Advances in Energy Technology

An accurate CMOS current mirror with negligible offset component, very high output resistance, and high bandwidth is presented in this paper. The proposed CM incorporates super-cascode configuration with Ramirez current mirror structure. This configuration not only eliminates the offset current term present at output terminal but also increases output resistance significantly without compromising the supply voltage requirement. The results of the proposed CM have been validated by carrying out its simulations using standard SPICE with the help of 0.18 µm CMOS technology for operating current range of 0–100 µa. These results show that proposed CM functions at a low supply voltage of 1.5 V, offers very high output resistance of 100 GΩ, fairly low resistance of approximately 4.16 KΩ at input terminal, and 3-db cutoff frequency of 7.20 MHz with negligible offset current component.

In this paper, geometric mean circuits using level shifted (LSFVF) and cascoded flipped voltage follower (CASFVF) have been proposed. In the proposed designs, flipped voltage follower (FVF) configuration used for biasing of MOS translinear loop has been replaced by LSFVF and CASFVF configurations. The proposed circuits based on FVF allow significant reduction in the supply voltage requirements. A comparative study of geometric mean circuit using different FVF structures is presented. LTSpice XVII has been used for simulation of designed circuits using 1.5 V supply voltage and 180 nm CMOS Technology. The simulated results show substantial improvement in output characteristics and significant improvement in percentage error obtained in output current.

In this paper, a new linear CMOS current differencing transconductance amplifier (CDTA) with improved characteristics has been designed and proposed. In this circuit, conventional transconductance amplifier of CDTA is replaced by a linear CMOS transconductance amplifier. This transconductance unit increases the linear operating current range of CDTA. It also provides the flexibility of electronic tuning of transconductance gain of amplifier by varying the biasing voltage. The simulations have been performed via Mentor Graphics Eldo Spice using 180 nm technology file of TSMC. The simulation results reflect the effectiveness in terms of bandwidth, gain and operating current range of the proposed circuit, and it has been observed that this circuit operates for a wider linear range as compared to conventional CDTA circuit.

This paper aims to design a modern scheme for WECS using squirrel cage induction generator to extract maximum possible power from wind source. One major concern about stability of WECS is due to continuous aerodynamic power variation. The major inefficiency of existing PI control-based scheme is high settling time, high operational noise, more aerodynamic power loss and less operational efficiency. The purposed scheme has overtaken all mentioned limitations of existing one. A modified PID control-based pitch angle control scheme is presented to enhance stability, decay noise and optimize power extraction. The modern PID control-based scheme has produced more efficiency after minimizing aerodynamic power loss. The simulation has been made using MATLAB/Simulink, results are analysed, and dynamic performances have been evaluated, ensuring superior performance of proposed application.

A massive MIMO system uses very large number of base station antennas in order to give service to many users. Pilot contamination because of inter-cell interference is suffered by a massive multiple input multiple output system. A smart pilot assignment (SPA) strategy has been utilised in this paper, so that the performance of user terminals can be increased that are suffering from pilot contamination severely. The inter-cell interference of each pilot sequence is firstly measured by the base station. This interference is caused by the users having same pilot sequence in the adjacent cells. Contrary to conventional strategy which assigns the pilot sequence randomly to the users, the SPA strategy does the assignment of pilot sequences to the user terminals having the worst channel quality with the smallest inter-cell interference, so that the performance can be improved. In this paper, simulations have been done for the verification of the performance gain of the proposed SPA scheme in massive MIMO systems.

A multiple band microstrip antenna is proposed in this paper. The antenna introduced has various GSM 900 and DCS 1800 smartphone capabilities. It is also used at 2.4 GHz for the application of Wi-Fi, Bluetooth and wireless local area network (WLAN). The proposed is based on FR4 epoxy with a limited ground plane on backside of the substrate with overall length × width is 50 × 90 mm. The suggested one shows strong resonance properties at several 0.9 GHz, 1.8 GHz, 2.45 GHz and 3.5 GHz frequencies. Antenna has return loss below −10 dB at multiple frequencies. Proposed antenna has reasonable gain at these frequencies. All the results are performed using 3D electromagnetic simulation software at high frequency for designing and simulating.

In this paper, the attendance management system is implemented for replacing the existing marking system, which is practiced manually. To achieve this, text recognition is used to scan the identity (ID) card and analyse it; furthermore, facial recognition is used based on demand. The ID card is scanned and by text recognition and verifies the student’s details with the database of registered students. Moreover, to avoid proxy in attendance, it also captures the image of the person handling and the image present in the database. For text recognition, the cloud services named Nanonets were adopted. For face recognition, the popular cloud services like Amazon web services (AWS) were adopted to create an application program interface (API) request. However, before sending this image to one of the cloud services for analysing and identifying the faces, initially, it uses open-source computer vision (OpenCV), a library of programming functions for image processing and to detect if a face is present in a given image database or not.

In this paper, a fuzzy logic-based MPPT controller is proposed for quadratic boost converter energized by a solar PV module. A quadratic boost converter exhibits high voltage conversion ratio with lower duty cycle and good efficiency. Unlike conventional controller design system which requires mathematical model of the system, the fuzzy logic controller design requires only expert knowledge of the system. This paper investigates the performance of the solar PV system at varying solar radiation and ambient temperature. Simulation of the power and control circuits has been done using MATLAB/Simulink software. Performance analysis based on MPPT efficiency for constant resistive load under standard and varying ambient condition demonstrates the superiority of fuzzy logic-based controller over incremental conductance-based controller.

This paper presents the implementation of microgrid along with its frequency control. Microgrid is a pioneering concept which takes into considerations various loads and sources which are operated under a microgrid control centre and provides electricity to a certain area. The proposed model includes various power generation sources like diesel generator (DG), wind generation, solar generation and two loads: residential load and asynchronous machine load (ASM). The photovoltaic (PV) farm modelled in microgrid has been studied with the purpose of showing the effect of partial shading on the microgrid. Furthermore, the wind farm which has been set up also takes into account a special wind profile in order to show the effect of abrupt change in wind speed and microgrid’s response. Now, in order to maintain the frequency of the microgrid, proportional integral (PI) controller and model predictive controller (MPC) have been used and their results are compared.

The event of isolated islands is not planned because it poses a risk to the safety of utility service workers, and the production and electrical power installations are damaged due to the asynchronous shutdown. Detection of islanding in the distributed generation (DG) system is a challenging issue with several security and protection issues. Once the DG is disconnected from the main source, efficient island detection is necessary to effectively detect the island. In this paper, the author examined the different problems related to single-phase photovoltaic (PV) systems connected to the network under island conditions. The chief goal of this paper is to detect and classify all possible islanding scenarios by developing a trained dataset and a feature extraction process. Simulation results from MATLAB/Simulink toolbox are used to determine the islands operating conditions.

In electronic circuit boards such as Arduino Uno and Raspberry Pi, thermal imaging (TI) is commonly used as non-destructive monitoring, fault diagnosis, and evaluation technique to inspect device failure. Due to the application of this popular development board, a constant call for effective component diagnosis is required for keeping the safe and healthy operation of these boards. In this paper, an emergent thermal imaging approach has been used for diagnose the faults in electronics circuit boards. The two states were analyzed, i.e., healthy board (HB) and faulty board (FB). The proposed approach was accomplished using 2D discrete wavelet transform (2D-DWT) for decomposition of the captured thermal images of electronic components followed by feature extraction. Principle component analysis (PCA) has been applied in order to reduce the dimensionality of the extracted feature vector, and there after, the selected features have been rated in order to achieve the much more optimum set of features. Finally, these feature vectors were forwarded for fault detection to the support vector machine (SVM) for further classifying the healthy and faulty states.

In complementary metal–oxide–semiconductor (CMOS) logic circuits, the main design requirement for low-power applications is to reduce the power dissipation. Dynamic power dissipation occurs in clock network which contributes up to 30–45% of total power dissipation of circuit mostly because of flip-flop are being used for sequencing which consumes lot of power therefore to reduce the power consumption flip-flop are being replaced with pulsed latch circuit with feedback. Conditional circuit is used in bidirectional shift register with bidirectional pulsed latch circuit. When it is compared with master–slave flip-flop, power reduction is 38%. Modified circuit is being implemented in 28 nm CMOS technology.

Recent times have witnessed a steep rise in wind power production across the globe. Doubly fed induction generator (DFIG)-based wind turbine has been extensively employed for wind power generation. However, increased penetration of wind energy generation has raised new concerns regarding operation of DFIG. One of the most important issues is the low voltage ride through (LVRT) capability of DFIG. A number of techniques have been proposed to enhance the LVRT capability of DFIG in order to enable it to function in accordance with the grid codes laid down by various countries.This paper attempts to present a critical survey of various LVRT enhancement techniques for DFIG-based wind turbines. For better perception, these methods are broadly categorized into four groups, viz. protection strategies-based, control strategies-based, FACTS-based and hybrid strategies. All these methods have been extensively analysed, and the analyses is presented in a critical manner. The advantages and drawbacks associated with each method have been discussed. The effectiveness of each method has been scrutinized by assessing them wrt various performance parameters. Finally, relevant conclusions have been drawn based on extensive survey, and an attempt has been made to identify future research possibilities in this field.

Traffic sign is important as they provide visual information to drivers. Traffic signal detection is a technique used detects traffic signs put on a road and inform it to a driver and it is an integral part of intelligent driver assistance. This paper proposed as hardware accelerator using cascade classifiers based on FPGA to detect the traffic signs. Cascade classifier consists of number of stages and adaptive boosting technique is trainer for the cascade classifier. FPGA is reconfigurable, cost efficient and more flexible than other hardware platforms. The system is implemented using model-based design (Simulink) and hardware software co-design. Proposed design is analyzed on a Xilinx board.

In this paper, instead of generating electricity by windmills turbine, artificial trees are used in which its leaves will act as horizontal turbine and generate electricity. These leaves are known as Aeroleaves. These leaves are like a blades of the artificial tree (essentially micro-wind turbines), which works at a speed as low as 3 m/s, regardless the direction of the wind. This paper discussed a tree-shaped structure design with a bunch of leaves acting mini-wind turbines. The design of these Aeroleaves is taken savonius type, which is simplest design and can catch the wind from all directions. Advantages of these unusual horizontal wind turbines are their adaptability to various locations. A 6 kW solar panel would take up nearly about 450 square feet of land space, which is not an optimal option who are living in apartments or houses without direct sunlight. But on the other hand, a wind tree’s curved branches could be installed to any rooftop or lesser space (balcony) and even in areas that do not get much wind. Each aeroleaf is connected to a DC generator, and all the generators terminals are connected to power convertor that will check and provide a constant DC voltage even if generated voltage is less or high due to wind speed variations. The terminals of the power convertors are then connected in series which adds up the generated voltage. Wiring of the all the supply runs through the branches of tree and goes to the battery panel at the bottom of tree. As the wind flows and rotates the turbine, electric energy is produced without any noise and got stored in batteries which can be used for various purposes. This paper concludes the new technique of generating electricity which is totally eco-friendly and is reliable. It can be used in last scale, and by some modification in design, it can be more efficient and profitable.

A combination of wind generator and battery storage provides a potential solution for electrification in remote areas. The intermittency issues associated with wind power can be suitably handled if battery storage is optimally integrated with it. The determination of optimal component size is a combinatorial optimization problem and must be adequately addressed in order to have reliability standards coupled with cost-effectiveness. This paper presents a reliability-based determination of component sizes for an isolated hybrid power system using wind generators as well as storage. A competent nature inspired algorithm called as Butterfly PSO has been used for optimization problem. The objective is minimization of economic parameter called as Levelized cost of energy (LCOE). Expected energy not served (EENS) has been used as the reliability constraint. A case study has been carried out for an isolated hybrid power system sited in India.

Thermal analysis contributes to achieving the major improvements in the design which affects the overall performance of the machine. It has been found that the induction motor fails frequently due to the overheating of various motor parts involved in operation under loaded conditions in electric vehicles. A computational analysis is required to predict the rise of temperature in all the parts of the motor. This paper investigates thermal analysis of 15 kW totally enclosed fan cooled (TEFC) induction motor using the finite element method (FEM). Computation of the temperature rise in various parts of the motors under rated load is performed by considering the Joule’s losses. The results give the temperature distribution in various parts of the motor. The simulated and experimental results are validating the proposed methodology.

The economic accomplishment of power system mainly consists of true power dispatch and reactive power regulation. Reactive power dispatch dominates these days due to the increase of connecting more number of non-conventional energy sources into the grid. The dispatch of optimal reactive power dispatch (ORPD) treated the problem of optimization for tuning the variables corresponding to PV bus voltages and tap settings of transformers and the rating of reactive power reimbursing devices. With the increasing demand for reactive power, its optimization in the electrical grid is an essential feature to maintain voltages within desirable limits for system reliability. ORPD is a non-continuous, nonlinear, complex, non-convex, and the problem of multimodel which implicates distinct as well as uninterrupted variables. Therefore, its results involve divergent objective functions corresponding to minimization of true power losses, improving voltage outline, voltage stability improvement, transmission cost minimization, minimization of investment cost of FACTS device and emission minimization, etc. A grid involving predictable thermal generators, renewable energy sources and with FACTS devices has been widely studied for ORPD. This paper presents a technological review on three topics which are reviewed in chronological order, with different meta-heuristic techniques applied on different IEEE bus systems for the elucidation of ORPD problems.

Facial recognition is a challenging research area since the past decade due to exponentially increasing intervention of humans to achieve anything and everything using technology. Facial recognition is a task; that is, trivially accomplished by human beings, even under fluctuating light and when faces are transformed by age or hindered by accessories or facial hair. Nevertheless, it has remained a perplexing computer vision challenge for decades. Due to upsurge in acceptance and accomplishment of deep learning models in computer vision, features extracted by convolutional neural networks (CNNs) can be employed for facial recognition. However, all the techniques involving deep learning require huge amounts of data, thereby increasing the computational requirements of any algorithm. In this paper, a method has been introduced with minimal computational requirements and minimal time complexity for identifying and recognizing faces which are surgically altered. Recognition is done by introducing a simple CNN for facial trait extraction which is followed by performing the classification task. Plastic surgery facial dataset is used for training, testing and validating the proposed deep network. This indeed is very beneficial for ensuring safety and security of an individual’s identity as it would assist to recognize offenders, impersonators or anyone who conceals their individuality.

Electricity plays a cardinal role in our lives, and the potential of monitoring and controlling real-time information has become pertinent in the present scenario. The foremost objective of this paper is to emphasize the point that the existing conventional energy meters lack a bi-directional communication mechanism to transmit the real-time values and also ineffective in controlling the non-technical losses such as erroneous meter reading and obscure meter modification occurring at the consumer premises. With a surge in the electricity cost these days, it is essential for the consumers to keep track of their electricity usage on a daily basis. To solve the above problem, this paper elucidates a computationally efficient method of establishing the communication between the Electricity Board and the consumer section for transmitting the consumer’s electricity consumption and the respective cost value using the Message Queuing Telemetry Transport (MQTT) application through the Internet of Things (IoT) platform. The optimal use of the Publish/Subscribe architecture in the MQTT protocol enables highly accessible solutions without reliance between the data producers and data consumers. The proposed prototype was implemented and tested which depicts that the operating speed does not exceed 500 microseconds and the real-time values can be monitored and controlled by the consumers in a reliable, fast, and cost-effective way.

Load forecasting is used to meet the demand and supply equilibrium in any distribution system. It is basically multi-variable and multi-dimension estimation problem. It helps electric utilities to make advance decisions about purchase and generation of electric power, switching of loads and infrastructure expansion, etc. This paper presents peak load prediction of a power transformer from the given datasets. Peak load data are obtained using supervisory control and data acquisition (SCADA). Curve fitting prediction is devised to calculate hourly load forecasting of the week days. However, this method does not provide accurate results as compared to machine learning (ML) algorithms. ML algorithms not only able to review large volumes of data but also provide accurate results with percentage error of only up to 10%. The load data are acquired from the DF 8000 software. To obtain the predictions, multiple regression model is applied to the dataset. A python data science platform is used for simulation work. The results obtained are presented.

Orthogonal frequency division multiplexing (OFDM) is a novel multi-carrier modulation scheme where linearly modulated streams of data are split into several sub-streams and each occupies much lesser bandwidth than the total bandwidth of the signal. As compared to other single-carrier transmission schemes, the prime benefit of OFDM is its capability to survive the critical channel conditions without the use of complex equalisers. Its inevitable role in prohibiting inter-symbol interference (ISI) and improving the signal-to-noise ratio (SNR) has enhanced the efficacy in communicating over large distances. As OFDM is favourable even in transmitting information involving very high rates of data and provides much better performance; therefore, it is the focal process behind the new generation wireless networks such as 4G mobile communications, digital television and audio broadcasting. M-ary quadrature amplitude modulation (M-QAM) is the most preferred modulation scheme in wireless communication popularly known for its high noise immunity, much more efficient utilisation of channel bandwidth (spectral efficiency) for the same average signal power and its reliability for higher data rate transmission over the same channel. The central concept of an OFDM system involving the QAM scheme for modulation of data has been discussed in this work. This study analyses and compares the bit error rate (BER) performance of OFDM systems against the parameters of signal-to-noise ratio (SNR) among different orders (‘M’ values) of QAM (i.e. 4, 16, 32, 64, 128 and 256-QAM) over AWGN channel. All simulations are performed in MATLAB only.

The recent advances in brain–computer interface (BCI) systems have resulted in successful implementations of multiple application domains using different paradigms. The imaginary speech paradigm has received less focus when compared to other paradigms. This study proposes deep learning models to classify and detect the imaginary pronunciations of vowels ‘a’ and ‘u’ using EEG data. The data was captured from three subjects with no psychological or neurological disorders. Channels Fz, Cz, C3, and C4 were used for analyzing the data. A bandpass filter with a frequency range of 1–45 Hz was used to filter the data. Three deep learning models are proposed in this study. These models are the convolution neural network (CNN) model, the long short-term memory (LSTM) model, and a model based on the combination of CNN and LSTM. The model containing combinations of CNN and LSTM performed best with the average classification accuracy of 84%. The CNN model and LSTM models yielded average classification accuracies of 51% and 63%, respectively.

Fused filament fabrication (FFF) is a type of additive manufacturing that manufactures the component by ejecting material, in the form of a filament; in slightly molten state to create a layer, these layers are superimposed over each other in the fusion state and allowed to cool and solidify. Various controllable and uncontrolled factors contribute to the mechanical properties of the product, resulting in impact on quality. In this research, a novel technique is applied to develop a model for optimal solutions. The objective of this research is (i) to find the effect of process parameters on the performance characteristics by application of adaptive neuro-fuzzy inference system (ANFIS) and also (ii) find the optimal level of these parameters for better quality characteristics of FFF process. ANFIS, in combination with principal component analysis (PCA), is applied first time to find the effect on six parameters on four performance characteristics.

Skin lesion characterization is a significant step as skin diseases are among the 4th common disease in the world. Skin conditions cause a major non-fatal disabilities, particularly in resource-poor zones. The main objective of our paper is to aid dermatologists in assessing the skin conditions of people living in such regions. It can also help dermatologists to detect in melanoma which could increase the survival rate of the patients upon early treatment. In our paper, we present a comparison between two techniques that can be used for classifying nine different skin lesions including malignant melanoma. The first method uses supervised learning in which the predefined features extracted from the skin lesion dataset trains the machine learning classifiers. The second technique makes use of transfer learning to train the deep neural networks (AlexNet and GoogLeNet) on clinical images of skin lesions which could then classify them. The results of the individual networks are then aggregated using ensembling techniques. We make use of the data collection from ISIC archive for training the deep neural network and for extracting features for the machine learning classifier. By transfer learning and ensembling the outputs of various trained network models, a maximum classification accuracy of 90.2% was achieved.

In the present scenario, the automation in the different types of machines plays very important role. It can be observed in our daily life, and there are only few things that are operated manually; most of the devices today are controlled automatically. Automatic system is preferred to the manual system. In the cities, there are street lights in the most of the streets. Due to the operation of the street lights, lots of energy are required for glowing the street lights. Sometimes it happens that even in the daytime the street lights glow uselessly. There are lot of loss of power because of the manual operation of the street lights. To reduce the power loss, it is necessary to apply some circuits and techniques. The aim of this paper is to reduce the amount of power lost in streets in the name of street lights. For that the street lights should glow whenever there is any object or any vehicle in the night and the lights should be switched off at the daytime. As a result of that our model will provide solution for energy saving.

This research work for optimal automatic generation control (AGC) of two area and four area power systems with diverse energy sources tuned with area control error (ACE) and brief summary of gray wolf optimization technique (GWO) in comparisons with particle swarm optimization (PSO), and firefly optimization are shown. This system establish connection for power systems as extended as thermal, diesel, nuclear, and many more sources interconnected with hybrid resources like solar power, wind energy, hydro power, electric vehicles, micro-grid, and smart grid. The interconnection of different sources with two areas, multi area and tie-line control with several controlling algorithms and soft computing techniques discussed. Paper has a decent work for related work and provides the simulation result to fulfill its objective also prepare it for future exploration with AGC in HVDC-AC link.

Application of BLDC motor is gradually growing day by day in every sector. But the primary problem with BLDC motors is ripples in the torque. The main reason for torque ripples in the BLDC motor is the presence of self-inductance per phase and different rate of rising and falling current in phases at the time of commutation. This paper represents a method for minimizing torque ripples by using a closed-loop DC-DC isolated buck–boost converter (flyback converter) before inverter. PI controller is employed in the feedback to make closed loop. Using flyback converter, there is isolation between the input and output sides so that inverter switches are protected in case of any fault condition. By using PI as a feedback, steady-state errors reduced and increase stability. Analysis using MATLAB or Simulink-centered simulations verifies the characteristics and reliability of the BLDC motor drive at constant speed with closed-loop flyback converter.

Switched coupled inductor boost converter (SCIBC) for renewable energy systems is outlined in this paper. The proposed converter will be obtained from a combination of traditional step-up converter and coupling inductor stacks. The proposed converter’s gain is determined by the number of couple inductors within the stack and duty cycle. The proposed converter refers to a single regulated power electronics switch. The frequency of 10 MHz is applied to lower the L, C values and to eliminate the ripples of the output signal (Vout and Iout). Proposed converter design and function are explored in depth. The proposed converter for single couple inductor stack is simulated in MATLAB (version 2015b), and the outcomes are checked using theoretical values and reliability of the BLDC motor drive at constant speed with closed-loop flyback converter.

This paper presents two novel three-level T-type legs which are modified using actual T-type legs in common emitter (CE) and common collector (CC)-type arrangements using SiC MOSFET. The proposed topology facilitates minimization in stress due to voltage on switch. This work can be further extended to analyze results of loss breakdowns of proposed five-level inverter and compared it with conventional topologies. Both the derived legs are independent, and poly-phase converters can be formed using these legs. Modulation scheme used in controlling is same as used in conventional topologies. In the proposed inverter, phase disposition (PD) modulation is used in order to make it easy for implementation. Other advantages being current flow through fewer devices results in reduction of conduction losses and as SiC MOSFET is used switching frequency can be increased for same amount of switching losses. Proposed topology is presented in detail, simulation results are analyzed, and conclusive remarks are presented.

One of the prime goals of the automobile industry has been the reduction of the weight of the products. This helps in improving the performance of the vehicles. Lightweight vehicles have various advantages ranging from handling to safety. Lesser weight means lesser kinetic energy, due to which the crashworthiness of the vehicle improves. Topology optimization has been performed to extract the geometrical shape of the chassis from the control volume (design domain) for the chassis, whose dimensions are based on the existing two-wheeler chassis of the commuter segment in Indian motorcycling market. The work has focused on the designing of a chassis for an electric motorcycle by incorporating the battery pack space inside the chassis space. The objective has been to minimize the volume and maximize the stiffness with application of relevant geometric restrictions. The final geometrical shape has been used to build the roll-cage structure of the chassis. The work has proposed three models with 70, 80, and 90% volume reduction and the 90% weight reduction model has been used to build manufacturable chassis based on the topology optimization output.

Smart management system not only entails optimal use of the energy of the demand side but also includes the factor of increased energy generation. It is the efficient approach of using energy so as to overcome the drawbacks of integrating renewable energy sources along with local grid. This paper proposes an algorithm which is fuzzy logic-based particle swarm optimization (PSO) for maximum power point tracking (MPPT) control to compensate the irregularities in power generation caused by various factors. The implementation of the PSO-based fuzzy algorithm was carried out by utilizing MATLAB and Python programming software while the evaluation was done at a testing facility in Delhi Technological University integrating a local resource of renewable energy, i.e., solar photovoltaic plant with smart management system. At the test site, the grid-connected system includes a photovoltaic generator coupled with a DC/DC boost which is regulated from a PSO-based fuzzy algorithm for MPPT regulation to draw out the maximum power obtainable at photovoltaic terminals. Its structure was made on the basis of most optimal utilization of the renewable energy resource, and in this case, the local photovoltaic grid is present in the system. The proposed algorithm was able to achieve a minimal change in values of generated and consumed power despite greater irregularities in solar irradiance and temperature.

Power system restoration in the distribution network is required for power supply restoration in a faulty area and to ensure the reliable operation of the system. The high level of faults does not arise usually but when they occur, they often result in power cut and loss for critical services, subsequently severely affecting the end-users. This generates a need of a reliable distribution networks that may be able to quickly restore the services for the affected areas. The infrastructure of the power system is changing everywhere. With the increasing bulkiness and intricacy in the infrastructure of the system, automatized distribution network is a need of time for staying updated, observing, and controlling. Service restoration is a complex multi-objective optimization problem. The restoration strategies have been changing over the past few years to manage the varying behavior of the system. An extensive difference has been seen in the strategies used to restore the service. In this paper, different methods that have been put forward in the literature to solve this problem are reviewed and presented.

To develop a robust management system, nowadays, worldwide industries are emphasizing to implement an efficient approach for shop floor management. In the present scenario, several approaches are used for shop floor management to identify and eliminate non-value-added activities of the production. The main aim of the present research paper is to describe the selection and implementation of approaches by researchers and industry persons for shop floor management and to identify a superior approach from the investigation of results found in previous research work. The novelty of this paper lies in the fact that few such studies have been done for the selection and identification of an efficient approach for shop floor management. This paper will help future researchers and the management team to select a suitable approach to improve shop floor management.

The cyber security issues become significantly progressively because of the current superannuated cyber infrastructure. A smart grid is an emerging modern architecture with a massively complex structure powered by ICT power equipment for the power system. Sensors, smart meters, and electrical appliances from the latest century are important equipment for the smart grid. This work provides a comprehensive analysis of potential risks to information security in a diverse climate. It introduces a cyber infrastructure smart brace device with analysis bearings inside and out. The anonymity, integrity, and transparency of the network must be improved by creating a comfortable and efficient cyber infrastructure for the smart grid. Recognition of threats, mitigation, verification, and core is an administration between all remaining research problems.

Nowadays, due to the problem of depletion of convention fuel sources and increasing pollution by internal combustion (IC) engine-based vehicles, the EVs are more beneficial and cost-effective and provide social benefits. Therefore, adoption of EVs is the alternate source of transportation and the growing replacement in transportation as they emit zero carbon emission, energy-efficient and are economical. For the expansion of EV market, the charging station infrastructure is very important to provide efficient energy for various EVs. This paper presents the optimal planning of charging infrastructure to establish efficient charging station location by considering the power losses, voltage and economic consideration in distributed system and also optimal sizing of distributed generation (DG) at that particular location. The minimum value of multi-objective function decides based on the system performance indices as power loss, charging cost, voltage deviation and reliability indices. The allocation of DGs and EV charging stations obtained by the multi-objective function is minimization using particle swarm optimization (PSO). The modified IEEE 33 bus distribution system for Durgapur City is considered for optimal allocation of the EV fast-charging station (FCS) and DGs with MATPOWER tool. This paper concludes that minimization of power losses, charging cost, voltage deviation and improved reliability of the system is increased leading to secure and stable EV charging stations with DGs.

Due to deregulation in the electricity market, the whole economic market management has been changed. With the increasing demand overload and violation of line, constraint leads to the congestion, thereby threatening the security of the system. Flexible AC transmission systems (FACTS) devices are used to avoid this congestion. The N-1 contingency is considered as an outage of one branch, which also increases the congestion in the system, active and reactive losses and violation of voltages at buses. Thyristor-controlled switched capacitor (TCSC) devices are optimally planned in the system with a multi-objective approach using PSO to relieve the congestion and maintain other performances under contingency. The multi-objective function is minimized to find the optimal placement of TCSC, which includes minimizing the voltage deviation and losses. PSO is applied to find the size and location of TCSC. Also, this paper discussed the amount of harmful gases like CO2, NO2, SO2 produced in a thermal power plant, and it is calculated in term of cost. This paper discussed the techno-economical–environmental effect. In this paper, the 14-bus system is implemented in MATLAB environment with MATPOWER tool for planning with N-1 contingency. The proposed methodology based on evaluation of voltage deviation, cost and loss as the objective function is useful for the optimal placement of TCSC.

This paper intends a solution towards the optimal reactive power dispatch (ORPD) within power systems incorporating solar power. And also gives an answer to the ORPD problem under load uncertainty for different random loads. The main objectives are to minimize the real power losses and voltage deviations by initiating solar energy source at one of the load bus systems with no change in constraints such as equality and inequality constraints. The reactive power dispatch problem is resolved with the firefly optimization technique. The suggested firefly algorithm is verified on IEEE 14-bus test system.

Recently due to the rapid rise in load demand, the power quality issue is increasing at a faster rate. The major cause behind this is the harmonics created by these loads. The main idea behind writing this paper is to justify the implementation of tuned harmonic passive filter along with the shunt active power filter for the reduction of harmonics in the system that is being created by the existence of nonlinear loads in the system. The MATLAB simulation is being carried out so to justify the fact that the harmonic passive filter along with the active power filter can slash the total harmonic distortion (THD) in the system to a significant level. Transient and the steady-state characteristics of the system are studied through the results obtained from the Simulink model. The results for the different cases have been studied thoroughly by comparing their THDs, and it has been found that the THD obtained from the above combination of the system is less than 1%, and therefore, it is well in accordance with the IEEE standard (IEEE 519).

Notch filters are extensively used to suppress the noise in biomedical application. Since the biosignals are very weak signals and having short duration, filters with high selectivity and better transient response are desirable. Transient response of notch filter increases with increase in quality factor and pole radius of notch filter. So, a different concept of IIR notch filters whose quality factor (Q) and pole radius (r) is varying with time have been implemented in this paper which results limiting the transient duration. The performance of these filters (with variable Q and r parameters) was compared with those notch filters that have constant Q and constant r parameters. The results revealed that the notch filters with variable quality factor and variable pole radius have better transient response as compared to the notch filters that have constant Q and constant r parameters. The performance of this Q-varying and r-varying IIR notch filters was also measured for the filtering of ECG signals corrupted by fixed frequency noise. The result shows that the Q-varying and r-varying IIR filter gives better performance over other conventional IIR notch filter.

This paper introduces a new approach to enhance solar photovoltaic performance by widening the principle of overlaid structures on photovoltaic modules. The paper emphasizes improving photon management that indirectly improves the electrical characteristics of photovoltaic modules. Two different types of structures have been discussed elaborately; the first structure that is taken is a simple and most preferred module, i.e., parabolic reflector, which has enormous potential in concentrating the power of photons. However, due to an excessive increase in photons, the solar photovoltaic module may lose its electrical characteristics due to which a filtered structure must be proposed which manages photons to its best level for a PV module. This leads to a better design at the nanolevel that will lead to the resonance of photons management and electrical characteristics thus honeycomb design is taken as an alternative. The paper also extends the concept of honeycomb and parabolic reflector to simulation level where results show a good level of comparison of the photon management. The paper tries to combine these two novel techniques to improve the efficiency of the solar photovoltaic module. Using the Simulink platform of MATLAB 2014a version and extending the concepts and mathematical equations to block diagram level in Simulink, the paper proposes a novel improvement in efficiency by using a parabolic reflector to 31.01% at around 10 AM in the experiment.

In this paper, we propose a few methodologies for composing music using deep learning algorithms and long short-term memory (LSTM) neural network. The LSTM model is created by training with a set of input files from a music library. The trained model then synthesizes music when an arbitrary note is provided. The quality of the music is calculated by comparing the harmony and few other parameters of the synthesized music with the trained files. The music library is made with a set of MIDI files, and based on the chosen library, a unique model shall be created. For the model creation, the library files are converted into a suitable format and encoded in order to make it compatible with the LSTM network. Though the outcome of this experiment is a continuous music, the harmony and notes can still be improved to solve the discontinuity problem.

A simple yet useful construction of a system comprising of a “second-order control system” attached to an “SSB-SC amplitude modulator”. Using the basic concepts of both, the latter can be controlled externally as a result of simple mathematical relationships established between the defining parameters of the two. These so-called defining parameters are the “damping ratio” and the “modulation index”. Other important and indirectly influenced/influencing parameters are the “natural frequency”, the “damped frequency”, and more importantly the “current” and “power” of the modulated signal. We have explicitly established mathematical relationships that allow us to influence most of the parameters of an SSB amplitude modulator except for its own carrier signal/modulation index (can be either of these two and solely depends on what the variable parameter(s) of the modulator apparatus involved is(are)). Similar designs have been proposed previously but they have failed to make use of the more power efficient single side band techniques that surprisingly also enable a greater ease of control and various other advantages which we have mentioned in our paper. Finally, after evaluating based on these previously mentioned parameters we prove that our design is better than the other one.

Due to the high directivity of ultrasonic speakers, they are used a lot. AM is the proposed modulation for them but FM is used even though there have not been much research done on the use of FM for such speakers. These modulated waves can be demodulated by exposing them no linearly with air. In this paper, we will try to a devise modulation technique that works on simultaneous FM and AM demodulation. The proposed method should provide a higher sound quality on comparatively low frequency, and we are also trying to reduce the power consumption of the suggested method. We are hoping to achieve that by modifying our original method with SSB instead of AM DSB. We will check the effectiveness using various parameters.

Electric vehicles are gaining popularity due to its environment-friendly and energy-efficient nature as compared to internal combustion-based vehicles in many countries around the world, including developing countries like India. However, there are certain challenges associated with the deployment of electric vehicles, particularly, in developing countries. With the rise in demand for electric vehicles, it becomes essential that proper infrastructure arrangements like charging station services are made available appropriately before the deployments. The charging station is supported by fossil fuels that end up within the emission of different gases like carbon monoxides, NOx, and SOx which result in environmental issues that can have adverse effects on human health. On the contrary, the charging stations which are based on solar photovoltaic will provide a sustainable and eco-friendly environment. It also reduces the burden on the prevailing electrical networks. Hence, the time is ripe for the research to be conducted within the domain of solar photovoltaic-based charging stations. This paper presents the feasibility analysis and a few of the essential aspects of various modes of operation of photovoltaic-based electric vehicles charging stations. Some recommendations and future directions are also provided in this paper.

Internet of things (IoT) has its application in various industries. IoT has gained popularity in industry-specific applications and also in miscellaneous applications. IoT has emerged as an ideal innovation in elevating the lifestyle standards. The demand of IoT integrated manufacturing industries has increased under Industry 4.0. In today’s era, when automation industry is at its peak, IoT is also emerging as an important tool in improving the time efficiency. In this paper, an attempt has been made to illustrate the different areas of applications of IoT. The role of IoT in manufacturing, transportation, digital factory, automotive industry, smart logistic service, smart manufacturing and Industry 4.0, Industrial IoT (IIoT) has been reviewed. The diversified fields where IoT has evolved and has elevated the features of their respective applications have been identified. Recent advancements, limitations and scope of IoT have also been discussed.

A performance study of three-phase three-level (TPTL), neutral point clamped (NPC), modified capacitor-assisted extended boost (MCAEB), Z-source multilevel inverter (ZSMLI) for photovoltaic (PV) applications is presented in this paper. With the intention of improving the voltage gain, boost factor and reducing the voltage stress across capacitors compared to conventional ZSMLI, a new topology for TPTL-NPC Z-source inverter (ZSI) is suggested using MCAEB network. The proposed topology uses sine reference-based simple boost control (SBC) technique for switching. Steady-state analysis of suggested topology proves that the boost factor is improved. Comparative analysis is done with conventional TPTL-NPC Z-source network. Simulation results are presented for MCAEB ZSI and conventional ZSI for shoot through (ST) and non-ST states. Boost factor is analyzed for different ST ratios and modulation indices. The result shows that the proposed TPTL-NPC inverter with MCAEB Z-source network outperforms conventional Z-source network in terms of voltage gain, boost factor, and reduced capacitor voltage stress.

Renewable energy and particularly solar energy have become very popular in recent years due to its abundant supply and free availability. It has become cost-competitive with other sources of energy. Testing a new power electronic converter or a new control algorithm on the converter requires a photovoltaic emulator since it may not always be possible to test the equipment live with solar panels, given their intermittency and large area required. Photovoltaic emulator technology has evolved considerably over the years, and many commercial emulators are available, though being somewhat expensive. This paper gives an overview on the topologies and control strategies that have been used for photovoltaic emulators to aid engineers and researchers intending to build an emulator for their laboratory experimentation. The issues involved and the relative merits have been outlined to enable people to choose a topology and control strategy for their respective applications. Scope for further work has been indicated.

In this paper, switched inductor improved quasi-Z-source inverter (SL-IqZSI) is proposed for electrical vehicle (EV) applications. In comparison with the existing SL-qZSI topology, boost capability and voltage gain of the proposed SL-IqZSI topology are enhanced considerably. The principle of operation, mathematical modeling of SL-qZSI, and proposed SL-IqZSI topologies were analyzed in detail. Exhaustive analysis is done using MATLAB/Simulink which validates the theoretical results of proposed SL-IqZSI topology. In this work, three-phase, two-level SL-qZSI and SL-IqZSI topologies were simulated using simple boost control technique (SBCT) for various shoot-through (K) and modulation indices (M) using sine and third harmonic injection (THI)-based reference signals.

Thermal comfort is the area which is explored extensively by the researchers to improve the performance of human being. This research is aimed at the analytical study of human body’s internal thermoregulation. To analyze the physiological behavior of the human body, an analytical mathematical model is built utilizing the steady-state energy balance principle. Different heat transfer modes defined in this model are as follows: heat transfer from convection, radiation, respiration and evaporation. By holding all other parameters constant, the behavior of mean body, core and necessary skin temperatures to achieve thermal comfort is observed by increasing the metabolic rate. The behavior of different heat transfer modes is investigated by rising the ambient temperature and holding all other parameters constant. The heat loss from body by convection, respiration and radiation to the atmosphere is found to decrease.

For power quality improvement designing, modelling and simulation of DSTATCOM are proposed in this paper like power factor improvement, zero voltage regulation and total harmonic distortion reduction in power distribution network for variable load by unit template technique or PI controller-based technique. To maintain the power quality, regulation and compensation are becoming important factors. By using a shunt compensation through an interfacing, it is better to propose compensator to get connected with PCC. The proposed compensator having an VSC is consisting of a DC bus capacitor based upon IGBT. As for as the performance of DSTATCOM is concerned, it is observed as the excellent one for variable loads with regulated voltage at PCC and self-supported DC link.

This paper discusses a simple current mirror (SCM) based on a novel dopingless tunnel FET (DLTFET) and compares it with existing MOSFET technology. The SCM is used to make a current control current source (CCCS), and an ideal SCM requires a device that must produce a constant current in its saturation region, and DLTFET is one of that devices. The simulation-based comparative analysis of SCM is done at 20 nm for N-type DLTFET and 22 nm, 32 nm, and 45 nm for NMOS in terms of current error, leakage current, input, and output resistance, transient analysis parameters. The simulated results have shown that DLTFET SCM performs better in comparison with MOS SCM as it shows the negligible current error, 99.99% lesser input resistance, and 99.6% greater output resistance.

Energy harvesting is the technique to harvest the ambient energy present in the environment into electrical energy. The lost energy in the form of heat, light, vibration and movement can be utilized to create self-sustaining energy systems. Energy harvesting is different from large-scale renewable energy generation as it harvests energy on a smaller scale. Energy harvesters are very useful in applications which limit the charging and frequent replacement of batteries. There are varieties of energy harvesters like piezoelectric, thermoelectric, pyroelectric, triboelectric, etc., are classified according to mechanism of energy conversion. Nanoparticles are promising materials for energy harvesting and storage.

Swarm robotics is one of the emerging domains of robotics, with its wide areas of application it is gaining a vital role in the modern society; thus, further researches in this domain is ever increasing around the world, while many swarm robots utilize optical mode of communication for local interactions which generally consist of modulated infrared signals. The influence of such communication on the performance of the swarm is still poorly understood. The current paper proposes a novel communication technique based on color-coded light signals which can be used for swarm robot system. By utilizing the colored light signals, the activity of the individual agent or a group of agents of the swarm can be easily monitored and thus their impact on the emergent behavior of the complete system can be studied.

Weeds are unwanted plants that grow in farmlands and compete with nutrients and sunlight for growth. If not removed, they could obstruct the growth of crops causing reduction in crop yield and increase in monetary losses of farmers. With the recent advancement in computational techniques, deep learning (DL) and artificial intelligence (AI) have provided valuable support in detecting weeds in farmlands. In this review, the development of weed detection process in the last five years using DL was done. The specific framework and models employed, data preprocessing and segmentation process used, performance according to classification accuracy and metrics were examined. The comparison of DL models with other traditional machine learning (ML) models (support vector machines, K-means and more) with respect to performance metric was done. It was found that deep learning offers greater accuracy and performance than other image processing techniques.

Solar photovoltaic refrigeration is one of the solutions to provide the refrigeration facility to remote areas, especially for storing the vaccines and milk preservation. Solar energy is a renewable and eco-friendly source of energy. This paper describes a review of the design and performance of various solar photovoltaic refrigeration systems. The different solar refrigeration systems inculcating phase change material (PCM), AC compressor, DC compressor, lead–acid batteries, inverters and monitoring systems have been discussed. DC compressors show better results than AC compressors. A variable speed DC compressor can reduce the size of solar PV and reduce the overall cost. Various studies showed that the solar photovoltaic refrigeration system could be successfully used for hot arid areas for refrigeration.

This paper presents the new design of the system in which grid and solar PV Array are integrated. The topology for design of such system is vector control in which measured signal has been compared with that of reference signal and passes through PID controller. The necessity of using renewable energy resource is to meet the load demand due to limited availability of conventional sources. In this paper, solar PV array and conventional AC source are connected with grid in order to fulfill the load demand. The output of solar PV array is connected with boost converter, and switching of boost converter is assessed with vector control using PO method. The conventional AC source is connected to DC grid via rectifier which is uncontrolled in nature. It is observed that during day time, load requirement is met by solar PV array while in night time, load requirement is fulfill by conventional sources. The output at grid terminal is maintained with better power quality in terms of THD and is used to fulfill the load demand in order to avoid the fluctuation in the grid.

This paper analyzes the performance of refrigerants HFC134a, HC600a, and HFO1234yf in a vapor compression refrigeration system (VCRS). The energetic and exergetic performance has been presented for the three refrigerants. It is concluded that HFC134a is the best choice in terms of thermodynamic aspects, yet the present global warming situation warrants it to be essentially replaced with environment-friendly refrigerants. HFO600a shows better exergetic performance; therefore, it has found a suitable substitute of HFC134a.

In the present multi-core technology era, the integration of accelerating numbers of cores into the microprocessor has a significant high power consumption issue. Due to the change of technology, the scaling down of CMOS is taking place, resulting in an increase of leakage current in the sub-threshold region increasing the device’s power. In the present scenario, all the electronic devices use nonvolatile memory, and power is the main center of attraction of all the memories. To overcome all the power-related issues, Spintronics—spin-based memory is the latest technology. Due to the effects of spins in MRAM, this type of memory can be considered a cohort embedded memory technology, and it can be treated as ubiquitous memory. This paper reviews with an introduction of memory, followed by MRAM’s working principle, then reading, writing, and MRAM mechanism have been discussed. Technologies to enhance writing ability and reliability issues are also discussed.

In today’s scenario, manpower wholly cannot finish all task; they need some other hand or help for support especially when we talk about large machines in industries and factories; an autonomous system is the basic requirement to cope up with the demands of market, robotization is the solution to all the above discussed issues. Designing a complete humanoid robot is a tough task, but we can use robotic manipulator (arms of robot) as a substitute; it will provide semi-automation and will also help in balancing the shortage of labor problem. In this paper, there is a detailed description of designing of dynamical model for manipulator, and for making it suitable for use in industries, an optimized manipulator is essential; hence, an optimization technique is also discussed in this work. As an optimized controller, it could be able to handle the uncertainties and by itself can adjust the parameter depending on the external and internal disturbances; this manipulator could find its potential application in welding, underwater robot, industries, painting, pot welding, and many more areas.

The renewable energy technology (RET) that accompanies the necessary integration of distributed energy resources (DER) and power distribution network is addressed by the small-scaled micro-grids. This paper presents a model of solar panel-based system for improving energy efficiency of a small-scaled micro-grid in residential area which includes pole mounted transformer, solar panel and battery controller. Furthermore, the load-flow measurements, steady-state parameters, initial state parameters, RLC line parameters and impedance parameters are also estimated using MATLAB (R2016a, Mathworks, Natick, USA). The solar panel-based small-scaled micro-grid can meet the efficiency, cost, environmental benefits and the challenging requirements such as security, quality, reliability and availability. The small-scaled micro-grid determines the energy efficiency of the system minimizing the losses, improving the load-flow parameters and steady-state parameters.

This paper presents a new hybrid optimization technique by combining artificial immune system (AIS) method with heuristic approach. Proposed optimization technique is applied for optimization of location distributed generator (DG) in loop distribution network. Objective for optimum placement is to trim down generation cost and power losses and enhancement of profile of voltage of a distribution network. The cost index (Ci), power loss index (Pi) and voltage profile index (VPi) are combined to form a multi-objective function to obtain optimal location of a DG. Two distinct cases are considered for two networks for validating the developed technique. Test systems considered are IEEE 30-bus and IEEE 14-bus distribution system. For supporting the superiority of the developed technique, results are compared against with other existing methodologies.

This research investigates the average efficiency of an L-shaped duct oscillating water column wave energy converter device (OWC-WEC) under the irregular incident waves environment. The OWC-WEC is situated over a uniform bed. To model the irregular incoming waves, the Pierson–Moskowitz spectrum with two most probable sea states is considered. To solve the problem, the boundary element method (BEM) is used. To study the efficiency of the L-shaped duct OWC device, free surface elevations outside, and within the device chamber, the average efficiency of the device are plotted as a function of the turbine damping coefficient. Moreover, the effects of chamber length, submergence depth, front wall protrusion length, and thickness of the OWC device on the performances are analyzed.

In prevailing COVID-19 pandemic, every nation is taking all suitable measures to avoid infection from and spreading of Corona virus through prominent measures like social distancing and lockdown. Peoples’ movement and business activities have been restricted in lockdown, and thus, time devoted by peoples to various house-hold activities and attitude/behaviour towards family members may change. Motivating from this, the study has been planned with an objective to know the awareness regarding Corona epidemic, social distancing and change in behaviour towards family members during lockdown. The outcome of an online survey, based on 274 responses, cited that the larger population is well aware about Corona epidemic, symptoms and safety measures. Almost all the people are following social distancing and taking care of other prescribed measures to avoid infection/spreading of Corona. They represent their affirmation for such obedience in near future also. Further, through correlation analysis, this has been observed that the obedience of social distancing and other prescribed measures is influenced by age, profession, qualification and awareness regarding Corona epidemic.

In this article, a tapered microchannel with ionic fluid is studied under the influence of electric field. Flow is actuated with the application of electric field. The generated flow is governed by Navier–Stokes equation, Poisson–Boltzmann, and Nernst–Planck equation. In COMSOL 5.5, we have selected creeping flow, electrostatic, and transport of diluted species physics to model this flow. This multiphyics model provided numerical solution of modeled problem. In this study, physical performance of all the parameters electro-osmotic parameter, electric field, and Helmholtz–Smoluchowski velocity is evaluated for velocity, pressure change, and volumetric flow.

With the progression in technology or using small size generators which is having greater efficiency, distributed generation is becoming more admired in recent scenario. The installation of distributed generation takes place near any load centers in the distribution network system. Thus, without having system transmission losses, the investment on transmission network can be reduced and, as a result, improved efficiency and steadfastness of the overall system. It is necessary to obtain the best deployment of distributed generators. In this paper, sensitivity analysis method for the placement of distributed generators is shown. This paper describes about the method and their objectives for the placement of the distributed generator optimally. The sensitivity-based method has proven efficacy in obtaining the best placement of DGs near any load centers. The result has reduced system losses and improved voltage profile in all the case studies and configuration of the network. The optimal settings of all distributed generation are obtained from the proposed optimization-based formulations which have significantly reduced power losses and improved voltage profile. The results of the case studies on 15-bus test systems reveal that in all the combinations of DGs considered, and the system loss has considerably reduced. The loss reduction found from load flow simulation matches with the relative sensitivity provided by the loss sensitivity factors.

Electric motor vehicles (EMV) will be a necessity very soon in the coming next years. The short range remains a big challenge for commercial use of these vehicles. In this paper, we have reviewed few techniques and systems to expand the range of the electric motor vehicle. A high range for electric motor vehicle is necessary because recharging stations are very less and too far from each other. EMVs have a remarkably shorter range as a contrast to the standard or usual vehicles with the combustion engine. Hence, it is significant to notify the chauffeur of an electric motor vehicle as precisely as possible about the definite range of energy consumption, and efforts should be made to improve range. The forecast for the range is more complex because it depends upon lots of factors including vehicle design, driver driving style, and the environment. The objective of this writing paper is to find out the effects of these factors and the methods to increase the range of electric motor vehicles.

Nowadays, time-series modelling techniques are widely used for prediction and forecasting of non-stationary data’s. The study analyses the continuous one-year ambient noise data using SVM and ARIMA modelling technique. The application of these techniques has been reported in time-series prediction and forecasting. A case study of each site of commercial is utilised to train the model. In SVM, tenfold cross-validation has been used to ascertain the optimum value of hyper-parameters (γ, ε, C). Box-Jerkin ARIMA technique has been also considered to simulate ambient day and night noise levels. Several statistical parameters such as MSE, RMSE, MAPE and R2 were used to ascertain the performance of proposed models. It was observed that SVM model outperforms ARIMA models.

A definite amount of power is lost while delivering power to the consumer. Moreover, the allocation of these losses becomes very important in the deregulated electricity environment. Indeed, the annual cost related to these power losses is worth of millions of dollars. Therefore, this is an opportunity for researchers to invent most judicious method for allocating losses. This paper provides a comparative study of circuit theory-based loss allocation for contemporary distribution system.

Energy storage is the focused area for the researchers and the academia; thus, in this paper, the different energy storage technologies are discussed in detail. The main working principles for most of the ESS are explained by analyzing their attributes for different specifications. The emerging concepts in transportation raised the role of storage solutions so it cannot be possible to specify any one storage solution that will be able to fulfill varying demands and performance characteristics; thus, different storage solutions are discussed. The comparison of different energy storage systems based on certain parameters like power, energy and efficiency demands is examined. The updated energy storage systems are also scrutinized along with their specifications for the electric vehicles. Finally, it can be said that in this paper various electrical energy storage methods for the electric vehicles are discussed with their analysis so that this will help the readers to understand about the potential energy storage solutions for the electric vehicle, plug-in or hybrid electric vehicles. The model used in this analysis replaced the previous relationships in which ESS capacity is unpredicted.