Proceedings of Third International Symposium on Sustainable Energy and Technological Advancements

Standalone solar photovoltaic (PV) systems emerge as a highly promising solution to ensure continuous and reliable electricity access to remote villages due to the unavailability of grid connections due to geographical challenges. This paper presents the feasibility analysis of standalone solar photovoltaic systems for remote area applications. The study utilizes a comprehensive approach, including bibliometric analysis and a detailed feasibility study through a real-world case scenario. The bibliometric analysis involves a systematic review and analysis of existing literature, mapping the evolution of research trends, identifying key authors, institutions, and journals, and assessing knowledge diffusion and growth patterns. The feasibility analysis evaluates the practical viability of standalone solar PV systems, considering technical, economic, environmental, and social dimensions. Additionally, a case study is presented to assess the economic viability, performance, and sustainability of a standalone solar PV system in a remote area. The study provides insights into the financial implications, energy generation capabilities, operational challenges, long-term sustainability, and optimization criteria, including net present cost (NPC) and levelized cost of energy (LCOE) system prospects.

Photovoltaic (PV) systems face challenges associated with Partial Shading Conditions (PSC) arising from fluctuating climatic conditions. To mitigate the impact of partial shading, solar PV arrays are subjected to reconfiguration. Optimization-based reconfiguration of PV arrays offers several advantages over other methods, such as physical array relocation. This paper introduces a novel approach for the reconfiguration of Photovoltaic (PV) arrays using the Horse Herd Optimization (HHO) algorithm. The HHO algorithm is employed to search the solution space and find the optimal arrangement of solar panels that maximizes energy output while considering practical constraints. Simulation results demonstrate the effectiveness of the HHO algorithm in achieving superior reconfigurations compared to traditional optimization methods. The proposed approach is applied on three shading patterns and compared with three existing approaches. The results shows the superiority of the proposed approach.

The article offers an optimistic approach to using battery energy storage devices (BESD) to reduce the effects of grid disturbances caused by switching events and the penetration of variable wind power. A bidirectional converter distribution static compensator (BDC DSTATCOM) is used to accomplish this. When the test system needs energy. In order to take control of the BDC DSTATCOM and store it in the BESD based on generation through power flow between the load bus and the BESD, synchronous reference frame theory (SRFT) based techniques are used. A test system comprising five buses was used for this investigation. Of the five buses, Bus1 provides a platform for sharing power between the electric grid (G) and the testing system; Bus2 integrates BESD with a 4 MW load (L1) capacity, which is connected by circuit breakers CB2 and CB3, respectively, to switch events ON and OFF and measure the effectiveness of BESD in mitigating grid disturbances in terms of transients during load switching; Bus3 and Bus4 are interfaced through trans-mission lines TL1 and TL2, providing power to local network loads. The Bus5, a 1.5 MW wind power plant (WPP) with transmission line TL3, integrates RE sources. In terms of power contribution to the load, voltage profile enhancement during switching events, grid parameters like voltage and current through evaluation of total harmonic distortions (THDs) is and analysis by the software MATLAB tool, its goals are to ascertain the effectiveness of the proposed method by power flow between BESD and the utility grid.

Wind energy gradually becomes a best alternative solution for power generation instead of fossil fuels with increasing installation capacity. Wind turbines experience fatigue loads in large scale with increasing size of wind turbine and these fatigue loads are highly variable with variation of wind speed. These loads impact severely on fatigue life of wind turbine components like rotor blades, in above rated wind speed condition. The fatigue life of wind turbine can be increased in above rated wind speed with pitch control of rotor blades, it helps to mitigate forces and enhances power quality generation. The objective function in present work is minimization of pitch error with proper selection of pitch actuating system and controller. Electrohydraulic pitch actuating systems become very popular over electromechanical systems due to its capability of generating higher forces with small size components and capability of withstanding external loads continuously irrespective of disturbance outside of the system. Feedforward fractional order PID controller is proposed for desire pitch of rotor blade. The control variables of feedforward fractional order PID controller were optimized by using advanced adaptive real coded genetic algorithm. Various wind profiles have been considered for investigating the performance of 1.5 MW wind turbine. The proposed algorithm has shown better performance compare to existing real coded genetic algorithm in terms of minimizing pitch error of rotor blade.Highlights The adaptive real code genetic algorithm has been proposed for optimization of feedforward fractional order PID controller by incorporating changes in mutation and crossover operations of real coded genetic algorithm. The proposed controller performance was studied with step, square signals and real wind data. The performance criteria of IAE (Integral Absolute Error), associated with pitch error of the proposed adaptive real coded genetic algorithm fractional order PID controller, has been improved by 43.18% compared to the real coded genetic algorithm fractional order PID.

In order to forecast the future of the electric vehicle (EV) market in India, this study investigates the use of deep learning and machine learning approaches, focusing specifically on the two- and three-wheeler categories. Numerous forecasting models, such as neural networks, support vector regression (SVR), and linear regression, are compared in the study. We looked at the time series and regression methods using the EV sales data. We discovered that the Neural Network technique produces superior forecast accuracy after analysing the errors produced by each. Next, we used the best neural network model to forecast the sales of electric vehicles (EVs) in India over the next five years. Sales of two- and three-wheeler electric vehicles (EVs) are expected to rise dramatically in India by 2027. This emphasises how much infrastructure and resources are needed to fulfil the increasing demand and keep up with global EV trends. The purpose of the paper is to act as a crucial resource for those involved in the electric vehicle business and to offer insightful analysis on the future trajectory of EV sales in India.

Modern power system are complex and interconnected in nature. The inclusion of different renewable energy sources (RES) create additional challenges in terms of frequency control and stability. This paper considers a two-area power system consisting of classical thermal power plant and RES such as photovoltaic and wind power. The simplified linearized model of the hybrid microgrid system has been developed where secondary and virtual inertia controllers has been designed for the study of frequency stability of the microgrid using Particle Swarm Optimization (PSO) and Teacher Learning Based Optimization (TLBO) under different operating scenarios.

Electric vehicles (EVs) emerge as a sustainable solution for curbing greenhouse gas emissions in the transportation sector. Typically equipped with Brushless Direct Current (BLDC) motors, known for regenerative braking, low maintenance, precise control, and high efficiency, EVs represent a promising avenue for eco-friendly mobility. This study introduces an innovative energy generation system leveraging photovoltaic (PV) technology to efficiently power the BLDC motor, facilitating rapid EV charging. The proposed method optimizes PV panel numbers through an Improved Zeta Converter, ensuring enhanced flexibility and a high voltage-gain ratio. An Artificial Neural Network (ANN) controller maintains a constant DC link voltage crucial for optimal operation. A 3-phase Voltage Source Inverter (VSI) supplies alternating current (AC) to the BLDC motor, with speed control facilitated by a conventional Proportional Integral (PI) controller. This approach yields an effective and swift-charging EV system, addressing application limitations and providing a practical solution for sustainable electric mobility where the settling time of the proposed ANN controller is 0.25 s and efficiency of the suggested converter is of 92.8%

Inadvertent islanding is one of the most serious issues associated with the power system. Therefore, unexpected islanding can lead to several technical issues, including deteriorating power quality, protective device failures, and personnel safety concerns. To overcome the above issues. An innovative passive islanding technology based on the rate of change of the superimposed positive sequence angle (ROCOSPSA) is developed in this study. The current and voltage signals are monitored at the DG terminal to get the computed islanding index (ROCOSPSA). If the value of ROCOSPSA is beyond a specified threshold, islanding may be identified. Otherwise, grid-connected mode. The distributed energy system was developed using the MATLAB/Simulink software. Simulation outcomes demonstrate that the suggested strategy efficiently distinguishes between islanding and non-islanding occurrences. Additionally, the suggested technique has a near-zero detection zone, no power quality issues, and islanding detection in 9 ms.

In power systems, both real power as well as reactive power play a significant role in transmission and distribution networks. So, in this paper, to reduce these losses a meta-heuristic technique that purely mimics the flying pattern based on Moth, i.e., known as the Moth Flame Optimization (MFO), which will be used in solving the optimal capacitor placement (OCP) as well as sizing of the shunt capacitor (SC) in allocation side. This paper focuses on how the real power losses and reactive power losses can be reduced by the best possible location and sizing of the capacitors in RDS. Most of the work is done by real power; reactive power is essential to control from the system steadiness part since fluctuation of voltage range can lead to an unintentional operation and also an early collapse of system components. OCP can be employed for voltage profile enhancement. The flow of power as well as network losses is acquired with the assistance of the data structures technique in this anticipated algorithm. The proposed MFO has been used to solve the IEEE-33 RDSs. The output results of the MFO algorithm had been compared with different comparable past results and also a distinguished advance is seen. The comparison result shows that the MFO is a very suitable optimization tactic for solving capacitor placement as well as sizing problems.

The paper emphasizes the acknowledgment, particularly in developed economies, of the significance of energy efficiency (EE) in reducing greenhouse gas emissions as part of the 2015 Paris Agreement. Developed nations have employed private Energy Service Company (ESCO) agencies to implement EE measures, aiming to cut CO2 emissions and combat climate change. The challenges and limitations encountered in developed economies underscore the need for careful consideration in achieving successful and sustainable EE deployment. India, a developing economy, established a government-funded Super ESCO that retrofitted households with energy-efficient LED lights successfully. It was followed by implementing a large-scale Public Lighting (PL) project. Despite an initial successful phase, sustainability issues emerged during the maintenance period, primarily related to consumer recovery, transparency, and collaboration challenges. The paper suggests potential remedies, including intermediary agencies for recovery, blockchain/distributed ledger technologies, and tradable formats to monetize savings, aiming to address the identified issues in developing economies.

The concept of locational marginal price is used to optimally price a congested network in a deregulated power system. The congestion isolates the power market mechanism and debilitates the competitive market by invalidating the more efficient and less priced generator sources in market competition. In the restructured power system, the players of the markets are offered financial transmission rights, which are contracts for difference to avert the risk associated with fluctuations in nodal pricing due to congestion, where the network configuration does not affect the trading of electrical energy. In this paper, the balancing of the amount of number of Financial Transmission Rights with the congestion surplus amount is revealed through a small three-bus system through optimal power flow calculation through DC load flow analysis.

Nowadays, the charging technologies of electric vehicles are a burning topic for researchers. As the world is trying to move from fuel-based vehicles to electric vehicles, rapidly, charging time is one of the biggest hurdles for researchers. Hence, selecting the best charging technology for an electric vehicle has become difficult. Various electric vehicle charging technologies have developed in recent years, and all have their advantages and limitations. The EV players need to select the most suitable combination among these for the best performance and acceptance. This work deals with a case study and a critical review of electric vehicle charging technologies. It will help researchers develop advancements in charging technologies and help EV players and customers decide and select the most suitable charging technology for their vehicles.

Renewable energy sources such as solar are becoming increasingly popular throughout the world. The structure of PV cells is demonstrated using a single-diode configuration. This paper presents the mathematical derivation of the single-diode configuration of the PV cell and models. In addition, the single diode configuration was modeled using the MATLAB Simulink model. The current–voltage and power-voltage waveforms were selected using a PV model at various temperatures and irradiance levels to explore the impact of temperature and irradiance on voltage and current waveforms. Then, assume that my PV cell had five parameters that were not given. Particle swarm optimization (PSO) techniques were used to identify this unknown parameter. The PSO's performance was compared to those of genetic algorithms (GAs), Villalva's Method, Accarino's Method, Iterative Method, and Silva's Method for single diode models of Kyocera (KC200GT). Instead of using a gradient-based approach, the PSO method utilizes a wide range of values for each parameter to generate the parameters of solar cells, allowing it to get as near as feasible to the parameters that are used in actual solar cells. This is feasible even without a reliable initial prediction. According to the observations of both simulated and real current–voltage data, the proposed method is more accurate and faster than the one previously utilized. Comparative research with various algorithms also helped us validate our observations.

In light of technological advancements and natural calamities, contemporary businesses are exhibiting a heightened inclination toward investigating energy efficiency, reliable power sources, and superior electricity quality for alternative energy sources, particularly renewable energy sources. Consequently, microgrids with distributed generation lay the groundwork for properly distributing power to consumers in an economical, safe, and successful manner. While meeting the system's demand and limitations, it is necessary to decrease the operating costs of a low-voltage microgrid that includes Plug-in Hybrid Electric Vehicles (PHEVs) and renewable energy sources (RESs) like photovoltaics, wind turbines, microturbines, or fuel cells. The proposed scheduling system describes the unknown PHEV and RES characteristics using the Monte Carlo simulation (MCS). The consequences of different PHEV behaviors on MGs are modeled by studying three different charging procedures. A hybrid metaheuristic method based on particle swarm optimization is described in this article. Optimizing distributed generation in microgrids is the goal of the price optimization issue, which is expressed as a nonlinearly limited mathematical problem. Results show that the hybrid method performs optimally when tested with low-voltage microgrids.

Microgrids that utilize renewable energy sources may encounter stability issues and subpar performance due to insufficient dampening during sudden increases in load. It is widely understood that a microgrid operates as a non-linear system. Preventing disruptions and outages in electric power necessitates the implementation of suitable control mechanisms for the microgrid's automated generation control. For this study, we employ a Genetic Algorithm (GA) optimization technique to enhance the frequency control of a two-area microgrid. This is achieved by utilizing a basic proportional-integral-derivation (PID) controller. Through the incorporation of renewable sources, the microgrid is capable of operating autonomously. The GA optimized the controller's gain while considering the criteria of integral time absolute error (ITAE). In this analysis, we will be examining the dynamic response of the GA-tuned controller in comparison to a conventional controller. Our focus will be on how the GA-tuned controller enhances the operational performance and stability of the microgrid.

The suggested study presents a microgrid that mostly uses non-conventional energy sources, with a battery energy storage system serving as an energy storage device and a diesel engine generator serving as the conventional source. This work proposes a noble frequency control approach for the suggested microgrid using Fuzzy Proportional Integral Derivative controllers and compared with the classical Proportional Integral Derivative controller. Both the controllers are optimized by a newly proposed Walrus Optimization Algorithm (WaOA) technique. An investigation is carried out on the suggested microgrid against various cyber-attacks with the proposed control techniques.

The proposed work deals with the multifunctional operation of a a distribution static compensator (DSTATCOM) integrated with photovoltaic (PV) and battery in a distribution system. It focuses on the improvement of quality and reliability of power along with a reduction in environmental pollution by using the combination of PV, battery and DSTATCOM. Depending on system dynamics such as load harmonics, load variation, unbalanced load, varying solar irradiance and battery SOC, the controller of DSTATCOM and battery energy storage system (BESS) operates to assure mitigation of power quality issues along with optimum utilisation of green power. Here, the DC-link is regulated by DSTATCOM, hence, BESS operates in reference power based current control mode. Also, the presence of a battery supports the DC-link voltage during any transient condition (such as load change) and smoothens the performance of DSTATCOM. The presented work is investigated under various dynamic conditions using MATLAB/Simulink and real-time simulation.

In this article, a modified synchronous reference function is presented for managing the grid-tied photovoltaic system’s interface inverter to reduce harmonics. The suggested approach also incorporates a PI controller to reduce steady-state inaccuracy. It has the effect of maintaining a consistent voltage level at the common DC point. The MSRF is made to analyze three-phase reference currents by taking the basic elements out of load current and grid voltage levels. The proposed MSRF offers several benefits, including improved boosted renewable energy sources’ ability to respond, variable phase and frequency, grid alignment, and minimal computational load on the MATLAB platform.

This paper deals with a battery management system of a photovoltaic system. A solar energy source and a battery bank intended to store excess energy produced by the photovoltaic (PV) array make up the system architecture. A bi-directional converter is essential for efficient power transmission to and from the battery. Battery charging and discharging are accurately regulated by the sophisticated Takagi–Sugeno Fuzzy (TS-Fuzzy) controller in the closed-loop control system. Comprehensive studies are carried out utilising the MATLAB/SIMULINK environment in order to assess the efficacy of the proposed method. The outcomes ought to demonstrate the system's strong performance in maximising PV array power capture and raising energy storage efficiency. The integration of smart control techniques for improved operational capabilities is emphasised in this research, which advances battery management tactics in renewable energy systems.

Lithium-ion (Li-ion) batteries find extensive application across various industries owing to their exceptional energy density and efficiency. However, these batteries are susceptible to cell-to-cell voltage variations, which can lead to capacity degradation, reduced overall performance, and safety risks. To address this issue, passive balancing techniques have emerged as an effective means to mitigate voltage imbalances within a battery pack. Passive balancing is a cost-effective and efficient technique employed to address these imbalances without the need for active control circuitry. The battery management system (BMS) employs the passive balancing technique for the Li-ion battery pack utilizing the bleed charge resistor approach. In this paper, a 3S-1P Li-ion battery pack is taken using the Constant-Current–Current–Voltage (CC-CV) charging method. The parameters like voltage, current, state of charge (SOC), and temperature of 3-RC equivalent circuit model (ECM) with passive balancing are studied using MATLAB/ SIMULINK.

This article presents a multi-carrier pulse-width modulation scheme for a re-configurable DC-DC BUCK converter topology. This combination yields a substantially higher enhanced switching frequency than the devices’ real switching frequency. The proposed re-configurable topology comprises multiple active switches connected in parallel, with their gate pulses derived from the modified and phase-shifted carrier waves. These switches operate alternately, effectively multiplying the operating frequency. Consequently, this allows for the utilization of slower and more cost-effective power devices, reducing filter requirements and enhancing overall system compactness. Further, in the proposed converter, the individual devices operating at lower frequencies help reduce overall losses, which reduces heat-sink requirements. The operation and analysis of the proposed concept are described in detail. Further, a comparative study, cost analysis, and thermal analysis are presented to highlight the scheme’s key features. Finally, the proposed scheme is validated through simulation and experimental results.

Research on enhancing the performance of a brushless DC Motor (BLDC) in an Electric Vehicle (EV) is one of the interesting areas in the automobile sector. Integration of BLDC motor with power converters and intelligent controllers implementation in EV optimizes its efficiency and performance. In this paper, an inverter-based brushless DC motor is designed and a closed-loop control system is developed with different controllers to control the speed and torque characteristics of the motor. Proportional Integral Derivative (PID) controller. Fuzzy Logic Controller (FLC) and Artificial Neural Network (ANN) controller are used in this work. The VSI operation is carried out with the help of all the controllers separately along with the switching logic circuit. The objective of the controllers is to regulate the speed of the rotor so that it can reach the level of reference speed. Reference speed and actual speed error are fed to the controller, based on the type of controller, input variables are chosen so that optimal control can be obtained for the VSI. Finally, a comparative analysis is presented to justify the suitable controller of the motor that can be fitted to an EV to improve its performance in this aspect. The above work model is designed and simulated with the help of the MATLAB/Sımulınk platform.

Renewable energy generation (REG) gradually contributes more to modern power distribution systems to fulfill the increasing load demand. Modern distribution grids can improve power distribution performance and distribution grid reliability by integrating energy storage devices. This research suggests a methodology considering both AIC for Li-ion battery energy storage systems (BSSs) and the distribution grid dependability. In this paper, the proposed methodology aims to maximize the reliability of the distribution grid with techno-economic performance improvement by minimizing the AENS, DD, and AIC on BSSs through optimal capacity and CDS operation of BSSs through the PSO technique. The projected work has been performed in the solar-integrated 28-bus Indian distribution grid with yearly analysis. Reliability and demand profile improvement can be observed through the reduction in SAENS and SADD, which express that 16% reliability enhancement and 12% improvement in technical performance have been achieved with a minimum AIC of INR 30.22 lakhs in the considered power delivery grid.

This study uses a novel nanotechnology-based biosensor for real-time freshness assessment to meet the urgent need for safe and fresh food. To improve sensitivity for targeted freshness indication detection, the biosensor integrates biological recognition components with nanomaterials, such as nanoparticles, nanocomposites, and nanotubes. Freshness-related analytes can be identified with precision thanks to biomolecules like aptamers, enzymes, and antibodies. The biosensor fits into portable devices for on-site freshness checks in the food supply chain and offers advantages like enhanced stability, lowered detection limits, and fast reaction times. The study presents fresh prospects for investigation and development in the field of food quality monitoring by examining manufacturing techniques, optimization strategies, and potential roadblocks. The real-time freshness status data provided by the biosensor has the potential to improve food industry quality control, decrease food waste, and increase customer safety. The study also offers a thorough re view of the developments in biosensor technology for freshness detection, including both electrochemical and optical modalities. It explores basic aspects of biosensor design such as advances in transduction processes and recognition components. The difficulties in identifying volatile markers and taking ambient factors into account while detecting freshness are discussed. Case studies from a variety of sectors show how adaptable and successful biosensors are in maintaining product freshness. The integration of biosensors into smart packaging systems is covered in the article, which provides real-time monitoring and enhances overall quality control. A useful tool for keeping up with the most recent advancements in freshness-detection biosensor applications, the article highlights future directions and new trends in the field of freshness-detecting biosensor research.

The areas that are out of reach from the grid can benefit with a tiny grid called a microgrid and this can get power from distributed generations. Renewable sources are the main source of power for the microgrids which are very much unpredictable in nature. The problem of frequency instability arises due to a power mismatch between the power-generating units and consumed power. When there is a deficit in power, the storage elements present in the isolated microgrid help to maintain the power and to do so the controller gives the command to various storage units. Therefore, this paper proposed a novel controller for microgrid frequency regulation by using a Parasitism Predation Algorithm (PPA) based PI-PD controller. The performance of the PPA-optimized PID for microgrid frequency regulation is investigated. The superiority of the PPA-based PI-PD controller is compared with those obtained from PPA-based PID, PD-PI controller.

The Sign Language Recognition project focuses on developing an efficient system to interpret and comprehend sign language gestures, crucial for the communication of the deaf and hearing-impaired community. By leveraging computer vision and machine learning techniques, the project aims to capture and analyze hand movements through Google's Python library, Media Pipe, and OpenCV. The process involves extracting joint coordinates from images, mapping them to gestures in American Sign Language (ASL), and translating these into textual language using a feed-forward neural network. This innovative approach utilizes key points rather than high-resolution images, significantly reducing the training dataset's size and overcoming variations in background and lighting conditions. By storing coordinate points in a CSV file, the system enhances space efficiency while addressing the challenges of gesture classification. Successful implementation holds promise in fostering inclusivity and accessibility, empowering individuals with hearing impairments to actively engage in education, employment, and social interactions within diverse aspects of everyday life.

Vibrationaloenergy harvesting has emerged as a viable battery-free solution for powering microelectromechanical systems (MEMS), particularly in extensive wirelessosensor networks utilized in aerospace and building infrastructures. This study focuses on the implementation of the Magnetostrictive Energy Harvesting (MEH) technique and conducts a comparative analysis with various other methods to enhance its advantages. The research assesses the performance of three widely used magnetostrictive materials—Terfenol-D (Tb0.3Dy0.7Fe2), Metglas (Fe81B13.5Si3.5C2), and Galfenol (Fe1-xGax)—in the development of magnetostrictive sensors. Utilizing finite element modeling, the paper anticipates stress analysis across different models, compares magnetostrictive materials (MsM), and deliberates on their respective merits and drawbacks. The stress distribution and output coil voltages of different MsM are simulated in COMSOL software. The findings are expected to contribute to the optimization of magnetostrictive sensor design and the advancement of energy harvesting and sensing technology.

As demand for high data rates, energy efficiency, low latency, and dependable connectivity in numerous application domains grows, wireless communication has made significant strides in recent years. Modulation techniques, which are crucial in determining the capacity and effectiveness of wireless communication networks, are one of the key elements driving these improvements. Wireless communication has long been based on conventional two-dimensional modulation techniques. However, a paradigm shift toward three-dimensional modulation techniques has drawn a lot of attention in order to fulfill the constantly expanding demands of contemporary wireless networks. These 3D modulation techniques make use of amplitude, phase, and the newly discovered polarization dimension to increase spectral efficiency and channel resilience, which also improves the performance of the receiver in terms of Bit Error Rate (BER). This work presents a comprehensive survey of 3D modulation schemes used in wireless communication, 5G, and beyond.

Mobile robots have been adopted into society to help with menial tasks like cleaning or sorting products. The issue is primarily the accuracy of the robot’s control, as the sensors may provide an incorrect reading or latency in the actuator. A PID controller can be implemented to increase the robustness of the control by having three tunable parameters in the algorithm. However, a fractional-order PID controller can achieve higher control and precision as it has five tunable parameters. Therefore, in this research, the mobile robot (AlphaBot2) will be implemented with a PID and fractional-order PID controller algorithm in Arduino to perform a path-tracking performance for various simple and complex patterns. The expected result is a higher precision control and performance for the fractional-order PID controller algorithm than the PID controller algorithm.

Beyond 22nm, a new transistor technology called FinFET (Fin-type field-effect transistor) presents an intriguing power-delay tradeoff. The work presented here involves the development of a Binary Content-Addressable Memory (BCAM) cell using 20nm FinFET technology. We measured the performance metrics and compared them to a CMOS-based design. The CAM cell design presented in this document is based on a NAND-type matchline (ML) matching scheme, which allows for parallel searching of multiple words. In this work, we analyse the advantages and disadvantages associated with each technology and present a comprehensive evaluation of their performance metrics, including power consumption, speed, Power-Delay Product (PDP) and Energy-Delay Product (EDP). The results indicate a significant improvement of 67.45%, 48.56% and 83.26%, 87.32% in power consumption, speed and PDP, EDP, respectively.

The primary focus of this paper is the navigation of a mobile robot using the Reptile Search Algorithm technique. A multifunctional 4WD UNO R3 robot car has been used for experimental purposes. RSA technique uses the sensory data fed in the Arduino board and helps in the path-mapping of the mobile robot from the start to the goal point. This algorithm and the sensory data together deal with a number of obstacles of various shapes and sizes while mapping the route from the start to the goal point. RSA helps in avoiding local minima during the quest of finding global minima. In this paper, both simulation and experimental works have been carried out. The obtained results show an average deviation of less than 5%. The results are depicted in terms of the time of navigation and the path length. The RSA technique lags in the Feature Selection, hence Feature Selection is the prime focus of this paper.

Balancing of rotors can be seen as time-consuming process when used in large industrial applications. The productivity decreases as the machine component must be brought to rest for balancing of specific components. In current research with the help of high precession Erbessd@ Instruments Rotor Balancing Machine minimization of residual unbalance is proposed. The rotor is balanced to acquire certain data such as amplitude, frequency, phase value, and residual unbalance which is obtained by the data acquisition system. After substituting the input values, the balancing calculator provides the compensating masses to be added at the required phase which balances the rotor. The data is stored for numerous iterations by changing the running parameters, obtaining the displacement at plane 1 and plane 2, and the RMS value. These output parameters are used to find the decision surface by applying AI Techniques such as ANFIS which would thereby, provide the residual unbalance, the amount of compensating mass to be added at what phase angle reducing the time of doing the experiments. Regression equations are also established to determine the changes in the independent variable associated with the changes in the dependent variable.

The presented research delves into the prediction of the kinematics for a bioinspired foldable flapping wing mechanism using an Artificial Neural Network. The investigation commences with the development of the kinematic parameterization for a foldable flapping wing UAV. Utilizing the established mathematical method of a foldable flapping wing mechanism, all datasets are generated to predict output parameters of links 4,7 and 8 (angular displacement) by modifying input parameters (fixed link length-crank radius ratio, angular displacement of crank, gear ratio, link-length ratio of special type of four bar mechanism) using MATLAB and verified by MSC ADAMS. The paper shows the implementation of a two-layer feed-forward neural network (a type of Artificial Neural Network), a subset of Machine Learning is employed to scrutinize the intricate relationships within the dataset using two algorithms under various geometrical conditions. The paper presents the comparison of the two algorithms i.e. (a) Levenberg–Marquardt algorithm and, (b) Bayesian Regularization algorithm of a two-layer feed-forward network. Out of this, the efficient algorithm is selected based on the performance parameters. This presented systematic approach needs no extensive experimental testing and support to make decisions based on predictions efficiently for all kinematic parameters of bioinspired foldable-flapping wing UAVs. This research may also be good for dynamic analysis to predict dynamic parameters for various mechanical systems.

The increasing global concerns about energy and the environment have created a growing demand for integrating distributed energy resources (DERs) into existing electrical infrastructures. To achieve this, peer-to-peer (P2P) energy trading is considered as, a potential solution to optimize the use of DERs. The proposed approach uses a private blockchain that is based on proof of authority for energy trading. A double auction mechanism is utilized for trading, which includes a multi-phase system for market clearing. To balance the load demand and clear the market, a smart contract is coded using the solidity language. In this work, a Flutter-based Android app is introduced for P2P energy trading that is built on the blockchain system, and it fully utilizes information about the participating prosumers and consumers, along with their electricity price and quantity. The reliability of the developed Android app is verified by testing it for P2P energy trading in the 0.4-kV Microgrid setup at the Clean Energy Research Lab, Nanyang Technological University (NTU) Singapore.

Crowdfunding is an innovative method to raise funds for various purposes, like developing innovative projects, charity, and many more. By using this approach, entrepreneurs who have limited funds to invest in their ideas can get more funding for their projects. There are a few crowdfunding platforms, such as Indiegogo and Kickstarter, that allow entrepreneurs to present their ideas to potential investors. In order to protect investors from fraud, these reliable third-party websites serve as an escrow for the money. However, these sites charge a higher fee for providing services. In this work, we replace intermediaries and their associated costs for fundraising between entrepreneurs and investors by using blockchain technology. Smart contracts are deployed on the blockchain to automate all operations of the framework, and it manages all operations based on the agreements and incentive rules as defined in the smart contract code. This enhances security and transparency among all the stakeholders. First, we designed the framework and then implemented it using Remix, and Solidity language is used to design smart contracts. The Ethereum blockchain network is used for storing and running smart contract transactions. For the front-end, we have user reactjs. Finally, the result of all the crowdfunding applications is described. This application would raise investment in renewable energy infrastructure projects and contribute to achieving the target of net zero emissions in the near future.

Aquaculture is a significant factor in strengthening global food security and contributing to nutritional well-being. Water quality parameters including pH, salinity, dissolved oxygen (DO), and temperature significantly influence the health, survival, productivity, and sustainability of aquaculture. Deep Learning (DL) techniques for predicting aquaculture water quality (A-WQ) not only overcome the limitations inherent in traditional methods, but also pave the way for accurate and efficient outcomes. In this work, we propose the use of Long Short-Term Memory (LSTM) and Bi-directional Long Short-Term Memory (Bi-LSTM) Deep Learning-Recurrent Neural Network (DL-RNN) models to predict aquaculture water quality parameters. Moreover, this paper is presented with a comprehensive analysis of the effect of hyperparameters ( $$h_p$$ h p ) on the prediction ability of the proposed Deep Learning models. The performance of the proposed LSTM and Bi-LSTM models is compared by means of both prediction accuracy and computational efficiency.

Modern power system is prone to several types of disruptions and contingencies and power system resiliency or survivability is the inherent ability of a power system to withstand and recover quickly from these severe contingencies. To quantify the resiliency of a power system under a specific contingency, a system resilience index has been proposed here in this paper. This power system resilience index can be evaluated from the power flow results data of the power system under any contingency condition. The lower the value of this resilience index, the more will be the resiliency or survivability of the system. In this paper, a modified IEEE 30-bus power system has been considered for the quantification, analysis, and prediction of its resiliency under several contingency conditions. Two machine learning tools have been implemented here for the prediction of system resiliency. An Artificial Neural Network (ANN) model and an Adaptive Fuzzy-Neuro Inference System (ANFIS) model have been prepared separately for the proper prediction of the resilience of the system at a particular contingency condition. A training dataset has been prepared by performing the load flow simulations of an IEEE-30 bus power system in MATLAB/Simulink software.

Microgrid is a combination of Distributed Generation (DG), loads & Energy storage systems basically a distribution system to cater to the load of a small community, colony or remote village, which may be operated to run either in grid tied or in an independent mode. There may be an impact on the grid side due to the addition of distributed generation (DG) particularly due to a change in the magnitude and direction of short circuit currents resulting in a major impact on the distribution side, causing false tripping or fail to trip the over-current protection relays in the power system. However, the protection logic designed must be able to meet the three basic protection needs namely selectivity, sensitivity and reliability in both modes of operation. The new protection logic frame must be adaptive and very sensitive in order to detect and isolate the minimum level fault currents very fast, ensuring a minimal interruption to the power system. In this paper, the protection issues are reviewed and an advanced protection scheme for AC Microgrids is developed and proposed. Here, one of the major challenges found while designing an AC microgrid is relay coordination. Simulation tests are conducted by applying three-phase fault in a 9-generator, 2-bus system designed using ETAP software.

Electricity fulfills one of the most fundamental needs in the modern society. Without electricity, it is difficult to conceive of what a comfortable existence on Earth would be like in the modern world. Because it enables us to transfer electricity from one form to another, an inverter is an essential invention among those that pertain to the field of electrical appliance invention. An inverter, in its most basic sense, is a device that transforms a DC energy source into an AC energy source. It is utilized in many contexts in the modern world. There is still a significant amount of investigation being carried out in this field. In this article, we will provide fundamental knowledge regarding the design of inverter hardware, but also provide a step-by-step description of how it is designed. I will take you by the hand and guide you step by step through the circuit and component requirements for developing an inverter. The researcher who is interested in getting a fundamental understanding of inverter design would find this work to be beneficial.