Control Applications in Modern Power Systems

The power balancing mechanism in power system is handled by automatic generation control (AGC), in which the secondary controller plays an important role. Here, in the present manuscript, we present the importance of secondary controller in single area thermal and two area thermal-thermal and thermal-hydro systems. It is observed that with secondary controller, the system dynamics do not possess any steady state error which was found without secondary controller. Various classical controllers namely proportional integral derivative (PID), proportional integral (PI) and integral (I) are used as secondary controllers and the corresponding gains are obtained by using particle swarm optimization (PSO) method and integral squared error (ISE) method. The PID controller shows superiority than I and PI controllers in the view of settling time, overshoots and oscillations. It is also observed that with PID controller the cost function value is considerably reduced. For this study, the electric governor is used for hydro generation.

In this paper, selection of suitable generation rate constraint (GRC) structure for optimal control of dual area thermal system is presented. Two different GRC models of open loop and closed loop structures are investigated in this work to find the one which suits better for the study of automatic generation control (AGC). Two-degree of freedom-PID (2DOF-PID) is enacted as regulator optimized with a hybridized soft computing mechanism of artificial electric field (HAEFA) algorithm. Controller parameter optimization procedure is laid with respective of minimizing the error squared over integral (ISE) function. Simulation results reveal the best suitable model of GRC for thermal system to obtain better functioning in AGC design.

Integration of Distributed Generators (DG) into Distribution Energy Network (DEN) became an important need, due to their technical advantages and economic benefits, as well as the contribution in power quality improvement and the reduction of the power losses. In this paper, is proposed a various version for hybrid Particle Swarm Optimization (PSO) algorithms based on chaotic maps and adaptive acceleration coefficients to optimally locate and size the Photovoltaic Distributed Generation (PV-DG) into DEN to minimize the Total Active Power Loss (TAPL), the Total of Voltage Deviation (TVD), and the Total Operation Time (TOT) of the overcurrent relay. The proposed algorithms were tested on the 28-bus DEN system, so that a study comparison was presented to identify the best hybrid PSO algorithm that delivers the best results in terms of achieving the best active losses reduction, enhancing the voltage profiles, and improving the overcurrent protection system.

In this article, a new system diminution technique is discussed to simplify the complexity of higher-order models. The proposed method focuses on the stability approach of Mihailov, make sure the stability of the simplified model if the large-scale process is stable. In this scheme, the nominal coefficients of the approximate reduced process are calculated by using the Mihailov criterion and the modified Cauer continuous fraction (MCCF) method that are used to determine the quantities of the numerator factorization. The usefulness of the solution suggested is demonstrated by differentiate the phase answers of the approximate reduced systems. Various performance error indices are employed as indices of results to compare the suggested method with current model reduction techniques.

This study provides a comparative analysis of two different swarm optimization techniques, i.e., flower pollination algorithm and ant colony optimization algorithm to find the PID controller parameters of a multi-area automatic generation control system. Dynamics of multi-area power system has been provided along with the linearized state-space model. Model order reduction is used to find the linearized model of the system. For controller purpose, PID controller is used. To find the parameters of PID controller, flower pollination and ant colony optimization algorithm have been used. Simulation results and comparative analysis have been provided.

The method of finding the voltage magnitude and angle of all buses based on measurement done on some buses is known as power system state estimation. Previously, we are too able to calculate the magnitude of bus voltages only. But nowadays, by using a phasor measurement unit, we can calculate the voltage and angle of the bus by placing a PMU in that bus. One of the advantages of PMU is in the area of state estimation. In this paper, we use PMU measurement in finding the state estimator of the IEEE 14 and 57 bus system using MATLAB programming. This method uses weighted least square (WLS) for calculating state estimator and post-processing approach for calculating PMU voltage and angle. By using conventional measurement, the method firstly calculates the state in polar form by WLS and then the final state is calculated with the help of PMU measurement.

The increment of load demand leads to voltage instability which is a major concern nowadays. It is required to transmit power in a efficient and economic manner. This problem can be solved either by increasing the generation or by enhancing the transmission capacity. But, due to financial constraints, it is not possible for constructing new generating stations. So, to improve the efficiency of the existing system, flexible AC transmission system (FACTS) controllers are incorporated. Since these controllers are costly, proper analysis is required for its siting. In this paper, the moth flame optimization (MFO) algorithm is used for the proper siting of FACTS controllers. Here, the proposed technique is compared with particle swarm optimization (PSO) and biogeography-based optimization (BBO) technique applied on IEEE 30 bus system, and the superiority of the MFO technique is demonstrated for reducing the active power losses in the transmission lines.

This paper explores the application of K-Nearest Neighbour (KNN) algorithm for detection of contingency due to line outage using the data from phasor measurement unit (PMU). In power system, contingency occurs due to outage of single or multiple lines. Detection of such contingency is needed in order to take corrective actions so that system stability is maintained. In this study, both the voltage phasors and current phasors are used to formulate the mathematical model. A new type of objective function is developed using the concept of Manhattan distance. The proposed method is tested on IEEE 5,14, and 57 bus networks. Detection rate of the suggested method is compared with some published methods and better performance is observed. The presented test results prove the effectiveness and viability of the proposed mechanism for detection of contingency.

This paper studies the effect of right-half plane zero (RPHZ) on the controller design of boost converter. Different techniques to mitigate RHPZ has been discussed in this paper. Circuit analysis of boost converter has been discussed in this paper, and frequency domain controller design for boost converter has been highlighted. The effect of RHPZ on the controller design has been highlighted. Simulation results have been provided for the voltage-mode controller design of boost converter.

This paper presents the power forecasting for grid-connected solar photovoltaic (PV) system using artificial intelligence nonlinear autoregressive exogenous (NARX) model. The NARX model consists of fifty hidden layers. The solar irradiation and temperature data generated from public websites for Udaipur, Rajasthan region, are used to train the NARX model. The corresponding power output of the simulated PV system is selected as target data. The Levenberg–Marquardt backpropagation function is used during the training. The complete system is modeled in MATLAB/Simulink environment. A neural network toolbox is utilized for training the system for future prediction. The simulation study is carried out in four cases. Simulation results show that the trained NARX model forecast power output in an effective manner with a root mean square error of 1.4270 for a one-year prediction.

The concept of frequency regulation for a multi-microgrid (MMG) model is investigated in this paper. The MMG consists of various distributed generators and energy storage units. In this paper, a hybrid energy storage model comprising battery energy storage unit (BESU) and superconducting magnetic energy storage (SMES) is proposed to effectively regulate the system frequency during various disturbances. Additionally, to improve the system’s dynamic performance, an intelligent swarm-based optimization algorithm is proposed to tune the PID controller parameters. To test the efficacy of the proposed SSA-PID controller an integral time absolute error (ITAE) based objective function is minimized. The dynamic response of the system is investigated under different scenarios by performing time domain analysis, and the results obtained are promising.

In this paper, a nonlinear sliding mode controller is designed for a linearized model of a constant DC voltage renewable source-based inverter. The inverter-based renewable DC voltage source system constitutes as a microgrid. The microgrid is located at distribution network side and generates power according to power demand in a specific region. Thus, a proposed controller is designed for islanded microgrid to achieve better performance and stability. The stability convergence of the control law is analyzed through Lyapunov stability theorem. The proposed controller improves overall system performance in presence of initial parameter variation by reducing over/under shoots, settling time, and oscillations. In addition, the proposed controller regulated inverter-based DG switching level so that it minimizes the voltage fluctuations. The performance, stability, and ability to keep in synchronism of the proposed control scheme are validated on an inverter-based battery storage model simulated in MATLAB©.

A strawberry algorithm (SBA) has been employed in a two area power system to optimize the parameters of proportional integral derivative (PID) controller. The PID controller is used as a load frequency control. Two type of schemes have been utilized for scrutinizing of SBA technique. Two area system without non-linearity is first schemes while second scheme is two area system with non-linearity like GRC, GDB, communication delays etc. The optimal controller parameters are obtained for both the systems using SBA by minimizing the performance index IAE (integral of absolute error). The performance of the obtained controllers is tested in MATLAB environment. The dynamic responses of controlled system are compared with the other techniques like TLBO and PSO.

In the PV curve of photovoltaic system, multiple peaks are present which makes the conventional MPPT techniques incapable to track global maximum power point (GMPP) effectively. The use of intelligent MPPT techniques such as ANN or intelligent hybrid MPPT techniques which combine intelligent MPPT technique with either of conventional, hybrid or optimization based MPPT technique is very essential for assurance of tracking GMPP with increased performance and efficiency of MPPT.

This article proposed the mixed approach technique using a genetic algorithm (GA) to find adequate numbers and optimum locations of isolators in the radial distribution system to increase the duration of power supply to customers. This paper also provides the information about the distribution system reliability indices which is used to enhance the overall system performance. Nowadays so many distribution companies are in the competitive market to provide reliable and cost effective power to their consumers. Reliable power supply is a challenging task these days for distribution companies. In this paper, the various reliability indices like SAIDI, SAIFI, ENS etc. have been calculated to determine the reliability of a radial distribution system. Foremost goal of this article is to decrease ENS (energy not served), so that we can enhance distribution system performance to deliver uninterrupted power supply to its valuable consumer. The suggested technique is tested on a radial distribution system containing 13 and 22 IEEE buses. The results show that the adequate number of isolators and their optimal locations enhance the reliability as well as appear to be cost effectiveness of power supply to their valuable consumers.

With the propitious steps taken by government of India in the field of electricity generation from clean energy resources, solar energy is most prominent. As per government, the maximum target is (100 GW) assigned for states rich in solar potential. As we all know that success of this mission will not only depend on the installation but on the long-term performance also. It should be ensured that the huge initial amount invested should be returned in proper time with satisfactory performance. Since the lifetime of such kind of systems is approx. 25 years and several other natural parameters varies across the country, therefore the approach of specified predictive maintenance should be preferred rather than generalized preventive maintenance. The overall yield can be monitored through the collected data from sensors also the degradation rate can be checked which is responsible for yield. In this paper, an attempt is made to highlight the present scenario of maintenance approaches adopted in this field with possible causes of degradation.

With the development of microgrid technology, combined heat power (CHP) dispatch becoming a popular preference for reducing cost and to meet the increased load demand in microgrid system. This research work intends to focus on uncertainty modeling of renewable energy sources and load using unscented transform (UT) technique. Moreover due to a new entity like CHP units having non-linear and non-convex characteristics, the operation management becomes more complex during the energy scheduling. Hence a meta-heuristic technique has been implemented to find the optimal operation cost under an efficient Energy Management Scheme (EMS). The velocity-update mechanism-based modified Particle Swarm Optimization (PSO) has been introduced to improve high search precision over stochastic solution and to deal with premature convergence. Statistical analysis has been shown in order to reflect effectiveness of proposed algorithm implemented in the microgrid with CHP integrated system.

In this research paper, a framework for optimal reactive power planning (RPP) in power transmission system is proposed. This is a comprehensive study for the installation of FACTS (flexible AC transmission system) devices and the minimization of operating cost. The locations for the equipment of FACTS devices are determined depending upon a set of mathematical calculations and considerations. Here, the operating cost is formulated as the sum of cost associated with real power loss, reactive power generation of generators, cost during line charging, and FACTS device cost. The control variables for RPP are addressed as optimization problem. So, in order to facilitate the solution of RPP, hybrid algorithm is used in this article. The proposed approach has been performed on standard IEEE 14 and IEEE 57 bus. A comparative study has been done among the simulation results and much better performance is noticed in case of hybrid algorithm.

Microgrids are a prominent area of research in the current era, as it offers solutions to many issues in the conventional grid, like increasing electricity costs, ageing infrastructure, lack of reliable supply of electricity to remote areas, etc. Though microgrids help in eliminating the above issues, it poses the problem of intermittent nature of Distributed Generation (DG) sources. A solution to the above issue can be Energy Storage Systems (ESSs) but they are costly. Interconnected microgrids are a promising research area in this regard. If a microgrid faces an energy shortage, it can absorb power from a neighboring microgrid. Since there are a number of renewable energy sources in interconnected microgrids, it may confront issues such as poor power quality and substandard dynamic performance that may arise due to the lack of sufficient reactive power support. To improve the voltage profile and maintain power quality in such a scenario, the Distribution Static Synchronous Compensator, popularly known as D-STATCOM may be adopted, which is a shunt active filter that provides adequate reactive power support. The operation of the D-STATCOM is often controlled by Proportional and Integral (PI) controllers. However, the PI controlled D-STATCOM has a slow response. Fast acting robust controllers, namely Fuzzy Logic Controllers (FLCs) are required for efficient D-STATCOM control, to ensure sufficient dynamic voltage control so as to improve power quality. This paper presents a comparative study of PI controlled and fuzzy logic controlled D-STATCOM in MATLAB/Simulink environment, for improving the voltage profile at the Point of Common Coupling (PCC) of a multi-microgrid interconnection scheme.

Generating power at affordable generating cost without any critical damage to the environment plays a crucial role in power system Operation and control. The Combined Economic and Emission dispatch problem (CEEDPs) deals with the reduction of generating cost without any harmful impact over the environment. Particle swarm optimization technique is utilized to reduce the generating cost as well as the rate of discharge simultaneously. This paper shows the effectiveness of this algorithm by creating variation in the Inertia weight function. This paper presents six different inertia weight factors that are used in this algorithm to reduce the generation and rate of discharge of harmful gases. These variations in the Inertia weight factor are used in the PSO for EELD’s of IEEE 30 bus system.

This paper presents the doubly fed induction generator(DFIG)-based grid integrated wind energy conversion system (WECS). An integrated power smoothing technique for DFIG-based WECS for fluctuating wind speed conditions is presented and discussed. Adaptive Neuro-Fuzzy Inference System (ANFIS) is employed to reduce deviations present in system response for a change in input wind speed. The novel ANFIS control is employed for both, grid side control and rotor side control, which gives a better performance. The proposed technique enhances the power quality along with safe operation by reducing faulty conditions in WECS. The system modelling is done using MATLAB/Simulink software for the ANFIS.

Loads play vital role in maintaining power quality in a low voltage distribution system as per IEEE standard 519–1992. The loads are classified in different ways based on their application, nature, and impact. Also various approaches of modeling loads are available in the literature. In this paper, a study is carried out to compile various load classification and modeling of few loads in the low voltage distribution system. Frequency domain analysis in s-domain has been carried out for specific class of loads. Simulation using PSCAD/EMTDC 4.2 has been done for a certain class of loads. An experiment is performed to obtain current and voltage waveforms of chosen class of load.

In the present situation, the populace is expanding at an exceptionally high rate because of which the utilization of intensity has been expanded. The exceptionally populated regions are experiencing the unpredictable system of overhead line issues like open circuit shortcomings, lightning dangers and so on which aren't on account of the underground link supply framework. The electrical parameters like electric field power, voltage conveyance, and vitality thickness and so on are results of appraised voltage and high voltage which assume a significant job on the constancy of underground links. The general execution of these underground force joins is critical for the suitable errand of the force system. Trustworthy period issues with them are associated with the contamination of polymer materials utilized for the spread because of electric, warm or ordinary weight. The electric field stress is the major concern while assigning the insulating properties to the underground cable. The main aim of the electrical field evaluation through the usage of stand-apart numerical strategies is to discover electric field stress and various parameters, which can be an unavoidable device in arranged quality concerned developments; particularly to consider discharging process and construction of underground cables having a very high rating. Here the finite element method (FEM) has been incorporated to discover mainly two-parameter i.e. energy density and partial discharge of a single-phase 500 kV underground cable based on the ethylene-propylene rubber (EPR) insulating material having some limiting conditions and the overall analysis has been done by the ELECTRO module of the integrated engineering software (IES) in two-dimensional mode.

Demand response is a potential solution for efficient control of smart grids with renewable sources integration. It enables all the consumers to adjust their demand profiles on the request from a centralized operator called as independent system operator (ISO). In the recent past, there has been increase in decentralized approach because of the disadvantages posed by the centralized approach. Data breach and security lapse are the major issues that are involved in a centralized approach. Hence, a novel system of control using blockchain technology has been researched for smart control, which involves smart contracts, distribution ledgers, and also peer-to-peer network communications. This paper gives a glimpse of this latest technology and how this can be useful in implementing demand response effectively in smart grids.

Wind energy is known to be the most promising renewable energy resources present on the earth. It is necessary to operate the WECS at optimal conditions so that we can achieve maximum output from the system. For this, many MPPT algorithms have been used till date. The maximum power point tracking-based controller is usually employed for regulating the speed of the generator such that the power coefficient of the Wind Turbine is maximized when the wind speed is lesser than the rated wind speed. In this paper, we represented perturb and observe algorithm (also known as HCS) based FL control for Doubly Fed Induction Generator (DFIG). It is unrealistic to calculate the turbine power and the turbine speed instantly. Fuzzy Logic can be used where exact estimation of something is not reliable. It trains our data according to the measurable input and provides the most efficient output. This control method gives us the optimum generator speed, at which if we make our generator to operate, it will maximize the total produced power by the generator.

Power system network is connected in radial and mesh distribution lines. Distributed Generation (DG) and Distribution Static Compensator (DSTATCOM) can be used to reduce power losses, improve voltage profile and to improve other electrical parameters. Solar Photovoltaic (SPV) and wind based DG has their own environmental as well as economic advantages. Improper size and location of DG and DSTATCOM may lead to poor voltage profile. Different algorithms are available for allocation of DG and DSTATCOM. Load sensitivity factor (LSF) is used to select most sensitive buses which are best suited for location of DG and DSTATCOM. DG and DSTATCOM can inject real and reactive power. In the proposed paper, DG is used to inject real power and DSTATCOM is used to inject reactive power only. Bacterial Foraging Optimization Algorithm (BFOA) and Whale Optimization Algorithm (WOA) are used for the same with minimum real power losses as an objective function. BFOA is nature inspired algorithm which is based on foraging behavior of Escherichia coli bacteria and WOA is based on foraging behavior of humpback whales. Proposed paper consists mathematical modeling of Solar PV and wind based DG and DSTATCOM, analysis and comparison of both algorithms for IEEE 33 bus radial as well as mesh distribution system. Computer load flow programs have been developed for implementation and analysis are performed in MATLAB programming. Results show better voltage profile and less power losses for mesh distribution system as compared to radial distribution system, considerably reduction in losses and improvement in voltage profile with DG and DSTATCOM which helps DNO in cost reduction.

In this modern era, electricity plays a highly crucial role in the growth and progress of any given country. Thus, proper management, time-to-time monitoring, and a correct billing system are very much essential. However, most of the energy providers in our country are still using the old conventional methods for billing, which are not only outdated but also highly inefficient. These methods lack bi-directional communication. Although there have been numerous advancements in metering, there are still many problems like electricity theft, no current connectivity, etc. So, our paper shows the use of a new and innovative system that will reduce the gap between the consumer and provider. It includes the making and designing of a portal or Website providing access to billing information. We have used HTML, CSS, JavaScript, and NodeJS for creating the user interface which will display all the information like energy consumption in kWh, the amount paid, the previous month’s expenditure, etc. We have also used MongoDB which is a source-available cross-platform document-oriented database program.

The ability to provide an uninterrupted power supply to the customers is known as reliability of the distribution system. There are two approaches to evaluate the reliability of the distribution network, historical estimation, and predictive estimation. Reliability indices assist to monitor the wide status of the system and also amplify the reliability and quality of the supply. Mechanical energy meters are still being used in our country. The advanced metering system is the most advanced digital computing technology to adjust and monitor the usage of power in the smart grid. By implementing a smart metering system, an accurate reliability system can be attained. Therefore, this paper calculates the reliability of advanced metering distribution systems using the reliability measures which include system average interruptions frequency index, system average interruptions duration index, consumer average interruption frequency index, consumer average interruption duration index, average service availability index, and average system interruption duration index.

The aim of this effort is to evaluate the performance of various empirical relationships available for computation of solar radiation over tilted surface. In this work we have considered four isotropic models and four anisotropic models for evaluation of radiation over titled surface. The angle of inclination was fixed equivalent to the geographical latitude of the area under study. Two different locations have been considered for computation purpose, New Delhi, the national capital of India, situated in the sub-tropical belt of globe and Bhopal, a city located in the central part of India, near the Tropic of Cancer. All the computations have been carried out in the MATLAB environment. It has been deduced that Badescu model has exhibited the lowest amount of error and has predicted the value of radiation nearest to the measured data. This is an isotropic model. While Klucher’s model has shown the highest error for the considered geographic locations. Thus, Badescu’s empirical relationship can be realized for calculation of solar irradiance over inclined planes in this geographical belt and can hence provide support for solar PV applications.

Optimal utilization of distributed energy resources in a microgrid is an essential requirement to ensure load requirements. Energy management system can optimize the reliability of a stand-alone microgrid with a solar PV-based active generator with energy storage. This work aims to design and develop an energy management system (EMS) for a hybrid solar battery-based system in a stand-alone microgrid. A hybrid solar battery energy storage system is modeled with its individual dedicated power converter units in MATLAB/Simulink. Based on the power generated and the system’s demand, the PV and the battery storage systems are scheduled to supply energy to the load, and the battery can capture the surplus energy requirement. The energy management system, along with the system model, is developed in MATLAB/Simulink environment, and the working of the proposed energy management system is validated for four different test cases.

Modern Power Systems tend to get more complex along with their constant growth. This is due to unpredictable rise in the loads and new power sources like windmills, hydropower plants etc. entering the system every year. This impetuous behaviour in the grid leads to confusions in the power generation and might cause an imbalance in the generation and consumption sides. Traditional machine learning algorithms lack the ability to help with this problem of the modern power system. More sophisticated algorithms are needed to help in solving such problem and successfully operating the power grid. This paper reviews the applications of Machine Learning in the two main aspects of the grid, i.e. Load Forecasting and Fault Detection. The drawbacks of implementing the same are discussed at the end of the article.

The contribution of renewable energy in the power system increased exponentially because they are pollution-free, and non-exhaustible sources of energy. Solar photovoltaic (PV) array output power is completely dependent on solar irradiance and ambient temperature, and electricity from the solar panel is less. So, to increase the output power, we need to arrange the solar panels in series and parallel to make solar arrays. In this series and parallel connection of solar panels some panels are affected by the shading of the adjacent buildings, trees, and clouds. Because of this effect, solar panels exhibit several peaks in their I-V and P-V characteristics curve. So, regular MPPT techniques unable to track these multiple peaks to produce the maximum power. This paper introduced a new Artificial Neural Network (ANN)-based MPPT technique which is used to monitor maximum power point (MPP) and improve the output power of the solar panels in the case of partial shading. The solar panels are modelled in MATLAB/Simulink system. The MPPT algorithm based on ANN is programmed through MATLAB programming. The ANN determines the duty cycle of the converter, which is varied according to the load matching feature, to ensure that the output power is close to equal the input power in a practical configuration. The proposed ANN is compared to the P&O method in this paper, and the proposed techniques are found to be superior and more efficient than the conventional P&O method.

Climate change is one of the most pressing issues in recent times. The main reason behind this is the emission of greenhouse gases. Burning of fossil fuels in thermal power plants for electricity generation contributes to this majorly. This can be reduced by adopting renewable energy resources like wind, solar etc. Solar PV power generation systems are being increasingly adopted due to technological advancements and cost reduction. Solar power generation is not reliable due to variability in the irradiance and temperature values throughout the day. If this variability can be forecasted this will help in proper load scheduling and make the system more reliable. The major focus of the study is to reduce the time for forecasting and to minimize the error for short-time duration i.e., One Hour ahead forecasting. In this paper solar PV power forecasting is done using two methods Artificial Neural Network (ANN) tuned using Genetic algorithm (GA), Artificial Neural Network tuned using Hybrid Genetics-based Particle Swarm Optimization (PSO) respectively and the results are compared. The ANN is simulated and the tuning of ANN as well as verification of results is done using MATLAB.

In this present work, we develop a model of two-area power grid system for load frequency control (LFC), a three-degree of freedom PID controller is referred and suggested as a special controller. The system under analysis includes a single power system having two control area, each of which comprises reheat type thermal unit generating source. Salp swarm algorithm (SSA) optimization technique is utilized for tuning controller to find the optimal value of the parameter gain of recommended 3DOF-PID control system for each control region or area. The optimization process is preceded using a function Integral time absolute error (ITAE) as an objective function to access the performance measure from simulink model and a 0.01p.u sudden perturbation of load for area-one. For achieving the better dynamic behavior of the system responses, such as time of settling, peak undershoot, and peak overshoot, evaluation of system’s dynamic performance. Further comparison of PID, 2DOF-PID and 3DOF-PID controllers are also included in this analysis.

Electromagnetic interference (EMI) generated in grid-connected solar photovoltaic (SPV) system is addressed in this research paper. The major emphasis has been given on the issues related to generate EMI magnitude due to PV panel capacitance to earth, Common Mode (CM) interference due to switching of inverters, and the length of DC cable in medium power grid-connected SPV system. The CM EMI is more dominant as compared to Differential Mode (DM) EMI. The simulation results of the generated EMI at 50 kW grid-connected SPV installed on the roof-top are shown. The results show that the conducted EMI generated by inverters changes its behavior under various circumstances. The generated EMI is compared against the CISPR 22 standard of electromagnetic compatibility. Switching control techniques are tested in this system. The output performance of these strategies is also discussed in this paper.

The worldwide population explosion has narrowed the balance between energy supply and demand to alarming levels, leading to problems like the energy crisis plaguing the entire world. This has resulted in a significant increase in the evolution of renewable energy sources (RES), like solar and wind, whose stocks have increased remarkably. This paper explores a technically feasible and economically relevant way to fulfil growing demands in a highly distributed scenario. It also elaborates a load flow methodology to comprehend the steady-state characteristics of distribution networks incorporating wind turbines and photovoltaic systems that can withstand radial and weakly meshed systems. This paper explores the effects of RES as distributed generation (DG) power sources in terms of bus voltage and power loss profiles in a distribution system. An IEEE-33 bus test system is simulated to ascertain the implementation and performance of the load flow algorithm in MATLAB.

Model-based approach along with few signal processing techniques like EMD is used for diagnosing synchronous generators. Maintenance is essential for machines, and many industries started to rely more on condition-based and preventive maintenance rather than scheduled and risk-based as it reduces cost and also ensures safety. To reduce the complexity and cost, experimental test rig requirement can be eliminated using Maxwell software. DEKF is used as it has two cascaded EK filters; it can estimate both state and parameters. Residual and IMF3 together will work as a reliable fault signature which helps in differentiating the stator inter-turn fault and a pseudo stator fault.

Stator inter-turn fault diagnosis of synchronous generators is necessary to avoid safety and efficiency concerns, as well as the overall breakdown of the machine. This work uses the Dual Sigma-Point Kalman filter for condition monitoring of a synchronous generator. By performing this method, the stator inter-turn faults can be diagnosed in their incipient stages. The analyses include testing of data with different load conditions and load currents to ensure the consistency of the proposed algorithm.

In this article, an internal model control (IMC) scheme-based proportional integral derivative (PID) controller is designed for controlling second order non-minimum phase system with time delay. The design uses IMC filters of higher order for realizing the controller. The novelty of work lies in the design controller based on maximum sensitivity. The proposed design also uses higher order Pade’s approximation for time delay. The closed loop performance is observed with nominal model, perturbed model and for noise in the measurement. The performance is computed using integral square error (ISE) and Integral absolute error (IAE). The controller effort is estimated using a measure called total variation (TV). Further, stability analysis is accomplished for variation in the model parameters and fragility analysis is carried out for uncertainties in the controller.

The extreme events (e.g., hurricanes, earthquakes, and floods) and man-made attacks have caused huge losses in the power grid and in the developed countries where the reliable electrical power supply is essential, and such events have gathered the attention of the researchers to develop new techniques to reduce their impact. The effect of such high-impact, low-frequency (HILF) events is measured through resiliency index, and various control, operation methods and planning strategies are proposed to improve grid resilience, but have limited application. Indian power system is among one of the largest synchronized grids in the world with a unique geography and topology. Hence, Indian power system experiences a varying degree of diversity in terms of weather and climate and faces more than one high-impact, low-frequency (HILF) events every year having a significant loss in the Indian electrical grid. This paper discusses a unified resilience evaluation with optimal placement of distributed generation (DG) for different penetration level. The proposed method is tested for the 33-bus distribution system, and results reveal that the with multiple energy sources in the grid, the resiliency can be improved during HILF events.

In this paper, we attempt to understand the limits to the loading of real and reactive powers at individual buses of radial feeders keeping the desired system constraints at specified values. To observe these effects, we apply the analysis to two types of IEEE radial feeders, namely an unbranched 12-bus feeder and a branched 15-bus feeder. For efficient operation, the feeder should have minimum system losses, and the voltage at all buses should be within the limit securing an acceptable value of voltage stability index (VSI). The individual bus loadings are increased till the system constraints are encountered. The extents to which increased loadings are possible are studied which are followed by accompanying conclusions. The study shows that certain buses of radial feeders can be loaded beyond their specified values without much compromise in the overall performance and without need for any additional infrastructure like distributed generators, capacitors or other costly equipment.

The paper deals with the development of background for defining Lyapunov functions for a wide range of linear dynamical objects. This background is based on assuming that the Lyapunov function is redundant energy in the considered object, and this energy is dissipated only during controlled motion. We assume the full derivative of the Lyapunov function for an autonomous motion of the control objects equals zero, and we use its summands to define linear algebraic equations. The solution of these equations allows us to find unknown terms of the Lyapunov function. The use of these terms, while the Lyapunov equation is being written down, shows that the left-hand expression in the Lyapunov equation is equal to the zero matrix. Thus, we avoid subjective assuming of quadratic form terms in the right-hand of the Lyapunov equation. We extend the proposed approach to the class dynamical system with uncertainty. This extension is performed by using interval methods, which allow defining object motions for minimal and maximal values of parameters. We show that for the control object, which parameters are not exactly known, one should consider two equations of object motions, which correspond to its trajectories on the boundaries of the intervals. Lyapunov functions are defined for these boundary trajectories. Since such an approach increases the number of the considered equations, we offer to decrease them by using hyper-complex numbers while object equations are written down.

Wind power plants are an important source of renewable based electric power generation in the power system. Onshore wind power plants have shown that wind energy can be successfully used for power generation. This paper focuses on modeling of wind resource based on Weibull probability density function and assessing the suitability of an offshore wind farm site based on wind power density (WPD). The novelty of this work is the application of kernel density estimation based probability distribution to a particular month wind speed data where the wind resource is having two peaks. It is observed at site under study that the most prevalent wind direction is southwest and it is followed by northeast direction. During June month, the wind power density is 1086.15 W/m2 and 1056.42 W/m2 at 100 m and 80 m heights, respectively. From May to September months, the WPD is more than 500 W/m2. Wind power density is greater than 216 W/m2 in every month except for the month of April where wind power densities are 109.79 W/m2 and 104.33 W/m2 at 100 m and 80 m heights respectively. Wind resource assessment shows that Dhanushkodi coastal offshore site has good wind profile for offshore wind farms.

The use of energy from only one type of renewable energy source (e.g., solar and wind generation) leads to a significant increase in the cost of electricity supply due to the need to install a backup power source (energy storage system or power system). This phenomenon is caused by the significant dependence of these sources on weather conditions. The wind speed is less than 4 m/s for a long time in summer, and solar energy is not available at all in dark day periods, which in December can last up to 16 h. Accordingly, the effective use of such systems is possible only if constructing a complex application of several sources of different nature (solar and wind generation). This measure allows increasing their energy efficiency by 30–50%. The combined use of these types of distributed generation is accompanied by the equalization of the daily energy intake due to the spread of the annual maximums of energy intake, but the daily fluctuations remain pretty significant. As a result of the conducted researches, it is established that when using the share of solar energy at the level 0.4… 0.55 of the total amount, the shortest period of energy deficit (at the level of 8 days per year) is observed. At other ratios between the shares of solar and wind energy, the period of energy deficit will increase. In case of increased requirements for providing responsible consumers with electricity, it is advisable to use systems with buffer electricity storage. The developed mathematical models allow calculating the equipment parameters used in the construction of efficient power supply systems.

Nowadays, climate change and the increased dependence of transportation sector on fossil fuels have created serious environmental problems, resulting in the popularity of Electric Vehicles (EVs). To support the usage and development of EVs, it is necessary to make available ample charging stations along with the support of governments for subsidies and electrical infrastructure development. We see renewable energy-based power generation systems being deployed in large scale in order to overcome the problems associated with fossil fuel-based electricity generation. A fuzzy logic (FL)-based centralized energy management control strategy for a solar-powered electric vehicle charging station (CS) is discussed in this paper. The main objective is to effectively operate the charging station in Photovoltaic (PV) standalone mode. The proposed electric vehicle charging system is modeled and simulated in MATLAB/Simulink software and verified system performance under varying irradiance and load conditions.

Electric Vehicles are promising alternatives for transportation as they can improve the environmental sustainability. Battery EVs (BEVs) or all-EVs are known as Plug-in Electric Vehicles (PEVs). Battery is the only source of power for PEVs. The large penetration of PEVs into the utility grid causes an increase in electricity demand and a significant shift in the demand curve's shape. To overcome this issue, charging of PEVs should undergo into a scheduled process. In this paper, charge scheduling of EVs by considering the solar PV power generation is presented. Vehicles are scheduled to charge based on the day-ahead forecasting of Solar PV generation. The proposed scheduling technique is developed using MATLAB. To verify the scheduling algorithm's performance, efficiency, and accuracy, tests were carried out under different scenarios for vehicles of different rating. In Australia, Time of Use Pricing (ToUP) is adopted and the pricing scheme of Australian Capital Territory (ACT) is considered in this work.

Precise gauging of the State of Charge (SoC) and State of Health of Lithium-Ion batteries is very basic for the ideal working of Battery Management Systems (BMS) locally available electric vehicles particularly with the end goal of range estimation, determination of time required for complete battery drain and Remaining Useful Life (RUL) forecasting. Current techniques for the assessment of SoC and SoH, for example, Coulomb Counting or Kalman Filtering either have low precision as on account of the former lacking accuracy and precision and the latter lacking adaptability and versatility. This paper presents an elective way to precisely gauge and anticipate the SoC and RUL of lithium-particle batteries utilizing Artificial Neural Networks to guarantee both exactness and versatility for ideal performance of Battery Management Systems (BMS).

IoT has important roles in smart grid to monitor and manage the different parameters of power systems from anywhere in the world by using internet. IoT provides automation to organize the work in easy manner. It enables sensing, monitoring, collecting and analyzing of large amount of data from different sources such as social media, machines, etc. Use of IoT is increasing in wireless technology as increasing the presence of smart appliances such as smart phones, smart watches which are important part of communication system. IoT enabled smart electricity meter is very essential part of smart grid. IoT enabled monitoring of solar PV, wind and other renewable based generation plays vital role in smart grid. In proposed paper, applications of IoT at different level of Smart Grid are analyzed. Also communication technologies and roles in smart grid with security management issues of IoT are analyzed.

In this paper, various cyber-physical system techniques are proposed to control and monitor the smart grid. Due to the increased dependency on IT and automated components of the electrical power system as in case of smart grid, the vulnerability of cyber security has increased, and thus, it leads to the importance of cyber security. Thus, cyber-physical energy systems such as smart grid can get influenced by cyber-attacks and cause unintentional tripping leading to the undesired power outage. Hence, the attacker types, attack types, their impact on the whole smart grid and its repercussions become significant so that they can be eliminated timely, and this is where the monitoring and controlling of smart grid by cyber security system become helpful. The potential attacks inject malicious control signals, and thus, it changes the normal operation of digital relays and circuit breakers causing relays to trip at an undesired time when it need not to trip, which in turn results in spurious tripping, when there is no fault in the system. However, detecting and discriminating anomalies or problems caused by the cyber-attack against the power system are yet to be satisfactorily achieved.