Particle Swarm Optimization algorithm (PSO) is found to be an effective meta-heuristic swarm-base... more Particle Swarm Optimization algorithm (PSO) is found to be an effective meta-heuristic swarm-based algorithm in solving modern time problems. Various improvements have been proposed in this algorithm in terms of internal computation, acceleration coefficients, stopping criteria, hybridization, velocity upgradation etc. The objective of this paper is to implement hybrid weights and, therefore, improve the quality of PSO algorithm. In the case of hybrid weights, we have combined two weights at a time. These weights are mixed in various but not in equal proportions and are tested against ten standard testing functions along with the pre-existing weights. By using this collection, we have analysed them on three parameters-mean, standard deviation, and minimum value achieved. Later on, after analysing the data, we found out that hybrid weights are an overall better option with respect to the pre-existing weights.
The key objective of the power system is to supply reliable power at the lowest possible cost and... more The key objective of the power system is to supply reliable power at the lowest possible cost and one of the most important and technical tools to achieve this is the optimal power flow (OPF) that determines the best operating conditions for power systems while taking into account system constraints including generator capabilities, line capacity, bus voltages, and line power flow limits. Traditional methods fail to meet the required objectives because they are ineffective in dealing with quantitative objectives, exhibit local convergence, and require a significant amount of computational time if the system is large, complex and hybrid. Moreover, the problem of OPF has grown more complicated as a result of a paradigm shift in the power industry that established competition in the electricity sector. Artificial Intelligence (AI) technologies have emerged in recent years that can address extremely complicated OPF problems. Because of their versatility, such AI approaches may deal with qualitative constraints in a seamless and efficient manner. The current study provides a comprehensive review of various computational intelligence-based optimization methodologies for solving OPF problems, with a focus on the modifications introduced over time and the computational features of each for various test cases. The recent evolution of population-based search algorithms and their hybridization for addressing OPF problems, particularly over the last two decades, has been investigated in this article.
Particle swarm optimization algorithm is an intelligent optimization technique applied to solve o... more Particle swarm optimization algorithm is an intelligent optimization technique applied to solve optimization problem in the field of Engineering, optimization design of electrical networks, aircraft, control systems, optimum design of chemical processing equipment, design of civil engineering structure, economic load dispatch, load flow, management system, medical etc. Various parameters of PSO are: population size(P), inertia weight, acceleration constants random number, Maximum number of iteration. In this paper an attempt is made to understand the PSO algorithm by solving Rosenbrock benchmark function manually. The impact of variations of parameters on PSO convergence has been studied. Each parameters is varied systematically keeping all other parameters fixed to some value. Rosenbrock benchmark function has been used to study the impact of parameters variations. It was found that PSO is sensitive to the values of these parameters, combination of various parameters for best accuracy and faster convergence has been determined.
Distributed energy resources, or distributed generation (DG), are the mainstay of modern power sy... more Distributed energy resources, or distributed generation (DG), are the mainstay of modern power systems aiming towards green energy generation via the effective integration of renewable energy sources. DG involvement in traditional power systems comprises the enhancement of power quality parameters, the utilization of renewable sources, cost optimization, and stable and reliable energy generation. The advantages of such a revolutionary approach can be achieved with the optimal sizing and allocation of DG by means of adequate optimization techniques, constraints, and optimized parameters. In this study, a comprehensive review of DG optimization is presented in light of recent developments, and a comparison is carried out based on the adopted optimization techniques, test system, enhanced parameters, and outcome evaluations.
The study aims to examine the effect of environment and economic variables on logistic performanc... more The study aims to examine the effect of environment and economic variables on logistic performance in India. In order to study the long run and short run association between the variables the study employed auto regressive distribution lag (ARDL) approach on a time series data from 2007 to 2018. The result revealed that foreign direct investment (FDI) has a positive relation with LPI whereas fossil fuel consumption in both the short and long run has a negative relation. On the other hand, GDP per capita has a negative relation with LPI while total greenhouse gas emissions has a positive relation, which is a sign of concern for environment sustainability. In the recent report published by the World Bank India’s rank has slipped down from 35th to 44th position worldwide whereby all the six dimensions have shown a downward trend. India being one of the largest customer oriented market would negatively affect the world economy if its logistics operation are poorly driven. This study hig...
The extraction of maximum power is a big challenge in solar photovoltaic-based power plants due t... more The extraction of maximum power is a big challenge in solar photovoltaic-based power plants due to varying atmospheric and meteorological parameters. The concept of array reconfiguration is applied for the maximum power extraction in solar PV plants. Using this approach, the occurrence of multiple peaks in P-V and I-V characteristics during partial shade can be smoothened and reduced significantly. Partial shading due to the movement of the cloud is considered in the research. The cloud movement mainly because of velocity and wind direction is used for creating various shading conditions. The main focus is to reduce the power losses during partial shading using a nature-inspired optimization approach to reconfigure the array for different types of shading conditions. A grey wolf optimizer-based bridge-linked total cross-tied (GWO-BLTCT) configuration is proposed in this paper. The performance of the proposed topology is compared with standard and hybrid topologies, namely, series-pa...
2022 International Conference for Advancement in Technology (ICONAT), 2022
The use of renewable energy resources is increasing to provide energy efficient and uninterrupted... more The use of renewable energy resources is increasing to provide energy efficient and uninterrupted supply of power. The use of solar photovoltaics (SPV) in the agriculture sector is gaining importance due to its availability in abundance and other advantages. This paper presents SPV based water pumping system (WPS) for rural agricultural practice in Indian scenario. The proposed water pumping system comprises of SPV system, maximum power point controller, boost converter, inverter, and 3-Φ induction motor driving a water pump. The stochastic nature of SPV power is a concern for electrical engineers. Thus, the use of maximum power point tracking (MPPT) algorithms is of key focus to improve the efficiency and ensure the robust performance of SPV system. The proposed SPV system incorporates an artificial intelligence method for designing MPPT algorithm. The paper introduces a grey wolf optimization inspired algorithm to extract the maximum power under varying irradiance conditions from SPV system. The performance of the proposed algorithm is compared with other algorithms for partially shaded conditions on the basis of computational burden, maximum power tracked, and fluctuations. The proposed controller is better with least rise time of 0.11 s, and 0% error in tracking the power.
2021 International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON), 2021
The idea of assisting solar PV in onboard battery charging system improves reliability of system ... more The idea of assisting solar PV in onboard battery charging system improves reliability of system consequently less burden on grid and improvement in per mile cost of vehicle is achieved and so an assisted charging system including bridgeless isolated Zeta converter as power factor correction (PFC) unit is introduced here. The converter operation is ensured in discontinuous conduction mode (DCM) for wide operating range whereas CUK converter having continuous output and input current features is used in solar PV is utilized efficiently for obtaining maximum power from solar PV. Further when the grid outage occurs, additional benefits include continuous battery charging, supply voltage control for wide variation. Also, system is observed at various irradiations in solar PV and results are presented here. Supply current is improved by using PFC and absence of diode bridge rectifier in bridgeless isolated Zeta converter makes it ripple free circuit by reducing switching stresses thereby decreasing Total harmonic distortion (THD) value to standard acceptable range i.e., below 5% showing improved and efficient charging system. Cascaded PI control is used to ensure the closed loop operation in PFC converter while solar PV uses the MPPT algorithm as P&O to extract the maximum of power from the PV panel and ensuring the continuity of battery charging in smooth way. Moreover, the PFC converter circuit uses CC-CV charging. The proposed system is validated using MATLAB Simulink.
The different energy assets such as solar panels and batteries help electrical engineers to manag... more The different energy assets such as solar panels and batteries help electrical engineers to manage and meet the increasing demand. The amalgamation of renewable energy resources with artificial intelligence is the key focus of providing high energy efficiency with alternative sources. This solution will not only meet electricity demand but also help in reducing greenhouse gas emissions as a result the efficient, sustainable and eco-friendly solution can be achieved which would contribute a lot to the smart grid environment. Here, a modified grey wolf optimizer approach is utilized to develop a novel energy management system for SPV-based microgrid considering modern power grid interactions. The proposed approach aims to provide a proficient microgrid that utilizes solar photovoltaic technology, and energy storage systems using an artificial intelligence algorithm-based microgrid control for optimal dispatch of energy in grid-connected systems. The performance of this novel energy management system is validated under sunny day and cloudy day, to emulate the stochastic nature of solar photovoltaic systems. A comparative study with mixed linear programming is also conducted that indicates towards the savings in 23.34% and 45.55% of the rolling cost for a clear and cloudy day respectively.
2021 8th International Conference on Signal Processing and Integrated Networks (SPIN), 2021
This paper presents onboard battery charging with a modified Zeta converter. Charging system is i... more This paper presents onboard battery charging with a modified Zeta converter. Charging system is incorporated with two clamping diodes and two extra switches at Zeta converter input resulting in low THD input current and better charging efficiency by mitigating the problems of conventional Zeta converter. An AC-DC conversion is employed by diode bridge rectifier (DBR) followed by modified Zeta converter as power factor correction (PFC) unit, and the flyback converter is used to synchronize the current of battery with the implementation of closed loop cascaded PI control during constant voltage (CV) mode and constant current (CC) mode. Zeta converter is operated in Discontinuous conduction mode (DCM) mode such that Zeta converter can work with better dynamic response, and low ripple at the output voltage. Linear PI control topology is employed here, which has cascaded control for the better working efficiency of the charger. Proposed converter operation is to achieve low total harmonic distortion (THD) within IEC 61000-3-2 standards. Due to inbuilt isolation, components are less and hence proving the system to be more reliable. Proposed system’s efficacy is validated in MATLAB SIMULINK.
2021 8th International Conference on Signal Processing and Integrated Networks (SPIN), 2021
The intent of paper is to deal with the design of onboard battery charger by interleaved Luo conv... more The intent of paper is to deal with the design of onboard battery charger by interleaved Luo converter topology cascaded with flyback converter to charge a battery. As per this configuration two Luo converters are interleaved and connected in parallel so that the current at input gets divided into them to ensure reliable operation of charger in discontinuous conduction mode. The charger efficiency gets improved because of the reduced stresses of current in the system moreover system works well due to inclusion of less ripple in current at input and output due to the cancellation in ripple within the circuit. Control technique involves closed loop control of cascaded PL in flyback to gain better and controlled results. Distortions in the circuit are reduced to a larger extent and THD is reduced within TEC 61000-3-2 standards. Prototype simulation is validated in MATLAB SIMULINK.
2018 2nd IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), 2018
Solar photovoltaic (PV) power estimation is difficult due to intermittent meteorological paramete... more Solar photovoltaic (PV) power estimation is difficult due to intermittent meteorological parameters. Thus, it is utmost important to predict solar PV power for proper operation and control of renewable energy based power system. Before predicting solar PV system power, forecasting of solar irradiation is essential. In this paper an intelligent model based on ANN is developed to forecast the solar radiation using aerosol index as input data. Based on daily weather classification, the artificial neural network (ANN) using the Nonlinear Auto-Regressive for Exogenous inputs (NARX) approach is implemented to forecast the next day 3-hour solar radiation outputs. To find a precise prediction for 3-hour ahead PV power generation, earlier models were designed by considering temperature, humidity, wind direction and wind speed data. The predicted results of the proposed PV forecasting model are evaluated on the basis of statistical indicators Mean Square Error (MSE). The percentage MSE of the proposed model using Aerosol Index (AI) is 4.67%. Thus, showing that the proposed model is found to be accurate and may be used for PV Power forecasting and also in smart energy management system.
In the modern power system, smart grid structure is getting admiration and edify a path of comple... more In the modern power system, smart grid structure is getting admiration and edify a path of complete greener way of energy generation. This paper highlights each aspects of this framework to justify the energy demand in a sheltered, economical, trustworthy and environmental manner even the absence of storage structure at large level. It also facilitates the simultaneous objectives of demand monitoring and control with the amalgamation of renewable distributed generation having smart devices and storage system. A comprehensive review has been demonstrated with the smart operations unlike traditional power grid even the presence of some limitations for the establishment of advanced grid-based power network. The analysis presented in this paper is driven by the eventuality of relic fuel calamity, ecological contamination, diminution of electrical energy, enlargement necessity of power system, newness to emergent trends in proficient energy utilization and regularly increasing electricit...
2019 International Symposium on Advanced Electrical and Communication Technologies (ISAECT), 2019
In most of the rural and isolated regions the grid supply is not available or continuous supply i... more In most of the rural and isolated regions the grid supply is not available or continuous supply is rarely available. The renewable energy resources as distributed generators can be utilized for such requirements. The appropriate selection of renewable energy resources is of utmost importance for designing the microgrid in distributed power generation systems. The paper focuses on the problem of optimal sizing and minimization of investment cost. The proposed method of optimal sizing is a two constraint based linear programming (TCLP) technique. This technique considers the variability of solar irradiance, temperature and wind of the selected site. The proposed technique is designed in two stages firstly, optimal sizing of solar photovoltaic (SPV) system and wind energy generation system (WEGS), secondly, sizing of the battery energy storage system (BESS) for complete off grid system to meet the load demands. The proposed technique aims to prevent over and under sizing by analyzing the load demand profile according to the time of usage (TOU). The assessment of loads into four different categories helped in generation of load demand profile for a year. Results of the optimization problem have demonstrated that the optimally designed distributed generation system has a payback period of 4.88 years.
The penetration of solar photovoltaic (PV) power in distributed generating system is increasing r... more The penetration of solar photovoltaic (PV) power in distributed generating system is increasing rapidly. The increased level of PV penetration causes various issues like grid stability, reliable power generation and power quality; therefore, it becomes utmost important to forecast the PV power using the meteorological parameters. The proposed model is developed on the basis of meteorological data as input parameters, and the impacts of these parameters have been analyzed with respect to forecasted PV power. The main focus of this research is to explore the performance of optimization-based PV power forecasting models with varying aerosol particles and other meteorological parameters. A newly developed intelligent approach based on grey wolf optimization (GWO) using multilayer perceptron (MLP) has been used to forecast the PV power. The performance of the GWO-based MLP model is evaluated on the basis of statistical indicators such as normalized mean bias error (NMBE), normalized mean absolute error (NMAE), normalized root-mean-square error (NRMSE) and training error. The results of the developed model show the values of NMBE, NMAE and NRMSE as 2.267%, 4.681% and 6.67% respectively. To validate the results, a comparison has been made with particle swarm optimization, Levenberg-Marquardt algorithm and adaptive neuro-fuzzy approach. The performance of the model is found better as compared to other intelligent techniques. The obtained results may be used for demand response applications in smart grid environment.
Particle Swarm Optimization algorithm (PSO) is found to be an effective meta-heuristic swarm-base... more Particle Swarm Optimization algorithm (PSO) is found to be an effective meta-heuristic swarm-based algorithm in solving modern time problems. Various improvements have been proposed in this algorithm in terms of internal computation, acceleration coefficients, stopping criteria, hybridization, velocity upgradation etc. The objective of this paper is to implement hybrid weights and, therefore, improve the quality of PSO algorithm. In the case of hybrid weights, we have combined two weights at a time. These weights are mixed in various but not in equal proportions and are tested against ten standard testing functions along with the pre-existing weights. By using this collection, we have analysed them on three parameters-mean, standard deviation, and minimum value achieved. Later on, after analysing the data, we found out that hybrid weights are an overall better option with respect to the pre-existing weights.
The key objective of the power system is to supply reliable power at the lowest possible cost and... more The key objective of the power system is to supply reliable power at the lowest possible cost and one of the most important and technical tools to achieve this is the optimal power flow (OPF) that determines the best operating conditions for power systems while taking into account system constraints including generator capabilities, line capacity, bus voltages, and line power flow limits. Traditional methods fail to meet the required objectives because they are ineffective in dealing with quantitative objectives, exhibit local convergence, and require a significant amount of computational time if the system is large, complex and hybrid. Moreover, the problem of OPF has grown more complicated as a result of a paradigm shift in the power industry that established competition in the electricity sector. Artificial Intelligence (AI) technologies have emerged in recent years that can address extremely complicated OPF problems. Because of their versatility, such AI approaches may deal with qualitative constraints in a seamless and efficient manner. The current study provides a comprehensive review of various computational intelligence-based optimization methodologies for solving OPF problems, with a focus on the modifications introduced over time and the computational features of each for various test cases. The recent evolution of population-based search algorithms and their hybridization for addressing OPF problems, particularly over the last two decades, has been investigated in this article.
Particle swarm optimization algorithm is an intelligent optimization technique applied to solve o... more Particle swarm optimization algorithm is an intelligent optimization technique applied to solve optimization problem in the field of Engineering, optimization design of electrical networks, aircraft, control systems, optimum design of chemical processing equipment, design of civil engineering structure, economic load dispatch, load flow, management system, medical etc. Various parameters of PSO are: population size(P), inertia weight, acceleration constants random number, Maximum number of iteration. In this paper an attempt is made to understand the PSO algorithm by solving Rosenbrock benchmark function manually. The impact of variations of parameters on PSO convergence has been studied. Each parameters is varied systematically keeping all other parameters fixed to some value. Rosenbrock benchmark function has been used to study the impact of parameters variations. It was found that PSO is sensitive to the values of these parameters, combination of various parameters for best accuracy and faster convergence has been determined.
Distributed energy resources, or distributed generation (DG), are the mainstay of modern power sy... more Distributed energy resources, or distributed generation (DG), are the mainstay of modern power systems aiming towards green energy generation via the effective integration of renewable energy sources. DG involvement in traditional power systems comprises the enhancement of power quality parameters, the utilization of renewable sources, cost optimization, and stable and reliable energy generation. The advantages of such a revolutionary approach can be achieved with the optimal sizing and allocation of DG by means of adequate optimization techniques, constraints, and optimized parameters. In this study, a comprehensive review of DG optimization is presented in light of recent developments, and a comparison is carried out based on the adopted optimization techniques, test system, enhanced parameters, and outcome evaluations.
The study aims to examine the effect of environment and economic variables on logistic performanc... more The study aims to examine the effect of environment and economic variables on logistic performance in India. In order to study the long run and short run association between the variables the study employed auto regressive distribution lag (ARDL) approach on a time series data from 2007 to 2018. The result revealed that foreign direct investment (FDI) has a positive relation with LPI whereas fossil fuel consumption in both the short and long run has a negative relation. On the other hand, GDP per capita has a negative relation with LPI while total greenhouse gas emissions has a positive relation, which is a sign of concern for environment sustainability. In the recent report published by the World Bank India’s rank has slipped down from 35th to 44th position worldwide whereby all the six dimensions have shown a downward trend. India being one of the largest customer oriented market would negatively affect the world economy if its logistics operation are poorly driven. This study hig...
The extraction of maximum power is a big challenge in solar photovoltaic-based power plants due t... more The extraction of maximum power is a big challenge in solar photovoltaic-based power plants due to varying atmospheric and meteorological parameters. The concept of array reconfiguration is applied for the maximum power extraction in solar PV plants. Using this approach, the occurrence of multiple peaks in P-V and I-V characteristics during partial shade can be smoothened and reduced significantly. Partial shading due to the movement of the cloud is considered in the research. The cloud movement mainly because of velocity and wind direction is used for creating various shading conditions. The main focus is to reduce the power losses during partial shading using a nature-inspired optimization approach to reconfigure the array for different types of shading conditions. A grey wolf optimizer-based bridge-linked total cross-tied (GWO-BLTCT) configuration is proposed in this paper. The performance of the proposed topology is compared with standard and hybrid topologies, namely, series-pa...
2022 International Conference for Advancement in Technology (ICONAT), 2022
The use of renewable energy resources is increasing to provide energy efficient and uninterrupted... more The use of renewable energy resources is increasing to provide energy efficient and uninterrupted supply of power. The use of solar photovoltaics (SPV) in the agriculture sector is gaining importance due to its availability in abundance and other advantages. This paper presents SPV based water pumping system (WPS) for rural agricultural practice in Indian scenario. The proposed water pumping system comprises of SPV system, maximum power point controller, boost converter, inverter, and 3-Φ induction motor driving a water pump. The stochastic nature of SPV power is a concern for electrical engineers. Thus, the use of maximum power point tracking (MPPT) algorithms is of key focus to improve the efficiency and ensure the robust performance of SPV system. The proposed SPV system incorporates an artificial intelligence method for designing MPPT algorithm. The paper introduces a grey wolf optimization inspired algorithm to extract the maximum power under varying irradiance conditions from SPV system. The performance of the proposed algorithm is compared with other algorithms for partially shaded conditions on the basis of computational burden, maximum power tracked, and fluctuations. The proposed controller is better with least rise time of 0.11 s, and 0% error in tracking the power.
2021 International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON), 2021
The idea of assisting solar PV in onboard battery charging system improves reliability of system ... more The idea of assisting solar PV in onboard battery charging system improves reliability of system consequently less burden on grid and improvement in per mile cost of vehicle is achieved and so an assisted charging system including bridgeless isolated Zeta converter as power factor correction (PFC) unit is introduced here. The converter operation is ensured in discontinuous conduction mode (DCM) for wide operating range whereas CUK converter having continuous output and input current features is used in solar PV is utilized efficiently for obtaining maximum power from solar PV. Further when the grid outage occurs, additional benefits include continuous battery charging, supply voltage control for wide variation. Also, system is observed at various irradiations in solar PV and results are presented here. Supply current is improved by using PFC and absence of diode bridge rectifier in bridgeless isolated Zeta converter makes it ripple free circuit by reducing switching stresses thereby decreasing Total harmonic distortion (THD) value to standard acceptable range i.e., below 5% showing improved and efficient charging system. Cascaded PI control is used to ensure the closed loop operation in PFC converter while solar PV uses the MPPT algorithm as P&O to extract the maximum of power from the PV panel and ensuring the continuity of battery charging in smooth way. Moreover, the PFC converter circuit uses CC-CV charging. The proposed system is validated using MATLAB Simulink.
The different energy assets such as solar panels and batteries help electrical engineers to manag... more The different energy assets such as solar panels and batteries help electrical engineers to manage and meet the increasing demand. The amalgamation of renewable energy resources with artificial intelligence is the key focus of providing high energy efficiency with alternative sources. This solution will not only meet electricity demand but also help in reducing greenhouse gas emissions as a result the efficient, sustainable and eco-friendly solution can be achieved which would contribute a lot to the smart grid environment. Here, a modified grey wolf optimizer approach is utilized to develop a novel energy management system for SPV-based microgrid considering modern power grid interactions. The proposed approach aims to provide a proficient microgrid that utilizes solar photovoltaic technology, and energy storage systems using an artificial intelligence algorithm-based microgrid control for optimal dispatch of energy in grid-connected systems. The performance of this novel energy management system is validated under sunny day and cloudy day, to emulate the stochastic nature of solar photovoltaic systems. A comparative study with mixed linear programming is also conducted that indicates towards the savings in 23.34% and 45.55% of the rolling cost for a clear and cloudy day respectively.
2021 8th International Conference on Signal Processing and Integrated Networks (SPIN), 2021
This paper presents onboard battery charging with a modified Zeta converter. Charging system is i... more This paper presents onboard battery charging with a modified Zeta converter. Charging system is incorporated with two clamping diodes and two extra switches at Zeta converter input resulting in low THD input current and better charging efficiency by mitigating the problems of conventional Zeta converter. An AC-DC conversion is employed by diode bridge rectifier (DBR) followed by modified Zeta converter as power factor correction (PFC) unit, and the flyback converter is used to synchronize the current of battery with the implementation of closed loop cascaded PI control during constant voltage (CV) mode and constant current (CC) mode. Zeta converter is operated in Discontinuous conduction mode (DCM) mode such that Zeta converter can work with better dynamic response, and low ripple at the output voltage. Linear PI control topology is employed here, which has cascaded control for the better working efficiency of the charger. Proposed converter operation is to achieve low total harmonic distortion (THD) within IEC 61000-3-2 standards. Due to inbuilt isolation, components are less and hence proving the system to be more reliable. Proposed system’s efficacy is validated in MATLAB SIMULINK.
2021 8th International Conference on Signal Processing and Integrated Networks (SPIN), 2021
The intent of paper is to deal with the design of onboard battery charger by interleaved Luo conv... more The intent of paper is to deal with the design of onboard battery charger by interleaved Luo converter topology cascaded with flyback converter to charge a battery. As per this configuration two Luo converters are interleaved and connected in parallel so that the current at input gets divided into them to ensure reliable operation of charger in discontinuous conduction mode. The charger efficiency gets improved because of the reduced stresses of current in the system moreover system works well due to inclusion of less ripple in current at input and output due to the cancellation in ripple within the circuit. Control technique involves closed loop control of cascaded PL in flyback to gain better and controlled results. Distortions in the circuit are reduced to a larger extent and THD is reduced within TEC 61000-3-2 standards. Prototype simulation is validated in MATLAB SIMULINK.
2018 2nd IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), 2018
Solar photovoltaic (PV) power estimation is difficult due to intermittent meteorological paramete... more Solar photovoltaic (PV) power estimation is difficult due to intermittent meteorological parameters. Thus, it is utmost important to predict solar PV power for proper operation and control of renewable energy based power system. Before predicting solar PV system power, forecasting of solar irradiation is essential. In this paper an intelligent model based on ANN is developed to forecast the solar radiation using aerosol index as input data. Based on daily weather classification, the artificial neural network (ANN) using the Nonlinear Auto-Regressive for Exogenous inputs (NARX) approach is implemented to forecast the next day 3-hour solar radiation outputs. To find a precise prediction for 3-hour ahead PV power generation, earlier models were designed by considering temperature, humidity, wind direction and wind speed data. The predicted results of the proposed PV forecasting model are evaluated on the basis of statistical indicators Mean Square Error (MSE). The percentage MSE of the proposed model using Aerosol Index (AI) is 4.67%. Thus, showing that the proposed model is found to be accurate and may be used for PV Power forecasting and also in smart energy management system.
In the modern power system, smart grid structure is getting admiration and edify a path of comple... more In the modern power system, smart grid structure is getting admiration and edify a path of complete greener way of energy generation. This paper highlights each aspects of this framework to justify the energy demand in a sheltered, economical, trustworthy and environmental manner even the absence of storage structure at large level. It also facilitates the simultaneous objectives of demand monitoring and control with the amalgamation of renewable distributed generation having smart devices and storage system. A comprehensive review has been demonstrated with the smart operations unlike traditional power grid even the presence of some limitations for the establishment of advanced grid-based power network. The analysis presented in this paper is driven by the eventuality of relic fuel calamity, ecological contamination, diminution of electrical energy, enlargement necessity of power system, newness to emergent trends in proficient energy utilization and regularly increasing electricit...
2019 International Symposium on Advanced Electrical and Communication Technologies (ISAECT), 2019
In most of the rural and isolated regions the grid supply is not available or continuous supply i... more In most of the rural and isolated regions the grid supply is not available or continuous supply is rarely available. The renewable energy resources as distributed generators can be utilized for such requirements. The appropriate selection of renewable energy resources is of utmost importance for designing the microgrid in distributed power generation systems. The paper focuses on the problem of optimal sizing and minimization of investment cost. The proposed method of optimal sizing is a two constraint based linear programming (TCLP) technique. This technique considers the variability of solar irradiance, temperature and wind of the selected site. The proposed technique is designed in two stages firstly, optimal sizing of solar photovoltaic (SPV) system and wind energy generation system (WEGS), secondly, sizing of the battery energy storage system (BESS) for complete off grid system to meet the load demands. The proposed technique aims to prevent over and under sizing by analyzing the load demand profile according to the time of usage (TOU). The assessment of loads into four different categories helped in generation of load demand profile for a year. Results of the optimization problem have demonstrated that the optimally designed distributed generation system has a payback period of 4.88 years.
The penetration of solar photovoltaic (PV) power in distributed generating system is increasing r... more The penetration of solar photovoltaic (PV) power in distributed generating system is increasing rapidly. The increased level of PV penetration causes various issues like grid stability, reliable power generation and power quality; therefore, it becomes utmost important to forecast the PV power using the meteorological parameters. The proposed model is developed on the basis of meteorological data as input parameters, and the impacts of these parameters have been analyzed with respect to forecasted PV power. The main focus of this research is to explore the performance of optimization-based PV power forecasting models with varying aerosol particles and other meteorological parameters. A newly developed intelligent approach based on grey wolf optimization (GWO) using multilayer perceptron (MLP) has been used to forecast the PV power. The performance of the GWO-based MLP model is evaluated on the basis of statistical indicators such as normalized mean bias error (NMBE), normalized mean absolute error (NMAE), normalized root-mean-square error (NRMSE) and training error. The results of the developed model show the values of NMBE, NMAE and NRMSE as 2.267%, 4.681% and 6.67% respectively. To validate the results, a comparison has been made with particle swarm optimization, Levenberg-Marquardt algorithm and adaptive neuro-fuzzy approach. The performance of the model is found better as compared to other intelligent techniques. The obtained results may be used for demand response applications in smart grid environment.
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Papers by Uma Nangia