Over the last few years, smart grids have become a topic of intensive research, development and deployment across the world. This is due to the fact that, through the smart grid, stable and reliable power systems can ...Over the last few years, smart grids have become a topic of intensive research, development and deployment across the world. This is due to the fact that, through the smart grid, stable and reliable power systems can be achieved. This paper presents a fuzzy logic control for dual active bridge series resonant converters for DC smart grid application. The DC smart grid consists of wind turbine and photovoltaic generators, controllable and DC loads, and power converters. The proposed control method has been applied to the controllable load＇s and the grid side＇s dual active bridge series resonant converters for attaining control of the power system. It has been used for management of controllable load＇s state of charge, DC feeder＇s voltage stability during the loads and power variations from wind energy and photovoltaic generation and power flow management between the grid side and the DC smart grid. The effectiveness of the proposed DC smart grid operation has been verified by simulation results obtained by using MATLAB and PLECS cards.展开更多
Renewable energy sources like solar,wind,and hydro are becoming increasingly popular due to the fewer negative impacts they have on the environment.Because,Since the production of renewable energy sources is still in ...Renewable energy sources like solar,wind,and hydro are becoming increasingly popular due to the fewer negative impacts they have on the environment.Because,Since the production of renewable energy sources is still in the process of being created,photovoltaic(PV)systems are commonly utilized for installation situations that are acceptable,clean,and simple.This study presents an adaptive artificial intelligence approach that can be used for maximum power point tracking(MPPT)in solar systems with the help of an embedded controller.The adaptive method incorporates both the Whale Optimization Algorithm(WOA)and the Artificial Neural Network(ANN).The WOA was implemented to enhance the process of the ANN model’s training,and the ANN model was developed using the WOA.In addition to this,the inverter circuit is connected to the smart grid system,and the strengthening of the smart grid is achieved through the implementation of the CMCMAC protocol.This protocol prevents interference between customers and the organizations that provide their utilities.Using a protocol known as Cross-Layer Multi-Channel MAC(CMCMAC),the effect of interference is removed using the way that was suggested.Also,with the utilization of the ZIGBEE communication technology,bidirectional communication is made possible.The strategy that was suggested has been put into practice,and the results have shown that the PV system produces an output power of 73.32 KW and an efficiency of 98.72%.In addition to this,a built-in regulator is utilized to validate the proposed model.In this paper,the results of various experiments are analyzed,and a comparison is made between the suggested WOA with the ANN controller approach and others,such as the Particle Swarm Optimization(PSO)based MPPT and the Cuckoo Search(CS)based MPPT.By examining the comparison findings,it was determined that the adaptive AI-based embedded controller was superior to the other alternatives.展开更多
文摘Over the last few years, smart grids have become a topic of intensive research, development and deployment across the world. This is due to the fact that, through the smart grid, stable and reliable power systems can be achieved. This paper presents a fuzzy logic control for dual active bridge series resonant converters for DC smart grid application. The DC smart grid consists of wind turbine and photovoltaic generators, controllable and DC loads, and power converters. The proposed control method has been applied to the controllable load＇s and the grid side＇s dual active bridge series resonant converters for attaining control of the power system. It has been used for management of controllable load＇s state of charge, DC feeder＇s voltage stability during the loads and power variations from wind energy and photovoltaic generation and power flow management between the grid side and the DC smart grid. The effectiveness of the proposed DC smart grid operation has been verified by simulation results obtained by using MATLAB and PLECS cards.
基金funding this research work through the Small Group Research Project under Grant Number RGP1/70/44.
文摘Renewable energy sources like solar,wind,and hydro are becoming increasingly popular due to the fewer negative impacts they have on the environment.Because,Since the production of renewable energy sources is still in the process of being created,photovoltaic(PV)systems are commonly utilized for installation situations that are acceptable,clean,and simple.This study presents an adaptive artificial intelligence approach that can be used for maximum power point tracking(MPPT)in solar systems with the help of an embedded controller.The adaptive method incorporates both the Whale Optimization Algorithm(WOA)and the Artificial Neural Network(ANN).The WOA was implemented to enhance the process of the ANN model’s training,and the ANN model was developed using the WOA.In addition to this,the inverter circuit is connected to the smart grid system,and the strengthening of the smart grid is achieved through the implementation of the CMCMAC protocol.This protocol prevents interference between customers and the organizations that provide their utilities.Using a protocol known as Cross-Layer Multi-Channel MAC(CMCMAC),the effect of interference is removed using the way that was suggested.Also,with the utilization of the ZIGBEE communication technology,bidirectional communication is made possible.The strategy that was suggested has been put into practice,and the results have shown that the PV system produces an output power of 73.32 KW and an efficiency of 98.72%.In addition to this,a built-in regulator is utilized to validate the proposed model.In this paper,the results of various experiments are analyzed,and a comparison is made between the suggested WOA with the ANN controller approach and others,such as the Particle Swarm Optimization(PSO)based MPPT and the Cuckoo Search(CS)based MPPT.By examining the comparison findings,it was determined that the adaptive AI-based embedded controller was superior to the other alternatives.
