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Simultaneous allocation of renewable energy sources and custom power quality devices in electrical distribution networks using artificial rabbits optimization

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Clean Energy 2023年第4期7卷 795-807页

作者： Ranga rao Chegudi Balamurugan Ramadoss ramakoteswara rao alla Department of Electrical Engineering Faculty of Engineering and TechnologyAnnamalai UniversityChidambaramTamilnadu 608002India Department of EEE RVR&JC College of EngineeringGunturAndhra Pradesh 522019India

This study suggests an optimal renewable energy source(RES)allocation and distribution-static synchronous compensator(D-STATCOM)and passive power filters(PPFs)for an electrical distribution network(EDN)to improve its performance and power quality(PQ).First,the latest metaheuristic artificial rabbits optimization(ARO)is used to locate and size solar photovoltaic(PV),wind turbine(WT)and D-STATCOM *** the second stage,ratings of single-tuned PPFs and D-STATCOMs at the RESs are determined,considering non-linear loads in the *** multi-objective function reduces power loss,improves the voltage stability index(VSI)and limits total harmonic *** using the IEEE 33-bus EDN compared the ARO results with those of previous *** the first scenario,ideally integrated D-STATCOMs,PVs and WTs reduced losses by 34.79%,64.74%and 94.15%,*** increases from 0.6965 to 0.7749,0.8804 and *** optimal WT integration of the first scenario outperformed the PVs and *** second step optimizes the WTs and PQ devices for non-linear *** and D-STATCOMs reduce the maximum total harmonic distortion of the voltage waveform by 5.21%with non-linear loads to 3.23%,while WTs and PPFs reduce it to 4.39%.These scenarios demonstrate how WTs and D-STATCOMs can improve network performance and *** computational efficiency of ARO is compared to that of the pathfinder algorithm,future search algorithm,butterfly optimization algorithm and coyote optimization *** speeds up convergence and improves solution quality and comprehension.

关键词： artificial rabbits optimization renewable distribution generation D-STATCOM power quality improvement loss reduction voltage stability enhancement

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Maximum power point tracking using decision-tree machine-learning algorithm for photovoltaic systems

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Clean Energy 2022年第5期6卷 762-775页

作者： P.Venkata Mahesh S.Meyyappan ramakoteswara rao alla Department of Electronics and Instrumentation Engineering Annamalai UniversityChidambaramIndia Department of Instrumentation Engineering Madras Institute of TechnologyChennaiIndia Department of EEE RVR&JC College of EngineeringGunturIndia

This work presents a machine-learning(ML)algorithm for maximum power point tracking(MPPT)of an isolated photovoltaic(PV)*** to the dynamic nature of weather conditions,the energy generation of PV systems is *** there is no specific method for effectively dealing with the non-linear data,the use of ML methods to operate the PV system at its maximum power point(MPP)is desirable.A strategy based on the decision-tree(DT)regression ML algorithm is proposed in this work to determine the MPP of a PV *** data were gleaned from the technical specifications of the PV module and were used to train and test the *** algorithms predict the maximum power available and the associated voltage of the module for a defined amount of irradiance and *** boost converter duty cycle was determined using predicted *** simulation was carried out for a 10-W solar panel with a short-circuit current of 0.62 A and an open-circuit voltage of 21.50 V at 1000 W/m^(2) irradiance and a temperature of 25℃.The simulation findings demonstrate that the proposed method compelled the PV panel to work at the MPP predicted by DTs compared to the existing topologies such asβ-MPPT,cuckoo search and artificial neural network *** the proposed algorithm,efficiency has been improved by>93.93%in the steady state despite erratic irradiance and temperatures.

关键词： boost converter decision tree maximum power point tracking photovoltaic system regression machine learning

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