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

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 units.In the second stage,ratings of single-tuned PPFs and D-STATCOMs at the RESs are determined,considering non-linear loads in the network.The multi-objective function reduces power loss,improves the voltage stability index(VSI)and limits total harmonic distortion.Simulations using the IEEE 33-bus EDN compared the ARO results with those of previous studies.In the first scenario,ideally integrated D-STATCOMs,PVs and WTs reduced losses by 34.79%,64.74%and 94.15%,respectively.VSI increases from 0.6965 to 0.7749,0.8804 and 0.967.The optimal WT integration of the first scenario outperformed the PVs and D-STATCOMs.The second step optimizes the WTs and PQ devices for non-linear loads.WTs 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 PQ.The computational efficiency of ARO is compared to that of the pathfinder algorithm,future search algorithm,butterfly optimization algorithm and coyote optimization algorithm.ARO speeds up convergence and improves solution quality and comprehension.

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

This work presents a machine-learning(ML)algorithm for maximum power point tracking(MPPT)of an isolated photovoltaic(PV)system.Due to the dynamic nature of weather conditions,the energy generation of PV systems is non-linear.Since 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 system.The data were gleaned from the technical specifications of the PV module and were used to train and test the DT.These algorithms predict the maximum power available and the associated voltage of the module for a defined amount of irradiance and temperature.The boost converter duty cycle was determined using predicted values.The 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 results.From the proposed algorithm,efficiency has been improved by>93.93%in the steady state despite erratic irradiance and temperatures.