Evaluation of Flashover Voltage Levels of Contaminated Hydrophobic Polymer Insulators Using Regression Trees, Neural Networks, and Adaptive Neuro-Fuzzy

Polluted insulators at high voltages has acquired considerable importance with the rise of voltage transmission lines. The contamination may lead to flashover voltage. As a result, flashover voltage could lead to service outage and affects negatively the reliability of the power system. This paper presents a dynamic model of ac 50Hz flashover voltages of polluted hydrophobic polymer insulators. The models are constructed using the regression tree method, artificial neural network (ANN), and adaptive neuro-fuzzy (ANFIS). For this purpose, more than 2000 different experimental testing conditions were used to generate a training set. The study of the ac flashover voltages depends on silicone rubber (SiR) percentage content in ethylene propylene diene monomer (EPDM) rubber. Besides, water conductivity (μS/cm), number of droplets on the surface, and volume of water droplet (ml) are considered. The regression tree model is obtained and the performance of the proposed system with other intelligence methods is compa ed. It can be concluded that the performance of the least squares regression tree model outperforms the other intelligence methods, which gives the proposed model better generalization ability

Implementation of MPPT Algorithm for Single-Stage Grid-Connected Photovoltaic system by using incremental conductance method

This paper presents simulation and implementation of maximum power point tracking algorithm for single stage three-phase grid-connected PV system by using incremental conductance method. The maximum efficiency is realized when PV works at its maximum power point, which is contingent on irradiation and temperature. Since the irradiation and temperature always change with time, a PV system which able to track the maximum power point needs to be established to produce more energy. the IC method shows a superior performance, lower oscillation and it took 3second to match MPP that abest time to give stability to the system. the control strategy is supported out using MATLAB/Simulink and experimentally validated with a dSPACE MicroLabBox controller.</p

Optimal PI microcontroller-based realization for technical trends of single-stage single-phase grid-tied PV

The paper introduces optimal PI controllers for a single-phase single-stage PV grid-tied inverter. In the proposed model, a time domain objective function based on the integral square error (ISE) of the current and voltage errors is formulated in order to obtain the PI controllers in an offline manner. The performance of the developed controllers is simulated via MATLAB/SIMULINK whereas a prototype was implemented to verify the effectiveness of the developed controllers. The developed controllers enable the developed inverter to provide active power to the utility grid with reactive power compensation. From the simulated and experimental results, the developed controller had better performance over different operating conditions. Besides, it is simple, low cost, and attractive for households’ energy production. Keywords: Grid connected photovoltaic inverters, Power control, Phase shift, Reactive power injectio

Optimal scheduling of DG and EV parking lots simultaneously with demand response based on self-adjusted PSO and K-means clustering

Funding Information: This study was supported by the Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, Espoo, Finland. Publisher Copyright: © 2022 The Authors. Energy Science & Engineering published by the Society of Chemical Industry and John Wiley & Sons Ltd.Recently, the proliferation of distributed generation (DG) has been intensively increased in distribution systems worldwide. In distributed systems, DGs and utility-owned electric vehicle (EV) to grid aggregators have to be efficiently scaled for cost-effective network operation. Accordingly, with the penetration of power systems, demand response (DR) is considered an advanced step towards a smart grid. To cope with these advancements, this study aims to develop an innovative solution for the day-ahead sizing approach of energy storage systems of EVs parking lots and DGs in smart distribution systems complying with DR and minimizing the pertinent costs. The unique feature of the proposed approach is to allow interactive customers to participate effectively in power systems. To accurately solve this optimization model, two probabilistic self-adjusted modified particle swarm optimization (SAPSO) algorithms are developed and compared for minimizing the total operational costs addressing all constraints of the distribution system, DG units, and energy storage systems of EV parking lots. The K-means clustering and the Naive Bayes approach are utilized to determine the EVs that are ready to participate efficiently in the DR program. The obtained results on the IEEE-24 reliability test system are compared to the genetic algorithm and the conventional PSO to verify the effectiveness of the developed algorithms. The results show that the first SAPSO algorithm outperforms the algorithms in terms of minimizing the total running costs. The finding demonstrates that the proposed near-optimal day-ahead scheduling approach of DG units and EV energy storage systems in a simultaneous manner can effectively minimize the total operational costs subjected to generation constraints complying with DR.Peer reviewe