Abstract
An adaptive approach for optimal tuning of a SMC for an automated voltage regulator system is displayed in this study. The approach is centered on hybrid of the GA and MOPSA. In addition, unique objective functions for the controller's parameter optimization are suggested. The performance of the resulting perfect sliding mode controller is confirmed by comparing it to controllers adjusted using various techniques that have been published in the literature. The simulation outcomes indicate that controllers tuned with the projected MOPSO and GA algorithms outperform controllers tuned with existing methods. In addition, a comparison study is performed to select the best controller for use in AVR systems. The suggested algorithm's major benefit is a considerable boost in convergence speed. With step changes and step load modifications in input wind power, the system model with built-in intelligent controller is generated in MATLAB/SIMULINK. The benefits of the recommended intelligent control algorithm are confirmed by comparing the outcomes of the sliding mode controller and the projected MOPSO self-tuned controller. The findings show that the hybrid Wind/PV system's reactive power adjustment capabilities. When used in conjunction with BES, it is extremely successful in optimising the voltage profile although providing active energy to local load.
Highlights
An adaptive approach for optimal tuning of a SMC for an automated voltage regulator system is displayed in this study
Other algorithms can be used for optimization, such as multi-objective extremal optimization (MOEO) [38], chaotic ant swarm (CAS) [36], [37], craziness PSO (CRPSO) and velocity update relaxation PSO (VURPSO) [35], harmony search algorithm (HSA) [34], artificial bee colony (ABC) [32], [33], symbiotic organisms search (SOS) [31], chaos optimization algorithm (COA) [30], many optimizing liaisons (MOL) [29], teaching–learning-based optimization (TLBO) [28], ant colony optimization (ACO) [26], [27], cuckoo search (CS) [24], [25], whale optimization algorithm (WOA) [23], improved kidney–inspired algorithm (IKA) [22], and others
To demonstrate the efficacy of MOPSO sliding mode control, the system is simulated in SIMULINKTM/MATLAB using a SMC and a MOPSO controller
Summary
In remote places where expanding the public grid is difficult, there is an increasing need to create and execute economic power solutions to meet power burdens while simultaneously minimizing potential environmental repercussions [1]. In off grid hybrid energy systems, the capacity of PV inverters to create or utilize reactive power to regulate voltage might be an answer to the power supply issue. A fuzzy-logic-based wavelet filtering control approach for smoothing the output fluctuation of wind and photovoltaic hybrid power production structure is presented in this work. This paper proposes an actual grid-connected use of battery energy storage and PV/Wind to manage direct current associated voltage and rectify reactive power while delivering active power to local consumers. For minimizing output power fluctuations of BESS/PV/wind hybrid power generating systems, sliding mode controller based a voltage smoothing control approach based on MOPSO is presented. This study presents an intelligent control based on a SMC technique with MOPSO for controlling reactive power of a battery, wind, or PV inverter to manage load bus voltage. The capacity of the hybrid model to change reactive power has been used to enhance power quality and system dependability
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