Water cycle algorithm-based PID controller for AVR

Abstract

Purpose In a power system, the purpose of automatic voltage regulator (AVR) is the voltage control of synchronous generator. Power system stability and security depends on the AVR. Design/methodology/approach The present work is concentrated on the precise terminal voltage control of AVR system and simultaneously maintaining the stability of the system. Therefore, an optimal proportional–integral–derivative (PID) controller is proposed. An optimization technique inspired from Mother Nature, i.e. water cycle algorithm (WCA) is used to evaluate the optimum parameter values of PID controller leading to WCA-tuned PID (WCA-PID). The performance of WCA-PID is compared with other controller reported in the literature. Findings Simulation results show that WCA-PID regulates the terminal voltage more preciously and accurately in comparison to other controller. Further, it is more robust toward parametric uncertainty, set-point tracking and disturbance rejection in comparison to other controller reported in the literature. Originality/value The work is not published anywhere else.

Performance evaluation of a novel improved slime mould algorithm for direct current motor and automatic voltage regulator systems

“…In terms of efficient tuning, the metaheuristic algorithms have also demonstrated greater capabilities for AVR system control, as well. Some of the metaheuristic optimization algorithms that were used to improve the performance of the abovementioned controllers are improved spotted hyena optimization (Zhou et al, 2020), water cycle algorithm (Pachauri, 2020), ant lion optimization (Pradhan et al, 2018), teaching–learning based optimization (Chatterjee and Mukherjee, 2016), artificial ecosystem-based optimization (Ćalasan et al, 2020), bacterial foraging optimization (Anbarasi and Muralidharan, 2016), African buffalo optimization (Odili et al, 2017), firefly algorithm (Bendjeghaba, 2014), particle swarm optimization (Ghosh et al, 2021; Li et al, 2017), improved jaya algorithm (Bhookya and Jatoth, 2020), multi-objective extremal optimization (Zeng et al, 2015), and swarm learning process (Pongfai et al, 2020). The list can further be extended.…”

A novel fractional order PID plus derivative (PIλDµDµ2) controller for AVR system using equilibrium optimizer

“…The AVR system parameters are taken the same as the compared studies, for a fairer comparison. The dynamic response of proposed FOPIDD controller is compared to EO algorithm-based PID, water cycle algorithm (WCA)-based PID (Pachauri, 2020), WOAbased PID (Ahmed M Mosaad, Attia and Abdelaziz, 2019), SCA-based PID (Bhookya and Kumar, 2019), CS-based PID (Sikander and Thakur, 2020) and improved kidney-inspired algorithm (IKA)-based PID (Ekinci and Hekimo glu, 2019). Table 4 shows the FOPID and PID controllers' parameters and transient response of AVR system.…”

Comparative Study of Optimization Techniques Based PID Tuning for Automatic Voltage Regulator System

“…As a result, several optimization techniques for tuning controller parameters have been proposed. Some of these optimization techniques which used as tuning methods to improve the performance of the (PID) controller are particle swarm optimization (PSO) [3,4], cuckoo search optimization (CSO) algorithm [5,6], moth flame optimization (MFO) algorithm [7,8], water cycle optimization (WCO) algorithm [9,10], teaching-learning-based optimization (TLBO) [11][12][13]. Hill climbing optimization (HCO) algorithm [14] is tested for the first time in the AVR system in this work.…”