Gravitational search algorithm for determining controller parameters in an automatic voltage regulator system

Abstract

This paper presents optimal tuning of the controller parameters of a proportional-integral-derivate (PID) controller for an automatic voltage regulator (AVR) system using a heuristic gravitational search algorithm (GSA) based on mass interactions and Newton's law of gravity. The determination of optimal controller parameters is considered an optimization problem in which different performance indexes and a performance criterion in the time domain have been used as objective functions to test the performance and effectiveness of the GSA. In the determining process of the parameters, the designed PID controller with the proposed approach is simulated under different conditions and the performance of the controller is compared with those reported in the literature. From the numerical simulation results it is clear that the GSA approach is successfully applied to reveal the performance and the feasibility of the proposed controller in the AVR system.

Sine-cosine algorithm-based optimization for automatic voltage regulator system

“…The controller types that have been studied for improving the dynamic response of AVR system are proportional-integral-derivative (PID), fractional order PID (FOPID), gray PID (GPID), and fuzzy logic PID (FLPID). In literature, heuristic optimization-based tuning methods that have been applied to improve performance of the fore mentioned controller types are particle swarm optimization (PSO) (Gaing, 2004), artificial bee colony (ABC) (Gozde and Taplamacioglu, 2011), teaching learning-based optimization (TLBO) (Chatterjee and Mukherjee, 2016; Priyambada et al, 2014), gravitational search algorithm (GSA) (Duman et al, 2016; Kumar and Shankar, 2015), chaotic ant swarm (CAS) optimization (Zhu et al, 2009), chaotic optimization based on Lozi map (COLM) (Coelho, 2009), pattern search algorithm (PSA) (Sahu et al, 2012), anarchic society optimization (ASO) (Shayeghi and Dadashpour, 2012), many optimising liaisons (MOL) (Panda et al, 2012), Taguchi combined genetic algorithm (TCGA) (Hasanien, 2013), local unimodal sampling (LUS) optimization (Mohanty et al, 2014), firefly algorithm (FA) (Bendjeghaba, 2014), bio-geography-based optimization (BBO) (Guvenc et al, 2016), Nelder-Mead algorithm (NMA) (Verma et al, 2015), ant colony optimization (ACO) (Suri babu and Chiranjeevi, 2016), cuckoo search (CS) algorithm (Sikander et al, 2018), grasshopper optimization algorithm (GOA) (Hekimoglu and Ekinci, 2018) and genetic algorithm (GA) tuned neural networks (NN) (Al Gizi et al, 2015). It is worth mentioning that, in literature, the most studied heuristic optimization methods that have either been proposed or used for comparison with other existing methods for AVR system are PSO, GA, ABC and DE.…”

Neurofuzzy robust backstepping based MPPT control for photovoltaic system

“…From Figures 7 and 8, it can easily be concluded that the proposed technique deviates the least under faults and then recovers and settles earlier than the backstepping and integral backstepping techniques. [20]. Based on these performance indices, it can be deduced that the proposed technique outperforms the backstepping and integral backstepping techniques in terms of providing the minimum accumulative error and achieving the best MPPT performance.…”

Neuro-adaptive backstepping integral sliding mode control design for nonlinear wind energy conversion system

“…To supplement the superior performance of the proposed NABISMC based MPPT paradigm against the FBLC, PID, SMC and NAISMC based MPPT benchmarks, the dynamic performance of all the stated MPPT candidates is also evaluated using four different well-known performance indices, expressed as follows [25][26][27]:…”