Controlling of an automatic voltage regulator using optimum integer and fractional order PID controller

Verma, Santosh Kumar; Yadav, Shekhar; Nagar, Shyam Krishna

doi:10.1109/wci.2015.7495525

Cited by 15 publications

(10 citation statements)

References 21 publications

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“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Hekimoğlu

2018

Transactions of the Institute of Measurement and Control

109

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A novel design method, sine-cosine algorithm (SCA) is presented in this paper to determine optimum proportional-integral-derivative (PID) controller parameters of an automatic voltage regulator (AVR) system. The proposed approach is a simple yet effective algorithm that has balanced exploration and exploitation capabilities to search the solutions space effectively to find the best result. The simplicity of the algorithm provides fast and high-quality tuning of optimum PID controller parameters. The proposed SCA-PID controller is validated by using a time domain performance index. The proposed method was found efficient and robust in improving the transient response of AVR system compared with the PID controllers based on Ziegler-Nichols (ZN), differential evolution (DE), artificial bee colony (ABC) and bio-geography-based optimization (BBO) tuning methods.

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Section: Introductionmentioning

confidence: 99%

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

Hekimoğlu

2018

Transactions of the Institute of Measurement and Control

109

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“…Those algorithms include Monarch Butterfly Optimization Algorithm (MBO) [24], Taguchi method [25], Slap Swarm Algorithm (SSA) [26], Artificial Bee Colony (ABC) [27], Bacterial Foraging Technique (BFT) [28], Memetic Algorithm (MA) [29], Firefly Optimization Technique (FOT) [30], Shuffled Frog Leaping (SFL) [31], Continuous Action Reinforcement Learning Automata (CARLA) [32], Differential Evolution (DE) and Teaching-Learning-Based Optimization (TLBO) algorithms [33,34], Pattern Search Algorithm (PSA) [35], Simulated Annealing (SA) [36], Finite Gradient [37], Global Neighborhood Algorithm (GNA) [38], Imperialist Competitive Algorithm (ICA) [39], Gravitational Search Algorithm [40], Vector-Based Swarm Optimization (VBSO) [41], Continuous Human Learning Optimizer (CHLO) [42], Artificial Electric Field (AEF) [43], Whale Optimization Algorithm (WOA) [44], Cuckoo Search (CS) [45,46], Jaya Optimization Algorithm (JOA) [45], Ant Colony Optimization (ACO) [21], Chaotic Ant Swarm (CAS) algorithm [47], Chaotic optimization algorithm [48] and Grey Wolf Optimizer (GWO) [49].…”

Section: Experimental Evaluation Of Optimal Tuning For Pidmentioning

confidence: 99%

Experimental Evaluation of Optimal Tuning for PID Parameters in an AVR System

Foroutan

Tootoonchian

2021

Scientia Iranica

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Automatic Voltage Regulator (AVR) is employed to stabilize the output voltage of the generators in the electric power plants. However, reliable performance of AVR depends on professional tuning of its PID controller's parameters. Therefore, different optimization algorithms are used to determine those parameters. The objective of the optimization is defined as minimizing the characteristics of transient step response such as settling time, rise time, overshoot, and steady state error. Then, to verify the optimization results, a simulator is built experimentally for AVR and PID system that can also be used for other studies on AVR systems. The experimental results are compared with those of MATLAB and Pspice Software. Close agreement between the simulation and experimental results confirms the success of the optimization.

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“…There are also carried out tests involving gradual, relatively slow voltage increase during load current measurement. For this reason, the software of the generator's microprocessor system uses a simple PID type voltage controller with the ability to operate in PI mode [16][17][18][19]. Figure 4 in the range of 0-3637 V which corresponds to the phase-to-phase voltage of 0-6300 V (0-U N ).…”

Section: Simulation Researchesmentioning

confidence: 99%

Control of synchronous machines set with microprocessorcontrolled excitation units in high-voltage tests station

Hyla¹

2019

ELECTROTECHNICAL REVIEW

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The paper presents idea and implementation of the control of synchronous motor-synchronous generator machines set used for supplying the high-voltage test station in the electrical equipment repair and service plant. In the excitation circuits of both machines the microprocessor-controlled excitation units were used. Possibilities of generator output voltage regulation by the field current control in the synchronous state of operation were introduced. Simulation model of Matlab-Simulink and results of simulation research was shown. Exemplary realization was disclosed. Verification measurements of the real machines set in the start-up process and synchronous state of operation were presented and discussed. Streszczenie. W artykule przedstawiono realizację sterowania zespołem napędowym silnik synchroniczny-generator synchroniczny przeznaczonym do zasilania stacji prób wysokonapięciowych. W obwodach wzbudzenia obu maszyn zastosowano mikroprocesorowo sterowane bloki zasilania wzbudzenia. Przedstawiono możliwości sterowania napięciem wyjściowym. Zamieszczono model symulacyjny oraz wyniki badań symulacyjnych. Zaprezentowano przykładowe wdrożenie. Przedstawiono wyniki pomiarów w układzie rzeczywistym. (Układ sterowania zespołem napędowym maszyn synchronicznych z mikroprocesorowo sterowanymi blokami wzbudzenia w stacji prób wysokonapięciowych)

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Controlling of an automatic voltage regulator using optimum integer and fractional order PID controller

Cited by 15 publications

References 21 publications

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

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

Experimental Evaluation of Optimal Tuning for PID Parameters in an AVR System

Control of synchronous machines set with microprocessorcontrolled excitation units in high-voltage tests station

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