Grasshopper optimization algorithm for automatic voltage regulator system

Hekimoğlu, Baran; Ekinci, Serdar

doi:10.1109/iceee2.2018.8391320

Cited by 76 publications

(60 citation statements)

References 19 publications

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“…Kapalı çevrim kontrol sistemlerinden daha iyi dinamik cevap elde edebilmek için kazanç-faz marjı, Cohen-Coon (CC) ve Ziegler-Nichols (ZN) gibi klasik ayarlama yöntemleri ile kontrolör parametrelerinin optimum veya optimuma yakın değerlerin bulunması zor olduğundan son yirmi yıl boyunca kontrolör parametrelerinin ayarı için çeşitli sezgisel-üstü optimizasyon yöntemleri önerilmiştir [2,3]. AVR sisteminin dinamik cevabını geliştirmek için incelenmiş kontrolör türleri oransal-integral-türevsel (PID), kesir dereceli PID (FOPID) ve PID artı ikinci dereceden türevseldir (PIDD 2 [18], çekirge optimizasyon algoritması (GOA) [19], simbiyotik organizmalar arama (SOS) algoritması [20], Jaya algoritması (JA) [21], sinüs kosinüs algoritması (SCA) [1], su dalgası optimizasyonu (WWO) [22] ve geliştirilmiş böbrek-ilhamlı algoritmadır (IKA) [23]. Tablo 1, son on yılda AVR sistemi için kullanılan ayarlama yöntemlerinin kısa ve öz bir literatür taramasını vermektedir.…”

Section: Literatür Taraması (Literature Survey)unclassified

Otomatik gerilim regülatör sistemi için karşıt tabanlı atom arama optimizasyon algoritması

Ekinci

Demirören

Zeynelgil

et al. 2020

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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 OBL strategy is used to improve the convergence of the ASO.  The performance of PIDD 2 controller is better than PID and FOPID controllers.  The proposed novel method is efficient and robust. Figure A. OBASO implementation block diagram for optimizing the AVR performance Purpose: This article presents a modified version of atom search optimization (ASO) algorithm that uses the opposition-based learning (OBL) to improve the search space exploration. Theory and Methods: OBL is a commonly used machine learning strategy for increasing the performance of meta-heuristic algorithms. As a new design method, the opposition-based ASO (OBASO) algorithm was proposed for the first time in determining the optimum values of the proportional-integral-derivative plus second order derivative (PIDD 2) controller parameters in an automatic voltage regulator (AVR) system. In the design problem, a new objective function, including the integral of time-weighted squared error (ITSE) and overshoot all together, was optimized with the proposed OBASO algorithm to find the best values of the PIDD 2 controller parameters. Results: The performance of the proposed OBASO tuned PIDD 2 (OBASO-PIDD 2) controller is compared to that of the classic ASO tuned PIDD 2 (ASO-PIDD 2) controller as well as the PID, fractional order PID (FOPID) and PIDD 2 controllers tuned with modern heuristic algorithms. Comparative transient and frequency response analyzes were conducted to assess the stability of the proposed approach. In addition, considering the possible changes in AVR parameters, the robustness of the proposed approach was tested. Conclusion: The extensive simulation results and comparisons with other existing controllers show that the proposed OBASO-PIDD 2 controller with a new objective function has a superior control performance and can highly improve the system robustness with respect to model uncertainties.

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Section: Literatür Taraması (Literature Survey)unclassified

Otomatik gerilim regülatör sistemi için karşıt tabanlı atom arama optimizasyon algoritması

Ekinci

Demirören

Zeynelgil

et al. 2020

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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“…The value of gain and time constant of each model of AVR system reviewed in many research papers [9][10][11][12][13][14][15][16][17][18] and the preferred values are given in Table -I. The Eqn.1provides transfer function of AVR system and Eqn.2confer transfer function of the system after placing the value in Eqn.1.…”

Section: Avr System Modelmentioning

confidence: 99%

“…The best value acquired from AI for controller but however it require more time to train the model and affects speedy response of convergence time in real application. At present, several intelligent optimization algorithms based on evolutionary technique have been recommended to adjust the controller parameters in the AVR system like Artificial Bee Colony Algorithm(ABC) [9], Biogeography-based optimization (BBO) [10], Cuckoo Search Algorithm(CS) [8], Grasshopper Optimization Algorithm (GOA) [11], Bacterial Foraging Optimization Algorithm(BFOA) [12], Improved Kidney-Inspired Algorithm (IKA) [13], Monarch Butterfly Optimization [14], particle swarm optimization [15] [16], sine-cosine algorithm (SCA) [17], Chaotic Differential Evolution Algorithm (CDE) [3],Flower Pollination Algorithm (FPA) [18]and many more such as Ant Lion Optimizer [19], Many Optimizing Liaisons (MOL) [20], Jaya [21] [22] and etc. In 2005, Karaboga reported ABC Algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…Shahrzad Saremi developed the GOA in 2017 [23]. The GOA is gifted to for electrical engineering applications includes renewable microgrid [24], economic load dispatch issue [25], voltageregulator [11]. Hekimoglu et al employed GOA for AVR system and gained better performance than ZN, DE and ABC in terms of transient performance [11].Serdar Ekinci et al [13] incorporated the IKA for AVR system and had a good stability and not pretentious by the fluctuations than ABC BBO, DE, GOA PSO and many more heuristic optimization .Anbarasi et al [12] incorporated theBFOA and confirmed the better responses, stability and robustness of AVR system as relating to ABC, DE algorithms, PSO and MOL.Sambariya [14] et al presented the MBO for the system and improved transient response debated.…”

Section: Introductionmentioning

confidence: 99%

“…The GOA is gifted to for electrical engineering applications includes renewable microgrid [24], economic load dispatch issue [25], voltageregulator [11]. Hekimoglu et al employed GOA for AVR system and gained better performance than ZN, DE and ABC in terms of transient performance [11].Serdar Ekinci et al [13] incorporated the IKA for AVR system and had a good stability and not pretentious by the fluctuations than ABC BBO, DE, GOA PSO and many more heuristic optimization .Anbarasi et al [12] incorporated theBFOA and confirmed the better responses, stability and robustness of AVR system as relating to ABC, DE algorithms, PSO and MOL.Sambariya [14] et al presented the MBO for the system and improved transient response debated. In 2108, Baran Hekimoglu [17] presented SCA for AVR system and debated that robustness, fast and effective search to give thefinest parameters of controller as compared with ZN, ABC, BBO, DE in transient analysis Vivekanandan et al [3] presented Chaotic DE for AVR system and discussed better performance for AVR system as compared with ZN, TL, DE.The scholar of Cambridge university X.S.Yang proposed the FPA in 2012.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enhanced Chaotic Grasshopper Optimization Algorithm Based PID Controller for Automatic Voltage Regulator System

Saravanan*,

Kumar,

Ibrahim

et al. 2019

IJRTE

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This work presents, the PID controller design for AVR system using Enhanced Chaotic Grasshopper Optimization Algorithm (ECGOA). The system response under the different settings are studied with PID controller for stable and minimum error operation. The gain of the controller revealed from traditional methods to recent optimization algorithm. The ECGOA will be implemented for AVR system with PID controller and the performance in-terms of transient response, robustness, stability and error is compared with existing optimization algorithm. The ECGOA based PID controller provides good response and examined upto ±50% of variation in several component of AVR system.

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Equilibrium optimizer tuned novel FOPID‐DN controller for automatic voltage regulator system

Paliwal

Srivastava

Pandit

2021

Int Trans Electr Energ Syst

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Summary This paper presents a novel fractional‐order PID plus derivative with filter coefficient (FOPID‐DN) controller for automatic voltage regulator (AVR) system using the MATLAB/Simulink environment. An AVR system is employed in the power system to maintain the terminal voltage at the desired level. Parameters of the proposed FOPID‐DN controller are optimally tuned by the recently developed Equilibrium Optimizer (EO) algorithm. Performance of the designed FOPID‐DN controller for an AVR system is compared with various controllers which are optimally tuned by different optimization algorithms. Comparative transient response analysis of the proposed technique has been carried out by considering vital transient response indicators like maximum overshoot, rise time, and settling time. Performance analysis of the proposed technique for an AVR system has been done by considering several input conditions. Robustness analysis of the EO algorithm tuned FOPID‐DN controller has been performed by varying the time constants of different components in an AVR system. The capability of the FOPID‐DN controller to handle nonlinearities of an AVR system has been investigated. The stability of the FOPID‐DN controlled AVR system has been analyzed. In addition, the disturbance rejection capability of the FOPID‐DN controlled AVR system has also been investigated. From the various simulation results obtained using the MATLAB/Simulink environment, it has been observed that the proposed FOPID‐DN‐based AVR system provided better performance as compared to different existing controllers.

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Grasshopper optimization algorithm for automatic voltage regulator system

Cited by 76 publications

References 19 publications

Otomatik gerilim regülatör sistemi için karşıt tabanlı atom arama optimizasyon algoritması

Otomatik gerilim regülatör sistemi için karşıt tabanlı atom arama optimizasyon algoritması

Enhanced Chaotic Grasshopper Optimization Algorithm Based PID Controller for Automatic Voltage Regulator System

Equilibrium optimizer tuned novel FOPID‐DN controller for automatic voltage regulator system

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