A particle swarm optimization, fuzzy PID controller with generator automatic voltage regulator

Gizi, Abdullah J. H. Al

doi:10.1007/s00500-018-3483-4

Cited by 31 publications

(13 citation statements)

References 32 publications

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“…Zeng et al [1] proposed a population-based iterated optimization algorithm with a multi-non-uniform mutation called RCEO-FOPID to design Fractional Order PID controllers (FOPID) in AVR systems. Gizi [2] designed a Fuzzy PID (FPID) controller by using Particle Swarm Optimization (PSO) and Sugeno Fuzzy Logic (SFL) to determine the optimal PID controller of generator parameters in the AVR system.…”

Section: Literature Reviewmentioning

confidence: 99%

Tuning of Novel Fractional Order Fuzzy PID Controller for Automatic Voltage Regulator using Grasshopper Optimization Algorithm

Khaniki

Hadi

Manthouri

2021

mjee

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One of the essential pieces of equipment in the power system is the Automatic Voltage Regulator (AVR) or synchronous generator excitation. The system's goal is to maintain the terminal voltage of the synchronous generator at the desired level. AVR is inherently uncertain. Hence, the proposed controller should be able to handle the problem. In this paper, Fractional Order Fuzzy PID (FOFPID) controller has been employed to control the system. To enhance the controller's performance, the Grasshopper Optimization Algorithm (GOA) is used to tune the controller's parameters. Unlike other methods, the FOFPID controller gains are not constant and alter in different operating conditions. The robustness of the controller has been investigated, and the comparative results show that the proposed controller has a better performance against other methods.

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Section: Literature Reviewmentioning

confidence: 99%

Tuning of Novel Fractional Order Fuzzy PID Controller for Automatic Voltage Regulator using Grasshopper Optimization Algorithm

Khaniki

Hadi

Manthouri

2021

mjee

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“…To compare the results of this study with the results of the literature, the constants of the transfer functions G a (s) , G e (s), G g (s) and G s (s) [11], [13], [32]- [35] are taken as the references. In Table 1, the ranges and selected values of these constants are given in detail.…”

Section: Avr System Modelingmentioning

confidence: 99%

Optimal Control of AVR System With Tree Seed Algorithm-Based PID Controller

Köse

2020

IEEE Access

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In this study, an optimal Tree-Seed Algorithm (TSA) algorithm-based Proportional-Integral-Derivative (PID) controller is proposed for automatic voltage regulator (AVR) system terminal tracking problem. PID controller gains K p, K i, and K d are optimized with the proposed TSA algorithm based on different objective functions. The TSA-based optimal PID controller's performance is compared with numerous PID controllers, which were developed using different meta-hermetic optimization algorithms in the literature. Several analysis methods including root locus, bode analysis, robustness, and disturbance rejection are studied and compared with reported works in the literature. It is shown that there is still a research gap to improve the tracking performance of the AVR system due to its importance in electrical systems. According to the obtained comparison results, it has been revealed that the proposed TSA-based PID controller improves tracking properties under load change thus it can be effectively used for synchronous generator automatic voltage regulator terminal voltage stability.

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“…For the above reasons, many new optimization methods have been developed 13 – 17 , such as a fuzzy-based PID 18 – 22 , a neural network-based PID 23 – 25 . Among them, particle swarm optimization algorithms have the characteristics of simple operation, high accuracy, and fast convergence and are widely used 26 – 29 .…”

Section: Introductionmentioning

confidence: 99%

Control system research in wave compensation based on particle swarm optimization

Tang

Xiong

et al. 2021

Sci Rep

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For the offshore wave compensation control system, its controller setting will directly affect the platform's compensation effect. In order to study the wave compensation control system and optimization strategy, we build and simulate the wave compensation control model by using particle swarm optimization (PSO) to optimize the controller's control parameters and compare the results with other intelligent algorithms. Then we compare the response errors of the wave compensation platform under different PID controllers; and compare the particle swarm algorithm's response results and the genetic algorithm to the system controller optimization. The results show that the particle swarm algorithm is 63.94% lower than the genetic algorithm overshoot, and the peak time is 0.26 s lower, the adjustment time is 1.4 s lower than the genetic algorithm. It shows that the control effect of the wave compensation control system has a great relationship with the controller's parameter selection. Meanwhile, the particle swarm optimization algorithm's optimization can set the wave compensation PID control system, and it has the optimization effect of small overshoot and fast response time. This paper proposes the application of the particle swarm algorithm to the wave compensation system. It verifies the superiority of the method after application, and provides a new research reference for the subsequent research on the wave compensation control systems.

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A particle swarm optimization, fuzzy PID controller with generator automatic voltage regulator

Cited by 31 publications

References 32 publications

Tuning of Novel Fractional Order Fuzzy PID Controller for Automatic Voltage Regulator using Grasshopper Optimization Algorithm

Tuning of Novel Fractional Order Fuzzy PID Controller for Automatic Voltage Regulator using Grasshopper Optimization Algorithm

Optimal Control of AVR System With Tree Seed Algorithm-Based PID Controller

Control system research in wave compensation based on particle swarm optimization

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