Fuzzy PID Controller for Automatic Generation Control of Interconnected Power System Tuned by Glow-Worm Swarm Optimization

Pilla, Ramana; Botcha, Nirupama; Gorripotu, Tulasichandra Sekhar; Azar, Ahmad Taher

doi:10.1007/978-3-030-30271-9_14

Cited by 24 publications

(8 citation statements)

References 24 publications

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“…The choice of 5-MF is based on the designer experience to make a compromise between the control complexity and the design error. Moreover, the only condition a membership function must satisfy is not to exceed the value 1 [42][43][44][45][46][47][48][49][50][51][52], as shown in Figs. 4-6.…”

Section: Control Strategy and Problem Formulationmentioning

confidence: 99%

“…Whereas for frequency stabilization for hybrid systems, Fuzzy-PIDF controllers were proposed in [47] based on the PSO algorithm and in [48] based on the competition over resources (COR) algorithm. In [49], a Fuzzy-PID controller was proposed for the automatic generation control of a hydro-thermal power system based on the glow-worm swarm optimization technique. The system response was improved and compared to the traditional PI and PID controllers.…”

Section: Introductionmentioning

confidence: 99%

“…Authors in [52] proposed a fuzzy self-tuning PID controller for LFC based on the Tribe-DE optimization algorithm in parametric uncertainties in addition to the presence of external perturbations. Although the previous fuzzy-based LFC structures had demonstrated a good system response, they could not be considered fully optimized controllers because the input and output scaling factors were the only optimized parameters while the boundaries of the membership functions were selected by the designer experience [37][38][39][40][41][42][43][44][45][46][47][48][49][50].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal Fuzzy PIDF Load Frequency Controller for Hybrid Microgrid System Using Marine Predator Algorithm

et al. 2021

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This paper presents a fully optimized fuzzy proportional-integral-derivative with filter (FPIDF) load frequency controller (LFC) for enhancing the performance of a hybrid microgrid system. The Marine Predator Algorithm (MPA), a recent optimization algorithm, is used to optimize the gains as well as the input scaling factors and membership functions of the proposed fuzzy PIDF controller. The controller performance is tested on a two-area hybrid microgrid system containing various renewable energy sources and energy storage devices. The effectiveness of the MPA based FPIDF controller is compared with conventional PIDF and FPIDF controllers based on other heuristic techniques presented in literatures. Moreover, different scenarios are implemented in this study to verify the robustness and sensitivity of the proposed controller to different step load perturbations, system parameters variations, wind speed fluctuation and solar irradiance variation. The dynamic response of the system is compared using different controllers in terms of settling time, maximum overshoot and undershoot values. The results are presented in the form of time domain simulations conducted via MATLAB/SIMULINK. INDEX TERMSFuzzy control, load frequency control, marine predator algorithm, microgrid system, PIDF controller, renewable energy, storage systems. NOMENCLATURE A Rotor swept area ACE Area control error B1, B2 Frequency bias coefficients BES Battery energy storage CE Change of error Predator step size control ratio Power coefficient COR Competition over resources E Error FF Fitness function FLC Fuzzy logic controller FPIDF Fuzzy proportional-integral-derivative with filter G Solar radiation ( ) Controller transfer function Solar irradiance under standard test conditions 1 , 2 Input scaling gains 12

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Section: Control Strategy and Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal Fuzzy PIDF Load Frequency Controller for Hybrid Microgrid System Using Marine Predator Algorithm

et al. 2021

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“…Proportional-integral-derivative (PID) controllers were implemented in the process control and other applications [31][32][33][34][35]. For these reasons, as explained in the previous section, the H-infinity PID loop shaping controller system, given in Figure 4, is obtained by elliptic functions.…”

Section: H ∞ Pid Loop Shaping Controller Synthesis By Elliptic Functionsmentioning

confidence: 99%

Robust H∞ Loop Shaping Controller Synthesis for SISO Systems by Complex Modular Functions

Azar

Serrano

Kamal

2021

MCA

Self Cite

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In this paper, a loop shaping controller design methodology for single input and a single output (SISO) system is proposed. The theoretical background for this approach is based on complex elliptic functions which allow a flexible design of a SISO controller considering that elliptic functions have a double periodicity. The gain and phase margins of the closed-loop system can be selected appropriately with this new loop shaping design procedure. The loop shaping design methodology consists of implementing suitable filters to obtain a desired frequency response of the closed-loop system by selecting appropriate poles and zeros by the Abel theorem that are fundamental in the theory of the elliptic functions. The elliptic function properties are implemented to facilitate the loop shaping controller design along with their fundamental background and contributions from the complex analysis that are very useful in the automatic control field. Finally, apart from the filter design, a PID controller loop shaping synthesis is proposed implementing a similar design procedure as the first part of this study.

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“…This review fall to this class of tuning. Some recent optimization techniques for PID tuning may include; Firefly algorithm (dos Santos Coelho and Mariani, 2012), chaotic optimization (dos Santos Coelho, 2009), evolutionary multi-objective optimization (Nisi et al, 2019), PSO (Azar et al, 2019) and glow-worm optimization (Pilla et al, 2019). v. Adaptive tuning methods: The adaptive tuning strategies are online real-time tuning methods which involve the use of automated online mechanisms to tune PID controller parameters gains.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems

Bassi

Dada

Abidemi

et al. 2022

Heliyon

172

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Fuzzy PID Controller for Automatic Generation Control of Interconnected Power System Tuned by Glow-Worm Swarm Optimization

Cited by 24 publications

References 24 publications

Optimal Fuzzy PIDF Load Frequency Controller for Hybrid Microgrid System Using Marine Predator Algorithm

Optimal Fuzzy PIDF Load Frequency Controller for Hybrid Microgrid System Using Marine Predator Algorithm

Robust H∞ Loop Shaping Controller Synthesis for SISO Systems by Complex Modular Functions

Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems

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