Intelligent fuzzy-based automatic voltage regulator with hybrid optimization learning method

Lawal, Maman Jimoh; Hussein, Suleiman U.; Saka, Bemdoo; Abubakar, Sadiq Umar; Attah, Idoko S.

doi:10.1016/j.sciaf.2023.e01573

Cited by 6 publications

(1 citation statement)

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“…Various research studies have explored the application of metaheuristic optimization algorithms to determine optimal controller gains in AVR systems [ 19 ]. For instance, in [ 20 ], the study enhances AVR performance using the adaptive neuro-fuzzy inference system (ANFIS) and compares it with scenarios without a controller and with a PID controller. The ANFIS, trained with a hybrid optimization learning scheme, shows improved performance metrics in MATLAB/Simulink simulations.…”

Section: Introductionmentioning

confidence: 99%

Optimizing AVR system performance via a novel cascaded RPIDD2-FOPI controller and QWGBO approach

Ekinci,

Snášel,

Rizk-Allah

et al. 2024

PLoS ONE

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Maintaining stable voltage levels is essential for power systems’ efficiency and reliability. Voltage fluctuations during load changes can lead to equipment damage and costly disruptions. Automatic voltage regulators (AVRs) are traditionally used to address this issue, regulating generator terminal voltage. Despite progress in control methodologies, challenges persist, including robustness and response time limitations. Therefore, this study introduces a novel approach to AVR control, aiming to enhance robustness and efficiency. A custom optimizer, the quadratic wavelet-enhanced gradient-based optimization (QWGBO) algorithm, is developed. QWGBO refines the gradient-based optimization (GBO) by introducing exploration and exploitation improvements. The algorithm integrates quadratic interpolation mutation and wavelet mutation strategy to enhance search efficiency. Extensive tests using benchmark functions demonstrate the QWGBO’s effectiveness in optimization. Comparative assessments against existing optimization algorithms and recent techniques confirm QWGBO’s superior performance. In AVR control, QWGBO is coupled with a cascaded real proportional-integral-derivative with second order derivative (RPIDD2) and fractional-order proportional-integral (FOPI) controller, aiming for precision, stability, and quick response. The algorithm’s performance is verified through rigorous simulations, emphasizing its effectiveness in optimizing complex engineering problems. Comparative analyses highlight QWGBO’s superiority over existing algorithms, positioning it as a promising solution for optimizing power system control and contributing to the advancement of robust and efficient power systems.

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Section: Introductionmentioning

confidence: 99%