An Implementation of Brain Emotional Learning Based Intelligent Controller for AVR System

Saat, Shahrizal; Ghazali, Mohd Riduwan; Ahmad, Mohd Ashraf; Mustapha, Nik Mohd Zaitul Akmal; Tumari, Mohd Zaidi Mohd

doi:10.1109/i2cacis57635.2023.10193647

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Cited by 3 publications

References 17 publications

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Using Non-Monopolize Search for Fast and Optimal PID Tuning of AVR System

Hanif,

Ahmad,

Rashid

et al. 2024

2024 International Conference on System Science and Engineering (ICSSE)

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Using Non-Monopolize Search for Fast and Optimal PID Tuning of AVR System

Hanif,

Ahmad,

Rashid

et al. 2024

2024 International Conference on System Science and Engineering (ICSSE)

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Fast PID Tuning of AVR System Using Memory-Based Smoothed Functional Algorithm

Islam,

Ahmad,

Hao

et al. 2024

2024 IEEE Symposium on Industrial Electronics &Amp;amp; Applications (ISIEA)

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A Novel Balanced Arithmetic Optimization Algorithm-Optimized Controller for Enhanced Voltage Regulation

Ekinci,

Çetin,

Izci

et al. 2023

Mathematics

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This work introduces an innovative approach that unites a PIDND2N2 controller and the balanced arithmetic optimization algorithm (b-AOA) to enhance the stability of an automatic voltage regulator (AVR) system. The PIDND2N2 controller, tailored for precision, stability, and responsiveness, mitigates the limitations of conventional methods. The b-AOA optimizer is obtained through the integration of pattern search and elite opposition-based learning strategies into the arithmetic optimization algorithm. This integration optimizes the controller parameters and the AVR system’s response, harmonizing exploration and exploitation. Extensive assessments, including evaluations on 23 classical benchmark functions, demonstrate the efficacy of the b-AOA. It consistently achieves accurate solutions, exhibits robustness in addressing a wide range of optimization problems, and stands out as a promising choice for various applications. In terms of the AVR system, comparative analyses highlight the superiority of the proposed approach in transient response characteristics, with the shortest rise and settling times and zero overshoot. Additionally, the b-AOA approach excels in frequency response, ensuring robust stability and a broader bandwidth. Furthermore, the proposed approach is compared with various state-of-the-art control methods for the AVR system, showcasing an impressive performance. These results underscore the significance of this work, setting a new benchmark for AVR control by advancing stability, responsiveness, and reliability in power systems.

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An Implementation of Brain Emotional Learning Based Intelligent Controller for AVR System

Cited by 3 publications

References 17 publications

Using Non-Monopolize Search for Fast and Optimal PID Tuning of AVR System

Using Non-Monopolize Search for Fast and Optimal PID Tuning of AVR System

Fast PID Tuning of AVR System Using Memory-Based Smoothed Functional Algorithm

A Novel Balanced Arithmetic Optimization Algorithm-Optimized Controller for Enhanced Voltage Regulation

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