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Fast PID Tuning of AVR System Using Memory-Based Smoothed Functional Algorithm
Voltage regulation within automatic voltage regulator (AVR) systems presents a significant engineering challenge due to uncertain load conditions. Consequently, employing a PID-based controller is crucial for maintaining AVR system performance. Recently, there has been a growing interest in utilizin...
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Main Authors: | , , , , |
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Format: | Conference Proceeding |
Language: | English |
Subjects: | |
Online Access: | Request full text |
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Summary: | Voltage regulation within automatic voltage regulator (AVR) systems presents a significant engineering challenge due to uncertain load conditions. Consequently, employing a PID-based controller is crucial for maintaining AVR system performance. Recently, there has been a growing interest in utilizing better optimization tools to tune the PID control parameters, providing an advantage in enhancing AVR system output response while maintaining the robustness and simplicity of PID controller. However, existing optimization tools for tuning PID controllers, particularly those based on multi-agent optimization, often involve a large number of function evaluations, resulting in high computational burdens. This study introduces a novel approach using a memory-based smoothed functional algorithm (MSFA) to tune the PID controller in AVR systems. The MSFA, which is in the class of single-agent based optimization techniques, requires fewer number of function evaluations per iteration, addressing the computational burden issue. Simulation analyses, including convergence curve of the fitness function, time response specification analysis of step response, stability analysis using Bode plot, computational effort analysis based on number of function evaluations (NFE), are conducted to assess the efficacy of the proposed MSFA-based PID controller for AVR systems. The results indicate that the proposed MSFA-based PID controller is highly effective in producing better function of demerit (FOD) with less values of NFE in comparison with other existing well-known based methods. |
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ISSN: | 2472-7660 |
DOI: | 10.1109/ISIEA61920.2024.10607268 |