Abstract
This paper proposes a novel Stability-Based Artificial Intelligence Method for predicting the optimum parameters of the proportional-integral-derivative controller in an automatic voltage regulator system. To implement the stability-based artificial intelligence method, first, parameters which are of great importance for the control of the system are applied to the system randomly, data are collected, and then artificial intelligence studies are carried out. The suggested approach has been applied to the system and compared with other control methods in the literature, namely the improved Kidney Inspired algorithm, Jaya algorithm, Tree Seed algorithm, Water Wave Optimization, and Biography-Based Optimization to test the robustness of the new method. The numerical results indicate that the proposed method significantly outperforms all other methods.
Highlights
In power systems, conventional voltage control can be grouped into three different stages
To implement the stability-based artificial intelligence method (S-AIM), the Kp, Ki, and Kd PID controller parameters are applied to the system randomly, data are collected, and artificial intelligence studies are carried out
The suggested approach has been applied to the automatic voltage regulator (AVR) system and compared to other control methods in the literature
Summary
Conventional voltage control can be grouped into three different stages. These stages are named as primary, secondary, and tertiary voltage control for a real-time, middle, and long-term stage. Automatic voltage regulators regulate the voltage of the generator bus bar by controlling the injection or absorption of reactive power for synchronous generators [1]. AVRs systematically take a wide range of input voltages and ensure a stable output voltage. The essential function of the AVR system is to automatically regulate generator voltage and maintain the output constant in the required range of voltage level for the generator without regard to the existing load. AVRs protect the surges from electrical surges and generator overload, and help generators resist overloads to avoid shorting [2], [3]
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