Emotion-Inspired Comprehensive Optimal Control for Transient Stability and Voltage Regulation in Power Systems

This paper presents a comprehensive controller that integrates intelligent and classic control solutions to enhance the transient stability and voltage regulation of power systems. The design process addresses the complex nonlinear dynamics inherent in power systems. Initially, a state feedback linearization (SFBL) control law is derived to transform these nonlinear dynamics into a closed-loop linear system. Subsequently, a linear quadratic regulator (LQR) optimal control is designed to ensure system stability while minimizing a cost function. Inspired by the emotional responses of the human brain, the voltage regulation controller is developed and fine-tuned using genetic algorithm optimization. This optimization adjusts the five gains of the emotional controller. Additionally, a fuzzy logicbased comprehensive controller is introduced to balance transient response and voltage regulation. The closed-loop control systems are evaluated under different conditions, such as variations in reactive and active power demands and three-phase ground faults at the line and generator terminal voltage. MATLAB simulations demonstrate the superiority of the comprehensive controller over an LQR controller. Additionally, the controller's performance is compared with a conventional setup, which includes a power system stabilizer (PSS) and an automatic voltage regulator (AVR), using hardware-in-the-loop (HIL) implementation with Plexim RT-Box and a TI C2000 microcontroller. The results show that the proposed controller provides improved stability response and voltage regulation.

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Publication Details

Journal
IEEE Transactions on Power Systems
Published
2025-05-19
DOI
https://doi.org/10.1109/tpwrs.2025.3571420
Citations
1
Primary Topic
Power Systems and Renewable Energy
Type
article
Field-Weighted Citation Impact
0.53
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article

Emotion-Inspired Comprehensive Optimal Control for Transient Stability and Voltage Regulation in Power Systems

Rasoul M. Milasi
1 citations
IEEE Transactions on Power Systems
Power Systems and Renewable Energy
0.53
article

Emotion-Inspired Comprehensive Optimal Control for Transient Stability and Voltage Regulation in Power Systems

Rasoul M. Milasi
article en
1 citations

Abstract

This paper presents a comprehensive controller that integrates intelligent and classic control solutions to enhance the transient stability and voltage regulation of power systems. The design process addresses the complex nonlinear dynamics inherent in power systems. Initially, a state feedback linearization (SFBL) control law is derived to transform these nonlinear dynamics into a closed-loop linear system. Subsequently, a linear quadratic regulator (LQR) optimal control is designed to ensure system stability while minimizing a cost function. Inspired by the emotional responses of the human brain, the voltage regulation controller is developed and fine-tuned using genetic algorithm optimization. This optimization adjusts the five gains of the emotional controller. Additionally, a fuzzy logicbased comprehensive controller is introduced to balance transient response and voltage regulation. The closed-loop control systems are evaluated under different conditions, such as variations in reactive and active power demands and three-phase ground faults at the line and generator terminal voltage. MATLAB simulations demonstrate the superiority of the comprehensive controller over an LQR controller. Additionally, the controller's performance is compared with a conventional setup, which includes a power system stabilizer (PSS) and an automatic voltage regulator (AVR), using hardware-in-the-loop (HIL) implementation with Plexim RT-Box and a TI C2000 microcontroller. The results show that the proposed controller provides improved stability response and voltage regulation.

IEEE Transactions on Power SystemsVol. 40(6)
Pennsylvania State University (US)
Affordable and clean energy
Openalex Percentile: Top 31%
Power Systems and Renewable Energy
0.53
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