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Improving load frequency controller tuning with rat swarm optimization and porpoising feature detection for enhanced power system stability

. 2024 Jul 02 ; 14 (1) : 15209. [epub] 20240702

Status PubMed-not-MEDLINE Language English Country England, Great Britain Media electronic

Document type Journal Article

Grant support
TN02000025 National Centre for Energy II Ministry of Education, Youth and Sports
CZ.10.03.01/00/22_003/0000048 Ministry of the Environment of the Czech Republic

Links

PubMed 38956157
PubMed Central PMC11220010
DOI 10.1038/s41598-024-66007-y
PII: 10.1038/s41598-024-66007-y
Knihovny.cz E-resources

Load frequency control (LFC) plays a critical role in ensuring the reliable and stable operation of power plants and maintaining a quality power supply to consumers. In control engineering, an oscillatory behavior exhibited by a system in response to control actions is referred to as "Porpoising". This article focused on investigating the causes of the porpoising phenomenon in the context of LFC. This paper introduces a novel methodology for enhancing the performance of load frequency controllers in power systems by employing rat swarm optimization (RSO) for tuning and detecting the porpoising feature to ensure stability. The study focuses on a single-area thermal power generating station (TPGS) subjected to a 1% load demand change, employing MATLAB simulations for analysis. The proposed RSO-based PID controller is compared against traditional methods such as the firefly algorithm (FFA) and Ziegler-Nichols (ZN) technique. Results indicate that the RSO-based PID controller exhibits superior performance, achieving zero frequency error, reduced negative peak overshoot, and faster settling time compared to other methods. Furthermore, the paper investigates the porpoising phenomenon in PID controllers, analyzing the location of poles in the s-plane, damping ratio, and control actions. The RSO-based PID controller demonstrates enhanced stability and resistance to porpoising, making it a promising solution for power system control. Future research will focus on real-time implementation and broader applications across different control systems.

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