State-of-the-Art Review: Models and Algorithms for Optimal Power System Design, Stabilization, and Reliability Enhancement

Abstract

The stability and reliability of power systems are essential for effective operation and design. Models and algorithms are instrumental in bolstering stability, especially using Power System Stabilizers (PSS) and refined control methods. In this study, a state-of-the-art review of models and algorithms for optimal power system design, stabilization, and reliability enhancement is conducted. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach was utilized to systematically identify and refine the focus on the topic using the Scopus database, while VOSviewer assisted in analyzing trends. Noticeable aspect from the results includes the prevalence of metaheuristic algorithms like genetic algorithms, particle swarm optimization, cuckoo search, and various others. The results also revealed that these algorithms are utilized for tasks such as optimal power system design, substation placement, parameter tuning for power system stabilizers, and load forecasting. The trends analysis further shows a notable shift towards learning algorithms, indicating an increasing interest in data-driven approaches to improve system performance. This paper contributes by providing a comprehensive review of optimization and machine learning techniques, including genetic algorithms and metaheuristics, for enhancing power system stability and resilience. It focuses on advanced methodologies for stabilizer design, Phasor Measurement Units (PMU) placement, distributed generation integration, and power system performance optimization, supported by practical applications and case studies. Finally, this work suggests future research directions to address gaps in existing knowledge.

Keywords

• Power system design genetic algorithms
• machine learning in power systems
• power network restoration metaheuristic algorithms
• power system stabilizer (PSS) optimization