A machine learning-driven support vector regression model for enhanced generation system reliability prediction

• Published in: Compel 2024-03, Vol.43 (1), p.37-49
• Main Authors: Bolourchi, Pouya, Gholami, Mohammadreza
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Artificial intelligence; Decisions; Effectiveness; Efficiency; Eigenvalues; Electrical loads; Forecasting; Kernel functions; Machine learning; Maintenance; Methods; Monte Carlo simulation; Performance measurement; Power supply; Regression models; Reliability analysis; Suppliers; Support vector machines; System reliability; Systems design
• ISSN: 0332-1649; 2054-5606; 0332-1649
• DOI: 10.1108/COMPEL-04-2023-0133

Purpose The purpose of this paper is to achieve high accuracy in forecasting generation reliability by accurately evaluating the reliability of power systems. This study uses the RTS-79 reliability test system to measure the method’s effectiveness, using mean absolute percentage error as the performance metrics. Accurate reliability predictions can inform critical decisions related to system design, expansion and maintenance, making this study relevant to power system planning and management. Design/methodology/approach This paper proposes a novel approach that uses a radial basis kernel function-based support vector regression method to accurately evaluate the reliability of power systems. The approach selects relevant system features and computes loss of load expectation (LOLE) and expected energy not supplied (EENS) using the analytical unit additional algorithm. The proposed method is evaluated under two scenarios, with changes applied to the load demand side or both the generation system and load profile. Findings The proposed method predicts LOLE and EENS with high accuracy, especially in the first scenario. The results demonstrate the method’s effectiveness in forecasting generation reliability. Accurate reliability predictions can inform critical decisions related to system design, expansion and maintenance. Therefore, the findings of this study have significant implications for power system planning and management. Originality/value What sets this approach apart is the extraction of several features from both the generation and load sides of the power system, representing a unique contribution to the field.