A Comprehensive Review of SCADA-Based Wind Turbine Performance and Reliability Modeling with Machine Learning Approaches

Abstract

The increasing reliance on wind energy to meet global energy demands has made wind turbine performance optimization and reliability a critical area of research. Supervisory Control and Data Acquisition (SCADA) data, which provides real-time operational insights into wind turbines, plays a pivotal role in predictive maintenance, fault detection, and energy output optimization. This review explores the current methodologies and advancements in wind turbine performance modeling and reliability analysis, with a particular emphasis on machine learning (ML) approaches. Existing studies that utilize SCADA data to implement various ML models, such as decision trees, neural networks, and ensemble learning techniques (bagging, boosting, stacking), are analyzed for their effectiveness in predicting turbine failures, improving energy efficiency, and optimizing maintenance schedules. Key findings from multiple studies are synthesized, highlighting the strengths, limitations, and real-world applications of these models. Challenges in data quality, model generalization, and the implementation of real-time ML-driven systems in wind farms are also addressed. This review aims to provide a comprehensive overview of the current state of SCADA-based wind turbine analysis and to offer a roadmap for future research that bridges the gap between data-driven models and their practical deployment in wind energy systems.