Fuzzy Evaluation and Prediction of Wind Turbine Reliability Based on Multi-source Data

In response to the high failure rates and expensive maintenance costs faced by wind turbines in complex operating environments, a fuzzy reliability assessment and health prediction approach for wind turbines based on SCADA system data is proposed. Initially, reliability-related indicators are selected based on the energy transfer process of the turbine and SCADA data indicators, establishing a multi-level reliability assessment index system encompassing critical components such as the rotor system, main bearing system, and generator system. Furthermore, a scientific method for determining indicator weights is established by integrating random forest algorithm importance measurements, expert scoring methods, triangular fuzzy number membership function solving, and fuzzy comprehensive evaluation methods, enabling real-time operational reliability assessment of the turbines. Subsequently, a neural network model based on multiple data features is developed to predict the health status of wind turbines, with model accuracy verified through error analysis. Finally, validation through practical case studies demonstrated that the proposed evaluation model can accurately assess turbine operational status, providing effective support for fault early warning and health management.