Current Challenges in Monitoring and Predictive Diagnostics of Large-Scale Wind Power Plants

Abstract

Relevance: the rapid expansion of large-scale wind power plants has made reliable operation, early fault detection, and lightning protection integrity critical engineering priorities. Due to turbine height, complex electrical architecture, and high lightning exposure, lightning protection reliability directly affects the safety and availability of the entire power system. Recent statistics indicate that 25–30 % of turbine failures in large wind farms are caused by lightning strikes, overvoltage events, or insulation breakdowns. Therefore, the development of real-time monitoring, AI-based forecasting, and predictive fault diagnostics systems has both scientific and practical significance.

Objective: to assess the reliability of lightning protection systems in large-scale wind farms, to monitor turbine operational conditions in real time based on SCADA data, and to develop AI-driven predictive diagnostic algorithms aimed at improving operational efficiency and reducing downtime.

Methods: the study utilized parameters including wind speed , rotor angular speed , active power , temperature , and grounding resistance . A comprehensive monitoring framework was established. The lightning protection model quantified surge-induced voltage as:  and the probability of insulation breakdown was determined using the Weibull function: Additionally, the overall turbine degradation index was defined as:

Results: the proposed monitoring and diagnostic system increased the accuracy of lightning protection fault detection by 23–27 %, reduced annual turbine downtime by 20 %, and improved overall operational efficiency by 3.4 %. Continuous monitoring of grounding resistance and real-time evaluation of conductivity provided an effective means to prevent insulation failures. The proposed approach enhances the reliability of large-scale wind power plants, optimizes maintenance scheduling, and minimizes energy production losses.