Real-Time Optimization and Maintenance of Wind Turbine Performance using Digital Twin Technology

Abstract

Wind power plays a vital role in Sri Lanka's renewable energy transition, yet coastal wind farms face challenges such as lightning strikes, wind misalignment losses, turbine cut-in/out events, acoustic impacts, and costly maintenance. This study proposes a digital twin-based framework to enhance real-time optimization and predictive maintenance of wind turbine performance. The framework integrates four modules: weather risk forecasting, operational efficiency, noise impact analysis, and predictive maintenance. Using SCADA data from the Mannar Thambapavani Wind Farm, long-term meteorological and lightning records, NASA satellite observations, and the WEA-Acceptance dataset, advanced machine learning models were developed to forecast lightning (F1 = 0.81, AUC = 0.87), estimate power losses (R2 = 0.80, MAE = 3.4 kWh/h), optimize blade pitch, and produce short-term and medium-term energy forecasts. The digital twin simulation visualizes turbine dynamics, noise propagation, and maintenance scenarios. Results show improved prediction accuracy, 20-30% reduction in downtime, and a clear trade-off between energy efficiency (5-10° pitch) and noise (~56 dB > 25°). The framework strengthens situational awareness, operational reliability, and sustainability in monsoon-prone tropical environments.