Advancing harmonic prediction for offshore wind farms using synthetic data and machine learning

Abstract

This study presents a novel forecasting model for accurate harmonic prediction in offshore wind farms (OWFs) using data augmentation and machine learning techniques. A Generative Adversarial Network (GAN) is employed to generate synthetic meteorological data, enhancing the training set for improved accuracy. The model utilizes wind speed data from Bozcaada, Turkey, and simulates voltage and current waveforms to predict Total Harmonic Distortion Voltage (THDV). Machine learning (Random Forest) and deep learning (LSTM, GRU) models are compared to assess prediction performance. Results show that the GAN-based data augmentation significantly enhances prediction accuracy. This study provides a valuable methodology for harmonic forecasting in OWFs, offering insights for future renewable energy system planning and grid stability.