Enhanced Anomaly Detection in Wind Energy Datasets: Superior Performance of LSTM-Based VAE-WGAN Over Isolation Forest and One-Class SVM

Detecting anomalies in wind energy data is crucial for a reliable and efficient wind power system, but it’s a complex challenge. Long Short-Term Memory (LSTM), Variational Autoencoders (VAEs), and Wasserstein Generation Adversarial Networks (WGANs) were all used together in this study. This is a new way to make it easier to find problems in wind energy. The proposed method in this paper, based on the above VAEs-WGANs, compares its performance to established algorithms like Isolation Forest and One-Class SVM, using metrics like precision, recall, and F1-score. The results show that the VAE-WGAN significantly outperforms these methods in identifying data irregularities. This research represents a significant advancement in wind energy anomaly detection, paving the way for more robust wind energy infrastructure.