| description abstract | Abstract. Fleet-wide condition monitoring is a well-known method for continuously assessing the operational health of an entire fleet, ensuring efficient and reliable performance. With the emergence of fleet-based monitoring, incorporating digital modeling, advanced diagnostics, and predictive maintenance has become possible. This approach compares physical machines to their digital counterparts, enabling more sophisticated fault detection. A critical challenge addressed by fleet monitoring is managing large data volumes efficiently, avoiding the need for excessive high-frequency data. The proposed approach leverages supervisory control and data acquisition (SCADA) data from similar wind turbines in the same region, assuming healthy operation for most turbines. Deviations in multiple measurements from normal conditions serve as fault indicators, enabling early detection and targeted interventions while reducing data processing demands. This study proposes an unsupervised learning method using statistical analysis of SCADA data, applied on a fleet of 22 wind turbines. After preprocessing to understand stochastic behaviors, a multifeature hierarchical clustering (MFHC) model identifies patterns and groups turbines based on operational characteristics. By analyzing extracted features, the model efficiently detects faults and optimizes performance without requiring labeled datasets. | |
