Remote diagnostics for power converter faults in wind turbines based on converter control system data

Power converters are among the most frequently failing subsystems of onshore and offshore wind turbines. In order to minimize the resulting downtime and production losses, the time to repair should be as low as possible. In practice, however, it is not uncommon for several turbine visits to be necessary, as information about the failure mode and the spare parts required can often only be determined on site. This paper presents a data-driven, interpretable workflow for the remote diagnosis of power-converter-related turbine shutdowns using converter control system data from an offshore wind farm. The study uses converter fault events and three data sources: high-resolution fast logs (4.5 kHz, -350 to +200 ms around a fault-induced trigger), 1 min operating data, and fault flags derived from event log data. From an initial 864 engineered features we remove low-variance and highly correlated features, apply a subsampled decision-tree inclusion-rate filter to retain 34 features, and estimate diagnostic impact via subsampled logistic regression. Results show that fast-log features and converter fault flags contain the most predictive information for classifying standstill severity after a fault-induced shutdown, while low-resolution operating data contribute little. Using four of the derived features yields the best cross-validated performance in a decision tree, with an accuracy of 0.89 and an F1 score of 0.86. The proposed approach is practical for industry use and offers the potential to provide explainable decision support for improving the first-time fix rate.