Katharina Fischer

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.

Based on maintenance data from over 1000 onshore and offshore wind turbines covering more than 4200 operating years, this study presents an analysis of failure rates, repair times, and maintenance resource requirements, focusing on subsystem-level reliability. Failure rates per turbine and megawatt are compared, and failure behaviour over time is examined. Next to failure events, further corrective and preventive maintenance interventions are analysed. To provide more detailed insights for operation and maintenance simulations, a distinction is made between total major component replacements and those specifically requiring a jack-up vessel. Results show that onshore wind turbines have higher failure rates per megawatt than offshore wind turbines. Key subsystems including the pitch system, the control system, and power converter system are identified as critical to overall wind turbine reliability for both onshore and offshore wind turbines. For the overall wind turbine system, a failure behaviour over time following a bathtub curve is identified, with distinct trends for individual subsystems.

Based on maintenance data from over 1000 onshore and offshore wind turbines covering more than 4200 operating years, this study presents an analysis of failure rates, repair times, and maintenance resource requirements, focusing on subsystem-level reliability. Failure rates per turbine and megawatt are compared and failure behaviour over time is examined. Next to failure events, further corrective and preventive maintenance interventions are analysed. To provide more detailed insights for operation and maintenance simulations, a distinction is made between total major component replacements and those specifically requiring a jack-up vessel. Results show that onshore wind turbines have higher failure rates per megawatt than offshore wind turbines. Key subsystems including the pitch system, the control system, and power converter system are identified as critical to overall wind turbine reliability for both onshore and offshore wind turbines. For the overall wind turbine system, a failure behaviour over time following a bathtub curve is identified, with distinct trends for individual subsystems.

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 ms 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 first-time fix rate.

Based on comprehensive field data covering more than 22,000 operating years of onshore and offshore wind turbines on five different continents, we present failure rates for the main components of the converter system, investigate their failure behavior with respect to trends and identify both design-related and site-specific factors having a significant effect on converter reliability. All converter components show prominent early failure behavior followed by a transition straight to degradation failures. Several design factors are identified as significant reliability drivers: namely the cooling concept of the converter, its position inside the wind turbine, its rated capacity as well as the IGBT-module manufacturer. In addition, a susceptibility to the environmental influences of mean absolute ambient humidity, mean ambient temperature, installation height and mean capacity factor is identified and quantified.

This study delves into the challenge of efficiently digitalising wind turbine maintenance data, traditionally hindered by non‐standardised formats necessitating manual, expert intervention. Highlighting the discrepancies in past reliability studies based on different key performance indicators (KPIs), the paper underscores the importance of consistent standards, like RDS‐PP, for maintenance data categorisation. Leveraging on established digitalisation workflows, we investigate the efficacy of text classifiers in automating the categorisation process against conventional manual labelling. Results indicate that while classifiers exhibit high performance for specific datasets, their general applicability across diverse wind farms is limited at the present stage. Furthermore, differences in failure rate KPIs derived from manual versus classifier‐processed data reveal uncertainties in both methods. The study suggests that enhanced clarity in maintenance reporting and refined designation systems can lead to more accurate KPIs.

Power converters in wind turbines exhibit frequent failures, the causes of which have remained unexplained for years. Field-experience based research has revealed that power- and thermal-cycling induced fatigue effects in power electronics do not contribute significantly to the field failures observed. Instead, clear seasonal failure patterns point to environmental influences, in particular to humidity, as a critical stressor and likely driver of converter failure. In addition to the electrical operating conditions, it is therefore important to also identify and characterize the climatic conditions that power converters in wind turbines are exposed to, both as a contribution to root-cause analysis and as a basis for the derivation of suitable test procedures for reliability qualification of components and systems. This paper summarizes the results of field-measurement campaigns in 31 wind turbines of seven different manufacturers spread over three continents. The temperature and humidity conditions inside the converter cabinets are characterized and related to the environmental conditions of the turbines and to their operation. The cabinet-internal climate is found to be subject to pronounced seasonal variations. In addition to the site-specific ambient climatic conditions and the operation of the turbines, the converter cooling concept is identified to significantly influence the climatic conditions inside the power cabinets.