In view of the frequent and costly failures of power converters in wind turbines, a large consortium of research institutes and companies has joined forces to investigate the underlying causes and key driving factors of the failures. This paper presents an exploratory statistical analysis of the comprehensive field data provided by the project partners. The evaluated dataset covers converter failures recorded from 2003-2017 during almost 7400 operating years of variable-speed wind turbines of different manufacturers and types, operating at onshore and offshore sites in 23 countries. The results include the distribution of failures within the converter system and the comparison of converter failure rates among turbines with different generator-converter concepts, from different manufacturers as well as from different turbine generations. By means of combined analyses of converter-failure data with operating and climate data, conditions promoting failure are identified. In line with the results of a previous, much smaller study of the authors, the present analysis provides further indications against the wide-spread assumption that thermal-cycling induced fatigue is the lifetime-limiting mechanism in the power converters of wind turbines. Instead, the results suggest that humidity and condensation play an important role in the emergence of converter failures in this application.

Power converters are among the most frequently failing components of wind turbines. Despite their massive economic impact, the actual causes and mechanisms underlying these failures have remained in the dark for many years. In view of this situation, a large consortium of three research institutes and 16 companies, including wind-turbine and component manufacturers, operators and maintenance-service providers has joined forces to identify the main causes and driving factors of the power-converter failures in wind turbines to create a basis for effective remedial measures. The present paper summarizes and discusses the results of this research initiative, which have been achieved through the evaluation of converter-specific failure and operating data of a large and diverse worldwide wind-turbine fleet, field measurements as well as post-mortem investigation of returned converter components. A key conclusion of the work is that the thermal-cycling induced fatigue of bond-chip contacts and die-attach solder, which is a known issue in other fields of power-electronics applications and which has been widely assumed to be the principle damage mechanisms also in wind turbines, is no relevant contributor to the observed converter failures in this application. Instead, the results indicate that environmental factors such as humidity and contamination but also design and quality issues as well as human errors play an important part in the incidence of these failures.

Frequent failures of power converters affect the availability of wind turbines and cause considerable maintenance costs. To enhance the reliability of power converters in wind turbines, the prevailing causes and modes of failures have to be identified. This publication contributes to root‐cause analysis of the power‐converter failures in wind turbines from a statistical point of view. For this purpose, the failure behavior of power‐converters is modeled via lifetime models as well as repairable‐system models. By means of regression models, covariates are incorporated, including both design‐related and site‐specific covariates. The analysis is based on a worldwide extensive field‐data collection covering more than 9000 turbines, including different turbine designs, sites, and ages. The results obtained by means of the applied regression models indicate that the location of the power converter within the turbine, the cooling system, the converter rated power, the DC‐link voltage, the IGBT‐module manufacturer, and the commissioning date of the turbine as design‐related covariates have a significant effect on the phase‐module failure behavior and with that on converter reliability. Among the site‐specific covariates, the analysis results confirm humidity as a likely significant driver of failures.

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.

SCADA operating data are more and more used across the wind energy domain, both as a basis for power output prediction and turbine health status monitoring. Current industry practice to work with this data is by aggregating the signals at coarse resolution of typically 10-min averages, in order to reduce data transmission and storage costs. However, aggregation, i.e., downsampling, induces an inevitable loss of information and is one of the main causes of skepticism towards the use of SCADA operating data to model complex systems such as wind turbines. This research aims to quantify the amount of information that is lost due to this downsampling of SCADA operating data and characterize it with respect to the external factors that might influence it. The issue of information loss is framed by three key questions addressing effects on the local and global scale as well as the influence of external conditions. Moreover, recommendations both for wind farm operators and researchers are provided with the aim to improve the information content. We present a methodology to determine the ideal signal resolution that minimized storage footprint, while guaranteeing high quality of the signal. Data related to the wind, electrical signals, and temperatures of the gearbox resulted as the critical signals that are largely affected by an information loss upon aggregation and turned out to be best recorded and stored at high resolutions. All analyses were carried out using more than one year of 1 Hz SCADA data of onshore wind farm counting 12 turbines located in the UK.