The usage of machine learning techniques is widely spread and has also been implemented in the wind industry in the last years. Many of these techniques have shown great success but need to constantly prove the expectation of functionality. This paper describes a new method to monitor the health of a wind turbine using an undercomplete autoencoder. To evaluate the health monitoring quality of the autoencoder, the number of anomalies before an event has happened are to be considered. The results show that around 35% of all historical events that have resulted into a failure show many anomalies. Furthermore, the wind turbine subsystems which are subject to good detectability are the rotor system and the control system. If only one third of the service duties can be planned in advance, and thereby the scheduling time can be reduced, huge cost saving potentials can be seen.

Maintenance work orders are commonly used to document information about wind turbine operation and maintenance. This includes details about proactive and reactive wind turbine downtimes, such as preventative and corrective maintenance. However, the information contained in maintenance work orders is often unstructured and difficult to analyze, presenting challenges for decision-makers wishing to use it for optimizing operation and maintenance. To address this issue, this work compares three different approaches to calculating reliability key performance indicators from maintenance work orders. The first approach involves manual labeling of the maintenance work orders by domain experts, using the schema defined in an industrial guideline to assign the label accordingly. The second approach involves the development of a model that automatically labels the maintenance work orders using text classification methods. Through this method, we are able to achieve macro average and weighted average $F_1$-scores of 0.75 and 0.85 respectively. The third technique uses an AI-assisted tagging tool to tag and structure the raw maintenance information, together with a novel rule-based approach for extracting relevant maintenance work orders for failure rate calculation. In our experiments, the AI-assisted tool leads to an 88% drop in tagging time in comparison to the other two approaches, while expert labeling and text classification are more accurate in KPI extraction. Overall, our findings make extracting maintenance information from maintenance work orders more efficient, enable the assessment of reliability key performance indicators, and therefore support the optimization of wind turbine operation and maintenance.

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.

With the background of numerous wind turbines phasing out of fixed feed‐in tariffs in the following years and an increasing share of negative electricity market prices on the spot market, the availability definitions of “time‐based” and “production‐based” availability are possibly no longer suitable for assessing the overall performance of a wind turbine. This paper introduces a novel definition: the “monetary‐based” availability. The differences between the established definitions and the “monetary‐based” availability are highlighted by comparing the measures on an empirical data set. Furthermore, results on the impact of scheduling planned downtimes towards a monetary‐based optimum show that revenues can be increased. By shifting only a small share of the annual downtime to an optimum to maximize the revenue from electricity, a strong increase in additional earnings and thereby an increasment of the monetary‐based availability can be achieved.

Maintenance work orders are commonly used to document information about wind turbine operation and maintenance. This includes details about proactive and reactive wind turbine downtimes, such as preventative and corrective maintenance. However, the information contained in maintenance work orders is often unstructured and difficult to analyze, presenting challenges for decision-makers wishing to use it for optimizing operation and maintenance. To address this issue, this work compares three different approaches to calculate reliability by performance indicators from maintenance work orders. The first approach involves manual labeling of the maintenance work orders by domain experts, using the schema defined in an industrial guideline to assign the label accordingly. The second approach involves the development of a model that automatically labels the maintenance work orders using text classification methods. Through this method, we are able to achieve macro average and weighted average F1-Scores of 0.75 and 0.85 respectively. The third technique uses an AI-assisted tagging tool to tag and structure the raw maintenance information, together with a novel rule-based approach for extracting relevant maintenance work orders for failure rate calculation. In our experiments the AI-assisted tool leads to a 88% drop in tagging time in comparison to the other two approaches, while expert labeling and text classification are more accurate in KPI extraction. Overall, our findings make extracting maintenance information from maintenance work orders more efficient, enable the assessment of reliability key performance indicators and therefore support the optimization of wind turbine operation and maintenance.