Automatised detection of sources for power curve deviations of horizontal axis wind turbines

Abstract: To face climate change and transform the electricity supply to an environmentally friendly generation, wind plays an important role. Due to a yearly increase in installed wind power turbines, in the European Union, the need for maintenance increases as well. For reducing the maintenance times and, with that, the standstill time and resulting economical losses, the time for troubleshooting must be reduced. This work aims to show that the troubleshooting process of wind turbines can be reduced to a minimum with the automation. This can be reached by creating a scatter plot of the active power over the wind speed curve and investigating the data points where the turbine is not performing as it should. The data is extracted from a wind farm located in Finland for the wind year 2021. The methodological approach taken in this study is to build a normalised threshold power curve and compare it to monthly binned power curves of two selected turbines. The deviation between the threshold and the monthly power curve is investigated, and the months with a high deviation are chosen for further analysis, which includes the separation of the outlier data into four different categories. The outlier in bins with a higher deviation than 5 % are selected. The four categories are further inspected, and the reasons for the curtailments are extracted and analysed. In summary, these results show that the analysis of curtailment reasons based on a scatter plot of the active power of a wind turbine is possible. Moreover, the troubleshooting process can be reduced in time. Due to practical constraints, this work cannot provide an analysis with a threshold power curve built with data from more than one year. This makes the results less objective since fluctuations, which can occur during only one year, cannot be minimised.