Automatic wind turbine operation analysis through neural networks

Abstract: This master thesis handles the development of an automatic benchmarking program for wind turbines and the thesis works as the theoretical basis for this program. The program is created at the request of the power company OX2 who wanted this potential to be investigated. The mission given by the company is to: 1. to find a good key point indicator for the efficiency of a wind turbine, 2. to find an efficient way to assess this and 3. to write a program that does this automatically and continuously. The thesis determines with a study of previous research that the best method to utilize for these kinds of continuous analyses are artificial neural networks which can train themselves on historical data and then assess if the wind turbine is working better or worse than it should with regards to its history. This comparison between the neural network predicted operation and the actual operation works as the measurement of the efficiency, the key point indicator for how the turbine work compared to how it historically should operate. The program is based on this principle and is completely written in MATLAB. Further testing of the program found that the best variables to use are wind speed and the blade pitch angle as input variables for the neural network and active power as the target used as the variable to predict and assess the operation. The final program was able to be fully automated and integrated into the OX2 system thanks to the possibility to continuously import wind turbine data through APIs. In the final testing was the program able to identify 75% of the anomalies manually found in the half year and in the five turbines used for this thesis, the small anomalies not found manually but identified by the program excluded.