In this paper, an artificial neural network (ANN) based algorithm is proposed as a solution to this problem. This algorithm is used to estimate wind turbine systems operational state and reliability. | Wind turbine systems operational state and reliability evaluation An artificial neural network approach International Journal of Data and Network Science 3 2019 323 330 Contents lists available at GrowingScience International Journal of Data and Network Science homepage ijds Wind turbine systems operational state and reliability evaluation An artificial neural network approach D. O. Aikhuelea A. Periolab and D. E. Ighravwea a Department of Mechanical and Biomedical Engineering Bells University of Technology Ota Nigeria b Department of Electrical and Computer Engineering Bells University of Technology Ota Nigeria CHRONICLE ABSTRACT Article history The increased role of wind turbine systems makes it important for its operational states to be Received December 28 2018 continuously monitored and optimized. This goal can be achieved using existing methods which Received in revised format May relies on closed-form expressions. The use of these methods however becomes challenging when 4 2019 interacting parameters cannot be fully presented with closed form expressions. In this paper an Accepted May 4 2019 Available online May 4 2019 artificial neural network ANN based algorithm is proposed as a solution to this problem. This Keywords algorithm is used to estimate wind turbine systems operational state and reliability. The proposed Wind turbine systems method is able to provide a more holistic approach to manage a wind turbine system with respect Artificial neural network to the problem mentioned above. Simulation results show that the developed ANN can predict the Downtime average number of failures per year distribution of failure and average downtime per failure with System reliability good accuracy. This was achieved using an ANN model with 5-15-3 architecture. The model gen- erates mean square errors of 10-3 10-3 and 10-3 at the training validation and testing stages respectively. The study is beneficial to wind turbine .
