Generally, the blades of wind turbine installed in cold regions often encounter the situation of icing on their surface in winter, which affects the performance of wind turbine greatly and reduces power generation. Blade icing of wind turbine is a typical alarm related to multivariates, such as environment temperature, humidity, blade rotation and so on. To successfully achieve a supervised classification, it is necessary to extract the correlated features with blade icing. This paper proposes an extraction algorithm using reward extremum which can extract a group of variables correlated to blade icing from many monitoring variables of wind turbine, and they affect each other. This algorithm, using reward function which can measure the reward or value of alarm problem, can effectively distinguish the variables correlated to blade icing from the uncorrelated ones without knowing the mean of variables and the relations between them. Whether the blade of wind turbine is iced or not is a binary logic, so logistic regression algorithm is better choice to detect blade icing. Hence, this paper proposes a hybrid model using logistic regression algorithm and extraction algorithm using reward extremum, which can be built by machine learning with historic monitoring data. This model can real-time detect blade icing by real-time monitoring data. A correlation variable set can be obtained by the application of extraction algorithm using reward extremum on the historic monitoring data; and then the weights of the correlation variables in the set can be gained from the historic data by using logistic regression algorithm, so a blade icing detection pattern can be found using logistic regression algorithm; finally, whether the blade of wind turbine is iced in delay time or not can be real-time detected with the help of the hybrid model by real-time monitoring data. It is proved by wind turbine data that this hybrid model can work well not only to get correlation variable set but also to improve the generalization performance of logistic regression and achieve better prediction results. The performance of the model on blade icing alarm of wind turbine provides a new way to find out multivariate correlation to alarm from the mass historic monitoring data.
Real-time monitoring, prognosis, and resilient control for wind turbine systems