A condition monitoring approach of multi-turbine based on VAR model at farm level

Abstract

A multi-turbine condition monitoring method using supervisory control and data acquisition (SCADA) data for large-scale wind farm is proposed. The method takes the difference between the SCADA data of each turbine with the median of other remaining turbines, and establishes condition vector consisting of the differences. Considering the autocorrelation of turbine SCADA data, vector autoregression (VAR) model is used to remove the autocorrelation in the condition vector of wind farm. Hotelling and multivariate exponentially weighted moving average (MEWMA) control chart are applied to monitor the residual vector. An industrial wind farm example is given to illustrate the proposed method. Compared with the existing turbine condition monitoring charts, the false alarm of proposed method is reduced for considering the autocorrelation of operation data, and monitoring strategy using MEWMA improves detected rate and expedites alarm time compared with Hotelling. The proposed method realizes monitoring multiple turbines simultaneously in farm by a fault indicator, which has important theoretical and engineering significance to the practical operations and maintenance activities in large-scale wind farm.