Time-Series Load Online Prediction of Wind Turbine Based on Adaptive Multisource Operational Data Fusion

Abstract

Aiming at the problem that the stochastic change of wind turbine generator (WTG) working conditions and the complex nonlinear relationship between load and operation data make it difficult to predict the short-term load online, this paper proposes an adaptive multi-information source data fusion online prediction method for WTG load. Random forest (RF) and WaveNet time series (WTS) are established as subinformation source models, and the influence of input features and historical data on load prediction is considered from horizontal and vertical dimensions. In order to reduce the influence of original data completeness on load prediction, the deviation degree of load prediction of RF and WTS is analyzed. The deviation degree of the subinformation source model is used as the basis for judgment, and it is fused into a multi-information source load model with adaptive deviation degree analysis to predict the loads on the blades, tower top, and tower bottom of the wind turbine. According to the 15 MW semisubmersible offshore WTG load prediction example, the prediction error of this method is about 4% under normal data conditions and 6% under abnormal data conditions, and the calculation time of 200 sets of test data is 0.053 s, which meets the needs of pitch control and has the potential to be applied in the optimization of pitch control strategy.