Detecting Wind Turbine Blade Icing with a Multiscale Long Short-Term Memory Network

Abstract

Blade icing is one of the main problems of wind turbines installed in cold climate regions, resulting in increasing power generation loss and maintenance costs. Traditional blade icing detection methods greatly rely on dedicated sensors, such as vibration and acoustic emission sensors, which require additional installation costs and even reduce reliability due to the degradation and failures of these sensors. To deal with this challenge, this paper aims to develop a cost-effective detection system based on the existing operation data collected from the supervisory control and data acquisition (SCADA) systems which are already equipped in large-scale wind turbines. Considering that SCADA data is essentially a multivariate time series with inherent non-stationary and multiscale temporal characteristics, a new wavelet-based multiscale long short-term memory network (WaveletLSTM) approach is proposed for wind turbine blade icing detection. The proposed method incorporates wavelet-based multiscale learning into the traditional LSTM architecture and can simultaneously learn global and local temporal features of multivariate SCADA signals, which improves fault detection ability. A real case study has shown that our proposed WaveletLSTM method achieved better detection performance than the existing methods.