A novel approach of wind turbine blade root load monitoring based on bearings vibrational data and a new AE-LSTM-Attention neural network

The monitoring of wind turbine blade root load (BRL) has always been a valuable yet highly challenging problem. BRL monitoring is often measured directly using strain sensors, which is expensive to install and poses safety risks. Thus, it is necessary to explore an indirect measurement method that establishes a mapping relationship between BRL and easily measurable data. This paper presents a non-intrusive BRL monitoring method based on the vibrational signals at bearings (including main bearing, gearbox bearing, and generator bearing) and a new proposed AE-LSTM-Attention neural network. The proposed neural network incorporates autoencoder (AE) pre-training process, long short-term memory (LSTM) neural network, and channel attention mechanism to overcome difficulties in extracting and fusing long-term, high-frequency, and multi-channel signal features. Additionally, considering the wind turbine transmission mechanism, an optimal LSTM step number selection criterion is proposed. The wind turbine’s physical feature is innovatively bridged with the neural network training parameters, e.g., setting the transmission ratio of the gearbox as the number of LSTM steps, by which the most cost-effective prediction performance can be achieved. Experiments were conducted using data from actual operating wind turbines, and the results show that the proposed method can successfully achieve non-intrusive monitoring of BRL characteristics and outperform traditional methods in terms of predictive performance. The mapping relationship between wind turbine vibration signals and BRL characteristics can be established efficiently and accurately.