Predictive Maintenance and Anomaly Detection of Wind Turbines Based on Bladed Simulator Models

초록

This paper proposes a novel data-driven scheme for condition monitoring and detecting anomalies in the wind turbine critical components based on advanced deep learning algorithms. The proposed method employs time-series data taken from a Bladed simulator model for the 5MW wind turbine. To emulate the characteristic behavior of essential wind turbine components, we develop supervised and unsupervised deep learning models using self-organizing map (SOM), long short-term memory auto encoder (LSTM-AE), and long short-term memory recurrent neural network (LSTM-RNN). Statistical process control(SPC) charts are used to evaluate the anomalous behavior predicted by the developed data-driven models. The proposed method is tested on a Bladed 5MW wind turbine model with 24 m/sec wind speed for validating its accuracy and applicability. Copyright (c) 2023 The Authors.

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