Frontiers in Energy Research (Apr 2022)
Anomaly Detection of Hydropower Units Based on Recurrent Neural Network
Abstract
Anomaly detection for hydraulic turbine unit has an important role in hydropower system. In hydropower systems, different components will produce n-dimensional heterogeneous time series with different characteristics at all times. Due to the characteristic evolution and time dependence, vibration-based anomaly detection for hydraulic turbine unit is extremely challenging. In this paper, we propose a conditional quantile regression based recurrent neural network (QRNN), which models the time dependence and probability distribution between random variables. The proposed method aims to extract the actual representation patterns from the fitted models and it can effectively detect anomalies in the non-uniform time series of feature evolution. The experimental results show that the proposed method has better accuracy in anomaly detection (error reduction by 34%) than the traditional method, and saves at least 25.6% of execution time.
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