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首页> 外文会议>Power Systems Computation Conference >An Anomaly Detection Approach Using Wavelet Transform and Artificial Neural Networks for Condition Monitoring of Wind Turbines' Gearboxes
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An Anomaly Detection Approach Using Wavelet Transform and Artificial Neural Networks for Condition Monitoring of Wind Turbines' Gearboxes

机译：基于小波变换和人工神经网络的风力发电机齿轮箱状态监测的异常检测方法

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摘要

This paper presents an anomaly detection approach using artificial neural networks and the wavelet transform for the condition monitoring of wind turbines. The method aims to attain early anomaly detection and to prevent possible false alarms under healthy operations. In the approach, nonlinear autoregressive neural networks are used to estimate the temperature signals of the gearbox. The Mahalanobis distances are then calculated to measure the deviations between the current states and healthy operations. Next, the wavelet transform is applied to remove noisy signals in the distance values. Finally, the operation information is considered together with the refined distance values to detect potential anomalies. The proposed approach has been tested with the real data of three 2 MW wind turbines in Sweden. The results show that the approach can detect possible anomalies before failure events occur and avoid reporting alarms under healthy operations.
机译：本文提出了一种基于人工神经网络和小波变换的风力发电机状态监测异常检测方法。该方法旨在实现早期异常检测并防止在正常操作下可能出现的误报。在该方法中，非线性自回归神经网络用于估计变速箱的温度信号。然后计算马氏距离，以测量当前状态与正常操作之间的偏差。接下来，应用小波变换以去除距离值中的噪声信号。最后，将操作信息与精确的距离值一起考虑以检测潜在的异常情况。该提议的方法已经在瑞典的三台2 MW风力发电机的实际数据中进行了测试。结果表明，该方法可以在故障事件发生之前检测出可能的异常情况，并避免在正常运行情况下报告警报。

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