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Vibration Adaptive Anomaly Detection of Hydropower Unit in Variable Condition Based on Moving Least Square Response Surface

机译:基于移动最小二乘响应面的水电机组变状态振动自适应异常检测。

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It is difficult to effectively analyze and identify the conditions of hydropower unit, due to its complex operation conditions, frequent start-stop conditions, continual working status switch, less fault samples, single static alarm threshold. Lots of test research shows that active power and working head are key factors which affect the operation conditions of hydropower unit. The health standard condition of unit is determined. An adaptive real-time anomaly detection model of hydropower unit vibration parameters is proposed based on moving least square response surface. In the proposed model, active power and working head are comprehensively considered. This model can adapt variable conditions of hydropower unit. The model is used to real time detect the anomaly of hydropower unit vibration parameters. The results show that this model can effectively evaluate the performance of unit vibration, can more accurately detect the abnormal of unit vibration.
机译:由于其复杂的运行条件,频繁的启停条件,连续的工作状态切换,更少的故障样本,单一的静态警报阈值,难以有效地分析和识别水力发电机组的条件。大量的试验研究表明,有功功率和工作扬程是影响水电机组运行状态的关键因素。确定单位的卫生标准条件。提出了基于移动最小二乘响应面的水电机组振动参数自适应实时异常检测模型。在提出的模型中,综合考虑了有功功率和工作头。该模型可以适应水电机组的可变条件。该模型用于实时检测水电机组振动参数异常。结果表明,该模型可以有效地评估单元振动的性能,可以更准确地检测单元振动的异常情况。

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