考虑时序趋势分析的周期性谐波异常识别

Periodic Harmonic Anomaly Recognition Considering Time Series Trend Analysis

考虑传统对谐波等稳态电能质量异常判别中仅以数值大小与限值对比而未考虑数据变化趋势的局限性,针对存在周期性的谐波运行工况,关注谐波监测数据的趋势变化特征,提出一种考虑时序趋势分析的谐波异常识别方法。首先,通过分段线性化提取主要趋势变化特征,并以模式值表征各段趋势;然后,分别由时序趋势相似性与数值离群占比确定趋势和数值异常指标,对二者加权组合,得到综合异常指标;并从历史数据中,选定时序分析周期与基准常态数据;最后,计算后续待识别数据的综合异常指标来辨识监测点是否存在谐波变化异常。通过仿真算例和实例应用分析证明了所提方法识别结果准确,适用性强且容易实现,可方便地集成到现有电能质量监测系统中。

The traditional identifications for the steady-state power quality anomalies,such as harmonics anomalies,only compare the data values with the threshold without considering the trend of the data changes.This paper puts forward a method of harmonic anomaly identification that takes time series trend analysis into account in view of the periodic harmonic operating conditions and the trend change characteristics of the harmonics monitoring data.Firstly,the main trend change features are extracted by piecewise linear representation,and the trend of each segment is represented by model values.Then,the trend and numerical value anomaly indexes are determined based on the similarity of the trend sequences and the outliers’proportion respectively,and the composite anomaly indexes are obtained when the two indexes are weighted.The analysis period of sequences and the benchmark normal data are selected from the historical data.Finally,the composite anomaly index of the subsequent data to be identified is calculated to identify whether there are harmonic variation anomalies at the monitoring point.Through simulation examples and case analysis,it is proved that the proposed method is accurate,applicable and easy to implement,and can be expediently integrated into the existing power quality monitoring system.