Frequency Anomaly Detection and Type Identification of PMU Based on Improved Dynamic Time Warping

HUANG Zimeng; YU Juan; XIANG Mingxu; ZHANG Jiangnan; LI Wenyuan

doi:10.7500/AEPS20220301010

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Frequency Anomaly Detection and Type Identification of PMU Based on Improved Dynamic Time Warping

Application Research | 更新时间：2025-06-16

- Frequency Anomaly Detection and Type Identification of PMU Based on Improved Dynamic Time Warping
- Automation of Electric Power Systems Vol. 46, Issue 24, Pages: 104-112(2022)
- 作者机构：
  
  1.输配电装备及系统安全与新技术国家重点实验室(重庆大学),重庆市 400044
  2.国网河南省电力公司电力科学研究院,河南省郑州市 450052
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(52077016);Sichuan Science and Technology Program(2021YFH0029);Academician-mentoring Scientific and Technological Innovation Project of Chongqing(cstc2021yszx-jscxX0002)
- DOI：10.7500/AEPS20220301010
  CLC：
- Received：01 March 2022，
  
  Revised：2022-06-13，
  
  Published Online：14 November 2022，
  
  Published：25 December 2022
- 稿件说明：
移动端阅览
黄子蒙,余娟,向明旭等.基于改进动态时间弯曲的PMU频率异常检测及类型识别[J].电力系统自动化,2022,46(24):104-112.

HUANG Zimeng,YU Juan,XIANG Mingxu,et al.Frequency Anomaly Detection and Type Identification of PMU Based on Improved Dynamic Time Warping[J].Automation of Electric Power Systems,2022,46(24):104-112.
黄子蒙,余娟,向明旭等.基于改进动态时间弯曲的PMU频率异常检测及类型识别[J].电力系统自动化,2022,46(24):104-112. DOI： 10.7500/AEPS20220301010.

HUANG Zimeng,YU Juan,XIANG Mingxu,et al.Frequency Anomaly Detection and Type Identification of PMU Based on Improved Dynamic Time Warping[J].Automation of Electric Power Systems,2022,46(24):104-112. DOI： 10.7500/AEPS20220301010.

摘要

高比例可再生能源电力系统中不确定性、复杂性和脆弱性问题日益凸显，亟须利用同步相量测量单元（PMU）的高质量量测数据支撑系统动态安全监控。然而，PMU数据受到各类因素影响，存在不同程度的质量问题，影响着数据的各类高级应用。对此，以频率量测数据为切入点，提出了一种数据驱动的PMU频率数据异常检测及类型识别方法。首先，归纳了频率数据的典型异常类型，并构造各类频率数据异常特征。进一步，提出了一种动态时间弯曲改进策略，通过动态调整弯曲窗口来有效量化各类异常特征。最后，基于局部离群因子法实现频率数据的异常检测及类型识别。以实际电网PMU频率数据为例，验证了所提方法的有效性。

Abstract

The problems of uncertainty

complexity and vulnerability in power systems with high proportion of renewable energy are increasingly prominent. It is urgent to use high-quality measurement data of synchronous phasor measurement units (PMUs) to support dynamic security monitoring of the system. However

PMU data is affected by various factors

and there are quality problems of varying degrees

which affect various advanced applications of data. In this paper

a data-driven PMU frequency data anomaly detection and type identification method is proposed based on the frequency measurement data. First

the typical types of frequency data anomalies are summarized

and the characteristics of various types of frequency data anomalies are constructed. Furthermore

an improved dynamic time warping strategy is proposed

which can effectively quantify various abnormal characteristic by dynamically adjusting the warping window. Finally

anomaly detection and type identification of frequency data are realized based on the local outlier factor method. The effectiveness of the proposed method is verified by taking the PMU frequency data of an actual power grid as an example.

关键词

Keywords

references

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