| 刘义艳,郝婷楠,张 伟.融合 RMT 特征值的电网异常状态检测[J].电子测量与仪器学报,2023,37(12):242-252 |
| 融合 RMT 特征值的电网异常状态检测 |
| Abnormal state detection of power system based on RMT eigenvalue fusion |
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| DOI： |
| 中文关键词: 电网 随机矩阵理论 特征值 评价指标 异常检测 |
| 英文关键词:power grid random matrix theory feature vector evaluation indicators abnormal detection |
| 基金项目:陕西省重点研发计划(2021GY-098)、国家重点研发计划项目(2021YFB1600202)、国家重点研发计划项目(2021YFB2601300)资助 |
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| 摘要点击次数: 233 |
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| 中文摘要: |
| 随着电网规模的不断扩大,电网监测数据变得越来越多元化、高速化、海量化,使得电网监测工作变得更加复杂和艰巨。
针对传统方法处理电网高维数据效率低、同步性差的问题,本文研究了一种利用随机矩阵理论(RMT)提取监测数据特征值实
现电网异常状态的检测方法。 首先,设计了电网内异常扰动类型,构建了一个矩阵窗口来选择时间序列内的监测信号,从而建
立高维矩阵;其次,应用 M-P 定律和单环定律进行矩阵变换,提取特征值并根据特征值分布情况来判断系统状态;然后,基于特
征值的线性统计,构建了多种评价指标,包括最大特征值(MESCM)、最小特征值(EME)、最大最小特征值之比(MME)和平均谱
半径(mean spectral radius,MSR)等指标;最后,比较了每个统计指标在电网出现短路故障、开路故障以及故障清除时的表现,以
实现电网状态识别、异常事件检测和电网稳定性评估。 案例测试结果表明,这些指标可以准确判断系统是否发生异常、检测异
常的起止时间,并评估电网的稳定性。 本文方法可以检测开路、短路等扰动事件,实现全局监测数据的同步处理,其计算量较
小、效率高,适用于大规模电网异常状态的检测。 |
| 英文摘要: |
| With the continuous expansion of the power grid scale, the power grid monitoring data presents a trend of diversification, highspeed and massive quantities. Consequently, the monitoring work of the power grid has become increasingly complex and challenging. In
order to solve the problems of low efficiency and poor synchronization of high-dimensional data processing in power system by traditional
methods, a monitoring method based on random matrix theory is being investigated to extract monitoring data feature values and detect
power grid anomalies. Firstly, disturbance situations within the power grid are designed, and a matrix window is constructed to select
monitoring signals within a time series, resulting in a high-dimensional matrix. Secondly, Marchenko-Pastur law and single-ring law are
applied for matrix transformation, and feature values are extracted based on the distribution of these values to judge the system's state.
Then, linear statistics based on feature values are used to construct various evaluation indicators, including maximum eigenvalue of
sample covariance matrix, energy with minimum eigenvalue, maximum-minimum eigenvalue, and mean spectral radius. Finally, the
performance of each statistical indicator during short-circuit faults, open-circuit faults, and fault clearance in the power grid is compared
to achieve power grid state recognition, anomaly event detection, and power grid stability assessment. The results show that these
indicators can accurately determine whether the system has anomalies, detect the start and end time of anomalies, and evaluate the
stability of the power grid. The method proposed by this paper can detect disturbances like open circuits and short circuits, and achieve
synchronized processing of global monitoring data with low computational complexity and high efficiency, making it suitable for detecting
anomalies in large-scale power grids. |
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