Large-scale power grid abnormal load identification method based on a power method and a parallel computing technology

Abstract

The invention discloses a large-scale power grid abnormal load identification method based on a power method and a parallel computing technology. The large-scale power grid abnormal load identification method comprises the following steps that step 1, data source matrixes Xs and z of all partitions are synchronously constructed; Step 2, the time window width T of each partition is determined and asampling starting moment t < 1 > is set; Step 3, a sliding window matrix X < z > of each partition is synchronously obtained ; 4, the sliding window matrixes X < z > of all the partitions are subjected to standardization processing synchronously, and non-Hermitian matrixes X < n > and X < z > of all the partition standards are obtained; 5, a sample covariance matrix S < z > of each partition is synchronously obtained ; 6, the maximum characteristic values max (th) and z (th) of the sample covariance matrix of each partition are quickly estimated by using a power method; 7, each partition estimates the signal-to-noise ratio z at the current moment, so that the dynamic threshold z of the maximum characteristic value of the sample covariance matrix of the corresponding partition is obtained;And 8, power grid state abnormity out-of-limit judgment is carried out. The method has the characteristics that the calculation efficiency can be remarkably improved, and the applicability to large-scale power grid application is enhanced.

Description

technical field [0001] The invention belongs to the technical field of power grid abnormality detection, and specifically designs a large-scale power grid abnormal load identification method based on power method and parallel computing technology. Background technique [0002] With the maturity of Wide Area Measurement System (WAMS) and the continuous evolution of smart grid, the explosive growth of data volume poses challenges to power grid data processing and knowledge extraction. Introducing big data thinking into power system analysis, mining knowledge and its value application from power data, and using high-performance computing technology to realize big data thinking analysis and evaluation of power grid operation status have important theoretical significance for ensuring the safety of the new generation of power systems. [0003] Random Matrix Theory (RMT) is a universal method that can understand the behavioral characteristics of complex systems from a high-dimensi...

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Problems solved by technology

However, most of the case studies in the above literature use small-scale system calculation examples with dozens of buses, and the adaptability of such methods to large-scale power systems has not yet b

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