ISSN 1674-0629
CN 44-1643/TK
CN 44-1643/TK
Fault Line Selection Method of Distribution Network Based on the Fusion of Parameter Optimized Variational Modal Decomposition and Improved K Clustering Criterion
Jianyuan WANG , Yuhui ZHANG , Cheng LIU
›› 2023, Vol. 17 ›› Issue (7) : 135 -145.
Fault Line Selection Method of Distribution Network Based on the Fusion of Parameter Optimized Variational Modal Decomposition and Improved K Clustering Criterion
In order to solve the problem that the existing transient line selection method is susceptible to fault phase angle, transition resistance, noise, harmonics and criterion threshold, a line selection method based on optimized parameter variational mode decomposition (VMD) and improved K-clustering criterion fusion is proposed. Firstly, the three key parameters of the decomposition process are dynamically optimized, and the number of VMD decomposition layers is determined by using the signal spectrum and component characteristics, and the optimal penalty factor is obtained by the arithmetic optimization algorithm, and the power frequency, noise and harmonic interference are eliminated. The modal center frequency is determined according to the number of decomposition layers and each modal spectrum to improve the decomposition efficiency. Secondly, the optimized VMD is used to obtain cosine similarity, high-frequency component amplitude and DC energy as complementary fault line selection criteria. Finally, the improved K clustering algorithm is used to achieve multi-criteria fusion, which makes up for the limitation of a single criterion. The theoretical analysis, simulation and test results show that the proposed method is suitable for the power grid with distributed power supply, and is not affected by the fault location, fault phase angle and transition resistance, and has excellent anti-harmonic and noise interference performance.
fault line selection / noise immunity / K center point clustering / arithmetic optimization algorithm / variation modal decomposition
the National Natural Science Foundation of China(52007027)
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