关键词: 电能质量, 综合评估, 图卷积网络, 指标, 关联性, 图, 邻接矩阵, 半监督训练

Abstract:

The increasingly widespread use of new power equipment has brought new disturbances to the power system and has placed increasing demands on power quality. In order to make full use of the power quality indicators in the national standards and to make a more comprehensive and integrated evaluation of power quality, this study proposes a power quality evaluation method based on graph convolutional network. A power quality assessment system with graded indicators is proposed according to the current national standards. The correlation between the various power quality assessment indicators is initially determined, and on this basis the indicator relationship diagram is determined, a graph neural network model is built and trained, and the error rate of the test set is 9.02%. A comparison and analysis with other assessment methods using actual measurement data of a power system proves that the proposed method is more effective in assessing power quality over a long time span.

Key words: power quality, comprehensive evaluation, graph convolutional network, indicator, correlation, graph, adjacency matrix, semi-supervised training