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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. [ABSTRACT FROM AUTHOR]

The detection of traffic congestion is now realized mostly by human monitoring and sensor monitoring. However， such detection devices are deficient in road-occupied electric power construction. To meet the needs of low equipment dependency and high accuracy of congestion detection in the road-occupied electric power construction， a detection method based on video data is proposed， which uses neural networks to extract features from video data and determine whether there is traffic congestion. In response to data deficiency in the road-occupied electric power construction， the generalization of the network is improved by making full use of the generic traffic scene dataset， and the adaptive learning method based on domain adversarial neural networks （DANN） is used to reduce the differential performance of two data domains in the feature extraction network. Semi-supervised learning （SSL） is proposed to reduce the manual labeling workload. The experimental results show that the proposed method can achieve an accuracy of 93.2% in traffic congestion detection and recognition in road-occupied electric power construction and has high application value.

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