Data-Driven Abnormity Assessment for Low-Voltage Power Consumption and Supplies Based on CRITIC and Improved Radar Chart Algorithms

With the wide deployment of advancing metering infrastructure (AMI) in power distribution systems, the quantity of power consumers' electricity data is increasing rapidly and the data also become more and more accurate. To make full use of these power consumers' AMI data, a data-driven abnormity assessment algorithm for low-voltage power consumers is proposed based on the CRITIC (CRiteria Importance Though Intercrieria Correlation) method and the improved radar chart method. First, the indexes that characterize the consumer's abnormal features of power consumption and supplies are extracted from the original AMI data. Then, the abnormity assessment algorithm is used to determine power consumers' abnormal features of power consumption and supplies by using the extracted indexes, in which the weights of indexes are determined by the CRITIC method and the assessment value of abnormal features is determined by the improved radar chart method. Next, the abnormity assessment algorithm is used again to assess power consumers' power consumption and supplies abnormities. Finally, the effectiveness of proposed algorithm is demonstrated in case studies by employing AMI data collected from power utilities in Zhejiang Province, China, and the results show that the algorithm can be used in actual applications.