Data-Driven Based Low-Voltage Distribution System Transformer-Customer Relationship Identification

Transformer-customer relationship identification refers to the determination of the physical connection relationship of electricity end-customers and their corresponding transformers. Such connection relationship is critical for distribution utilities to maintain their end-customer profiles. However, management of transformer-customer relationship becomes one of the most emerging challenges due to large number of end-customers and lack of measurement devices in low-voltage distribution systems. To address the above issue, this paper proposes an end-customer data-driven method to identify transformer-customer relationship in low-voltage distribution grid by utilizing the customer field data obtained from advanced metering infrastructure. Specifically, the incidence convolution identification method is proposed to build up the unique mapping relationship between end-customers and their transformers based on the principle of energy conservation. Then the voltage correlation maximization model based on Markov Random Field is proposed, where the voltage correlation matrix is exacted and combined with the adjacency matrix to establish an optimization model to correct the potential abnormal transformer-customer relationship. Finally, the effectiveness of the proposed method is verified by using practical utility tests.