Consumer phase identification under incomplete data condition with dimensional calibration

Abstract As results of poor communication quality, human error, electricity theft and other reasons, the obtained power consumption data of consumers from smart meters is incomplete and cannot fully reflect the power consumption of low-voltage distribution network (LVDN), which affects the recognition performance of the existing power-based and current-based consumer phase identification (CPI) methods. To tackle the limitation under incomplete data condition, this paper develops multi-dimensional calibration in CPI based on voltage characteristics in LVDN. Firstly, multi-dimensional correlation characteristics of consumers in LVDN are deduced to provide theoretical foundation, including the correlation characteristics among consumers and that between consumers and low-voltage buses of distribution transformer. Then, multiply CPI processes are designed for different smart meter incomplete ratios to increase the algorithm’s robustness to incomplete data. In which, a consumer clustering method based on correlation characteristics among consumers is developed to decrease the variables in the identification process and a calibration method combined location index and the correlation characteristics among consumers is proposed to correct the phase connectivity of users far away low-voltage buses. Finally, the performance of the proposed algorithm is verified on a real-world LVDN in Guangdong. The comparison analysis between the proposed method and other published methods and the impact of the threshold coefficients on the identification accuracy are also investigated. Further, the application results of the proposed method in pilot LVDNs are presented. The results indicate that the proposed method achieves higher CPI accuracy than other published methods when the obtained power consumption data of consumers is incomplete and has good applicability in practice.