Machine Learning Based Network Parameter Estimation Using AMI Data

The expansion of distribution power systems and the growing penetration of distributed energy resources present new challenges for situational awareness. Calibrating the extended system model with sensor system measurements and maintaining usability is one critical task. This paper presents a distribution network parameter estimation (DNPE) approach using machine learning (ML) and metering data to improve extended distribution system model quality. The reliability model can improve the ability of endpoint data to be translated into network-level situational awareness in real-time and help distribution system operators (DSOs) solve branch flow and voltage problems. In addition, a data analytic and automated processing scheme is proposed to improve the sensor data quality and prevent misleading information. The effectiveness of the proposed method is verified with advanced metering infrastructure (AMI) data on an actual utility feeder model while considering the higher penetration of photovoltaic power generation. The test of DNPE and study results are demonstrated in this paper.