A Data-Driven Topology and Parameter Joint Estimation Method in Non-PMU Distribution Networks

Abstract

For distribution networks, topology and line parameter information is the pre-requisite of further functions such as network analysis, operation and control. To identify the topology or estimate the parameters, a lot of existing literature relies on the knowledge of feasible topologies or full-scale deployment of advanced measuring devices, such as phase measurement units (PMUs), which is unrealistic in practice. Therefore, we propose a data-driven topology and parameter joint estimation method for non-PMU distribution networks, only using historical measurement data from smart meters. First, the topology label matrix, as a unique label of the network topology and corresponding parameters, is created and calculated based on historical data. The numeric characteristics of the topology label matrix is further analyzed to extract the topology and parameter information. Subsequently, a clustering based topology identification and parameter estimation methodology is proposed as a data-driven linear regression model. To reduce the coupling during regression, a power supply path matrix is utilized to improve the accuracy. Moreover, to improve the credibility of the method, an error-correction mechanism is proposed to indicate whether the inaccurate identification exists and how to correct it. Finally, the proposed method is tested in IEEE 33-node, 123-node networks and a large-scale system. The results demonstrate that the proposed method can provide an accurate estimation of the topology and line parameters based on samples of measurement with noise and is also effective in a large-scale system.