Identifying Topology of Power Distribution Networks Based on Smart Meter Data
This addresses the need for accurate topology identification in power distribution networks, which is crucial for efficient operation, though it appears incremental as it builds on existing PCA and graph-theoretic methods.
The paper tackles the problem of inaccurate network topology information in low-voltage power distribution networks by proposing a data-driven approach using smart meter energy measurements, and demonstrates the method through simulations on randomly generated networks and the IEEE Roy Billinton test system.
In a power distribution network, the network topology information is essential for an efficient operation of the network. This information of network connectivity is not accurately available, at the low voltage level, due to uninformed changes that happen from time to time. In this paper, we propose a novel data--driven approach to identify the underlying network topology including the load phase connectivity from time series of energy measurements. The proposed method involves the application of Principal Component Analysis (PCA) and its graph-theoretic interpretation to infer the topology from smart meter energy measurements. The method is demonstrated through simulation on randomly generated networks and also on IEEE recognized Roy Billinton distribution test system.