Estimating distribution grid topologies: A graphical learning based approach

Abstract

Abstract-Distribution grids represent the final tier in electric networks consisting of medium and low voltage lines that connect the distribution substations to the end-users/loads. Traditionally, distribution networks have been operated in a radial topology that may be changed from time to time. Due to absence of a significant number of real-time line monitoring devices in the distribution grid, estimation of the topology/structure is a problem critical for its observability and control. This paper develops a novel graphical learning based approach to estimate the radial operational grid structure using voltage measurements collected from the grid loads. The learning algorithm is based on conditional independence tests for continuous variables over chordal graphs and has wide applicability. It is proven that the scheme can be used for several power flow laws (DC or AC approximations) and more importantly is independent of the specific probability distribution controlling individual bus's power usage. The complexity of the algorithm is discussed and its performance is demonstrated by simulations on distribution test cases.

Physics informed topology learning in networks of linear dynamical systems

“…7(a) with network dynamics as described by (7). Here p i are modeled as filtered white Gaussian noise (colored noise unlike iid Gaussian in [36]) to generate time series data for evaluation of the proposed algorithm. The output at each node is sampled at 0.01s.…”

Graphical Models in Meshed Distribution Grids: Topology Estimation, Change Detection & Limitations

“…Researchers have looked at multiple approaches, both active and passive, in learning using varying measurement type and availability. Example of such schemes include greedy methods [5], [6], voltage signature based methods [7], [8], probing schemes [9], imposing graph cycle constraints [10] and iterative schemes for addressing missing data [11], [12]. In contrast to the referred work that employ static voltage samples, learning schemes that exploit dynamic voltage measurements are reported in [13], [14].…”

“…[15] uses signs in inverse covariance matrix of voltage magnitudes for topology identification, but limited to radial topologies in grids with constant r/x (resistance to reactance) line ratio. [6], [16] uses voltage conditional independence tests for guaranteed topology identification, but limited to radial distribution grids in single and three-phase networks. [17] discusses topology change detection using an approximate graphical model (Markov random field).…”

“…Add edge (ij) in G. It is worth mentioning that prior work on learning radial distribution grids exactly using graphical models [6] cannot be directly applied here as presence of multiple paths in loopy grids render conditional independence tests ineffective. On the other hand, approximate algorithms such as in [31] assume, based on realistic data, that the edges between two-hop neighbors in the graphical model are weaker than direct links and hence can be omitted.…”

Identification of Critical Transmission Lines in Complex Power Networks

“…All lines are represented by the edge set E, E = {e 1 The topological analysis-based method can provide a quick and effective way to identify the network structure and find propagation paths of power flow transfer [34]. Some new approaches, such as the compressive sensing-based approach [35] and graphical learning-based approach [36,37] were proposed in recent years. Based on Dijkstra's algorithm [38], CAA is proposed in this study.…”