Exact Topology and Parameter Estimation in Distribution Grids with Minimal Observability

Abstract

Limited presence of nodal and line meters in distribution grids hinders their optimal operation and participation in real-time markets. In particular lack of real-time information on the grid topology and infrequently calibrated line parameters (impedances) adversely affect the accuracy of any operational power flow control. This paper suggests a novel algorithm for learning the topology of distribution grid and estimating impedances of the operational lines with minimal observational requirements -it provably reconstructs topology and impedances using voltage and injection measured only at the terminal (enduser) nodes of the distribution grid. All other (intermediate) nodes in the network may be unobserved/hidden. Furthermore no additional input (e.g., number of grid nodes, historical information on injections at hidden nodes) is needed for the learning to succeed. Performance of the algorithm is illustrated in numerical experiments on the IEEE and custom power distribution models.

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

“…Similarly, we can show that ∆J vv (j, j) + ∆J θθ (j, j) > 0. For edge (ij) removal, it follows similarly that the opposite sign in (12) is derived. Hence the result holds.…”

“…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].…”

Learning Graphs From Linear Measurements: Fundamental Trade-Offs and Applications

“…Graph learning has been widely used electric grids applications, such as state estimation [11,12] and topology identification [38,16]. Most of the literature focuses on topology identification or change detection, but there is not much recent work on joint topology and parameter recovery, with notable exceptions of [28,46,34]. Moreover, there is little exploration on the fundamental performance limits (estimation error and sample complexity) on topology and parameter identification of power networks, with the exception of [48] where a sparsity condition is provided for exact recovery of outage lines.…”

Predicting the Voltage Distribution for Low Voltage Networks using Deep Learning

“…It is assumed that the LV circuit is a near-balance circuit, however, in reality, this is not often the case. Identification of customer phase is an active area of research, with voltage clustering [10] and energy data correlation [11] are some of the methodologies proposed for customer phase identification. The later technique is more suitable if, for each customer on the circuit, high granularity demand power data at every half hour or less is available.…”