Structure Learning in Power Distribution Networks

Topology detection in power distribution system using kernel‐node‐map deep networks

“…A number of data‐driven approaches are reported in the literature, such as graph theory and graphical modelling [18], probabilistic graphical models [19, 20]. In [1], a collection of all possible topologies compared with the real‐time time‐series signatures of voltage measurements is presented, but it may be argued that line parameters are not available all the time.…”

Outage Detection for Distribution Networks Using Limited Number of Power Flow Measurements

“…Using time series measurements from PMUs, a topology identification method is proposed in [19]. A topology detection method using optimal matching loop power is proposed in [20]. The network topology is identified using smart meter data in [21].…”

“…The mixed-integer quadratic programming is used for topology identification in [23]. A topology identification algorithm using voltage correlation data is proposed in [20]. The research in [24], [25] relying on a limited number of line flow sensors is more relevant to the work presented in this paper.…”

Deep‐learning‐based power distribution network switch action identification leveraging dynamic features of distributed energy resources

“…Papers [10–12] proposed a signature‐based approach to compare the time series data of a switch action with a library of signatures. Paper [13] treated a distribution network as a graph and utilised the statistical characteristics of power injections at different nodes to learn the graph's structure. Multivariate Wiener filtering was utilised in [14] to reconstruct the topology of a power distribution system by eliminating spurious edges of graphs.…”