Distribution circuit state estimation using a probabilistic approach

Past work on distribution circuit state estimation has focused on the adoption of a transmission state estimator approach, without necessarily accounting for the specific requirement of a distribution circuit-based analysis. On distribution circuits, typically, there are very few available real-time measurements, and thus, researchers have treated customer load demand estimates as pseudo-measurements in a weighted-least-squares formulation. This can lead to convergence problems and also, the approach effectively assumes that all bus load demands are normally distributed (Gaussian) which may not be valid on distribution circuits. This paper presents an alternative approach to distribution circuit state estimation using a probabilistic extension of the radial load flow algorithm while accounting for real-time measurements as solution constraint. The algorithm which takes advantage of the radial nature of distribution circuits also accounts for other issues specific to distribution circuits. Namely, the algorithm accounts for nonnormally distributed loads, incorporates the concept of load diversity (load correlation) and can interact with a load allocation routine. The effectiveness of the algorithm is illustrated through comparisons made with Monte Carlo simulations