Wenyu Wang

Wenyu Wang, Nanpeng Yu

Accurate phase connectivity information is essential for advanced monitoring and control applications in power distribution systems. The existing data-driven approaches for phase identification lack precise physical interpretation and theoretical performance guarantee. Their performance generally deteriorates as the complexity of the network, the number of phase connections, and the level of load balance increase. In this paper, by linearizing the three-phase power flow manifold, we develop a physical model, which links the phase connections to the smart meter measurements. The phase identification problem is first formulated as a maximum likelihood estimation problem and then reformulated as a maximum marginal likelihood estimation problem. We prove that the correct phase connection achieves the highest log likelihood values for both problems. An efficient solution method is proposed by decomposing the original problem into subproblems with a binary least-squares formulation. The numerical tests on a comprehensive set of distribution circuits show that our proposed method yields very high accuracy on both radial and meshed distribution circuits with a combination of single-phase, two-phase, and three-phase loads. The proposed algorithm is robust with respect to inaccurate feeder models and incomplete measurements. It also outperforms the existing methods on complex circuits.

Wenyu Wang, Nanpeng Yu

Accurate estimates of network parameters are essential for modeling, monitoring, and control in power distribution systems. In this paper, we develop a physics-informed graphical learning algorithm to estimate network parameters of three-phase power distribution systems. Our proposed algorithm uses only readily available smart meter data to estimate the three-phase series resistance and reactance of the primary distribution line segments. We first develop a parametric physics-based model to replace the black-box deep neural networks in the conventional graphical neural network (GNN). Then we derive the gradient of the loss function with respect to the network parameters and use stochastic gradient descent (SGD) to estimate the physical parameters. Prior knowledge of network parameters is also considered to further improve the accuracy of estimation. Comprehensive numerical study results show that our proposed algorithm yields high accuracy and outperforms existing methods.