Structure Learning and Statistical Estimation in Distribution Networks - Part II

2015/02/27 by Deepjyoti Deka, Deka, Deepjyoti, Scott Backhaus +3
Engineering · #Control Systems and Identification #FOS: Mathematics #Optimization and Control (math.OC) #Power Quality and Harmonics #Power System Optimization and Stability

paper · pdf · doi:10.48550/arxiv.1502.07820

openalex publication_date 2015/02/27 · openalex created_date 2022/08/16 · openalex updated_date 2026/07/28

Abstract

Part I of this paper discusses the problem of learning the operational structure of the grid from nodal voltage measurements. In this work (Part II), the learning of the operational radial structure is coupled with the problem of estimating nodal consumption statistics and inferring the line parameters in the grid. Based on a Linear-Coupled (LC) approximation of AC power flows equations, polynomial time algorithms are designed to complete these tasks using the available nodal complex voltage measurements. Then the structure learning algorithm is extended to cases with missing data, where available observations are limited to a fraction of the grid nodes. The efficacy of the presented algorithms are demonstrated through simulations on several distribution test cases.

Related

• Structure Learning and Statistical Estimation in Distribution Networks - Part I
• Estimating Distribution Grid Topologies: A Graphical Learning based\n Approach
• Learning Topology of Distribution Grids using only Terminal Node Measurements
• Topology Estimation using Graphical Models in Multi-Phase Power\n Distribution Grids
• Learning Topology of the Power Distribution Grid with and without Missing Data