Inverse Power Flow Problem

Abstract

This paper formulates an inverse power flow problem which is to infer a nodal admittance matrix (hence the network structure of a power system) from voltage and current phasors measured at a number of buses. We show that the admittance matrix can be uniquely identified from a sequence of measurements corresponding to different steady states when every node in the system is equipped with a measurement device, and a Kron-reduced admittance matrix can be determined even if some nodes in the system are not monitored (hidden nodes). Furthermore, we propose effective algorithms based on graph theory to uncover the actual admittance matrix of radial systems with hidden nodes. We provide theoretical guarantees for the recovered admittance matrix and demonstrate that the actual admittance matrix can be fully recovered even from the Kron-reduced admittance matrix under some mild assumptions. Simulations on standard test systems confirm that these algorithms are capable of providing accurate estimates of the admittance matrix from noisy sensor data.

References in corpus (5)

• A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems
• Data-driven Discovery of Cyber-Physical Systems
• On Identification of Distribution Grids
• Inverse Power Flow Problem
• Event Detection and Localization in Distribution Grids with Phasor Measurement Units

Cited by in corpus (16)

• Physics-Guided Deep Neural Networks for Power Flow Analysis
• Inverse Power Flow Problem
• Power Systems Topology and State Estimation by Graph Blind Source Separation
• Event Detection and Localization in Distribution Grids with Phasor Measurement Units
• Preliminary Exploration on Digital Twin for Power Systems: Challenges, Framework, and Applications
• PaToPa: A Data-Driven Parameter and Topology Joint Estimation Framework in Distribution Grids
• Conditions for Estimation of Sensitivities of Voltage Magnitudes to Complex Power Injections
• Data-Driven Power Flow Linearization: A Regression Approach
• Data-driven Optimal Power Flow: A Physics-Informed Machine Learning Approach
• Ensemble Learning Based Convex Approximation of Three-Phase Power Flow
• Identification of AC Networks via Online Learning
• Unbalanced Multi-Phase Distribution Grid Topology Estimation and Bus Phase Identification
• A Learning-to-Infer Method for Real-Time Power Grid Multi-Line Outage Identification
• PaToPaEM: A Data-Driven Parameter and Topology Joint Estimation Framework for Time Varying System in Distribution Grids
• Learning Graphs from Linear Measurements: Fundamental Trade-offs and Applications
• Ensemble learning based linear power flow