Distribution Network Admittance Matrix Estimation With Linear Regression

Topology identification and parameter estimation form the basis for the operation and control of the distribution network with little or no observability. This paper proposes a method to estimate the admittance matrix for open-loop distribution networks based only on active/reactive power injection and voltage magnitude data. We derive the linear relationship between the admittance matrix and the measurements based on decoupled linear power flow (DLPF) equations. A total least squares regression method is proposed based on analysis of the characteristics of the open-loop grid admittance matrix. Case studies on the IEEE 33-bus system demonstrate the proposed method's effectiveness, accuracy, and efficiency.

Automated summary

This paper presents a method for estimating the admittance matrix of open-loop distribution networks based on active/reactive power injection and voltage magnitude data, improving the operation and control capabilities of the distribution network. By deriving the linear relationship between the admittance matrix and the measurements using decoupled linear power flow equations, and proposing a total least squares regression method, accurate estimation of the admittance matrix is achieved. Case studies on the IEEE 33-bus system demonstrate the effectiveness, accuracy, and efficiency of the proposed method.