A State Estimation Method of Distribution Network Based on Maximum Likelihood Estimation

Abstract

The invention relates to a power distribution network state estimation method based on the maximum likelihood estimation theory, belonging to the power system scheduling automation and power grid simulation technology field. The method of the invention comprises steps of assuming that the power pseudo measurement of the power distribution network load is obtained through a method, obtaining a pseudo measurement error group through the power distribution network load true value, generating a distribution model of the pseudo measurement error by adopting a kernel density estimation method and establishing a state estimation model considering the load pseudo measurement through the maximum likelihood estimation theory. The invention has a good fitting distribution effect. If only the probability of the data sample is existence and continuous, the invention can fit any distribution form of the data without any transcendental probability distribution assumption. As a result, the invention can solve the difficulty that the load capacity pseudo measurement of the power distribution network cannot comply with the normal distribution, further proposes the maximum likelihood estimation of the new probability density function, and improves the accuracy of estimation result of the power distribution network.

Description

technical field [0001] The invention relates to a distribution network state estimation method based on maximum likelihood estimation, which uses historical data and pseudo-measurement data of the distribution network and a kernel density estimation method to generate a distribution model of pseudo-measurement errors, thereby passing The principle of maximum likelihood estimation establishes a state estimation model considering load pseudo-measurement, which belongs to the field of power system dispatch automation and power grid simulation technology. Background technique [0002] State estimation is one of the core technologies of the smart distribution network. However, the data collection of the current distribution network is huge and complex. The hardware installation of the measurement points requires a lot of money and energy, which is unrealistic. The data is seriously insufficient, so the state estimation of the distribution network relies on the pseudo-measurement ...

AI Technical Summary

Problems solved by technology

Considering the large scale of the distribution network, limited by investment and maintenance costs, there are few real-time measurement devices actually installed, and the real-time measurement data is seriously insufficient. Therefore, the distribution network state estimation relies on pseudo-measurement of load, and the error distribution model of pseudo-measurement significantly affects Accuracy of State Estimation

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