Two-stage linear weighted least-square power system state estimation method

Abstract

The invention provides a two-stage linear weighted least-square power system state estimation method which is characterized by comprising the following steps of: forming a network model, and calculating a node admittance matrix and a branch-node relationship matrix; transforming a measurement vector and a state vector; forming an accurate linear measurement equation; performing the first stage of linear weighted least-square estimation to obtain the estimated value of the transformed state vector; performing inverse transformation, and performing the second stage of linear weighted least-square estimation to obtain the estimated values of the voltage amplitudes and phase angles of all nodes; and identifying bad data. According to the two-stage linear weighted least-square power system state estimation method provided by the invention, more scientific state estimation result can be obtained, the calculation efficiency is higher, and the engineering application prospect is good.

Description

technical field [0001] The invention relates to the field of power system scheduling automation, in particular to a two-stage linear weighted least squares power system state estimation method. Background technique [0002] Power system state estimation is the basis and core of the energy management system. Its function is to filter the real-time information provided by the data acquisition and monitoring system (SCADA), so as to obtain the estimated value of the state variables (voltage amplitude and phase angle) of the whole network. Furthermore, estimated values ​​such as branch power and node injection power can be obtained. [0003] Since foreign scholars proposed the first model of state estimation in 1970, scholars and engineers at home and abroad have conducted a large number of in-depth research and practice on state estimation. Now almost every dispatch center in the world has deployed state estimators. The basic position of state estimation in the safe operation ...

AI Technical Summary

Problems solved by technology

Problems faced by robust state estimation: 1) Difficulty in global optimization; 2) Computational efficiency is lower than nonlinear WLS

From a mathematical point of view, this nonlinear optimization model and its corresponding solution method have several unavoidable disadvantages: 1) It is easy to fall into the local optimum, that is, it is difficult to obtain the global optimal solution, especially some robust states Estimate; 2) In order to obtain an acceptable solution, multiple iterations are required. This nonlinear optimization problem sometimes has difficulty in converging, or even does not converge, and the solution is time-consuming; 3) The Jacobian matrix and the residual sensitivity matrix are not constant matrices , so when using LNR to identify bad data, it is necessary to delete bad data and re-run the cyclic operation of state estimation multiple times, and if the EI method is used to identify bad data and estimate multiple measurement errors, it is also necessary to re-run the Linear State Estimation

The academic community has conducted some research on the above three issues. For example, foreign scholars have proposed to use the trust region method to enhance the convergence of state estimation, but so far, the above three issues have not been well resolved. The fundamental reason is that Due to the nonlinear characteristics of the traditional state estimation model

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