Electric power system bilinear anti-error estimation method based on bilinear protruding optimization theory

Abstract

The present invention discloses an electric power system bilinear anti-error estimation method based on a bilinear protruding optimization theory. A bilinear theory is introduced, and a non-linear measurement equation is converted to a two-phase linearity measurement equation; the sparse characteristic of the rough error is calculated and measured, the anti-error estimation is converted to the two-phase strict protruding optimization problem; and each phase identifies the sparse measurement rough error based on the ADMM, rejects the rough error in the measurement to employ the WLS for solution, and maintains the WLS advantages. The test results of the IEEE standard system and the national real power grid show that: because the bilinear theory is introduced, the calculation efficiency of the electric power system bilinear anti-error estimation method is higher than that of a traditional WLS estimator, the ADMM technology greatly identifies the spare measurement rough error to allow the estimation precision of the electric power system bilinear anti-error estimation method to be better than that of a traditional anti-error estimator.

Description

technical field [0001] The invention relates to a bilinear robustness estimation method of a power system based on a bilinear convex optimization theory, and belongs to the technical field of power system monitoring, analysis and control. Background technique [0002] State estimation estimates the real-time operating state of the power system based on telemetry raw data. Based on the results of state estimation, the energy management system (EMS) performs a series of subsequent analysis and calculations. Therefore, state estimation is an essential part of EMS. The traditional weighted least squares (weighted least estimation, WLS) estimation can efficiently estimate the best state of the system when the measurement noise obeys a strict Gaussian distribution. However, due to the aging of measuring instruments, long-distance transmission of data, and even bad data injected maliciously by humans, it is inevitable that the estimated results of WLS will be affected by bad data (...

AI Technical Summary

Problems solved by technology

The robust estimator improves the accuracy of state estimation at the cost of increasing computational complexity. However, compared with the WLS estimator, its lower computational efficiency also limits its application in engineering practice to some extent.

[0004] In the AC power network model, the current state estimation mainly relies on the nonlinear measurements provided by the supervisory control and data acquisition (SCADA), which makes the state estimation essentially a nonlinear (non-convex) optimization problem. The most common method is to approximate the linearized iterative solution with the Gauss-Newton method, which may have the following disadvantages: 1) Sensitive to the initial value; 2) Easy to fall into the local optimal solution; 3) The convergence is difficult to guarantee

[0005] Based on the above descriptions, it can be seen that the state estimation of the power system currently faces two difficulties: 1) the nonlinear relationship between the quantity measurement and the state quantity makes the state estimation equivalent to solving a non-convex optimization problem; 2) the measurement gross error Existence has a great impact on the accuracy of WLS estimation. Although the traditional robust estimation method can suppress the influence of gross measurement errors, the calculation efficiency is low

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