SCADA data calibration method based on WAMS information

Abstract

The invention discloses an SCADA data calibration method based on WAMS information. The method includes the steps of firstly, performing self-identification on relevant data of a WAMS system to verify the correctness of the data itself; secondly, linearly estimates a state value of each node in the system through a PMU, substituting the state value of each node into an SCADA measurement equation to obtain a calculation result corresponding to the SCADA, then obtaining a difference between the SCADA measured value and the SCADA measured value by the PMU estimation, and also calculating a standardized residual value to achieve the purpose of detection; and thirdly, subjecting the WAMS data to measurement transformation to form SCADA data at the equivalent time by using a nonlinear model, updating a Jacobian matrix with the iteration, and replacing bad data of the SCADA by the data communication between the two systems to achieve the purpose of calibration. The method applies PMU measurement information to the bad data detection and identification, overcomes the phenomenon of residual contamination and so on, and also has good detection and identification effects on the problem of the occurrence of bad data to the key item measurement in an SCADA system. The operation safety and reliability of a power system are improved.

Description

technical field [0001] The invention relates to a data calibration method, in particular to a SCADA data calibration method based on WAMS information. Background technique [0002] Power system state estimation is an important network analysis function in EMS, and it is also the basis for various advanced applications such as power grid security assessment, preventive control, and operation analysis. Most of the measurement data is obtained through the data acquisition and monitoring system (SCADA). The quality of the data plays an important role in the stable operation of the power grid. And the channel of data transmission is not smooth and other reasons, it is very likely to contain bad data with large errors. As a result, the measurement data used for state estimation may contain bad data with large errors in addition to normal measurement noise (these noises can be removed by state estimation filtering). However, the existence of bad data, the most direct result may l...

AI Technical Summary

Problems solved by technology

The current bad data identification methods mainly include residual search method, non-quadratic criterion method, zero residual method, overall type estimation identification method, etc. These methods may appear "residual pollution" and "residual flooding", which may lead Missed detection and misjudgment of bad data

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