Power grid robust state prediction method based on multi-dimensional state matrix sliding matching

Abstract

The invention provides a power grid robust state prediction method based on multi-dimensional state matrix sliding matching. The power grid robust state prediction method comprises the steps of obtaining a node admittance matrix; acquiring SCADA instrument quantity measurement and PMU device quantity measurement, and constructing a state quantity set based on mixed state estimation; forming a historical state quantity database; predicting the future state of the nodes of the smart power grid by using a multi-dimensional state matrix sliding matching method to obtain a prediction result; adopting an improved power balance equation detection method to detect whether abnormal data exists in a current system or not, if yes, a residual distribution deviation degree detection method is adopted to judge whether the abnormality is caused by abrupt change of quantity measurement, if yes, correction is carried out, bad data are removed, and reliable data support is provided for state predictionin future. The method has better prediction robustness, information in a historical database can be effectively utilized, and the safety and reliability of the intelligent power grid and the capability of resisting malignant data and bad data are improved.

Description

technical field [0001] The invention belongs to the safety technology of electric power system, and in particular relates to a robust state prediction method of power grid based on sliding matching of multi-dimensional state matrix. Background technique [0002] With the promotion of intelligent equipment and the continuous improvement of the automation level of the power system, the traditional power system is gradually transitioning to the smart grid. As a new generation of key infrastructure of the power grid, the smart grid is characterized by the deep integration of networked virtual resources and physical equipment, and the establishment of massive information communication between the primary system and the secondary system of the power grid with the help of integrated computing, communication network and control technologies. Architecture and information integration channels provide an effective way to achieve a high degree of integration between information systems ...

AI Technical Summary

Problems solved by technology

[0004] However, there are some deficiencies in the existing state prediction and bad data detection methods: 1) It has a good detection effect for bad data with a large difference, but a high rate of missed detection for bad data with a small difference; 2) It lacks strong concealment The mechanism of malicious data injection attack detection; 3) The occurrence of quantitative measurement mutations will make the prediction inaccurate, and the prediction robustness is poor

Images