PMU Data Reconstruction Method Based on Spatiotemporal Information Feature Fusion
Through a multi-dimensional view learning method based on spatiotemporal information feature fusion, PMU data is reconstructed in real time online, solving the problem of insufficient accuracy and reliability of existing PMU data, and improving the accurate data reconstruction and dynamic scheduling capabilities of the power system in steady state and transient state.
CN117290738BActive Publication Date: 2025-06-27BEIJING XINYUAN INTERNET TECHNOLOGY CO LTD
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Patent Information
- Application Number
- CN202311475379.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-11-07
- Publication Date
- 2025-06-27
- Estimated Expiration
- 2043-11-07
AI Technical Summary
Technical Problem
The accuracy, reliability and completeness of existing PMU measurement data leads to difficulties in real-time global state estimation and dynamic scheduling of power systems.
Method used
A multi-dimensional view learning method based on the fusion of spatiotemporal information features is adopted, and the PMU data is reconstructed in real-time online through the combination of global time view, global space view, local time view and local space view, combined with the system operation status.
Benefits of technology
Accurate and reliable data reconstruction in steady-state and transient situations are realized, and the quality of PMU data and the dynamic scheduling capabilities of the system are improved.
✦ Generated by Eureka AI based on patent content.
Abstract
The present invention relates to a PMU data reconstruction method based on spatio-temporal information feature fusion. This method analyzes the synchronize phasor measurement data measured on-site, summarizes four cases of low-quality data, and uses mathematical analysis and correlation analysis methods to identify the operating state of the system; then combines the multi-view learning method with the power grid operation mechanism to obtain four view methods with spatio-temporal information features, and uses the four view methods to preliminarily predict the reconstruction values of low-quality target data; finally, trains the non-low-quality data of the target system, adaptively updates the weights of each prediction value through the overall error of each of the four algorithms in the transient and steady-state stages, and integrates the prediction results of the four view methods to obtain the final data reconstruction value. The method of the present invention can effectively perform real-time data reconstruction on dynamic systems, providing an effective guarantee for the application of synchronize phasor data in power systems.
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