一种基于交流特征的异常采样数据检测方法及系统
By adopting an anomaly detection method based on AC characteristics, combined with dynamic thresholds and multiple criteria, the problem of anomaly detection in sampling data in power systems has been solved. This method effectively identifies and corrects fly-through points and periodic anomalies, improving the reliability and accuracy of the data.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- BEIJING SIFANG JIBAO ENG TECH
- Filing Date
- 2025-09-29
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies are insufficient for effectively detecting and processing anomalies such as fly-throughs and periodic distortions in sampled data in power systems, affecting the accuracy of protection and control and the reliability of measurement data. Furthermore, fixed detection thresholds or single criteria make them unsuitable for complex field environments.
An abnormal sampling data detection method based on AC characteristics is adopted. By calculating the difference between voltage and current, a dynamic and effective judgment threshold is set to filter out zero drift data, identify flying points and periodic anomalies, and correct abnormal data. The threshold is dynamically adjusted in combination with system frequency and sampling frequency, and anomalies are identified using multiple criteria.
It enables sensitive capture of sudden anomalies, adapts sensitivity adjustment to different application scenarios, comprehensively identifies periodic anomalies, improves the reliability and accuracy of sampling data, and enhances the decision-making credibility of downstream protection and control equipment.
Smart Images
Figure CN121114551B_ABST