考虑PMU数据特征的新能源场站扰动检测方法OA

The Phasor Measurement Unit provides data support for dynamic analysis and power fluctuation monitoring in renewable energy stations.Due to harsh operating environments,deployed PMUs often exhibit measurement anomalies,and continuous faulty data can impact the detection of real disturbances.Based on measurement data from a renewable energy demonstration base,this paper proposes a disturbance detection method for renewable energy stations considering PMU data characteristics.The method analyzes four types of PMU frequency anomalies in renewable energy stations and reveals the differences in spatiotemporal similarity between faulty data and disturbances across different PMUs.Considering both point and sequence anomalies'local and global features,this paper proposes an improved Local Outlier Factor algorithm for point anomaly detection,which effectively reduces false alarm rate by incorporating clustering algorithms to compare the differences in point density.An improved denoising variational autoencoder model is introduced for sequence anomaly detection,enhancing feature extraction and setting dynamic thresholds,which improves detection accuracy.To further distinguish faulty data from disturbances,this paper develops an improved dynamic kernel correlation disturbance detection method,which calculates a spatiotemporal similarity matrix to measure feature differences,ensuring accurate disturbance event detection.Simulation and real data confirm the effectiveness of the proposed method.