Online Detection of Low-Quality Synchrophasor Data Considering Frequency Similarity

TL;DR

This paper introduces an online method for detecting low-quality synchrophasor data by analyzing time and frequency domain features, effectively identifying data issues without prior offline studies.

Contribution

It presents a novel real-time detection approach that leverages frequency similarity features to identify low-quality data under various conditions.

Findings

Effective detection of low-quality data demonstrated through case studies.

No offline training required, enabling real-time application.

Improved accuracy in distinguishing data quality issues.

Abstract

This letter proposes a new approach for online detection of low-quality synchrophasor data under both normal and event conditions. The proposed approach utilizes the features of synchrophasor data in time and frequency domains to distinguish multiple regional PMU signals and detect low-quality synchrophasor data. The proposed approach does not require any offline study and it is more effective to detect low-quality data with apparently indistinguishable profiles. Case studies from recorded synchrophasor measurements verify the effectiveness of the proposed approach.