Abstract
In this paper, an online data-driven approach is proposed for the detection of low-quality synchrophasor measurements. The proposed method leverages the spatio-temporal similarities among multiple-time-instant synchrophasor measurements and formulates the low-quality synchrophasor data as spatio-temporal outliers. A density-based local outlier detection technique is proposed to detect the spatio-temporal outliers. This data-driven approach involves no system modeling information. The detection algorithm can operate under both normal and fault-on system conditions, with fast computation speed suitable for online applications. Case studies on both synthetic and real-world synchrophasor data verify the effectiveness of the proposed approach.
| Original language | English (US) |
|---|---|
| Article number | 7762220 |
| Pages (from-to) | 2817-2827 |
| Number of pages | 11 |
| Journal | IEEE Transactions on Power Systems |
| Volume | 32 |
| Issue number | 4 |
| DOIs | |
| State | Published - Jul 2017 |
| Externally published | Yes |
Keywords
- Data mining
- data quality improvement
- outlier detection
- spatio-temporal similarity
- synchrophasor
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering