Abstract
Transient stability simulation studies of large-scale power systems often generate large amounts of data. This can make it difficult for power system planners to understand the overall system response or to identify portions of the system with unusual signals. In this paper we present a novel approach that utilizes clustering to extract common features in the voltage magnitude and frequency response signals, and to identify outliers. The geographic visualization of the results is also discussed. Results are demonstrated using the IEEE 118-bus system and a 16000-bus real-world system model.
| Original language | English (US) |
|---|---|
| Article number | 6621055 |
| Pages (from-to) | 966-973 |
| Number of pages | 8 |
| Journal | IEEE Transactions on Power Systems |
| Volume | 29 |
| Issue number | 2 |
| DOIs | |
| State | Published - Mar 2014 |
| Externally published | Yes |
Keywords
- Clustering
- feature extraction
- model errors
- power system visualization
- spark lines
- transient stability data
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering