Abstract
The increasing amount of data recorded during power system operations and recently developed data-driven methods make online sensitivity identification (SI) a possibility. However, due to the inherent properties of power systems - nonlinearity, time variance, and collinearity - the effective data that carry the sensitivity information are insufficient. Consequently, the online SI information collected with existing methods may result in unexpected estimates. In this paper, a sufficient effective data condition that guarantees the success of online SI is proposed. The inherent properties of power systems and their impacts on this condition are then investigated. A series of metrics to qualify online whether the data meet the condition is put forward to assess the online SI results. A method is also proposed to select the effective data to improve the online computational efficiency. Finally, the findings and methods are validated in an eight-generator 36-node bus system with operations data recorded from actual power systems.
| Original language | English |
|---|---|
| Article number | 7707399 |
| Pages (from-to) | 2756-2766 |
| Number of pages | 11 |
| Journal | IEEE Transactions on Power Systems |
| Volume | 32 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - Jul 2017 |
| Externally published | Yes |
Keywords
- data collinearity
- data explosion
- Data quality
- locally weighted linear regression (LWLR)
- sensitivity identification (SI)
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering