Abstract
Measurement sensors installed in the smart transmission system can acquire big data for electromechanical dynamics monitoring. The time-series data obtained carry information of instantaneous relationship of system oscillation modes with respect to operating conditions. To extract this information, this paper proposes a parallel processed online supervised learning algorithm called k-nearest neighbors 'locally weighted linear regression' (KNN-LWLR), which is an extensive combination of two famous machine-learning algorithms: 1) the KNN learning; and 2) LWLR learning. Its mathematical derivation, implementation, parameter tuning, and application to electromechanical oscillation mode prediction are first described. The proposed algorithm is then validated based on an 8-generator 36-node system with the real operations data.
Original language | English |
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Article number | 7302048 |
Pages (from-to) | 844-852 |
Number of pages | 9 |
Journal | IEEE Transactions on Industrial Informatics |
Volume | 12 |
Issue number | 2 |
DOIs | |
Publication status | Published - Apr 2016 |
Externally published | Yes |
Keywords
- instantaneous electromechanical dynamics monitoring
- k-nearest neighbors learning
- locally weighted regression learning
- smart transmission grid
ASJC Scopus subject areas
- Control and Systems Engineering
- Information Systems
- Computer Science Applications
- Electrical and Electronic Engineering