Instantaneous Electromechanical Dynamics Monitoring in Smart Transmission Grid | IEEE Journals & Magazine | IEEE Xplore

Instantaneous Electromechanical Dynamics Monitoring in Smart Transmission Grid


Abstract:

Measurement sensors installed in the smart transmission system can acquire big data for electromechanical dynamics monitoring. The time-series data obtained carry informa...Show More

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.
Published in: IEEE Transactions on Industrial Informatics ( Volume: 12, Issue: 2, April 2016)
Page(s): 844 - 852
Date of Publication: 26 October 2015

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