| Abstract: |
| The recent development of phasor measurement technique opens the way for real-time post-disturbance transient
stability assessment (TSA). Following a disturbance, since the transient instability can occur very fast, there is an
urgent need for fast TSA with sufficient accuracy. This paper first identifies the tradeoff relationship between the
accuracy and speed in post-disturbance TSA, and then proposes an optimal self-adaptive TSA method to optimally
balance such tradeoff. It uses ensemble learning and credible decision-making rule to progressively predict the
post-disturbance transient stability status, and models a multi-objective optimization problem to search for the
optimal balance between TSA accuracy and speed. With such optimally balanced TSA performance, the TSA
decision can be made as fast as possible while maintaining an acceptable level of accuracy. The proposed method
is tested on New England 10-machine 39-bus system, and the simulation results verify its high efficacy. |
| Key words: Ensemble learning, Extreme learning machine (ELM), Intelligent system (IS), Multi-objective optimization,Transient stability assessment |
| DOI：10.1186/s41601-018-0091-3 |
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| Fund: |
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