Journal
IEEE TRANSACTIONS ON POWER SYSTEMS
Volume 34, Issue 2, Pages 864-872Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2018.2872505
Keywords
Transient stability; phasor measurements; convolutional neural networks; principal component analysis
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Funding
- Fund for Improvement of S& T Infrastructure program, Department of Science and Technology, India [SR/FST/ETII-063/2015(C) (G)]
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A continuous Online Monitoring System (OMS) for power system stability based on Phasor Measurements (PMU measurements) at all the generator buses is proposed in this paper. Unlike the state-of-the-art methods, the proposed OMS does not require information about fault clearance. This paper proposes a convolutional neural network, whose input is the heatmap representation of the measurements, for instability prediction. Through extensive simulations on standard IEEE 118-bus and IEEE 145-bus systems, the effectiveness of the proposed OMS is demonstrated under varying loading conditions, fault scenarios, topology changes, and generator parameter variations. Two different methods are also proposed to identify the set of critical generators that are most impacted in the unstable cases.
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