Abstract
This paper presents a spatiotemporal feature learning method for cause identification of electromagnetic transient events in power grids. The proposed method is formulated based on the availability of time-synchronized high-frequency measurements and using the convolutional neural network as the spatiotemporal feature representation along with softmax function for the classification. Despite the existing threshold-based, or energy-based events analysis methods, such as support vector machine autoencoder, and tapered multi-layer perceptron neural network, the proposed feature learning is carried out with respect to both time and space. The effectiveness of the proposed feature learning and the subsequent cause identification is validated through the Electromagnetic Transients Program (EMTP) simulation of different events such as line energization, capacitor bank energization, lightning, fault, and high-impedance fault in the IEEE 30-bus, and the real-time digital simulation of the Western System Coordinating Council (WSCC) 9-bus system.
Original language | English |
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Article number | 106255 |
Journal | International Journal of Electrical Power and Energy Systems |
Volume | 123 |
DOIs | |
State | Published - Dec 2020 |
Scopus Subject Areas
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering
Keywords
- Cause identification
- convolutional neural network (CNN)
- electromagnetic transient event (EMTE)
- real-time digital simulator (RTDS)