Cause Identification of Electromagnetic Transient Events using Spatiotemporal Feature Learning
This addresses the problem of accurately identifying causes of transient events for power grid operators, but it appears incremental as it builds on existing methods like CNNs and softmax.
The paper tackles cause identification of electromagnetic transient events in power grids by proposing a spatiotemporal unsupervised feature learning method using CNNs, validated through simulations on IEEE 30-bus and WSCC 9-bus systems with events like line energization and faults.
This paper presents a spatiotemporal unsupervised feature learning method for cause identification of electromagnetic transient events (EMTE) in power grids. The proposed method is formulated based on the availability of time-synchronized high-frequency measurement, and using the convolutional neural network (CNN) as the spatiotemporal feature representation along with softmax function. Despite the existing threshold-based, or energy-based events analysis methods, such as support vector machine (SVM), autoencoder, and tapered multi-layer perception (t-MLP) 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 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 (RTDS) of the WSCC 9-bus system.