Toward the Prediction Level of Situation Awareness for Electric Power Systems Using CNN-LSTM Network

• Published in: IEEE transactions on industrial informatics 2021-10, Vol.17 (10), p.6951-6961
• Main Authors: Wang, Qi, Bu, Siqi, He, Zhengyou, Dong, Zhao Yang
• Format: Article
• Language: English
• Subjects: Adequacy; Artificial neural networks; Contingency; Convolutional neural network (CNN); Data mining; Data models; deep learning; Electric power; Electric power systems; Fault location; long short-term memory (LSTM) recurrent neural network; Low level; Measuring instruments; Model accuracy; Neural networks; Phasor measurement units; Phasors; Power measurement; Power system stability; Power systems; Recurrent neural networks; situation awareness (SA); Situational awareness; spatiotemporal data mining; Spatiotemporal phenomena; Stability
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2020.3047607

Situation awareness (SA) has been recognized as a critical guarantee for the stable and secure operation of electric power systems, especially under complex uncertainties after renewable energy integration. In this article, an artificial-intelligence-powered solution is presented to reach a full realization of SA covering perception, comprehension, and prediction, the last of which is more advanced but challenging and hence has not been discussed in any literature before. A novel SA model is proposed by aggregating two powerful deep learning structures: convolutional neural network (CNN) and long short-term memory (LSTM) recurrent neural network. The proposed CNN-LSTM model has superiority to achieve collaborative data mining on spatiotemporal measurement data, i.e., to learn both spatial and temporal features simultaneously from phasor measurement units data. Two functional branches are designed within the SA model: a contingency locator to detect the exact fault location at present and a stability predictor to predict stability status of the system in the future. Test results have shown high performance (accuracy) of the model even on a low level of data adequacy. The proposed SA model can promisingly facilitate very fast postfault actions by the system operators to prevent the power system from any unstable operational status.