A new intelligent time-series prediction technique for coherency identification performance enhancement






Time-series prediction, coherency identification, nonlinear autoregressive exogenous, artificial neural network, power system


The manuscript proposes a time-series prediction technique to enhance the response time of the coherency identification technique. The proposed methodology utilizes the nonlinear autoregressive exogenous neural network (NARX) algorithm to predict the generator speed deviations following a disturbance in the system. Consequently, the coherency identification technique based on independent component analysis (ICA) is utilized on the predicted system responses. The effectiveness of the proposed approach is demonstrated on of the IEEE 16-generator 68-bus test system model. The result shows that the proposed technique is able to predict $0.2$s ahead following a disturbance in the system accurately. Therefore, the NARX allows a $0.2$s head start for the ICA to determine the real-time coherent generator group in the system. Furthermore, the result shows that the proposed approach is able to identify the coherent group of generators based on the predicted generator speed deviation in all cases considered in this study, accurately. Conclusively, the result implies that the proposed technique is able to speed up the overall coherency identification process in a power system operation.

Author Biography


He received the B.Eng degree in electrical engineering and the M.Eng degree in electrical-power from Universiti Teknologi Malaysia (UTM) in 2008 and 2010, respectively. Consequently, he obtained the Ph.D. degree at the Department of Electrical and Electronic Engineering, Imperial College London, London, U.K. Currently, he is an academician in Universiti Tun Hussein Onn Malaysia (UTHM). Also, he is Editor of the IEEE Open Access Journal of Power and Energy. His current research interests include of power system dynamics, coherency identification, and adaptive protection in power system.


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