EPE  Vol.5 No.4 B , July 2013
Accuracy Improvement in CCT Estimation of Power Systems by iRprop-RAN Hybrid Neural Network

This paper proposes a new Initial CCT (Critical Clearing Time) estimation method using a hybrid neural network composed of iRprop (Improving the Resilient back PROPation Algorithm) and RAN (Resource Allocation Network). In transient stability study, CCT evaluation is very important but time consuming due to the fact it needs many iteration of time domain simulations gradually increasing the fault clearing time. The key to reduce the required computing time in this process is to find accurate initial estimation of CCT by a certain handy method before going to the iterative stage. As one of the strongest candidates of this handy method is the utilization of the pattern recognition ability of neural networks, which enable us to jump to a close estimation of the real CCT without any heavy computing burden. This paper proposes a new hybrid neural network which is a combination of the well-known iRprop and RAN. In the proposed method, the outputs of the hidden units of RAN are modified by multiplying the contribution factors calculated by an additional iRprop network. Numerical studies are done using two different test systems for the purpose of confirming the validity of the proposal. The result of the proposed method is the best. Properly evaluating the contribution of each input to the hidden units, the estimation error obtained by the proposed method is improved further than the original RAN based estimation.

Cite this paper: T. Kumano and S. Netsu, "Accuracy Improvement in CCT Estimation of Power Systems by iRprop-RAN Hybrid Neural Network," Energy and Power Engineering, Vol. 5 No. 4, 2013, pp. 999-1004. doi: 10.4236/epe.2013.54B191.

[1]   K. Ikenono and S. Iwamoto, “Generalization of Transient Stability Solution using Neural Network Theory,” Trans IEEJ, Vol. 111, No. 7, 1991, pp. 723-728.

[2]   A. L. Bettiol, A. Souza, J. L. Todesco and J. R. Tesch, Jr, “Estimation of Critical Clearing Times Using Neural Networks,” 2003 IEEE Bologna Power Tech Conference Proceedings. doi:10.1109/PTC.2003.1304446

[3]   H. Takahashi and T. Kumano, “Available Transfer Capability Screening Considering Transient Stability by Support Vector Machine,” 2008 IEEE PES General Meeting, Pittsburgh. doi:10.1109/PES.2008.4596499v

[4]   A. Wada and T. Kumano, “Fast Estimation of Transient Stability Cconstrained ATC by Relevance Vector Machine,” IEEE 2nd International Power and Energy Conference, 2008, doi:10.1109/PECON.2008.4762451v

[5]   B. S. Mahanand, S. Suresh, N. Sundararajan, M. A. Kumar, “Alzheimer's Disease Detection Using a Self-adaptive Resource Allocation Network classifier,” The 2011 International Conference on Neural Networks, pp. 1930-1934.doi:10.1109/IJCNN.2011.6033460

[6]   S. Shanthi and V. M. A. Bhaskaran, “Computer Aided Detection and Classification of Mammogram Using Self-adaptive Resource Allocation Network Classifier,” 2012 International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME), pp. 284-289. doi:10.1109/ICPRIME.2012.6208359

[7]   H. C. Lou and W. Z. Dai, “A Novel Non-linear Model Predictive Controller Based on Minimal Resource Allocation Network and Its Application in CSTR PH Process,” 7th World Congress on Intelligent Control and Automation, 2008. WCICA 2008, pp. 5672-5676. doi:10.1109/WCICA.2008.4593855v

[8]   C. Igel and M. Husken, “Empirical Evaluation of the Improved Rprop Learning Algorithms,” Neurocomputing, Vol. 50, 2003, pp. 105-123. doi:10.1016/S0925-2312(01)00700-7

[9]   J. Platt, “A Resource-Allocating Network for Function Interpolation,” Neural Computation, Vol. 3, No. 2, 1991, pp. 213-225.doi:10.1162/neco.1991.3.2.213v

[10]   Y. Kitauchi, “Scheme of Power System Stability Enhancement using Margin to Apparatus Limitation (Part II)-Verification of Power System Stability Improvement using Highly Voltage Control on IEEJ WEST 30-machine System Model-”, CRIEPI REPORT R04010, 2005.

[11]   P. M. Anderson and A. A. Fouad, Power System Control and Stability, Iowa State University Press, 1977.