This paper proposes a new Initial CCT (Critical Clearing
Time) estimation method using a hybrid neural network composed of iRprop
(Improving the Resilient backPROPation 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.
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