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Hybrid approach using counterpropagation neural network for power-system network reduction

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5 Author(s)
Lo, K.L. ; Dept. of Electron. & Electr. Eng., R. Coll. Building, Glasgow, UK ; Peng, L.J. ; Macqueen, J.F. ; Ekwue, A.O.
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A hybrid counterpropagation neural network and Ward-type equivalent approach for power system network reduction is proposed for improving the conventional external system equivalent technique. The proposed Ward-type equivalent technique not only possesses the good properties of the extended Ward equivalent, but can also update the parameters of the equivalent model for representing real-time topology changes of the external system. Another improvement is that a counterpropagation neural network is used to match the boundary equivalent power injections. The new hybrid approach combines the simplicity of Ward-type equivalent techniques with the speed of artificial neural networks. Test results demonstrate that the hybrid approach is very efficient and highly accurate compared to the external system equivalent

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Generation, Transmission and Distribution, IEE Proceedings-  (Volume:144 ,  Issue: 2 )