Abstract
This article presents an evolutionary based wavelet network (EBWN) technique for real-time power dispatch regarding both fuel cost and environmental impact of emissions. The wavelet networks, constructed by an evolutionary computing algorithm, are composed of a three-layer structure that contains the wavelet, weighting, and summing nodes, respectively. To make the computed outputs fit the historical ones, the parameters of translation and dilation in the wavelet nodes and the weighting factors in the weighting nodes are tuned. Taking the advantages of global search abilities of the evolutionary programming (EP) and the multi-resolution as well as localization natures of the wavelets, the evolutionary based wavelet networks can thus construct and identify the inherent characteristics of the nonlinear system. Once the networks are trained properly, they can be applied to the real-time power dispatch environment. The effectiveness of the proposed approach has been demonstrated by two examples, the IEEE 30-bus 6-generator system and the Taipower Company (TPC) real system. Comparisons of learning performances are made to the existing artificial neural networks (ANNs) method.
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