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Amalgamate Economic & Emission Dispatch Applying Radial Basis Function Backboned Neural Network

Journal: GRD Journal for Engineering (Vol.2, No. 5)

Publication Date:

Authors : ; ; ; ;

Page : 362-375

Keywords : Economic Dispatch (ED); Electric Power Generation Systems; Redial Neural Network; Economic Emission Dispatch (EED); RBF; BPA;

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The efficient and optimum economic operations of electric power generation systems have always occupied an important position in the electric power industry. This involves allocation of the total load between the available generating units in such a way that the total cost of operation is kept at a minimum. In recent years this problem area has taken on a suitable direction as the public has become increasingly concerned with environmental matters, so that economic dispatch now includes the dispatch of systems to minimize pollutants, as well as to achieve minimum cost. In addition, there is a need to expand the limited economic optimization problem to incorporate constraints on system operation to ensure the security of the system, thereby preventing the collapse of the system due to unforeseen conditions. The purpose of the traditional Economic Dispatch (ED) problem is to find the most economical schedule of the generating units while satisfying load demand and operational constraints. This involves allocation of active power between the units, as the operating cost is insensitive to the reactive loading of a generator, the manner in which the reactive load of the station is shared among various on line generator dos not affect its economy. Citation: Sharad Chandra Rajpoot, G.E.C. Jagdalpur , Bastar, Chhattisgarh, India; Prashant Singh Rajpoot ,L.C.I.T., Bilaspur, Chhattisgarh, India; Sanjay Kumar Singhai ,G.E.C. Bilaspur, Chhattisgarh, India; Anil Kumar Shukla ,G.E.C. Bilaspur, Chhattisgarh, India. "Amalgamate Economic & Emission Dispatch Applying Radial Basis Function Backboned Neural Network." Global Research and Development Journal For Engineering 25 2017: 362 - 375.

Last modified: 2017-05-18 22:56:20