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Hassan, E.E., Zakaria, Z. and Rahman, T.K.A. (2012) Improved Adaptive Tumbling Bacterial Foraging Optimization (ATBFO) for Emission Constrained Economic Dispatch Problem. Proceedings of the World Congress on Engineering, 2, 1-4.

has been cited by the following article:

  • TITLE: Development of Hybrid Algorithm Based on PSO and NN to Solve Economic Emission Dispatch Problem

    AUTHORS: R. Leena Rose, B. Dora Arul Selvi, R. Lal Raja Singh

    KEYWORDS: Particle Swarm Optimization (PSO), Economic Dispatch (ED), Economic Dispatch Problems (EDPs), Genetic Algorithm (GA), Neural Network (NN)

    JOURNAL NAME: Circuits and Systems, Vol.7 No.9, July 19, 2016

    ABSTRACT: The electric power generation system has always the significant location in the power system, and it should have an efficient and economic operation. This consists of the generating unit’s allocation with minimum fuel cost and also considers the emission cost. In this paper we have intended to propose a hybrid technique to optimize the economic and emission dispatch problem in power system. The hybrid technique is used to minimize the cost function of generating units and emission cost by balancing the total load demand and to decrease the power loss. This proposed technique employs Particle Swarm Optimization (PSO) and Neural Network (NN). PSO is one of the computational techniques that use a searching process to obtain an optimal solution and neural network is used to predict the load demand. Prior to performing this, the neural network training method is used to train all the generating power with respect to the load demand. The economic and emission dispatch problem will be solved by the optimized generating power and predicted load demand. The proposed hybrid intelligent technique is implemented in MATLAB platform and its performance is evaluated.