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.