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Study on the Calculation of Economic Dispatch of Power System Based on Improved Particle Swarm Optimization Algorithm

Author: ZhangXiaoQing
Tutor: ZhangJing
School: Harbin University of Science and Technology
Course: Control Theory and Control Engineering
Keywords: Power System Economic Dispatch Particle Swarm Optimization Dynamic Double Population
CLC: TP18
Type: Master's thesis
Year: 2014
Downloads: 88
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Abstract


With the increasing progress of science and technology, the development ofall walks of life almost all must rely on the support of electric power.The securityand stability of power system operation is related to the development of thenational economy. Economic dispatch problem calculation plays an importantrole in the operation and control of power system, meeting the power supplyreliability and power quality premise, the economic operation of the powersystem is optimized, which makes the system achieve great economic benefit.Therefore, research on economic dispatch of power system is of great practicalvalue. In this paper, power system economic dispatch is studied with theimproved particle swarm optimization algorithm.The particle swarm optimization algorithm in the iterative optimizationprocess in the presence of the "convergence" phenomenon, we use the improvedparticle swarm optimization algorithm, the immune algorithm in the populationdiversity maintaining mechanism is introduced into the basic particle swarmalgorithm, so as to maintain the diversity of population. In addition, the swarm isdivided into two sub groups in the improved particle swarm algorithm, and sizeof the two sub population is adjusted dynamically in the process of evolution.realization of dynamic double population optimization.In order to reflect the superiority of improved particle swarm optimizationalgorithm for function optimization, the improved particle swarm algorithm isapplied to the Rosenbrock, Rastrigin and Griewank three standard test functionsfor the extremum, get the optimized result of simulation curve and the bestfitness value. Then optimization results are compared with the particle swarmoptimization results and the result shows that the improved particle swarm algorithm has certain superiority.Finally, for economic dispatch of power system, the fuel cost based onpower system is regard as the objective function. The network loss of powersystem is considered. The valve point system in the power generation processeffect is ignored. Build the power system model that meet the power balanceconstraint and motor operating constraint. The IEEE30system is used in powersystem. The improved particle swarm algorithm is used in simulation whichparameters on the fuel consumption is known. And the results were comparedwith the optimization of the basic particle swarm optimization algorithm. Theresults show that, the optimization results of system losses and costs are less thanthe basic particle swarm algorithm, an improved particle swarm algorithm caneffectively solve the power system economic dispatch problem.

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CLC: > Industrial Technology > Automation technology,computer technology > Automated basic theory > Artificial intelligence theory
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