vioft2nntf2t|tblJournal|Abstract_paper|0xf4ff8fba030000006a0f000001000700 Power generating system has the responsibility to ensure that adequate power is delivered to the load, both reliably and economically. Any electrical system must be maintained at the desired operating level characterized by nominal frequency and voltage profile. But the ability of the power system to track the load is limited due to physical and technical consideration. Hence, a Power System Control is required to maintain a continuous balance between power generation and load demand. The quality of power supply is affected due to continuous and random changes in load during the operation of the power system. Load Frequency Controller (LFC) and Automatic Voltage Regulator (AVR) play an important role in maintaining constant frequency and voltage in order to ensure the reliability of electric power. The fixed gain PID controllers used for this application fail to perform under varying load conditions and hence provide poor dynamic characteristics with large settling time, overshoot and oscillations. In this paper, Evolutionary Algorithms (EA) like, Enhanced Particle Swarm Optimization (EPSO), Multi Objective Particle Swarm Optimization (MOPSO), and Stochastic Particle Swarm Optimization (SPSO) are proposed to overcome the premature convergence problem in a standard PSO. These algorithms reduce transient oscillations and also increase the computational efficiency. Simulation results demonstrate that the proposed controller adapt themselves appropriate to varying loads and hence provide better performance characteristics with respect to settling time, oscillations and overshoot.
A. Soundarrajan, S. Sumathi, G. Sivamurugan
P.S.G. College of Technology, Tamil Nadu, India
Load Frequency Control (LFC), Automatic Voltage Regulator (AVR), Evolutionary Algorithm (EA), Enhanced Particle Swarm Optimization (EPSO), Multi Objective Particle Swarm Optimization (MOPSO), and Stochastic Particle Swarm Optimization (SPSO)
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| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 1 , Issue: 2 , Pages: 88 - 97 )
Date of Publication :
October 2010
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201
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