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Evolutionary Algorithms for Load Frequency Control in Two Area Interconnected Power System
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A Power system is an interconnected electric network which makes available, electric power generated by the generating plants to the loads through transmission lines. Modern power systems are large, highly complex and organized in the form of regional grids, which are interconnected to facilitate power transfer between areas via tie-lines. Interconnections make the system more reliable, since the power can be borrowed from the neighboring area. It is the responsibility of power generating system 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. 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) play an important role in maintaining constant frequency in order to ensure the reliability of electric power. In order improve the performance and stability of this control loop, PID controllers are normally used. But these fixed gain controllers fail to perform under varying load conditions and hence provide poor dynamic characteristics. Also the conventional PSO based PID controllers will have large settling time, overshoot and oscillations. In order to achieve better dynamic performance, system stability and sustainable utilization of generating systems, PID gains must be well tuned. 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. 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.
Keywords
Load Frequency Control (LFC), Evolutionary Algorithm (EA), Enhanced Particle Swarm Optimization (EPSO), Multi Objective Particle Swarm Optimization (MOPSO), and Stochastic Particle Swarm Optimization (SPSO).
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