Informatics Publishing Limited

A. Soundarrajan ¹, S. Sumathi ², G. Sivamurugan ²

Affiliations
1 Information Technology Department, PSG College of Technology, Coimbatore-641004, Tamilnadu, IN
2 Electrical and Electronics Engg. Department, PSG College of Technology, Coimbatore-641004, Tamilnadu, IN

Abstract

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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A. Soundarrajan ¹, S. Sumathi ², G. Sivamurugan ²

Affiliations
1 Department of Information Technology, PSG College of Technology, Tamil Nadu, IN
2 Department of Electrical and Electronics Engineering, PSG College of Technology, Tamil Nadu, IN

Abstract

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.

Keywords

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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