Voltage and frequency control in power generating system using hybrid evolutionary algorithms

Abstract

A power generating system has the responsibility to ensure that adequate power is delivered to the load, both reliably and economically. The quality of power supply is affected due to continuous and random changes in load during the operation of the power system. Hence, a power system control is required to maintain a continuous balance between power generation and load demand. Load Frequency Controller and Automatic Voltage Regulator play an important role in maintaining constant frequency and voltage in order to ensure the reliability of electric power. In order to improve the performance and stability of these control loops, proportional-integral-derivative (PID) controllers are normally used. But these fixed gain controllers fail to perform under varying load conditions and hence provide poor dynamic characteristics with a large settling time, overshoot and oscillations. In order to achieve a better dynamic performance, system stability and sustainable utilization of generating systems, PID gains must be well tuned. In this paper, Evolutionary Algorithms like, Enhanced Particle Swarm Optimization, Multi Objective Particle Swarm Optimization, and Stochastic Particle Swarm Optimization are proposed to find the optimum gains of the PID controller to control the voltage and frequency of the generating system within the permissible limit. These algorithms offer a more stable and faster convergence towards the best PID gains and also with minimum computational time. These algorithms are appropriate to model the uncertainties found in the power demand and improve the flexible nature of the controllers. Simulation results demonstrate that the proposed controllers adapt themselves appropriately to varying loads and hence provide better performance characteristics with respect to settling time, oscillations and overshoot.