Affiliations: Department of Electrical and Electronics Engineering, Rajagiri School of Engineering and Technology, Kakkanad, Kochi, Kerala, India
Correspondence:
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Corresponding author: Anju G. Pillai, Department of Electrical and Electronics Engineering, Rajagiri School of Engineering and Technology, Kakkanad, Kochi, Kerala, India. E-mail: [email protected].
Abstract: Automatic Generation Control (AGC) is an important tool to ensure the stability and reliability of power systems. For stable operation of power systems, the frequency of the system should be reserved within the nominal value. Towards this, the estimation of states is of supreme implication. In this paper, a comparison is made on the estimation of the states using Kalman estimator method and optimal control approach to the Automatic Generation Control (AGC) of an isolated power system. The performance of optimized Linear Quadratic Regulator (LQR) in pole placement is compared with Kalman estimator. Optimization algorithms such as Genetic Algorithm and Particle Swarm Optimization are used to optimize positive definite matrices Q and R, weighting matrices of a LQR controller. Kalman estimator estimates the states of the system by measuring only one output signal which in this paper is mentioned as the change in frequency for the system considered. The comparison is made on the basis of the mean of the variances of the output, using the mentioned approaches. Study is conducted under different noise levels for independent Monte Carlo simulations. Modeling of an isolated power system is done using Simulink/MATLAB.
Keywords: Automatic Generation Control (AGC), Genetic Algorithm (GA), Linear Quadratic Regulator (LQR), Particle Swarm Optimization (PSO), kalman estimator, single area power system
