Abstract
This paper investigates a renewable energy resources application to the Load-Frequency Control of interconnected power system. The Proportional plus Integral(PI) controller gains of the two area interconnected thermal power system with the fast acting energy storage devices are designed based on Control Performance Standards (CPS) using conventional/Beta Wavelet Neural Network(BWNN) approaches. The energy storing device Hydrogen generative Aqua Electrolizer (HAE) with fuel cell can efficiently damp out the electromechanical oscillations in the power system because of their efficient storage capacity in addition to the kinetic energy of the generator rotor, which can share the sudden changes in power requirements. The system was simulated and the frequency deviations in area 1 and area 2 and tie-line power deviations for 1% and 5% step- load disturbance in area 1 are obtained. The comparison of frequency deviations and tie-line power deviations of the two area interconnected thermal power system with HAE designed with BWNN Controller are found to be superior than that of output response obtained using PI Controller.
Highlights
Load-Frequency Control (LFC) is a very essential control stratergy in electric power system design to ensure reliable operation
It is well known that the main objectives of LFC in multi-area power systems are to keep the tie-line power flows in a prescribed tolerance and to fix the frequency of each area within the permissible limit [1,2,3]
The simulation studies of the two-area interconnected thermal power system were performed with the plus Integral (PI) controller and Beta Wavelet Neural Network (BWNN) Controller for a step load disturbance of (0.01pu MW and 0.05 pu MW) in area-1 and the corresponding frequency deviations and tie-line power deviations are plotted for easy comparison and are presented in figures 5 to 10
Summary
Load-Frequency Control (LFC) is a very essential control stratergy in electric power system design to ensure reliable operation. Several adaptive control techniques have been suggested to overcome these shortcomings of the conventional techniques The attempt of such control is to extend the stability margin of the power systems [4,5,6,7]. The artificial intelligence neural network and fuzzy logic control approaches have been applied successfully to the controllers used in load frequency control. Such intelligent control systems [8,9,10,11,12] are independent of the power system mathematical model parameters, but they can work with the available system time responses. It is easy to increase the capacity and free from degradation due to fast charging and discharging action [15,16]
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