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Design of 2-Dof Pid Controller for Load Frequency Control of Two Area Power System using Mfo Algorithm
Rajveer Singh1, Saurabh Kumar Kesarwani2, Neelesh Kumar Gupta3, Haroon Ashfaq4

1Rajveer Singh, Electrical Engineering Department, Jamia Millia Islamia, New Delhi, India.
2Saurabh Kumar Kesarwani, Electrical Engineering Department, Jamia Millia Islamia, New Delhi, India.
3Neelesh Kumar Gupta, Department of Electrical Engineering, NIT Jamshedpur, Jharkhand, India.
4Haroon Ashfaq*, Electrical Engineering Department, Jamia Millia Islamia, New Delhi, India.

Manuscript received on February 01, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 158-161 | Volume-9 Issue-3, February, 2020. | Retrieval Number: C5009029320/2020©BEIESP | DOI: 10.35940/ijeat.C5009.029320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The paper endeavours to analyse the load frequency control for two area system. In this paper, two areas has been considered in which non-reheated type of turbine in both area are used and whose secondary loop consists a latest controller called 2 degree-of-freedom PID (2-DOF-PID) controller. The parameter of the this controller is been optimized by the latest meta heuristic algorithm also called Moth flame optimization algorithm (MFO) to minimize the deviation in frequency of area and tie-line power respectively. The same processes are repeated with PID controller and Integral controller whose parameters are also optimized by MFO. A comparison is made among the result of these and 2-DOF-PID controller prove its superiority over the other controller for minimizing the deviation which occurs in frequency of the area as well as the tie-line power.
Keywords: Load frequency control, 2-area power system, 2-DOF PID Controller, PID controller, Moth–Flame Optimization (MFO) algorithm, Genetic Algorithm (GA), PSO (Particle Swarm Optimization) algorithm.