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arXiv · 1701.08081

Design of PI Controller for Automatic Generation Control of Multi Area Interconnected Power System using Bacterial Foraging Optimization

Naresh Kumari·Nitin Malik·A. N. Jha·Gaddam Mallesham
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Abstract

The system comprises of three interconnected power system networks based on thermal, wind and hydro power generation. The load variation in any one of the network results in frequency deviation in all the connected systems.The PI controllers have been connected separately with each system for the frequency control and the gains (Kp and Ki) of all the controllers have been optimized along with frequency bias (Bi) and speed regulation parameter (Ri). The computationally intelligent techniques like bacterial foraging optimization (BFO) and particle swarm optimization (PSO) have been applied for the tuning of controller gains along with variable parameters Bi and Ri. The gradient descent (GD) based conventional method has also been applied for optimizing the parameters Kp, Ki,Bi and Ri.The frequency responses are obtained with all the methods. The performance index chosen is the integral square error (ISE). The settling time, peak overshoot and peak undershoot of all the frequency responses on applying three optimization techniques have been compared. It has been observed that the peak overshoot and peak undershoot significantly reduce with BFO technique followed by the PSO and GD techniques. While obtaining such optimum response the settling time is increased marginally with bacterial foraging technique due to large number of mathematical equations used for the computation in BFO. The comparison of frequency response using three techniques show the superiority of BFO over the PSO and GD techniques. The designing of the system and tuning of the parameters with three techniques has been done in MATLAB/SIMULINK environment.

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Naresh Kumari, Nitin Malik, A. N. Jha, Gaddam Mallesham. 2017-01-26. Design of PI Controller for Automatic Generation Control of Multi Area Interconnected Power System using Bacterial Foraging Optimization. https://doi.org/10.21817/ijet/2016/v8i6/160806236

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