Design of PI Controller for Automatic Generation Control of Multi Area Interconnected Power System using Bacterial Foraging Optimization
January 26, 2017 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Naresh Kumari, Nitin Malik, A. N. Jha, Gaddam Mallesham
arXiv ID
1701.08081
Category
eess.SY: Systems & Control (EE)
Cross-listed
cs.NE
Citations
7
Venue
arXiv.org
Last Checked
2 months ago
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.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Systems & Control (EE)
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
Incremental Gradient, Subgradient, and Proximal Methods for Convex Optimization: A Survey
R.I.P.
๐ป
Ghosted
Wireless Network Design for Control Systems: A Survey
R.I.P.
๐ป
Ghosted
Learning-based Model Predictive Control for Safe Exploration
R.I.P.
๐ป
Ghosted
Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function
R.I.P.
๐ป
Ghosted
Novel Multidimensional Models of Opinion Dynamics in Social Networks
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Language Models are Few-Shot Learners
R.I.P.
๐ป
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
๐ป
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
๐ป
Ghosted