DocumentCode
255479
Title
Performance study of Mine Blast Algorithm for automatic voltage regulator tuning
Author
Majumdar, S. ; Mandal, K. ; Chakraborty, N.
Author_Institution
Dept. of Power Eng., Jadavpur Univ., Kolkata, India
fYear
2014
fDate
11-13 Dec. 2014
Firstpage
1
Lastpage
6
Abstract
Proportional Integral Derivative (PID) controllers are extensively used in industry for process instrumentation application. PID controllers have also found widespread application in Power System Control. To achieve effective control optimal tuning of PID gains of the controller is necessary. This controller gain tuning problem is a multimodal non-convex optimization problem. This paper proposes a tuning strategy based on Mine Blast Algorithm (MBA); a population based algorithm for tuning the controller. This algorithm is a newly developed optimization technique. The motivation of this study is to determine if MBA presents a better alternative than traditional soft computing based optimization methods. The algorithm simulates the behavior of exploding mines in a mine field. This algorithm has been used to find optimal values of PID gains. The performance of MBA is compared with results obtained from Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). All the codes have been developed in house in Matlab environment. MBA has demonstrated up to 40% reduction in computational burden while maintaining controllers output characteristics.
Keywords
concave programming; genetic algorithms; particle swarm optimisation; three-term control; voltage regulators; GA; MBA; Matlab environment; PID controllers; PSO; automatic voltage regulator tuning; controller gain tuning problem; genetic algorithm; mine blast algorithm; multimodal nonconvex optimization problem; particle swarm optimization; population based algorithm; proportional integral derivative controllers; Equations; Genetic algorithms; Optimization; Sociology; Statistics; Time factors; Tuning; Automatic Voltage Regulator (AVR); Genetic Algorithm; Mine Blast Optimization; Optimization; Particle Swarm Optimization;
fLanguage
English
Publisher
ieee
Conference_Titel
India Conference (INDICON), 2014 Annual IEEE
Conference_Location
Pune
Print_ISBN
978-1-4799-5362-2
Type
conf
DOI
10.1109/INDICON.2014.7030488
Filename
7030488
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