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Analytic hierarchy process based automatic generation control of multi-area interconnected power system using Jaya algorithm
S.P. Singh, , V.P. Singh, M.G. Babu
Published in Elsevier Ltd
2017
Volume: 60
   
Pages: 35 - 44
Abstract
Controller design aspect for automatic generation control (AGC) of multi-area power system is often regarded as an optimization problem with different conflicting objectives. The controller must fulfill these objectives to produce a feasible set of solution as per the requirements of the designer. This paper explores the design and performance analysis of a proportional-integral-derivative (PID) controller employing Jaya algorithm for AGC of an interconnected power system. A filter with derivative term is used with PID controller to minimize the effect of noise in the input signal. The different performance measures considered are integral-time-multiplied-absolute-error (ITAE), the minimization of peak overshoots and the settling times of the deviations in the frequencies and tie-line power. Furthermore, these performance measures i.e. objectives are combined to form a single objective problem using analytic hierarchy process (AHP). Recently proposed Jaya algorithm, which is free from algorithm-specific parameters, is used for minimizing this single objective. The two-area interconnected linear power system model is considered for the simulation process. The simulation studies are carried out under five different cases with diverse sets of disturbances. The efficacy and superiority of the proposed Jaya algorithm based PID controller is shown by comparing simulation results with other algorithms like particle swarm optimization (PSO), differential evolution (DE), Nelder-Mead simplex (NMS), elephant herding optimization (EHO) and teacher learner based optimization (TLBO). Time-domain simulations and statistical analysis are presented to validate the effectiveness of the proposed controller. © 2017 Elsevier Ltd
About the journal
JournalData powered by TypesetEngineering Applications of Artificial Intelligence
PublisherData powered by TypesetElsevier Ltd
ISSN09521976