Multiobjective optimization using weighted sum Artificial Bee Colony algorithm for Load Frequency Control

Kanendra Naidu

doi:10.1016/j.ijepes.2013.10.022

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Multiobjective optimization using weighted sum Artificial Bee Colony algorithm for Load Frequency Control

2014-02-01

103

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Kanendra Naidu

Hazlie Mokhlis *

A.H.A. Bakar

DOI：10.1016/j.ijepes.2013.10.022

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Abstract

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This paper presents the implementation of multiobjective based optimization of Artificial Bee Colony (ABC) algorithm for Load Frequency Control (LFC) on a two area interconnected reheat thermal power system. The ABC algorithm is currently being applied in many research works due to the local and global search capability of the algorithm. This paper uses the weighted sum approach of the ABC to optimize the PID controller's gains to provide a compromise between the frequency response's settling time and maximum overshoot. The composite objective function comprising both objectives is characterized by the performance criterions - Integral of Time Multiplied Absolute Error (ITAE) and Integral of Time Weighted Squared Error (ITSE). Analysis is carried out to determine the best weightage set for this investigation. A performance index based on Least Average Error (LAE) is formulated to calculate the index of each weightage set. In order to ensure effective compensation in the system output, the PID controllers for both areas are tuned simultaneously. The tuning performance of the algorithm is evaluated by comparing the performance of the proposed controller with conventional PI and PID controller. The robustness of the proposed algorithm is further investigated by evaluating the response of the system under simultaneous step load perturbation (SLP), changing load demand and collectively varying system parameters in the range of +/-50%. The simulation result shows the dynamic response of the controller emphasizes on the compromise between the settling time and maximum overshoot of the frequency response. Furthermore, the proposed algorithm is robust enough to operate under different operating conditions and system parameter variations. 2013 Elsevier Ltd. All rights reserved.

Keywords:

Multiobjective optimization

Weighted sum approach

Load Frequency Control (LFC)

PID

Artificial Bee Colony (ABC) algorithm

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