Fuzzy analytic hierarchy process based generation management for interconnected power system

Decision makers consistently face the challenge of simultaneously assessing numerous attributes, determining their respective importance, and selecting an appropriate method for calculating their weights. This article addresses the problem of automatic generation control (AGC) in a two area power sy...

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Bibliographic Details
Published in:Scientific reports Vol. 14; no. 1; pp. 11446 - 24
Main Authors: Varshney, T., Waghmare, A. V., Singh, V. P., Meena, V. P., Anand, R, Khan, Baseem
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 20.05.2024
Nature Publishing Group
Nature Portfolio
Subjects:
639/166
639/166/4073
639/166/987
AGC
AHP
Algorithms
Decision making
Fuzzy AHP
Herding
Hierarchies
Humanities and Social Sciences
Jaya optimization algorithm
multidisciplinary
Objective function
Optimization algorithms
Optimization techniques
PID controller
Power system
Science
Science (multidisciplinary)
Statistical analysis
Power system
Jaya optimization algorithm
PID controller
AHP
Fuzzy AHP
AGC
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:Decision makers consistently face the challenge of simultaneously assessing numerous attributes, determining their respective importance, and selecting an appropriate method for calculating their weights. This article addresses the problem of automatic generation control (AGC) in a two area power system (2-APS) by proposing fuzzy analytic hierarchy process (FAHP), an multi-attribute decision-making (MADM) technique, to determine weights for sub-objective functions. The integral-time-absolute-errors (ITAE) of tie-line power fluctuation, frequency deviations and area control errors, are defined as the sub-objectives. Each of these is given a weight by the FAHP method, which then combines them into an single final objective function. This objective function is then used to design a PID controller. To improve the optimization of the objective function, the Jaya optimization algorithm (JOA) is used in conjunction with other optimization techniques such as sine cosine algorithm (SCA), Luus–Jaakola algorithm (LJA), Nelder–Mead simplex algorithm (NMSA), symbiotic organism search algorithm (SOSA) and elephant herding optimization algorithm (EHOA). Six distinct experimental cases are conducted to evaluate the controller’s performance under various load conditions, with data plotted to show responses corresponding to fluctuations in frequency and tie-line exchange. Furthermore, statistical analysis is performed to gain a better understanding of the effectiveness of the JOA-based PID controller. For non-parametric evaluation, Friedman rank test is also used to validate the performance of the proposed JOA-based controller.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-61524-2