Grey wolf optimizer-assisted R-method-based weighted minimization for automatic generation control in two-area interconnected power system

Krishna, P. J.; Meena, V. P.; Patnana, Naresh; Mathur, Abhishek; Singh, V. P.

doi:10.1007/s40435-022-01070-9

Cited by 5 publications

(3 citation statements)

References 47 publications

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“…MADM techniques like rank sum weight, AHP and rank exponent are implemented for AGC problem of 2-APS in the literature 16 – 19 . AGC ensures maintaining the overall power balance of the system that is dependent on critical factors such as frequency deviation, area control errors, and tie-line power deviation 20 , 21 .…”

Section: Introductionmentioning

confidence: 99%

Fuzzy analytic hierarchy process based generation management for interconnected power system

Varshney,

Waghmare,

Singh

et al. 2024

Sci Rep

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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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Section: Introductionmentioning

confidence: 99%

Fuzzy analytic hierarchy process based generation management for interconnected power system

Varshney,

Waghmare,

Singh

et al. 2024

Sci Rep

Self Cite

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“…Additionally, the jellyfish search optimizer (JSO) [10] simulates the pulsating motion of jellyfish. Moving beyond, the whale optimization algorithm (WOA) [16] draws inspiration from the social behavior of humpback whales, and the grey wolf optimization algorithm (GWO) [17] mimics the hunting techniques of gray wolves. Further diversifying the optimization approaches are the elephant herding optimization (EHO) [18], emulating the herding behavior of elephants, and the jaya algorithm (JA) [19], driven by the concept of communal optimization.…”

Section: Introductionmentioning

confidence: 99%

Automatic Generation Control of a Hybrid PV-Reheat Thermal Power System Using RIME Algorithm

Ekinci,

Can,

Ayas

et al. 2024

IEEE Access

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This study focuses on the automatic generation control (AGC) system, which is crucial for maintaining balance between power generation and demand in power systems. The implementation of the AGC system needs to be more precise due to the increasing uncertainty surrounding renewable energy sources (RESs) and changes in demand. The objective of this study is to investigate the AGC functions in a two-area hybrid power system that combines a PV system with a reheat thermal system. To improve system performance, we utilize a proportional-integral (PI) controller. We utilized a recently developed optimization method, RIME, for tuning controller parameters. This technique has not been studied before in AGC processes. Furthermore, the optimization procedure utilizes a modified version of the integral time-multiplied absolute error (ITAE) objective function. The study compares the performance of the RIME-tuned PI controller under various scenarios, including changes in thermal system load, load variations in both areas, and robustness considerations, with well-known techniques in the literature, such as black widow optimization algorithm (BWOA), salp swarm algorithm (SSA), shuffled frog leaping algorithm (SFLA), firefly algorithm (FA) and genetic algorithm (GA). Our comparative study demonstrates that the proposed controller outperforms state-of-the-art approaches in terms of overshoot values and damping durations for both system frequency and tie-line power changes. The study provides valuable information on the effectiveness of the RIME-tuned PI controller in controlling AGC processes in complex hybrid power systems.INDEX TERMS Automatic generation control, RIME algorithm, Hybrid PV-reheat thermal system, ITAE, frequency deviation.

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“…Several researchers have integrated MCDA techniques in their studies [14][15][16] to rank and determine appropriate weights for each sub-objective function within an overall objective function designed for automatic generation control (AGC) systems. In AGC, key factors essential for maintaining the overall power balance of the system include frequency deviation, area control errors, and tie-line power deviation 17 .…”

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confidence: 99%

Investigation of rank order centroid method for optimal generation control

Varshney,

Waghmare,

Singh

et al. 2024

Sci Rep

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Multi-criteria decision-making (MCDM) presents a significant challenge in decision-making processes, aiming to ascertain optimal choice by considering multiple criteria. This paper proposes rank order centroid (ROC) method, MCDM technique, to determine weights for sub-objective functions, specifically, addressing issue of automatic generation control (AGC) within two area interconnected power system (TAIPS). The sub-objective functions include integral time absolute errors (ITAE) for frequency deviations and control errors in both areas, along with ITAE of fluctuation in tie-line power. These are integrated into an overall objective function, with ROC method systematically assigning weights to each sub-objective. Subsequently, a PID controller is designed based on this objective function. To further optimize objective function, Jaya optimization algorithm (JOA) is implemented, alongside other optimization algorithms such as teacher–learner based optimization algorithm (TLBOA), Luus–Jaakola algorithm (LJA), Nelder–Mead simplex algorithm (NMSA), elephant herding optimization algorithm (EHOA), and differential evolution algorithm (DEA). Six distinct case analyses are conducted to evaluate controller’s performance under various load conditions, plotting data to illustrate responses to frequency and tie-line exchange fluctuations. Additionally, statistical analysis is performed to provide further insights into efficacy of JOA-based PID controller. Furthermore, to prove the efficacy of JOA-based proposed controller through non-parametric test, Friedman rank test is utilized.

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Grey wolf optimizer-assisted R-method-based weighted minimization for automatic generation control in two-area interconnected power system

Cited by 5 publications

References 47 publications

Fuzzy analytic hierarchy process based generation management for interconnected power system

Fuzzy analytic hierarchy process based generation management for interconnected power system

Automatic Generation Control of a Hybrid PV-Reheat Thermal Power System Using RIME Algorithm

Investigation of rank order centroid method for optimal generation control

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