Whale optimization algorithm based MPPT control of a fuel cell system

Perçin, Hasan Bektaş; Caliskan, Abuzer

doi:10.1016/j.ijhydene.2023.03.180

Cited by 20 publications

(5 citation statements)

References 39 publications

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“…Statistical analysis is crucial for deriving meaningful insights from complex datasets. 66 In this study, various statistical tests were used to compare the performance of the five algorithms. The goal was to identify performance variances and ensure that any observed differences were statistically significant, making the conclusions robust to random data fluctuations.…”

Section: Discussionmentioning

confidence: 99%

Nature‐ınspired algorithms for optimizing fractional order PID controllers in time‐delayed systems

Güven,

Mengi

2024

Optim Control Appl Methods

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Time‐delayed systems frequently appear, especially in sectors such as fluid flow processes, chemical procedures, and the food industry. This paper addresses the optimization of parameters for a fractional order PID (FOPID) controller, which is used to control a time‐delayed system, using five distinct algorithms inspired by nature. These algorithms are NewBAT, Cuckoo search (CS), Firefly (FF), Gray Wolf Optimizer (GWO), and Whale optimization algorithm (WOA). The FOPID controller parameters, namely KP, KI, KD, λ and μ, have been optimized using these algorithms. During the optimization process, the integral of the time absolute error (ITAE) was considered as the primary measurement criterion. In addition to this value, the maximum overshoot, settling time, time to reach the maximum value, and error values were examined. Simulations conducted with the obtained parameters tested the system's resilience to disturbances introduced at the output, and the controller responses were also evaluated during these tests. The reactions of the determined parameters to different reference inputs were analyzed, and the results are presented in graphs and tables. The efficiency and reliability of the optimization algorithms were substantiated by comprehensive statistical analyses. These analyses play a critical role in algorithm selection and objective evaluation of the results. Simulation studies were conducted in the Matlab and Simulink environments. The FOMCON Toolbox was used for fractional‐order processes.

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Section: Discussionmentioning

confidence: 99%

Nature‐ınspired algorithms for optimizing fractional order PID controllers in time‐delayed systems

Güven,

Mengi

2024

Optim Control Appl Methods

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“…Mirjalili and Lewis are credited with conceptualizing the WOA in 2016, a novel metaheuristic algorithm drawing inspiration from the predatory behaviours characteristic of humpback whales [81]. Despite its relatively nascent introduction to the field, WOA has been proficiently used to address engineering conundrums across many domains, including but not limited to clustering, classification, image processing, networking, and scheduling [82].…”

Section: Methodsmentioning

confidence: 99%

“…Whale optimization algorithm whales [81]. Despite its relatively nascent introduction to the field, WOA has been proficiently used to address engineering conundrums across many domains, including but not limited to clustering, classification, image processing, networking, and scheduling [82].…”

Section: 55mentioning

confidence: 99%

Exploring solar energy systems: A comparative study of optimization algorithms, MPPTs, and controllers

Güven

2024

IET Control Theory & Appl

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This study elucidates the use of optimization algorithms to identify the controller parameters employed in adjusting the current and voltage values of loads powered by solar energy systems and battery groups. Parameters for these controllers were independently derived using a combination of ant colony optimization with Levy flight, hybrid firefly‐particle swarm optimization, hybrid gravitation search algorithm‐particle swarm optimization, alongside the implementation of Jaya and whale optimization algorithms. The results from each method were juxtaposed for thorough analysis. In addition, three distinct Maximum Power Point Tracker (MPPT) algorithms were employed in the system: perturbation and observation, open circuit voltage, and incremental conductance (IC). To assess the system’s adaptability to real‐world conditions, it was tested against varying temperatures and sunlight levels. Moreover, potential changes in the loads were considered by varying the load. The efficacy of the controllers was examined by altering both the environment and load. The effectiveness of the controllers was examined by referring to the integral of time‐weighted absolute error value. The system was simulated using MATLAB/Simulink software. This study demonstrates that the fractional‐order PID controller achieves the most effective results, the Jaya algorithm provides the best controller parameters, and the IC technique exhibits the highest performance in MPPT.

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“…If the concept of individual escape experience is introduced into the GOA, even if the best gazelle individual accidentally falls into the local optimal solution, other gazelle individuals can still escape from the local solution by relying on their individual escape experience. The combined method can be modeled using Equation (17), and the positions and velocities of particles can also be updated,…”

Section: Combination Of Pso and Goamentioning

confidence: 99%

“…Bishla Sandeep and team [16] employed the chimpanzee optimization algorithm for optimizing the scheduling of batteries in electric vehicles. Percin Hasan Bektas and Caliskan Abuzer [17] utilized the whale optimization algorithm to control fuel cell systems. Jagadish Kumar N. and Balasubramanian C. [18] implemented the widow optimization algorithm for cloud service resource scheduling, effectively reducing the cost of cloud services.…”

Section: Introductionmentioning

confidence: 99%

Multi-Strategy Improved Particle Swarm Optimization Algorithm and Gazelle Optimization Algorithm and Application

Qin,

Zeng,

Sun

et al. 2024

Electronics

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In addressing the challenges associated with low convergence accuracy and unstable optimization results in the original gazelle optimization algorithm (GOA), this paper proposes a novel approach incorporating chaos mapping termed multi-strategy particle swarm optimization with gazelle optimization algorithm (MPSOGOA). In the population initialization stage, segmented mapping is integrated to generate a uniformly distributed high-quality population which enhances diversity, and global perturbation of the population is added to improve the convergence speed in the early iteration and the convergence accuracy in the late iteration. By combining particle swarm optimization (PSO) and GOA, the algorithm leverages individual experiences of gazelles, which improves convergence accuracy and stability. Tested on 35 benchmark functions, MPSOGOA demonstrates superior performance in convergence accuracy and stability through Friedman tests and Wilcoxon signed-rank tests, surpassing other metaheuristic algorithms. Applied to engineering optimization problems, including constrained implementations, MPSOGOA exhibits excellent optimization performance.

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Whale optimization algorithm based MPPT control of a fuel cell system

Cited by 20 publications

References 39 publications

Nature‐ınspired algorithms for optimizing fractional order PID controllers in time‐delayed systems

Nature‐ınspired algorithms for optimizing fractional order PID controllers in time‐delayed systems

Exploring solar energy systems: A comparative study of optimization algorithms, MPPTs, and controllers

Multi-Strategy Improved Particle Swarm Optimization Algorithm and Gazelle Optimization Algorithm and Application

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