Jellyfish Search Optimization Algorithm for MPP Tracking of PV System

Alam, Afroz; Verma, Preeti; Tariq, Mohd; Sarwar, Adil; Alamri, Basem; Zahra, Noore; Urooj, Shabana

doi:10.3390/su132111736

Cited by 20 publications

(26 citation statements)

References 29 publications

(34 reference statements)

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“…Alam et al 67 used JSO to track the global maximum power point (GMPP) of the solar photovoltaic (PV) module under partial shading conditions. Their results suggested that the JSO method has good tracking speed and accuracy.…”

Section: Applicationsmentioning

confidence: 99%

Recent advances in use of bio-inspired jellyfish search algorithm for solving optimization problems

Chou

Molla

2022

Sci Rep

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The complexity of engineering optimization problems is increasing. Classical gradient-based optimization algorithms are a mathematical means of solving complex problems whose ability to do so is limited. Metaheuristics have become more popular than exact methods for solving optimization problems because of their simplicity and the robustness of the results that they yield. Recently, population-based bio-inspired algorithms have been demonstrated to perform favorably in solving a wide range of optimization problems. The jellyfish search optimizer (JSO) is one such bio-inspired metaheuristic algorithm, which is based on the food-finding behavior of jellyfish in the ocean. According to the literature, JSO outperforms many well-known meta-heuristics in a wide range of benchmark functions and real-world applications. JSO can also be used in conjunction with other artificial intelligence-related techniques. The success of JSO in solving diverse optimization problems motivates the present comprehensive discussion of the latest findings related to JSO. This paper reviews various issues associated with JSO, such as its inspiration, variants, and applications, and will provide the latest developments and research findings concerning JSO. The systematic review contributes to the development of modified versions and the hybridization of JSO to improve upon the original JSO and present variants, and will help researchers to develop superior metaheuristic optimization algorithms with recommendations of add-on intelligent agents.

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Section: Applicationsmentioning

confidence: 99%

Recent advances in use of bio-inspired jellyfish search algorithm for solving optimization problems

Chou

Molla

2022

Sci Rep

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“…The first population of jellyfish swarm is formed according to Equation (33), then Jellyfish move around their location and this update will be formed according to Equation (34) [34]: ( ) ( )…”

Section: Jellyfish Optimization Techniquementioning

confidence: 99%

Harmonic Mitigation by Optimal Allocation of Tuned Passive Filter in Distribution System

El‐Ela¹,

Allam²,

Mubarak³

et al. 2022

EPE

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The purpose of this work is to determine the appropriate allocation of a single tuned passive filter "STPF" in a real distribution network at Tala city to prevent harmonic distortion caused by nonlinear loads. This design will cover the ideal filter parameter as well as the best filter position. The filter design is based on single and multi-objective functions that optimize the cost of the proposed filters, real power losses, total harmonic distortion "THD," and individual harmonic distortion "IHD". Upper and lower limitations for filter parameters, quality factor, voltage and harmonic distortion limits are determined using inequality constraints. This will be done under the equality constraint of power balance. The challenge of optimization is solved using two modern algorithms, jellyfish optimization technique "JFOT" and arithmetic optimization algorithm "AOA", in which the placements of the filters and their parameters are selected. IEEE 33-bus radial system and an actual system as a part of the Egyptian network at Tala city are used for demonstrating the results obtained by the proposed techniques. The results reveal that the system's harmonic distortion of current and voltage is reduced less than the limitations of the IEEE 519 standards.

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“…𝑋 𝑖 (𝑡 + 1) = 𝑋 𝑖 (𝑡) + 𝑟1(0,1). 𝑑𝑟𝑖𝑓𝑡 ⃗⃗⃗⃗⃗⃗⃗⃗⃗⃗ (13) Also, 𝑋 𝑖 (𝑡 + 1) = 𝑋 𝑖 (𝑡) + 𝑟1(0,1). (𝑥 * − 𝛽𝜎 𝑟1(0,1)) ( 14)…”

Section: Ocean Currentmentioning

confidence: 99%

“…Until this moment, the JFO has not been evaluated with servomechanism parameter identification. Nevertheless, it was used for maximum power point tracking and parameter identification of solar photovoltaic systems in [13] and [14] respectively.…”

Section: Introductionmentioning

confidence: 99%

Systems identification of servomechanism parameters using jellyfish, particle swarm and constraint optimization

Nyong-Bassey¹,

Epemu²

2022

Nig. J. Tech.

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In this paper, DC servomechanism parameters were identified offline using Jellyfish, particle swarm and constraint optimization techniques in a MATLAB simulation environment with experimental data. Specifically, the unknown parameters of the servomechanism were identified using a two-step approach. Initially, the first-order transfer function of the servomechanism which is characterized by a DC gain and time constant was determined analytically using the experimental open-loop speed step response of the servo motor. Next, by iterative minimization of a fitness score derived from the root mean squared error between the experimental and simulated position response of the servomechanism of an equivalent state-space model structure, the servomechanism parameters were identified. The simulated angular position step response of the servomechanism with the particle swarm, Jellyfish and constraint optimization algorithm, showed excellent agreement with the experimental data in descending order and was consistent with the fitness score of 1.9035, 0.0083, and 0.00706 respectively.

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Jellyfish Search Optimization Algorithm for MPP Tracking of PV System

Cited by 20 publications

References 29 publications

Recent advances in use of bio-inspired jellyfish search algorithm for solving optimization problems

Recent advances in use of bio-inspired jellyfish search algorithm for solving optimization problems

Harmonic Mitigation by Optimal Allocation of Tuned Passive Filter in Distribution System

Systems identification of servomechanism parameters using jellyfish, particle swarm and constraint optimization

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