Single and multi-objectives based on an improved golden jackal optimization algorithm for simultaneous integration of multiple capacitors and multi-type DGs in distribution systems

Elseify, Mohamed A.; Hashim, Fatma A.; Hussien, Abdelazim G.; Kamel, Salah

doi:10.1016/j.apenergy.2023.122054

Cited by 17 publications

(6 citation statements)

References 64 publications

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“…Inspired by the foraging behaviour of golden jackals, GJO mimics the search and adaptability strategies of these animals in its quest for optimal solutions. It has shown promise in various application areas, including engineering, economics, and logistics, making it a valuable tool for tackling real-world optimization challenges [6]- [7].…”

Section: ░ 3 Resultsmentioning

confidence: 99%

Optimizing Capacitor Placement in Distribution Systems Under Variable Loading Conditions with Golden Jack Optimization (GJO)

Reddy,

Kiran,

Kumar

et al. 2024

IJEER

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In modern society, the demand for electricity is ever-growing, making the minimization of power losses in distribution systems paramount. One significant aspect contributing to these losses is the strategic placement of capacitors within the distribution network. Efficient capacitor placement not only reduces power losses but also enhances the overall performance and reliability of the system. In today's world, where electricity is indispensable, minimizing power losses in the distribution system holds significant importance. This research introduces the Golden Jack Optimization (GJO) algorithm as a novel approach to address the challenge of capacitor placement in distribution systems. GJO, inspired by the foraging behavior of jackals, exhibits unique characteristics such as adaptability and efficiency in finding optimal solutions this paper proposes an innovative algorithm specifically designed for this purpose. To validate the effectiveness of the algorithm, extensive testing and experimentation have been conducted on the IEEE 69-bus system. This study aims to provide a practical and efficient solution to the challenge of power loss reduction within distribution systems. By harnessing the power of GJO and the insights gained from testing on the IEEE 69-bus system, we contribute to the ongoing efforts to enhance the efficiency and sustainability of electricity distribution networks. The results are compared with MPSO, TSM, GA-Fuzzy algorithms and proposed algorithm shows superior performance.

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Section: ░ 3 Resultsmentioning

confidence: 99%

Optimizing Capacitor Placement in Distribution Systems Under Variable Loading Conditions with Golden Jack Optimization (GJO)

Reddy,

Kiran,

Kumar

et al. 2024

IJEER

View full text Add to dashboard Cite

show abstract

“…In the future, Authors can use many optimization algorithms and embedded them in the network for better accuracy. These algorithms can be any algorithm such as Snake Optimizer (SO) 45 , Fick’s Law Algorithm (FLA) 46 , Jellyfish Search (JS) 47 , Dandelion Optimizer (DO) 48 , Aquila Optimizer 49 – 51 , Atom Search Optimization (ASO) 52 , Water Cycle Algorithm (WCA) 53 , Bald Eagle Search (BES) 54 , African Vultures Optimization Algorithm (AVOA) 55 , Archimedes Optimization Algorithm (AOA) 56 , Beluga Whale Optimization (BWO) 57 , Hunter Prey Optimization (HPO) 58 , INFO 59 , Supply Demand Optimizer 60 , 61 , Reptile Search Algorithm (RSA) 62 , Golden Jackle Optimization (GJO) 63 , and more.…”

Section: Discussionmentioning

confidence: 99%

A CNN-based model to count the leaves of rosette plants (LC-Net)

Deb,

Dhal,

Das

et al. 2024

Sci Rep

Self Cite

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Plant image analysis is a significant tool for plant phenotyping. Image analysis has been used to assess plant trails, forecast plant growth, and offer geographical information about images. The area segmentation and counting of the leaf is a major component of plant phenotyping, which can be used to measure the growth of the plant. Therefore, this paper developed a convolutional neural network-based leaf counting model called LC-Net. The original plant image and segmented leaf parts are fed as input because the segmented leaf part provides additional information to the proposed LC-Net. The well-known SegNet model has been utilised to obtain segmented leaf parts because it outperforms four other popular Convolutional Neural Network (CNN) models, namely DeepLab V3+, Fast FCN with Pyramid Scene Parsing (PSP), U-Net, and Refine Net. The proposed LC-Net is compared to the other recent CNN-based leaf counting models over the combined Computer Vision Problems in Plant Phenotyping (CVPPP) and KOMATSUNA datasets. The subjective and numerical evaluations of the experimental results demonstrate the superiority of the LC-Net to other tested models.

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“…To assess the efficiency of the mPDO, it is compared with the original PDO, bat algorithm (BA) 59 , PSO 59 , improved golden jackal optimization (IGJO) 60 , novel heuristic approach (NHA), novel stochastic fractal search algorithm (SFSA), quasi-oppositional-chaotic symbiotic organisms search algorithm (QOCSOS) 29 , and chaotic quasi-oppositional barnacles mating optimizer (CQOBMO-7) 30 . It is observed from Table 3 that the mPDO consistently beats the basic PDO and the state-of-the-art optimizers, exhibiting the lowest power loss when incorporating single and multiple PVs into the test system.…”

Section: Numerical Simulation and Discussionmentioning

confidence: 99%

Boosting prairie dog optimizer for optimal planning of multiple wind turbine and photovoltaic distributed generators in distribution networks considering different dynamic load models

Elseify,

Hashim,

Hussien

et al. 2024

Sci Rep

Self Cite

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Deploying distributed generators (DGs) supplied by renewable energy resources poses a significant challenge for efficient power grid operation. The proper sizing and placement of DGs, specifically photovoltaics (PVs) and wind turbines (WTs), remain crucial due to the uncertain characteristics of renewable energy. To overcome these challenges, this study explores an enhanced version of a meta-heuristic technique called the prairie dog optimizer (PDO). The modified prairie dogs optimizer (mPDO) incorporates a novel exploration phase inspired by the slime mold algorithm (SMA) food approach. The mPDO algorithm is proposed to analyze the substantial effects of different dynamic load characteristics on the performance of the distribution networks and the designing of the PV-based and WT-based DGs. The optimization problem incorporates various operational constraints to mitigate energy loss in the distribution networks. Further, the study addresses uncertainties related to the random characteristics of PV and WT power outputs by employing appropriate probability distributions. The mPDO algorithm is evaluated using cec2020 benchmark suit test functions and rigorous statistical analysis to mathematically measure its success rate and efficacy while considering different type of optimization problems. The developed mPDO algorithm is applied to incorporate both PV and WT units, individually and simultaneously, into the IEEE 69-bus distribution network. This is achieved considering residential, commercial, industrial, and mixed time-varying voltage-dependent load demands. The efficacy of the modified algorithm is demonstrated using the standard benchmark functions, and a comparative analysis is conducted with the original PDO and other well-known algorithms, utilizing various statistical metrics. The numerical findings emphasize the significant influence of load type and time-varying generation in DG planning. Moreover, the mPDO algorithm beats the alternatives and improves distributed generators' technical advantages across all examined scenarios.

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Single and multi-objectives based on an improved golden jackal optimization algorithm for simultaneous integration of multiple capacitors and multi-type DGs in distribution systems

Cited by 17 publications

References 64 publications

Optimizing Capacitor Placement in Distribution Systems Under Variable Loading Conditions with Golden Jack Optimization (GJO)

Optimizing Capacitor Placement in Distribution Systems Under Variable Loading Conditions with Golden Jack Optimization (GJO)

A CNN-based model to count the leaves of rosette plants (LC-Net)

Boosting prairie dog optimizer for optimal planning of multiple wind turbine and photovoltaic distributed generators in distribution networks considering different dynamic load models

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