A novel parallel hurricane optimization algorithm for secure emission/economic load dispatch solution

Rizk‐Allah, Rizk M.; El‐Sehiemy, Ragab A.; Wang, Gai-Ge

doi:10.1016/j.asoc.2017.12.002

Cited by 130 publications

(62 citation statements)

References 20 publications

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“…The proposed MIW-PSO method is employed to simulate the case studies on an Intel® Core™ i5- The values of FC, emission coefficients, transmission loss coefficients, generation limits of unites and price of penalty factor (d = 3 ($/kg)) are given in [2,9,11,20,23,24].…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Numerous studies with respect to ED with FC and PE have been improved using enhanced intelligent algorithms. These improved methods such as Pareto genetic algorithm (NPGA) [7,8], modified group search optimization (MGSO) algorithm [9], differential harmony search (DHS) [10], parallel hurricane optimization algorithm (PHOA) [11], real coded chemical reaction optimization (RCCRO) [12], biogeography/based optimization (BBO) [13] and seeker optimisation algorithm (SOA) [14] have been published. However, these methods are affected by performance in large-scale systems.…”

Section: Introductionmentioning

confidence: 99%

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A New Enhancement on PSO Algorithm for Combined Economic-Emission Load Dispatch Issues

Al-Rubayi¹,

Kdair²,

Flaih³

2020

IJIES

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Economic dispatch issues in power system aim to try getting an optimal plan for the power generators to minimize the fuel cost (FC) in parallel with satisfying system constraints. This paper proposes a new enhancement based on particle swarm optimization (PSO) algorithm called multiple inertia weight PSO (MIW-PSO) to solve the combined economic and emission load dispatch (CEELD) issues in the modern electrical power systems. Two electrical test systems are investigated in this study to validate the competence of the proposed algorithm. The obtained results for CEELD case using MIW-PSO compared with MOCPSO indicate a promising performance in terms of minimizing FC and pollutant emission (PE) are reduced 84.96 $/h and 12.01 kg/h for the first test system. As well as, for the second test system, compared with NSGA-RL are reduced 0.241 $/h and 3.15 kg/h. Moreover, the proposed algorithm has more accuracy, better convergence time, and higher quality solutions for the minimum CEELD compared with other methods.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Enhancement on PSO Algorithm for Combined Economic-Emission Load Dispatch Issues

Al-Rubayi¹,

Kdair²,

Flaih³

2020

IJIES

View full text Add to dashboard Cite

show abstract

“…We also plan to apply other deep learning models, such as the state-of-art Mask RCNN [36], to identify many types of rock in the same image. In addition, we will study various mature optimization algorithms [37][38][39] to improve computing efficiency. These efforts should greatly improve large-scale geological mapping and contribute to the automation of mapping.…”

Section: Discussionmentioning

confidence: 99%

Rock Classification from Field Image Patches Analyzed Using a Deep Convolutional Neural Network

et al. 2019

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The automatic identification of rock type in the field would aid geological surveying, education, and automatic mapping. Deep learning is receiving significant research attention for pattern recognition and machine learning. Its application here has effectively identified rock types from images captured in the field. This paper proposes an accurate approach for identifying rock types in the field based on image analysis using deep convolutional neural networks. The proposed approach can identify six common rock types with an overall classification accuracy of 97.96%, thus outperforming other established deep-learning models and a linear model. The results show that the proposed approach based on deep learning represents an improvement in intelligent rock-type identification and solves several difficulties facing the automated identification of rock types in the field.

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“…Harmonic and voltage compensation control methods for the microgrids were discussed. The economic emission load dispatch problem in present power system was solved by proposing a parallel hurricane optimization algorithm [15]. By protecting the constraints of the system within the range, generating units operating conditions and the transmission line operating above congestion limits were taken into consideration as well.…”

Section: Literature Survey On Economic Dispatch Of Hybrid Systems / Mmentioning

confidence: 99%

A review to economic dispatch of hybrid microgrids

Vankadara¹,

Raglend²

2019

Serb J Electr Eng

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Hybrid Microgrids are being widespread for power generation in remote locations. The improvement in non-conventional energy sources and rise in the price of existing electrical energy production sources led to the advancement of hybrid renewable sources. Economic characteristics of these technologies are adequately capable to include in emerging power generation. Advances and research in solar, wind and other non-conventional energy sources are required to continue for improving their performance, creating techniques for exactly forecasting their outputs and reliably integrating with other conventional sources used for generation. Economic dispatch is being taken into consideration as the optimal output of the electricity generation facilities. These are to be met as per the load at the lowest possible cost. The problem on Economic Dispatch (ED) and different technical constraints considering power balance in the network satisfying the objective are to be formulated. This paper presents a brief review on economic dispatch of hybrid microgrids.

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A novel parallel hurricane optimization algorithm for secure emission/economic load dispatch solution

Cited by 130 publications

References 20 publications

A New Enhancement on PSO Algorithm for Combined Economic-Emission Load Dispatch Issues

A New Enhancement on PSO Algorithm for Combined Economic-Emission Load Dispatch Issues

Rock Classification from Field Image Patches Analyzed Using a Deep Convolutional Neural Network

A review to economic dispatch of hybrid microgrids

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