Grey Wolf Optimization-Based Optimum Energy-Management and Battery-Sizing Method for Grid-Connected Microgrids

Nimma, Kutaiba Sabah; Al-Falahi, Monaaf D.A.; Nguyen, Hung D.; Jayasinghe, Shantha Gamini; Mahmoud, Thair S.; Negnevitsky, Michael

doi:10.3390/en11040847

Cited by 72 publications

(41 citation statements)

References 43 publications

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“…Grey wolf optimizer, a newly swarm intelligence algorithm introduced by Mirjalili et al [51], is a powerful meta-heuristic algorithm, which has the ability to compete with other algorithms including PSO, GA, DE and many other algorithms in terms of solution accuracy, minimum computational effort, and aversion of premature convergence [69,70]. Because of these advantages, it has been gained a very big research interest by tremendous audiences from several domains and successfully applied in the fields of global optimization [71], control engineering [72,73], feature selection [74], scheduling problems [75,76] in recent years.…”

Section: Grey Wolf Optimizermentioning

confidence: 99%

A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM

et al. 2019

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Dissolved gas analysis (DGA) is a widely used method for transformer internal fault diagnosis. However, the traditional DGA technology, including Key Gas method, Dornenburg ratio method, Rogers ratio method, International Electrotechnical Commission (IEC) three-ratio method, and Duval triangle method, etc., suffers from shortcomings such as coding deficiencies, excessive coding boundaries and critical value criterion defects, which affect the reliability of fault analysis. Grey wolf optimizer (GWO) is a novel swarm intelligence optimization algorithm proposed in 2014 and it is easy for the original GWO to fall into the local optimum. This paper presents a new meta-heuristic method by hybridizing GWO with differential evolution (DE) to avoid the local optimum, improve the diversity of the population and meanwhile make an appropriate compromise between exploration and exploitation. A fault diagnosis model of hybrid grey wolf optimized least square support vector machine (HGWO-LSSVM) is proposed and applied to transformer fault diagnosis with the optimal hybrid DGA feature set selected as the input of the model. The kernel principal component analysis (KPCA) is used for feature extraction, which can decrease the training time of the model. The proposed method shows high accuracy of fault diagnosis by comparing with traditional DGA methods, least square support vector machine (LSSVM), GWO-LSSVM, particle swarm optimization (PSO)-LSSVM and genetic algorithm (GA)-LSSVM. It also shows good fitness and fast convergence rate. Accuracies calculated in this paper, however, are significantly affected by the misidentifications of faults that have been made in the DGA data collected from the literature.

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Section: Grey Wolf Optimizermentioning

confidence: 99%

A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM

et al. 2019

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“…Grey wolf optimizer is one of the metaheuristic algorithms widely applied in energy domain due to the decreased amount of entities. Most of these researches apply the Grey wolf optimizer algorithm directly and solve the issues of smart energy networks . Our proposed modified Grey Wolf Optimizer in MapReduce environment efficiently maintains the balance between exploration and exploitation in the progressing population.…”

Section: Discussionmentioning

confidence: 99%

“…Most of these researches apply the Grey wolf optimizer algorithm directly and solve the issues of smart energy networks. [50][51][52][53] Our proposed modified Grey Wolf Optimizer in MapReduce environment efficiently maintains the balance between exploration and exploitation in the progressing population. In future, we plan to apply our MapReduce-based Modified Grey Wolf Optimizer for solving the problem of creating and ensuring the successful functioning of smart energy networks using a complex of renewable energy sources of various nature.…”

Section: Discussionmentioning

confidence: 99%

A MapReduce‐based modified Grey Wolf optimizer for QoS‐aware big service composition

Bhaskar

Jatoth

Gangadharan

et al. 2019

Concurrency and Computation

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Summary Big services are the collection of interrelated web services across virtual and physical domains, integrating service oriented computing and big data. The rapid growth of Big services that offer similar functionality with varying QoS attributes makes the process of selection and composition of these big services as highly challenging and complex. In this paper, we develop an efficient QoS‐aware Big service composition approach by applying a MapReduce based Modified Grey Wolf Optimizer (MR‐MGWO) that explores more search space, especially in a multidimensional environment. Our approach ensures an optimal balance of exploration and exploitation that enhances the convergence rate and minimizes the computational time. The empirical analysis illustrates that the performance of MR‐MGWO is superior to other similar approaches for solving Big service composition.

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“…Several studies have implemented GWO and compared its results with other algorithms. These studies found that GWO provides competitive optimization results compared to other swarm and evolutionary algorithms such as particle swarm optimization (PSO) [56][57][58], differential evolution (DE) [56], gravitational search algorithm (GSA) [56,57], genetic algorithm (GA) [58] and ant colony optimization (ACO) [59].…”

Section: B Grey Wolf Optimization (Gwo)mentioning

confidence: 99%

Power management optimization of hybrid power systems in electric ferries

Al-Falahi

Nimma

Jayasinghe

et al. 2018

Energy Conversion and Management

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General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.? Users may download and print one copy of any publication from the public portal for the purpose of private study or research. ? You may not further distribute the material or use it for any profit-making activity or commercial gain ? You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us at vbn@aub.aau.dk providing details, and we will remove access to the work immediately and investigate your claim. Abstract-The integration of more-electric technologies, such as energy storage systems (ESSs) and electric propulsion, has gained attention in recent years as a promising approach to reduce fuel consumption and emissions in the maritime industry. In this context, hybrid power systems (HPSs) with direct current (DC) distribution are currently gaining a commendable interest in research and industrial applications. This paper examines the impact of using HPS with DC distribution and a battery energy storage system (BESS) over a conventional AC power system for short haul roll-on/roll-off (RORO) ferries. An electric ferry with a HPS is modeled in this study and the power management system is simulated using the Matlab/Simulink software. The result is validated using measured load profile of a ferry. The performance of the DC HPS is compared with the conventional AC system based on fuel consumption and emission reductions. An approach to estimate the fuel consumption of the diesel engine through calculation of specific fuel oil consumption (SFOC) is also presented. This study uses two optimization techniques: a classical power management method namely Rule-Based control (RB) and a meta-heuristic power management method known as Grey Wolf Optimization (GWO) to optimally manage the power sharing of the proposed HPS. Fuel consumption and emission indicators are also used to assess the performance of the two power management methods. The simulation results show that the HPS provides a 2.91 % and 7.48 % fuel consumption reduction using RB method and GWO method respectively. It is apparent from the result that the HPS has more fuel savings while running the diesel generator sets (DGs) at higher operational efficiency. It is interesting that the proposed HPS using both power management methods provided a 100 % emission reduction at berth. Finally, it was found that using a meta-heuristic optimization algorithm provides better fuel and emission reductions than a classical method.Keywords-Battery, DC power system, electric ferry, energy storage system, hybrid power system, power management.

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Grey Wolf Optimization-Based Optimum Energy-Management and Battery-Sizing Method for Grid-Connected Microgrids

Cited by 72 publications

References 43 publications

A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM

A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM

A MapReduce‐based modified Grey Wolf optimizer for QoS‐aware big service composition

Power management optimization of hybrid power systems in electric ferries

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