Using the gray wolf optimizer for solving optimal reactive power dispatch problem

Sulaiman, Mohd Herwan; Mustaffa, Zuriani; Mohamed, Mohd Rusllim; Alfandi, Omar

doi:10.1016/j.asoc.2015.03.041

Cited by 340 publications

(151 citation statements)

References 23 publications

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“…The generator voltage magnitudes are continuous nature whereas the transformer tap settings, and reactive power injected from capacitor banks are discrete variables. Thus, the RPD problem is considered as a highly nonlinear, nonconvex optimization problem, consisting of both continuous and discrete control variables [1]- [2]. It has been solved effectively by conventional optimization techniques [3] such as interior point methods [4], [5], linear programming (LP) [6], nonlinear programming [7], quadratic programming [8], mixed integer nonlinear programming [9], and sequential quadratic programming [10].…”

Section: Q Cmentioning

confidence: 99%

Integrated Strategies of Backtracking Search Optimizer for Solving Reactive Power Dispatch Problem

Shaheen

El‐Sehiemy

Farrag

2018

IEEE Systems Journal

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This paper proposes backtracking search optimizer (BSO) for solving the reactive power dispatch (RPD) problem. The RPD problem is highly nonlinear, nonconvex optimization problem and is consisting of both continuous and discrete control variables. It aims to find the optimal settings of the generator voltages, tap positions of tap changing transformers, and the amount of reactive compensation that able to optimize the transmission power losses. BSO has simple structure and single control parameter. It has two new tuned mutation and crossover operators that control the amplitude of the search-direction matrix and search space boundaries. In this paper, five diversified generation strategies of mutation factor of BSO to solve the RPD. The proposed BSO strategies are applied to the IEEE standard 14-, 30-, 57-bus test systems, and a real power system at West Delta region system as a part of the unified Egyptian network. Simulation results show that the BSO strategy, which almost generates mutation factor concentrated on the mean value of the normal distribution (BSO 4) assigns multidirections through the search space and supports the diversity of populations. To show the effectiveness and superiority of proposed BSO, the obtained results are compared with different algorithms reported in the literature.Index Terms-Backtracking search optimizer (BSO), evolutionary algorithms, power losses, reactive power dispatch (RPD), statistical indices, tuned mutation strategies.

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Section: Q Cmentioning

confidence: 99%

Integrated Strategies of Backtracking Search Optimizer for Solving Reactive Power Dispatch Problem

Shaheen

El‐Sehiemy

Farrag

2018

IEEE Systems Journal

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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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“…The GWO is utilized to solve many optimization problems in different fields and successfully provides highly competitive results [49]- [52].…”

Section: Grey Wolf Search Algorithmmentioning

confidence: 99%

Estimating the Parameters of Software Reliability Growth Models Using the Grey Wolf Optimization Algorithm

Sheta¹,

Abdel-Raouf²

2016

ijacsa

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Abstract-In this age of technology, building quality software is essential to competing in the business market. One of the major principles required for any quality and business software product for value fulfillment is reliability. Estimating software reliability early during the software development life cycle saves time and money as it prevents spending larger sums fixing a defective software product after deployment. The Software Reliability Growth Model (SRGM) can be used to predict the number of failures that may be encountered during the software testing process. In this paper we explore the advantages of the Grey Wolf Optimization (GWO) algorithm in estimating the SRGM's parameters with the objective of minimizing the difference between the estimated and the actual number of failures of the software system. We evaluated three different software reliability growth models: the Exponential Model (EXPM), the Power Model (POWM) and the Delayed S-Shaped Model (DSSM). In addition, we used three different datasets to conduct an experimental study in order to show the effectiveness of our approach.

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Using the gray wolf optimizer for solving optimal reactive power dispatch problem

Cited by 340 publications

References 23 publications

Integrated Strategies of Backtracking Search Optimizer for Solving Reactive Power Dispatch Problem

Integrated Strategies of Backtracking Search Optimizer for Solving Reactive Power Dispatch Problem

Power management optimization of hybrid power systems in electric ferries

Estimating the Parameters of Software Reliability Growth Models Using the Grey Wolf Optimization Algorithm

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