Optimal allocation of multi-type distributed generators using backtracking search optimization algorithm

El‐Fergany, Attia A.

doi:10.1016/j.ijepes.2014.09.020

Cited by 323 publications

(119 citation statements)

References 33 publications

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“…BSA was used in [45] and [46] for circular and concentric circular antenna array designs, respectively. BSA was also employed for solving different optimization problems [47][48][49][50][51].…”

Section: Introductionmentioning

confidence: 99%

Pattern Nulling of Linear Antenna Arrays Using Backtracking Search Optimization Algorithm

Güney

Durmuş

2015

International Journal of Antennas and Propagation

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An evolutionary method based on backtracking search optimization algorithm (BSA) is proposed for linear antenna array pattern synthesis with prescribed nulls at interference directions. Pattern nulling is obtained by controlling only the amplitude, position, and phase of the antenna array elements. BSA is an innovative metaheuristic technique based on an iterative process. Various numerical examples of linear array patterns with the prescribed single, multiple, and wide nulls are given to illustrate the performance and flexibility of BSA. The results obtained by BSA are compared with the results of the following seventeen algorithms: particle swarm optimization (PSO), genetic algorithm (GA), modified touring ant colony algorithm (MTACO), quadratic programming method (QPM), bacterial foraging algorithm (BFA), bees algorithm (BA), clonal selection algorithm (CLONALG), plant growth simulation algorithm (PGSA), tabu search algorithm (TSA), memetic algorithm (MA), nondominated sorting GA-2 (NSGA-2), multiobjective differential evolution (MODE), decomposition with differential evolution (MOEA/D-DE), comprehensive learning PSO (CLPSO), harmony search algorithm (HSA), seeker optimization algorithm (SOA), and mean variance mapping optimization (MVMO). The simulation results show that the linear antenna array synthesis using BSA provides low side-lobe levels and deep null levels.

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Section: Introductionmentioning

confidence: 99%

Pattern Nulling of Linear Antenna Arrays Using Backtracking Search Optimization Algorithm

Güney

Durmuş

2015

International Journal of Antennas and Propagation

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“…Then the algorithms of PSOGSA and MFO provide the optimal RDGs sizing and placement from the chosen buses. To study the performance and efficiency of the proposed PSOGSA and MFO on 33-bus and 69-bus IEEE distribution network, the operation of RDGs is chosen at two different power factors unity and 0.866 that are supposed as two types of RDGs according to [17,21]. Type-A at unity power factor is capable of injecting only active power (e.g., photovoltaic "PV" system) and Type-C is capable of injecting both active and reactive power (e.g., wind turbine "WT") that is considered in this section at optimal power factor 0.866.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Now, the loss sensitivity factor (LSF (ij) ) can be evaluated by considering the first derivative of P lineloss(ij) in Equation (23) with respect to the active power load P j,e f f as in the following expression [17,21]:…”

Section: Sensitivity Factors Analysismentioning

confidence: 99%

Impact of Optimum Allocation of Renewable Distributed Generations on Distribution Networks Based on Different Optimization Algorithms

et al. 2018

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Integration of Renewable Distributed Generations (RDGs) such as photovoltaic (PV) systems and wind turbines (WTs) in distribution networks can be considered a brilliant and efficient solution to the growing demand for energy. This article introduces new robust and effective techniques like hybrid Particle Swarm Optimization in addition to a Gravitational Search Algorithm (PSOGSA) and Moth-Flame Optimization (MFO) that are proposed to deduce the optimum location with convenient capacity of RDGs units for minimizing system power losses and operating cost while improving voltage profile and voltage stability. This paper describes two stages. First, the Loss Sensitivity Factors (LSFs) are employed to select the most candidate buses for RDGs location. In the second stage, the PSOGSA and MFO are implemented to deduce the optimal location and capacity of RDGs from the elected buses. The proposed schemes have been applied on 33-bus and 69-bus IEEE standard radial distribution systems.

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“…BSA structure including initialization, selection-I, mutation, crossover, and selection-II is explained in details below [30][31][32][33][34]:…”

Section: Design Of Bsa For Optimal Flc Speed Controllermentioning

confidence: 99%

Fuzzy logic speed controller optimization approach for induction motor drive using backtracking search algorithm

et al. 2016

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Optimal allocation of multi-type distributed generators using backtracking search optimization algorithm

Cited by 323 publications

References 33 publications

Pattern Nulling of Linear Antenna Arrays Using Backtracking Search Optimization Algorithm

Pattern Nulling of Linear Antenna Arrays Using Backtracking Search Optimization Algorithm

Impact of Optimum Allocation of Renewable Distributed Generations on Distribution Networks Based on Different Optimization Algorithms

Fuzzy logic speed controller optimization approach for induction motor drive using backtracking search algorithm

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