Optimal setting of STATCOM based on voltage stability improvement and power loss minimization using Moth-Flame algorithm

Ebeed, Mohamed; Kamel, Salah; Youssef, Heba

doi:10.1109/mepcon.2016.7836988

Cited by 19 publications

(7 citation statements)

References 12 publications

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“…Moths can travel at night by flying in accordance with the position of the moon by using an analogous angle, a movement termed as transverse orientation. The method helps in travelling in straight and long path [39]. Transverse orientation is effective, yet it is commonly observed that moths move around man-made lights in a spiral manner.…”

Section: A Moth Flame Optimization (Mfo) Algorithmmentioning

confidence: 99%

An Evolutionary Algorithm-Based Vehicular Clustering Technique for VANETs

et al. 2022

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Many precious lives are lost world-wide due to road accidents. To counter this issue, the ultimate solution is Vehicular ad hoc networks (VANETs). Due to the high mobility of vehicles and varying network topology in VANETs, efficient communication among the vehicular nodes is of extreme importance. To enhance communication proficiency in VANETs, clustering is a renowned procedure. Therefore, a clustering algorithm based on Moth-Flame Optimization (MFO), titled AMONET, is projected which can effectively work in high mobility nodes scenario of VANETs. AMONET is based on bio inspired procedure and creates optimized clusters for reliable and efficient communication. Our algorithm is assessed experimentally with well-known procedures such as Ant Colony Optimization (ACO), Comprehensive Learning Particle Swarm Optimization (CLPSO) and Multi Objective Particle Swarm Optimization (MOPSO). Several experiments are conducted to measure the comparative efficiency of these procedures. The average cumulative results for all the grid sizes are 27.1% for AMONET while 36.3% for ACO, 54.9% for CLPSO and 58.7% for MOPSO. The results signify that AMONET produces near ideal results, covers the entire network and generates least number of clusters. It is an efficient technique to accomplish vehicular clustering with the purpose of improving the network's overall performance and consequently reducing the routing cost of the vehicular network.

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Section: A Moth Flame Optimization (Mfo) Algorithmmentioning

confidence: 99%

An Evolutionary Algorithm-Based Vehicular Clustering Technique for VANETs

et al. 2022

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“…They provide a robust tool for solving multi-modal, discrete, stochastical-based and multi-objective non-linear constrained power system problems with a very high degree of computational efficiency. Examples include but not limited to genetic algorithm [18], particle swarm optimization [19], brainstorm optimization [20], moth-flame algorithm [21], adaptive cuckoo search algorithm [22], strength pareto multi-objective evolutionary algorithm [23], differential evolution [24], biogeography based optimization [25], gravitational search algorithm [26], fuzzy logic [27], artificial neural network [28], harmony search [29], Grey wolf optimization [30], hybrid fruit fly firefly optimization [31], bee colony algorithm [32], opposition krill herd algorithm [33], flower pollination algorithm [34] and firefly algorithm [35].…”

Section: Introductionmentioning

confidence: 99%

Optimal Placement of FACTS Devices Using Filter Feeding Allogenic Engineering Algorithm

Mutegi

Nnamdi

2022

Technol Econ Smart Grids Sustain Energy

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Flexible Alternating Current Transmission System (FACTS) are a useful tool for power system stability improvement and control. This research developed a new algorithm for optimal placement of FACTS devices named the Filter Feeding Allogenic Engineering (FFAE) algorithm inspired by the filter feeding and the motile behaviour of Allogenic Engineers.Kenya's 87-Bus, 25-Generator, 132 kV and 220 kV transmission network was used as the test case in Matlab's environment. The said algorithm was used to solve the twin objectives on minimization of active power loss and voltage deviations. One metaheuristic technique-the whale optimization algorithm (WOA)-and one sensitivity based method-the fast voltage stability index (FVSI)-were used to test the accuracy of the proposed method. The computation time was 0.283 s and 0.308 s for FFAE and the whale optimization algorithm respectively mainly because of the use of Kalman filtration to achieve targeted iterations and using a more robust algorithm. The FFAE algorithm summed the total active power losses to 99.911% whereas the whale optimization algorithm gave a total of 99.770% indicating superior computational ability of the new method.

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“…Many contributions were introduced for detecting the optimal size and location of certain FACTS devices to achieve certain objective functions. These contributions were achieved by many optimization techniques, like particle swarm optimization (PSO) and its modifications [2][3][4][5], biography-based optimization (BBO) [5], moth flame optimization (MFO) [6], gray wolf optimization (GWO) [7], improved harmony search (IHS) algorithm [8], cuckoo search algorithm (CSA) [9], teaching learning-based optimization (TLBO) [10,11], the dragonfly algorithm (DA) [12], and the Pareto envelope-based selection algorithm [13]. Also, some of the contributions involving FACTS devices were achieved by hybrid techniques, like the hybridizations between artificial bee colony (ABC) and the gravitational search algorithm (GSA) in [14]; differential evolution (DE) and BBO, known as the hybrid DE-based BBO algorithm, in [15]; and chemical reaction optimization (CRO) with quasi-oppositional-based optimization in [16].…”

Section: Introductionmentioning

confidence: 99%

Optimal SVC allocation in power systems using lightning attachment procedure optimization

Awad¹,

Kamel²,

Youssef³

2020

Turk J Elec Eng & Comp Sci

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Flexible AC transmission systems (FACTS) technology is widely adopted and utilized to maintain the performance of power systems. However, the improvements of power system performance achieved by FACTS devices depend on the right sizing and allocation of such devices. For technical and economic considerations, a FACTS device's location and size should be selected very carefully in order to maximize its benefits to the power system. In this paper, the sizing and location of a static VAR compensator (SVC) are optimally determined using a new optimization technique called lightning attachment procedure optimization (LAPO). The optimal allocation of the SVC is determined regarding the improvement of voltage deviation index and the reduction of total active power losses. The system is optimized in two cases: once without SVC installation in order to find out the optimal settings of the system that achieve the objective functions, and another time with SVC installation to determine its optimum sizes and locations by which the required objective functions are achieved. Then the system performance is analyzed after optimization with and without SVC devices to show the impact of the optimum sizing and location of the SVC on the system. The study is validated using the standard IEEE 30-bus system, while the developed LAPO is performed by MATLAB M-Files and the system performance analysis in different cases is performed by NEPLAN software.

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Optimal setting of STATCOM based on voltage stability improvement and power loss minimization using Moth-Flame algorithm

Cited by 19 publications

References 12 publications

An Evolutionary Algorithm-Based Vehicular Clustering Technique for VANETs

An Evolutionary Algorithm-Based Vehicular Clustering Technique for VANETs

Optimal Placement of FACTS Devices Using Filter Feeding Allogenic Engineering Algorithm

Optimal SVC allocation in power systems using lightning attachment procedure optimization

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