Localization and sizing of FACTS devices for optimal power flow in a system consisting wind power using HBMO

Aghaebrahimi, M. R.; Golkhandan, Reza Kazemi; Ahmadnia, Sajjad

doi:10.1109/melcon.2016.7495372

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…Teimoury et al [187] hybridized K-means with the honey bee mating algorithm to resolve the problems associated with the K-means clustering algorithm to improve the performance of the clustering algorithm. Aghaebrahimi, Golkhandan, and Ahmadnia [188] combined the K-means algorithm with HBMO to solve the problem of localization and sizing of flexible AC transmission systems (FACTS) in a power system to reduce the generation, transmission, and power costs.…”

Section: Honey Bee Mating Optimizationmentioning

confidence: 99%

K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions

2021

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K-means clustering algorithm is a partitional clustering algorithm that has been used widely in many applications for traditional clustering due to its simplicity and low computational complexity. This clustering technique depends on the user specification of the number of clusters generated from the dataset, which affects the clustering results. Moreover, random initialization of cluster centers results in its local minimal convergence. Automatic clustering is a recent approach to clustering where the specification of cluster number is not required. In automatic clustering, natural clusters existing in datasets are identified without any background information of the data objects. Nature-inspired metaheuristic optimization algorithms have been deployed in recent times to overcome the challenges of the traditional clustering algorithm in handling automatic data clustering. Some nature-inspired metaheuristics algorithms have been hybridized with the traditional K-means algorithm to boost its performance and capability to handle automatic data clustering problems. This study aims to identify, retrieve, summarize, and analyze recently proposed studies related to the improvements of the K-means clustering algorithm with nature-inspired optimization techniques. A quest approach for article selection was adopted, which led to the identification and selection of 147 related studies from different reputable academic avenues and databases. More so, the analysis revealed that although the K-means algorithm has been well researched in the literature, its superiority over several well-established state-of-the-art clustering algorithms in terms of speed, accessibility, simplicity of use, and applicability to solve clustering problems with unlabeled and nonlinearly separable datasets has been clearly observed in the study. The current study also evaluated and discussed some of the well-known weaknesses of the K-means clustering algorithm, for which the existing improvement methods were conceptualized. It is noteworthy to mention that the current systematic review and analysis of existing literature on K-means enhancement approaches presents possible perspectives in the clustering analysis research domain and serves as a comprehensive source of information regarding the K-means algorithm and its variants for the research community.

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Section: Honey Bee Mating Optimizationmentioning

confidence: 99%

K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions

2021

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“…The consequences for TCSC allocation have been given in Table 3. As shown in this table, the degree of compensation of TCSC is -0.74 p.u in line (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) gives MSL of 123% with the cost of installation is US$ 2.87×10 6 . Figure 5.…”

Section: Scenario 2: Tcsc Onlymentioning

confidence: 99%

“…An a portion of the expressed goals in the literature are : network loadability enhancement, line thermal constraints violation [7]. Some interesting studies are focused on the investigation of loss reduction, voltage profile enhancement [8,9], voltage stability improvement, fuel cost reduction [10] ,reliving transmission line congestion [11] and enhancement available transfer capability (ATC) [12]. Each of the above stated objectives enhance power system performance.…”

Section: Introductionmentioning

confidence: 99%

A probabilistic multi-objective approach for FACTS devices allocation with different levels of wind penetration under uncertainties and load correlation

EL-Azab¹,

Omran²,

Mekhamer³

et al. 2020

IJECE

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This study presents a probabilistic multi-objective optimization approach to obtain the optimal locations and sizes of static var compensator (SVC) and thyristor-controlled series capacitor (TCSC) in a power transmission network with large level of wind generation. In this study, the uncertainties of the wind power generation and correlated load demand are considered. The uncertainties are modeled in this work using the points estimation method (PEM). The optimization problem is solved using the Multi-objective particle swarm optimization (MOPSO) algorithm to find the best position and rating of the flexible AC transmission system (FACTS) devices. The objective of the problem is to maximize the system loadability while minimizing the power losses and FACTS devices installation cost. Additionally, a technique based on fuzzy decision-making approach is employed to extract one of the Pareto optimal solutions as the best compromise one. The proposed approach is applied on the modified IEEE 30-bus system. The numerical results evince the effectiveness of the proposed approach and shows the economic benefits that can be achieved when considering the FACTS controller.

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Optimal placement and sizing of FACTS devices for optimal power flow in a wind power integrated electrical network

Biswas

Arora

Mallipeddi

et al. 2020

Neural Comput & Applic

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Localization and sizing of FACTS devices for optimal power flow in a system consisting wind power using HBMO

Cited by 7 publications

References 9 publications

K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions

K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions

A probabilistic multi-objective approach for FACTS devices allocation with different levels of wind penetration under uncertainties and load correlation

Optimal placement and sizing of FACTS devices for optimal power flow in a wind power integrated electrical network

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