Optimal Allocation of DSTATCOM in Distribution Network Using Whale Optimization Algorithm

Balamurugan, P.; Yuvaraj, T.; Muthukannan, P.

doi:10.48084/etasr.2302

Cited by 31 publications

(13 citation statements)

References 16 publications

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“…This similarity of results shows the performance, accuracy and relevance of our tool, i.e., BVSI-GA, to position a compensator in a distribution network. The optimal power of DSTATACOM found is 1200kVAr contrary to the other authors who used the methods and tools (BAT-VSI) [8], GA [3], AI [3], (PeSOA_FUZZY) [19] and (WOA_VSI) [20] who found higher power ratings with losses that remain relatively identically to the 152.5kW. These very efficient results confirm the optimality of the GA-BVSI tool and its robustness.…”

Section: Session (I): System With Single Dstatcomsupporting

confidence: 55%

“…These very efficient results confirm the optimality of the GA-BVSI tool and its robustness. It can then be deduced that the optimal sizing to improve the performance of the 69-bus network is more efficient and less expensive with the methods: (GA-BVSI) proposed and (BAT-VSI) in [8] than with the methods: simple genetics in [3], immune algorithm in [3], combined penguin algorithm in [19] and combined whale algorithm in [20]. In view of its accessibility and technical performance and its ease of finding weaknesses in an electrical system, its use can be strongly recommended to distribution network managers.…”

Section: Session (I): System With Single Dstatcommentioning

confidence: 99%

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DSTATCOM Positioning in Radial Distribution Networks Using BVSI and Genetic Algorithm

Ibrahim¹,

Fifatin²,

Oloulade³

et al. 2022

EPES

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The distribution networks of African cities are for the most part in degradation of the required quality due to the exponential evolution of the loads due to demography and industry. In this paper, a hybrid approach based on an analyticmetaheuristic method called (BVSI-GA) developed in the MATLAB environment was used to dimension and position in an ultra-optimal way a DSTATACOM in a quasi-disaster network of Maradi in Niger. Indeed, the positioning of a DSTATCOM with a power of 1840 kVAr at node 40 in this 123-bus distribution network has contributed to reduce the losses in this network by 19.08% with a voltage profile improvement of 2.44%. On the other hand, the exploration of a multiple positioning in order to better secure and make this 123-node network more reliable in case of failure of any of the multiple DSTATACOMs contributed to reducing losses by 25.76%, i.e., a performance improvement of 6.68% compared to the mono-DSTATACOM. As for the tension profile in this case was improved by 2.78% compared to the initial case and by 0.34% to the mono positioning case. The method used in this study is accessible to operators and is effective in predicting and performing distribution networks in African countries, which are nowadays confronted with various contingencies that must be controlled within the limits of tolerable time.

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Section: Session (I): System With Single Dstatcomsupporting

confidence: 55%

Section: Session (I): System With Single Dstatcommentioning

confidence: 99%

DSTATCOM Positioning in Radial Distribution Networks Using BVSI and Genetic Algorithm

Ibrahim¹,

Fifatin²,

Oloulade³

et al. 2022

EPES

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“…A Bio-Inspired Cuckoo Search technique has been used for optimizing the allocation of the DSTAT-COM for loss reduction under different load models [11]. In [12], the whale optimization algorithm has been applied for optimizing the allocation of DSTATCOM in the distribution network. In [13], the differential evolution algorithm has been used to assign the location and size of DSTATCOM for cost and loss reduction.…”

Section: Introduction a Literature Surveymentioning

confidence: 99%

Stochastic Optimal Planning of Distribution System Considering Integrated Photovoltaic-Based DG and DSTATCOM Under Uncertainties of Loads and Solar Irradiance

et al. 2021

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Optimal planning of integration the Photovoltage Distributed Generation (PV-DG) and DSTATCOM is a crucial task due to the stochastic variations of PV output power and the load demand which are related to solar irradiance variations and the activities of the customers, respectively. In this article, the optimal planning problem of the PV-DG and DSTATCOM system is solved. The proposed model considers the uncertainties of the solar irradiance and the load demand for a multi-objective function, including the cost reduction, the voltage profile, and stability index improvement. Modified Ant Lion Optimizer (MALO) is proposed to enhance the basic ALO searching ability using two strategies. The first strategy is based on Levy Flight Distribution (LFD) to strengthen the exploration of the algorithm and avoid the premature of the basic ALO. In contrast, the second strategy is based on updating the solutions in a spiral orientation to improve the exploitation of the algorithm. The IEEE 69-bus and 118-bus radial distribution systems are used to demonstrate the effectiveness of the proposed method, and the yielded simulations are compared with the basic ALO and other well-known optimization techniques for power loss minimization under deterministic conditions. The simulation results demonstrate that the techno-economic benefits can be increased considerably by optimal inclusion of two PV-DGs and DSTATCOMs compared with a single system.

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“…Power loss in electric distribution systems (EDSs) takes a high portion in the total power loss of the electricity system. There are lots of techniques for reducing power loss such as distributed generation placement [1], capacitor placement [2][3][4], and network reconfiguration. Among these methods, network reconfiguration is the most efficient.…”

Section: Introductionmentioning

confidence: 99%

Network Reconfiguration for an Electric Distribution System with Distributed Generators based on Symbiotic Organisms Search

Duong

Nguyen

2019

Eng. Technol. Appl. Sci. Res.

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This paper proposes a method of network reconfiguration based on symbiotic organisms search (SOS) algorithm for reducing power loss of the electric distribution system. The SOS is a recent developed meta-heuristic algorithm inspired from the symbiotic interaction strategies of organisms for surviving and propagating in the ecosystem. Compared to other algorithms, SOS does not need any control parameters during the searching process. The advantages of the proposed SOS method have been validated in two electric distribution systems. Three network cases have been considered for each system, consisting of performing network reconfiguration on the system without distributed generator (DG) placement, the system installed type-P DGs and the system installed type-PQ DGs. The comparison results with particle swarm optimization and other previous methods show that the proposed SOS can be a promising technique for the problem of electric network reconfiguration.

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Optimal Allocation of DSTATCOM in Distribution Network Using Whale Optimization Algorithm

Cited by 31 publications

References 16 publications

DSTATCOM Positioning in Radial Distribution Networks Using BVSI and Genetic Algorithm

DSTATCOM Positioning in Radial Distribution Networks Using BVSI and Genetic Algorithm

Stochastic Optimal Planning of Distribution System Considering Integrated Photovoltaic-Based DG and DSTATCOM Under Uncertainties of Loads and Solar Irradiance

Network Reconfiguration for an Electric Distribution System with Distributed Generators based on Symbiotic Organisms Search

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