Power system reconfiguration in a radial distribution network for reducing losses and to improve voltage profile using modified plant growth simulation algorithm with Distributed Generation (DG)

Rajaram, R.; Kumar, Kaushal; Rajasekar, N.

doi:10.1016/j.egyr.2015.03.002

Cited by 199 publications

(99 citation statements)

References 14 publications

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“…The 33‐bus 11‐kV system was composed of one to 32 sectional lines (closed switches) and 33 to 37 tie lines (open switches). The total real power load demand was 3715 kW, and reactive power load demand was 2300 kVAr.…”

Section: Simulation and Results Analysismentioning

confidence: 99%

Multi‐objective grey wolf optimizer algorithm for combination of network reconfiguration and D‐STATCOM allocation in distribution system

Selvaraj

Kanimozhi

2019

Int Trans Electr Energ Syst

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Summary The distribution flexible alternating current system (D‐FACT) devices are being increasingly placed in the distribution system due to their several practical and environmental benefits. In this work, a novel grey wolf optimizer (GWO) algorithm as a meta‐heuristic technique is applied to solve the reconfiguration problem, the installation of distribution static compensator (D‐STATCOM) problem, and the combination of these two problem. The aim of this research is to diminish the total power loss and the load‐balancing (LB) index of the radial distribution system. First, the index vector (IV) method is employed to suggest the candidate busses for D‐STATCOM allocation. Then, the GWO algorithm is applied to establish the locations and sizes of the D‐STATCOM from the candidate busses. An overall accurateness and consistency of the proposed GWO algorithm has been validated and experienced on a 33‐bus, a 69‐bus, and a real‐time 31‐bus radial distribution system, India with three kinds of load levels. In addition, the proposed method results are ideal as compared with other intellectual methods like genetic algorithm (GA) and fuzzy GA.

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Section: Simulation and Results Analysismentioning

confidence: 99%

Multi‐objective grey wolf optimizer algorithm for combination of network reconfiguration and D‐STATCOM allocation in distribution system

Selvaraj

Kanimozhi

2019

Int Trans Electr Energ Syst

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“…The penetration level of DGs' capacity and the reconfiguration scheme have to be selected in such a way so that the voltage stability margin after reconfiguration would be more than before reconfiguration (15).…”

Section: Stability Margin Constraintmentioning

confidence: 99%

“…[10][11][12][13] Esmaeili et al 14 propose a multi-objective framework for simultaneous network reconfiguration and power allocation of DGs in distribution networks. In Rajarm et al, 15 a modified plant growth simulation algorithm is proposed that considers DGs in the process. The objective function of this paper is reducing the power losses and improving the voltage profile.…”

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confidence: 99%

Multi‐objective optimization model for distribution network reconfiguration in the presence of distributed generations

Zarei

Zangeneh

2017

Int Trans Electr Energ Syst

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Summary With increasing annual energy demand and significant growth in the use of distributed generation (DG), distribution companies are exploring ways to improve the technical specifications of the distribution networks such as power losses, customer interruption costs, and voltage stability and on the other hand to increase the penetration level of DGs. In this paper, a multi‐objective optimization model is proposed for distribution network reconfiguration (DNR) to decrease the total costs of energy losses and energy not supplied (ENS) and increase the voltage stability and DG penetration level. Seasonal hourly load curves are applied, and best configuration is determined for each loading condition. Uncertainties regarding the load variations and volatile power production of wind turbine are modeled using a 2‐point estimate method, and a benefit‐cost analysis is used to assess the effects of depreciation cost in DNR problem. The proposed model is applied on 2 typical test systems.

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“…Moreover, some researches suggest the minimum limit in distribution networks to be 0.90 p.u. as given in [20]. It is also important to mention that the reason for being not so useful in case 1 is that it has few instances when the load is higher than that of nominal load value for which the optimal sizes were calculated.…”

Section: ) Static Loadmentioning

confidence: 99%

Voltage profile improvement in radial distribution networks with analytical method of simultaneous optimal DG sizing

Shahzad

Ahmad

Gawlik

et al. 2016

2016 18th Mediterranean Electrotechnical Conference (MELECON)

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This paper investigates the usefulness of analytical expressions for finding optimal sizes of Distributed Generators (DGs). Results in this study revealed some interesting and useful features of analytical expression in regard to voltage profile improvement. Method was applied on IEEE 37 node radial test system and simulations were performed in DigSILENT PowerFactory coupled with Python. The validation of results was performed by comparing the voltage profiles of proposed method with two study cases; one without any DG while other with DGs of sizes equal to the cumulative load. DGs of different types, based on their output power, have been tested and results are presented. Moreover, results proved that the proposed method is better as compared to the methods in other study cases for static and time varying loads.

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Power system reconfiguration in a radial distribution network for reducing losses and to improve voltage profile using modified plant growth simulation algorithm with Distributed Generation (DG)

Cited by 199 publications

References 14 publications

Multi‐objective grey wolf optimizer algorithm for combination of network reconfiguration and D‐STATCOM allocation in distribution system

Multi‐objective grey wolf optimizer algorithm for combination of network reconfiguration and D‐STATCOM allocation in distribution system

Multi‐objective optimization model for distribution network reconfiguration in the presence of distributed generations

Voltage profile improvement in radial distribution networks with analytical method of simultaneous optimal DG sizing

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