Optimal placement of UPFC based on techno-economic criteria by hybrid CSA-CRO algorithm

Sen, Deepro; Acharjee, Parimal

doi:10.1109/appeec.2017.8308909

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…Esmaili et al [5] presented a novel coordinated design of UPFC for power system stability considering culture-PSO-co evolutionary (CPCE) algorithm. Reddy et al [6] proposed optimal allocation of UPFC critical based to enhance the voltage stability of system considering genetic algorithm based upgraded differential evolution algorithm In recent days, evolutionary algorithms [7] such as artificial algae algorithm (AAA) [8], modified particle swarm optimization (M-PSO) [9], imperialist competitive algorithm [10], dolphin echolocation optimization [11], Cuckoo search algorithm and chemical reaction optimization [12], genetic algorithm [13], sine-cosine algorithms [14] are proposed for optimal placement of UPFC for unified PF control. The studies [15], [16] proposed power congestion control strategy to increase the stability of the network.…”

Section: Introductionmentioning

confidence: 99%

Optimal siting and sizing of unified power flow controller using sensitivity constrained differential evolution algorithm

Reddy

Rao²,

Rao³

2022

IJECE

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This paper presents Sensitivity constrained placement of unified power flow controller (UPFC) considering active-power flow sensitive index (APFSI) and static voltage stability index (STATIC-VSI) to minimize active-power losses and to improve power transmission capacity. The sensitive factors are derived with respect to voltage, phase angle and current to formulate APFSI. Transmission line impedance parameters along with active and reactivepower flow measurements are considered to formulate static-VSI. Sensitivity constrained differential evolutionary (SCDE) algorithm is proposed for parameter setting through which power control and minimization of losses in system can be achieved. Testing is performed on IEEE-5, 14 and 30-bus networks in MATLAB and results indicate that SCDE is robust optimization technique compared to conventional method and genetic algorithm (GA)

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