2023
DOI: 10.1016/j.epsr.2022.108899
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Load shedding strategy using online voltage estimation process for mitigating fault-induced delayed voltage recovery in smart networks

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Cited by 8 publications
(4 citation statements)
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References 19 publications
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“…Methods Description FIDVR quantifying and modelling [1] Presenting an index for determining FIDVR severity using load's amount and short-circuit level [2] Using entropy and divergence of voltage density for determining FIDVR severity [3,4] Using ensemble-based learning model for determining FIDVR severity [5,6] Three-phase/three-sequence modelling of network for off-line FIDVR simulating [7] Time-series load flow for off-line FIDVR simulating [8] Presenting dynamic model based on Lyapunov function for IM loads during FIDVR [9] Presenting aggregate model including stall-mode parameters for IM loads during FIDVR Network-side methods for mitigating FIDVR [10,11] Study on the effect of injected reactive power from DGs on FIDVR severity [12][13][14] Determining the contribution of DGs for mitigating FIDVR using expert rules, drop of voltage/VAr, and optimization problem [15] Study on the effect of injected reactive power from Energy-storages on FIDVR severity [16][17][18][19][20] Allocation of VAr sources for mitigating FIDVR using trajectory sensitivity analysis, optimization algorithm, controllability covariance, mesh adaptive direct search algorithm and Voronoi diagram [21] Utilizing PV-STATCOM for mitigating FIDVR [22,23] Presenting voltage control strategy for VAr sources during FIDVR Load-side methods for mitigating FIDVR [24] Using voltage slope for LS against FIDVR [25] Improving UVLS relays using fuzzy controller against FIDVR [26] Using angle difference between voltage and current of loads for LS against FIDVR [27] Using active and reactive powers of loads for LS against FIDVR [28] Using imaginary part of admittance of feeder for LS against FIDVR [29] Local under-impedance load shedding method [30] Using changes in apparent power of IM loads for determining LS location [31] Optimization-based method for determining LS amou...…”
Section: Categorymentioning
confidence: 99%
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“…Methods Description FIDVR quantifying and modelling [1] Presenting an index for determining FIDVR severity using load's amount and short-circuit level [2] Using entropy and divergence of voltage density for determining FIDVR severity [3,4] Using ensemble-based learning model for determining FIDVR severity [5,6] Three-phase/three-sequence modelling of network for off-line FIDVR simulating [7] Time-series load flow for off-line FIDVR simulating [8] Presenting dynamic model based on Lyapunov function for IM loads during FIDVR [9] Presenting aggregate model including stall-mode parameters for IM loads during FIDVR Network-side methods for mitigating FIDVR [10,11] Study on the effect of injected reactive power from DGs on FIDVR severity [12][13][14] Determining the contribution of DGs for mitigating FIDVR using expert rules, drop of voltage/VAr, and optimization problem [15] Study on the effect of injected reactive power from Energy-storages on FIDVR severity [16][17][18][19][20] Allocation of VAr sources for mitigating FIDVR using trajectory sensitivity analysis, optimization algorithm, controllability covariance, mesh adaptive direct search algorithm and Voronoi diagram [21] Utilizing PV-STATCOM for mitigating FIDVR [22,23] Presenting voltage control strategy for VAr sources during FIDVR Load-side methods for mitigating FIDVR [24] Using voltage slope for LS against FIDVR [25] Improving UVLS relays using fuzzy controller against FIDVR [26] Using angle difference between voltage and current of loads for LS against FIDVR [27] Using active and reactive powers of loads for LS against FIDVR [28] Using imaginary part of admittance of feeder for LS against FIDVR [29] Local under-impedance load shedding method [30] Using changes in apparent power of IM loads for determining LS location [31] Optimization-based method for determining LS amou...…”
Section: Categorymentioning
confidence: 99%
“…As LS from one bus also affects the voltage recovery of other buses, simultaneous investigation of all buses using centralized strategies will have better results than the separate examination of each bus using decentralized ones. The repetition‐based LS method, which LS locations are obtained through changes in the apparent power of the motor loads, and also the optimization‐based method, where the location and amount of LS are calculated using the MILP, have been presented in [30] and [31]. The authors in [32] have compared between the existence LS methods for dealing with FIDVR.…”
Section: Introductionmentioning
confidence: 99%
“…Some studies have proposed using measurement data to characterise the motor load slip rate [13]. However, owing to the increasing complexity of the load model, measuring the motor load response accurately is difficult [26]. Moreover, a method involving calculating the Thevenin parameters was proposed for evaluating the voltage stability of a power system in real time [27].…”
Section: Literature Reviewmentioning
confidence: 99%
“…The load shedding strategy using online voltage estimation process for mitigating fault-induced delayed voltage recovery in smart networks is also an important past work to be addressed due to its capability to perform voltage estimation in power system. This has been highlighted in [8] as one of the important works in load shedding strategy. Other than these works, other work which addressed load shedding using the application of artificial intelligence is the work conducted by Isa et.…”
Section: Introductionmentioning
confidence: 99%