Dynamic simulation of voltage collapses

Deuse, Jacques; Stubbe, M.

doi:10.1109/59.260913

Cited by 60 publications

(23 citation statements)

References 2 publications

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“…Techniques for sensitivity and uncertainty analysis are precisely presented in [2] using Monte Carlo analysis, response surface analysis, differential analysis, Fourier amplitude sensitivity test, fast probability integration and Sobol"s variance decomposition. Dynamic analysis method of small signal method [4,5] and simulation methods based on time domain [6,7] are used to address the dynamic problem of voltage instability. A computational feasible method to assess impact of uncertainties due to dynamic behaviour of power system in simulation is shown in [8].…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Voltage Stability Considering Effects of WTG and Load Uncertainties Using Differential Evolution

Sharma¹,

Varma²,

Choube³

2018

IJRET

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Section: Introductionmentioning

confidence: 99%

Enhancement of Voltage Stability Considering Effects of WTG and Load Uncertainties Using Differential Evolution

Sharma¹,

Varma²,

Choube³

2018

IJRET

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“…However, the voltage instability is a dynamic problem. To address this problem, dynamic analysis methods of small signal method [4,5] and time domain simulation method [6][7][8] were presented. On the basis of small signal method, different bifurcations such as saddle node bifurcation, Hopf bifurcation and singularity-induction bifurcation can be obtained, and dynamic performance of power system was studied [4,5].…”

Section: Introductionmentioning

confidence: 99%

Voltage stability analysis considering the uncertainties of dynamic load parameters

Zhang

Tse

Wang

et al. 2009

IET Generation, Transmission &Amp; Distribution

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Many uncertainties exist in power systems and they will affect the stability analysis results. Voltage stability considering uncertainty in load parameters will be discussed. With the assumption that parameter variation is normal distribution, the probabilistic characteristics of eigenvalues under the uncertainties of dynamic load parameters can be obtained. Distribution of the critical eigenvalue will determine the stability probability of a power system. The stability margin can be inferred from the probabilistic critical load level, which is the maximal load level where system is 'probabilistically' stable. Case studies on three test systems illustrate that the stability margin will be reduced with load uncertainty. The proposed probabilistic results are validated using deterministic method of Monte Carlo on multi 10 000 sample studies.

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“…There has been lots of research [3][4][5][6][7][8][9] indicating the risk imposed by critical operating conditions, which often lead to ultimate collapse of electric power systems. Basically, the critical condition analysis refers to identification of critical load buses through evaluation of L-index [10].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Critical Conditions in Electric Power Systems by Feed Forward and Layer Recurrent Neural Networks

Panda¹,

Sahoo²,

Satpathy³

et al. 2014

ijeei

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Abstract:In this paper, critical conditions in electric power systems are monitored by applying various neural networks. In order to accomplish the stated goal, the authors tried several combinations of Feed Forward Neural Network and Layer Recurrent Neural Networks by imparting appropriate training schemes through supervised learning in order to formulate a comparative analysis on their performance. Once, training goes successful, the neural network learns how to deal with a set of newly presented data through validation and testing mechanism so as to evolve the best network structure and learning criteria. The proposed methodology has been tested on the standard IEEE 30-bus test system with the support of MATLAB based neural network toolbox. The results presented in this paper signify that the multi-layered feed forward neural network with Levenberg-Marquardt back propagation algorithm gives best training performance of all possible cases considered in this paper, thus validating the proposed methodology.

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Dynamic simulation of voltage collapses

Cited by 60 publications

References 2 publications

Enhancement of Voltage Stability Considering Effects of WTG and Load Uncertainties Using Differential Evolution

Enhancement of Voltage Stability Considering Effects of WTG and Load Uncertainties Using Differential Evolution

Voltage stability analysis considering the uncertainties of dynamic load parameters

Analysis of Critical Conditions in Electric Power Systems by Feed Forward and Layer Recurrent Neural Networks

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