An improved voltage stability index and its application

Jia, Hongjie; Yu, Xiaodan; Yu, Yixin

doi:10.1016/j.ijepes.2005.08.012

Cited by 67 publications

(29 citation statements)

References 9 publications

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“…Therefore, VSMI is a suitable indicator for voltage stability monitoring. To determine the collapse point, PV curve is drawn using the continuous Power Flow (CPF) technique [7,28]. CPF is used because the conventional power flow (Gauss-Seidel or NewtonRaphson) method fails to converge as the operating point reaches the nose point (voltage collapse point) of PV curve.…”

Section: Problem Formulationmentioning

confidence: 99%

“…(6), the first term C 1 m P m i¼0 nðf ðx i Þ À y i Þ, represents the empirical error, which is estimated by the e-insensitive loss function in Eq. (7). The second item ||x|| 2 /2, is the regularization term.…”

Section: Svm Regression Theorymentioning

confidence: 99%

“…The traditional method for voltage stability analysis relied on static analysis using the conventional power flow method such as Gauss-Seidel or Newton-Raphson method. In reference [4][5][6][7][8], numerous voltage stability indexes based upon conventional power flow have been proposed. The main drawback of these techniques is the singularity of the Jacobian matrix at the maximum loading point.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Genetic algorithm based support vector machine for on-line voltage stability monitoring

Sajan

Kumar

Tyagi

2015

International Journal of Electrical Power & Energy Systems

105

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Section: Problem Formulationmentioning

confidence: 99%

Section: Svm Regression Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Genetic algorithm based support vector machine for on-line voltage stability monitoring

Sajan

Kumar

Tyagi

2015

International Journal of Electrical Power & Energy Systems

105

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“…In steady state voltage stability studies, the P-V curve, as shown in Figure 3, has been usually used and its nose point considered as the system voltage collapse point. However, in literature it has been shown that in the systems with inconstant-power loads (voltage dependent loads such as constant current and constant impedance loads), the real voltage collapse point is the SNB of the bifurcation curve (or point B' ' on P-V curve in Figure 3) instead of the nose point (NP) of P-V curve (point B' ) [39]. Nevertheless, when all the loads are constant-power type, nose point just coincides with the saddle point node [40].…”

Section: Loading Margin Maximization (F 2 )mentioning

confidence: 99%

Multiobjective clearing of coupled active and reactive power market considering power system security

Amjady

Rabiee

Shayanfar

2010

Int Trans Elec Energy Syst

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SUMMARYThis paper presents coupled active and reactive power market which is cleared in the form of multiobjective framework. The main objective function of the coupled market clearing is the offer cost of generators for their active power generation along with Total Payment Function (TPF) of generators for reactive power compensation plus Lost Opportunity Cost (LOC). Besides that, overload index, voltage drop index, and also voltage security margin are the other objective functions of the Optimal Power Flow (OPF) problem used to clear the coupled market. A Multi-objective Mathematical Programming (MMP) formulation is implemented to solve the OPF problem of the coupled market clearing. The proposed MMP formulation is also equipped with a fuzzy approach to choose the best compromise solution according to the specific preference among various Pareto optimal solutions. The effectiveness of the proposed coupled market and its clearing scheme is examined based on the IEEE 24-bus Reliability Test System (IEEE 24-bus RTS).

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“…Voltage stability evaluation, considered in the most of previous research works in the area, usually focuses on the determination of VSM of the power system. Various indices have been proposed for this purpose in the literature such as VSM based on the energy functions [9,10], load margins [3,6,11], Lindex [16,17], etc. In voltage stability prediction, the VSM is not of interest.…”

Section: The Problem Descriptionmentioning

confidence: 99%

Dynamic voltage stability prediction of power systems by a new feature selection technique and probabilistic neural network

Amjady

Velayati

2010

Euro. Trans. Electr. Power

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SUMMARYWith continued increase in the electrical energy demand and tendency towards maximizing economic benefits in power transmission system, especially in the liberalized electricity markets, real-time voltage security analysis has become a growing concern in electric power utilities. However, static analysis methods, such as power flow based methods, have difficulty in evaluating voltage stability and some voltage stability feasible region boundaries may not be correctly analyzed by these methods due to the use of simple models for the system components. On the other hand, dynamic modeling and evaluation of voltage stability are complex, expensive, and time consuming. In this paper, a new feature selection technique combined with a probabilistic neural network (PNN) is proposed for this purpose. A major difference of our paper with the previous research works in the area is that this paper proposes voltage stability prediction, while the previous works usually focus on the voltage stability evaluation. The proposed dynamic voltage stability prediction method is examined on the IEEE 14-bus and New England 39-bus test systems and the effectiveness of the proposed method is demonstrated. Also, the effect of different load models, branch contingencies, and generator contingencies is evaluated. Another advantage of the proposed prediction method is that it can be used for varied topologies and configurations of the power system.

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An improved voltage stability index and its application

Cited by 67 publications

References 9 publications

Genetic algorithm based support vector machine for on-line voltage stability monitoring

Genetic algorithm based support vector machine for on-line voltage stability monitoring

Multiobjective clearing of coupled active and reactive power market considering power system security

Dynamic voltage stability prediction of power systems by a new feature selection technique and probabilistic neural network

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