Voltage collapse risk assessment

Silva, Armando M. Leite da; Coutinho, I.P.; Souza, Antônio Carlos Zambroni de; Prada, R.B.; Rei, Andrea M.

doi:10.1016/s0378-7796(99)00087-5

Cited by 26 publications

(16 citation statements)

References 4 publications

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“…In [14], an approach is proposed for evaluating the VIR and the expected VSM considering uncertainties associated with the load forecasting error. These probabilistic indices have been estimated using two techniques: the MCSM and the Tangent Vector Method [18].…”

Section: Bts-65mentioning

confidence: 99%

Voltage Stability Probabilistic Assessment in Composite Systems: Modeling Unsolvability and Controllability Loss

Rodrigues

Prada

Silva

2010

IEEE Trans. Power Syst.

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Several papers have recognized the effect of uncertainties in voltage stability analysis through probabilistic methods. In these papers, the unstable states are generally identified by the unsolvability of the power flow equations or by violations in the voltage stability margin limit. However, voltage stability problems may also be associated with a loss in voltage controllability, when a voltage control action has an effect which is contrary to what is usually expected. The main aim of this paper is to include unstable states caused by unsolvability and voltage controllability loss in the voltage stability probabilistic assessment. This goal is achieved through the combination of three techniques: the Monte Carlo Simulation Method, the nonlinear optimal power flow and the D' matrix method. These three techniques permit the inclusion of a new issue in the computation of voltage instability risk: the unstable states stemming from controllability loss. Index Terms-Composite systems, interior point method, MonteCarlo simulation, optimal power flow, probabilistic methods, sensitivity analysis, voltage stability.

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Section: Bts-65mentioning

confidence: 99%

Voltage Stability Probabilistic Assessment in Composite Systems: Modeling Unsolvability and Controllability Loss

Rodrigues

Prada

Silva

2010

IEEE Trans. Power Syst.

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“…The minimum total load critical point and the on-scenario critical load are depicted as, respectively, and . Examples can be found of the application of Monte Carlo simulations in voltage collapse risk assessment, e.g., [10]. It must however be stressed that the calculation time of a single continuation power flow takes considerably more time as compared to a simple power flow.…”

Section: A Dealing With Uncertaintiesmentioning

confidence: 99%

“…With longer term forecasting, e.g., when analyzing a grid peak scenario, the wind speed probability density function (pdf) displays a Weibull distribution with Weibull scale parameter and shape parameter (Fig. 6) which is related to a standard normal variable as (10) Wind speed is related to power generation by the generators power curve which can be approximated as piecewise linear, based on cut-in speed, maximum power output, and cut-out speed. When considering only one stochastic injection in the grid, the probabilistic load margin shows a relatively small variance.…”

Section: A Scenario I-dispersed Wind Generationmentioning

confidence: 99%

A Probabilistic Formulation of Load Margins in Power Systems With Stochastic Generation

Haesen

Bastiaensen

Driesen

et al. 2009

IEEE Trans. Power Syst.

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This paper discusses the impact of uncertain power injections in the grid on the load margin. Two common analyses of voltage stability are those aiming for the closest saddle node bifurcation and those assuming a prefixed direction of load and production increase. In the case of large renewable based generation units or a significant degree of dispersed generation, the loading margin has to be interpreted as a stochastic variable itself. This allows to interpret load margins at different levels of probability of voltage collapse with or without corrective actions undertaken. The probabilistic margin is assessed with a minimum number of samples by use of a stochastic response surface method implementation. The method is illustrated on the IEEE 24-bus and 118-bus system considering stochastic wind generation and dispersed generation.

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“…This assessment should consider all contingencies, including those that have a small probability of occurrence, as their impact on the power system's operation could be severe. Extensive studies regarding the application of a risk-based concept in voltage security and stability can be seen in [2], [10]- [14]. In [2], only a limited set of transmission line outages are considered and thus, the risk index value computed may not provide reliable information on the current status of a power system.…”

Section: Introductionmentioning

confidence: 99%

“…In [2], only a limited set of transmission line outages are considered and thus, the risk index value computed may not provide reliable information on the current status of a power system. In [10], probabilistic load flow and Monte Carlo simulation are used to determine the risk of voltage collapse without taking into account the severity of the collapse. To speed up the risk assessment procedure, contingency screening is implemented in [11] and [12], but this method can be very time consuming for a large-sized power system.…”

Section: Introductionmentioning

confidence: 99%

Risk-based voltage collapse assessment using generalized regression neural network

Marsadek

Mohamed

Nopiah

2011

Proceedings of the 2011 International Conference on Electrical Engineering and Informatics

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This paper describes the implementation of a fast and accurate intelligent technique using generalized regression neural network to assess the risk of voltage collapse in power systems. The risk of voltage collapse is defined as the product of the probability of transmission line outage and its severity associated with voltage collapse. The effect of weather in the probability of transmission line outage is taken into account in which the failure rate of each transmission line with respect to weather conditions is calculated. A new severity function model that utilises the voltage collapse prediction index is also considered in this assessment method. The performance of the generalised regression neural network is evaluated using mean absolute and mean square errors. The proposed risk based voltage collapse assessment method has been validated on a real power system.

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Voltage collapse risk assessment

Cited by 26 publications

References 4 publications

Voltage Stability Probabilistic Assessment in Composite Systems: Modeling Unsolvability and Controllability Loss

Voltage Stability Probabilistic Assessment in Composite Systems: Modeling Unsolvability and Controllability Loss

A Probabilistic Formulation of Load Margins in Power Systems With Stochastic Generation

Risk-based voltage collapse assessment using generalized regression neural network

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