The performance of North American nuclear power plants during the electric power blackout of August 14, 2003

Maldonado, G. Ivan

doi:10.1109/nssmic.2004.1466907

Cited by 17 publications

(15 citation statements)

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“…Taking the core fuel and coolant temperature module for example, Figure 12 shows the trajectory sensitivity analysis results with changes of ±10% to Ω/μc under a step change of 0.01 for Nr, or a positive step change of 10 degrees for 1 T  . The parameter influence of Ω/μc on output variables (i.e., TF, Tav and 2 T  ) existed constantly as Nr changed.…”

Section: Parameter Sensitivity Analysismentioning

confidence: 99%

“…The parameter influence of Ω/μc on output variables (i.e., TF, Tav and 2 T  ) existed constantly as Nr changed. The trajectory sensitivities eventually dropped to zero after about 30 s under step change of 1 T  (see Figure 12). Thus, there was a significant difference in the sensitivity of Ω/μc to each output variable under two cases.…”

Section: Parameter Sensitivity Analysismentioning

confidence: 99%

“…Nuclear power units are sensitive to system voltage and frequency fluctuations, because a sudden load rejection or cutting machine connected to the power grid may cause a big impact on the grid voltage and frequency stability [1,2]. The second-generation, or the improved second-generation nuclear technology were PCTRAN software in Section 5.…”

Section: Introductionmentioning

confidence: 99%

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Parameter Identification with the Random Perturbation Particle Swarm Optimization Method and Sensitivity Analysis of an Advanced Pressurized Water Reactor Nuclear Power Plant Model for Power Systems

et al. 2017

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Abstract:The ability to obtain appropriate parameters for an advanced pressurized water reactor (PWR) unit model is of great significance for power system analysis. The attributes of that ability include the following: nonlinear relationships, long transition time, intercoupled parameters and difficult obtainment from practical test, posed complexity and difficult parameter identification. In this paper, a model and a parameter identification method for the PWR primary loop system were investigated. A parameter identification process was proposed, using a particle swarm optimization (PSO) algorithm that is based on random perturbation (RP-PSO). The identification process included model variable initialization based on the differential equations of each sub-module and program setting method, parameter obtainment through sub-module identification in the Matlab/Simulink Software (Math Works Inc., Natick, MA, USA) as well as adaptation analysis for an integrated model. A lot of parameter identification work was carried out, the results of which verified the effectiveness of the method. It was found that the change of some parameters, like the fuel temperature and coolant temperature feedback coefficients, changed the model gain, of which the trajectory sensitivities were not zero. Thus, obtaining their appropriate values had significant effects on the simulation results. The trajectory sensitivities of some parameters in the core neutron dynamic module were interrelated, causing the parameters to be difficult to identify. The model parameter sensitivity could be different, which would be influenced by the model input conditions, reflecting the parameter identifiability difficulty degree for various input conditions.

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Section: Parameter Sensitivity Analysismentioning

confidence: 99%

Section: Parameter Sensitivity Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Parameter Identification with the Random Perturbation Particle Swarm Optimization Method and Sensitivity Analysis of an Advanced Pressurized Water Reactor Nuclear Power Plant Model for Power Systems

et al. 2017

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“…The failure rate of the electric substations, regardless external event, has been gathered from the existent literature (Nack 2005). In the case of a general loss of external power grid the nuclear plant is assumed to shut down safely (UnplannedOutage), according to procedures (Maldonado 2004).…”

Section: Internal Failure Sectionmentioning

confidence: 99%

Robust vulnerability analysis of nuclear facilities subject to external hazards

Tolo

Patelli

Beer

2016

Stoch Environ Res Risk Assess

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Natural hazards have the potential to trigger complex chains of events in technological installations leading to disastrous effects for the surrounding population and environment. The threat of climate change of worsening extreme weather events exacerbates the need for new models and novel methodologies able to capture the complexity of the natural-technological interaction in intuitive frameworks suitable for an interdisciplinary field such as that of risk analysis. This study proposes a novel approach for the quantification of risk exposure of nuclear facilities subject to extreme natural events. A Bayesian Network model, initially developed for the quantification of the risk of exposure from spent nuclear material stored in facilities subject to flooding hazards, is adapted and enhanced to include in the analysis the quantification of the uncertainty affecting the output due to the imprecision of data available and the aleatory nature of the variables involved. The model is applied to the analysis of the nuclear power station of Sizewell B in East Anglia (UK), through the use of a novel computational tool. The network proposed models the direct effect of extreme weather conditions on the facility along several time scenarios considering climate change predictions as well as the indirect effects of external hazards on the internal subsystems and the occurrence of human error. The main novelty of the study consists of the fully computational integration of Bayesian Networks with advanced Structural Reliability Methods, which allows to adequately represent both aleatory and epistemic aspects of the uncertainty affecting the input through the use of probabilistic models, intervals, imprecise random variables as well as probability bounds. The uncertainty affecting the output is quantified in order to attest the significance of the results and provide a complete and effective tool for riskinformed decision making.

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“…Thus, ensuring the safety and stability of power networks and NPPs is a critical issue [5]. Numerous power outages at home and abroad indicate that the coordination between power plants and grids is a crucial factor in ensuring the safety and stability of power systems [6][7][8][9][10][11][12]. Some major achievements have been made in the coordination of control and protection system between units and the grid in recent years [13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Interaction and Coordination among Nuclear Power Plants, Power Grids and Their Protection Systems

Song

et al. 2016

Energies

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Nuclear power plants (NPPs) have recently undergone rapid development in China. To improve the performance of both NPPs and grids during adverse conditions, a precise understanding of the coordination between NPPs and grids is required. Therefore, a new mathematical model with reasonable accuracy and reduced computational complexity is developed. This model is applicable to the short, mid, and long-term dynamic simulation of large-scale power systems. The effectiveness of the model is verified by using an actual NPP full-scope simulator as a reference. Based on this model, the interaction and coordination between NPPs and grids under the conditions of over-frequency, under-frequency and under-voltage are analyzed, with special stress applied to the effect of protection systems on the safe operation of both NPPs and power grids. Finally, the coordinated control principles and schemes, together with the recommended protection system values, are proposed for both NPPs and grids. These results show that coordination between the protection systems of NPPs and power networks is a crucial factor in ensuring the safe and stable operation of both NPPs and grids. The results can be used as a reference for coordination between NPPs and grids, as well as for parameter optimization of grid-related generator protection of NPPs.

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The performance of North American nuclear power plants during the electric power blackout of August 14, 2003

Cited by 17 publications

References 0 publications

Parameter Identification with the Random Perturbation Particle Swarm Optimization Method and Sensitivity Analysis of an Advanced Pressurized Water Reactor Nuclear Power Plant Model for Power Systems

Parameter Identification with the Random Perturbation Particle Swarm Optimization Method and Sensitivity Analysis of an Advanced Pressurized Water Reactor Nuclear Power Plant Model for Power Systems

Robust vulnerability analysis of nuclear facilities subject to external hazards

Interaction and Coordination among Nuclear Power Plants, Power Grids and Their Protection Systems

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