Method of acquiring probability static state voltage stabilization margin based on Quasi monte carlo simulation and kernel density estimation

A technology of static voltage stability and voltage stability margin, applied in the field of obtaining probabilistic static voltage stability margin based on quasi-Monte Carlo simulation and kernel density estimation, to achieve the effect of high calculation accuracy

Active Publication Date: 2017-07-28
SHANGHAI MUNICIPAL ELECTRIC POWER CO +2
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However, most of the traditional methods are based on deterministic models, ignoring uncert...

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  • Method of acquiring probability static state voltage stabilization margin based on Quasi monte carlo simulation and kernel density estimation
  • Method of acquiring probability static state voltage stabilization margin based on Quasi monte carlo simulation and kernel density estimation
  • Method of acquiring probability static state voltage stabilization margin based on Quasi monte carlo simulation and kernel density estimation

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Embodiment Construction

[0021] Such as figure 1 As shown, this embodiment uses the IEEE 118 standard system as an example for description:

[0022] This embodiment divides the system into four partitions A, B, C, and D. Area A includes nodes 1-33, Area B includes nodes 34-59, Area C includes nodes 60-79, and Area D includes nodes 80-118. Set the node load active and reactive power to obey the normal distribution, the expectation is equal to the load active and reactive power under the basic working conditions, the coefficient of variation (standard deviation / expectation) is: A area 7%, B area 4%, C area 9%, D zone 5%. Each generator is equivalent to 4 identical units, and the failure probability of each unit is 0.09. Set the sampling scale to 1000, use QMC (quasi Monte Carlo) to obtain input variable samples, use the direct method to calculate the voltage stability critical point, and obtain the stability margin samples, then use DKDM to obtain the probability distribution of the stability margin.

[0...

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Abstract

A method of acquiring a probability static state voltage stabilization margin based on Quasi monte carlo simulation and kernel density estimation is disclosed. The method comprises the following steps of according to power grid data, carrying out pretreatment to acquire an input random variable matrix; and then using a diffusion-based kernel density method (DKDM) to acquire a probability density of a voltage stabilization margin and cumulative probability distribution. In the invention, through introducing the Quasi monte carlo simulation, an input random variable sample is acquired so as to increase calculating efficiency of a simulation method; the diffusion-based kernel density method is used to accurately acquire a probability distribution function of the stabilization margin; and only a small sampling scale is needed and high calculating precision can be acquired.

Description

Technical field [0001] The invention relates to a technology in the field of power system control, in particular to a method for obtaining probabilistic static voltage stability margin based on quasi-Monte Carlo simulation and nuclear density estimation. Background technique [0002] With the development of my country's power system, speeding up the development of hydropower in the Southwest, large-scale development of wind power and solar power, relying on UHV and smart grids to transport clean energy in the central and western regions to the eastern load center is the main way to fully implement the national new energy development plan. Under this development model, the DC power receiving scale of some power grids in eastern my country has increased significantly, the output demand of internal units has decreased, and the peak shaving factor has been superimposed. A large number of local thermal power units need to be shut down, and the power grid shows the characteristics of st...

Claims

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Application Information

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IPC IPC(8): H02J3/00
CPCH02J3/00H02J2203/20
Inventor 李禹鹏崔勇冯楠杨增辉苏运郭强严正徐潇源马骏宇
Owner SHANGHAI MUNICIPAL ELECTRIC POWER CO
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