A probabilistic power flow determination method and system based on power system

Abstract

The invention discloses a probabilistic power flow determination method and a system based on a power system. The method comprises the following steps of: acquiring power data of a plurality of wind turbines; A Gaussian mixture model of input variables of power system is constructed according to the power data of all wind turbines. Genetic algorithm is used to solve the parameters of Gaussian mixture model. The parameters include the weight coefficient of each Gaussian sub-component, the mean value of each Gaussian sub-component and the variance of each Gaussian sub-component. Gaussian MixtureModel with Input Variables Determined by Parameters; Obtaining the linear equation model of tidal current equation; According to the Gaussian mixture model of input variables and the linear equationmodel of power flow equation, the joint probability density function of output variables of power system is obtained to determine the probabilistic power flow of power system. The invention can greatly reduce the fitting error, has good fitting effect on the output of the fan, thereby improving the analysis precision of the probability tidal current, more accurate analysis on the stability of theline, and the whole process is simple and the calculation speed is fast.

Description

technical field [0001] The present invention relates to the field of power systems, in particular to a method and system for determining a probability flow based on a power system. Background technique [0002] At present, large-scale new energy sources represented by wind and light have been connected to the grid. How to describe the power fluctuation characteristics of these new energy sources has become a difficult point in recent years. [0003] Many scholars use the Weibull distribution to fit the wind power fluctuation characteristics, and use the Beta distribution to fit the photovoltaic power fluctuation characteristics. Due to the large fluctuation and strong randomness of new energy power, these single distribution models cannot be well Fitting these fluctuation characteristics, and the fitting accuracy of these single models is not very high, so consider using multi-distribution model fitting. [0004] The Gaussian Mixture Model (GMM) is a multi-distribution mode...

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Problems solved by technology

[0003] Many scholars use the Weibull distribution to fit the wind power fluctuation characteristics, and use the Beta distribution to fit the photovoltaic power fluctuation characteristics. Due to the large fluctuation and strong randomness of new energy power, these single distribution models cannot be well Fitting these fluctuation characteristics, and the fitting accuracy of these single models is not very high, so consider using multi-distribution model fitting

[0004] The Gaussian mixture model (GMM) is a multi-distribution model that can fit these fluctuation characteristics well. However, the traditional GMM is modeled using the maximum expectation algorithm. This algorithm has poor convergence and makes the model accuracy low.

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