A Dynamic Equivalence Modeling Method of Wind Farm Based on Dynamic Gray Relational Analysis Method

Abstract

The invention relates to a method for modeling dynamic equivalence of a wind power farm based on a dynamic grey-relevancy analysis method. The method comprises the following steps: surveying relevancy between any time data value of actually measured operating data and historical data during actually measured operating data preprocessing by adopting autocorrelation analysis, mining an implicit rule in the data, selecting reasonable time span, and further determining small data samples at K moments in the span to serve as simulation model input according to different wind speed scales, so that the influence of simulation model precision and analysis and calculation duration due to too few data samples or multifarious redundancy is reduced. Moreover, an association degree matrix is established to serve as a clustering index by adopting the dynamic grey-relevancy analysis method, and complex relation and grey property in an operating state of each wind generation set are considered into the fleet clustering process, so that the fleet clustering result is reasonable, and the accuracy of a dynamic equivalence model of the wind power farm is greatly improved.

Description

technical field [0001] The invention relates to the technical field of renewable energy grid connection, and more specifically, relates to a dynamic equivalent modeling method for wind farms based on a dynamic gray relational analysis method. Background technique [0002] Wind power generation has attracted more and more attention from all over the world because of its advantages such as large growth space for installed capacity, rapid cost reduction, safety, and energy never being exhausted. However, wind energy has the characteristics of randomness, intermittence and instability. With the continuous expansion of the capacity of wind turbines and the scale of wind farms, the impact of wind power grid integration on the stability of power systems is becoming more and more significant. According to incomplete statistics, there were 193 wind turbine off-grid accidents in 2011 alone. Among them, the largest 2.24 large-scale wind power off-grid accident in Jiuquan, Gansu Provinc...

AI Technical Summary

Problems solved by technology

[0005] Among them, the modeling process of the stand-alone equivalent method is simple. It is usually assumed that the input wind speeds of all wind turbines are the same, and the entire wind farm is equivalent to one wind turbine. However, for large wind farms, due to topography and wake effects and Due to the influence of time lag, the distribution of wind speed in the wind farm is uneven, and the wind speed of wind turbines varies greatly. Using the single-machine equivalent method usually has a large error

[0006] The most commonly used and simplest method in multi-machine equivalence is to classify and aggregate wind farms according to their arrangement positions, often treating the same exhaust fan as a single wind turbine, taking into account the differences in the operating conditions of different row fans and reducing the wind power consumption. However, in actual wind farms, there may be large wind speed differences even for fans with the same exhaust

[0007] In the existing technology, the relevant equivalent model treats all data samples equally, and there are many input vectors, complicated and redundant data samples, which is not conducive to analysis, which will have a certain adverse effect on simulation accuracy and simulation analysis time; on the other hand, the current State-of-the-art related equivalence models generally use a single state quantity or all monitoring item quantities of each wind turbine as the input of the clustering model to perform unit equivalence division, and lack of consideration for the correlation and grayness between the operating conditions of each wind turbine , and the operating state of each wind turbine is affected by various factors such as weather, power grid operation, temperature, etc. and is not deterministic, which is a complex nonlinear process

[0008] Therefore, the above unit equivalence division method does not consider the correlation between the operating states of wind turbines comprehensively and clearly, which will affect the rationality of the clustering results and reduce the simulation accuracy

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