A dynamic equivalence method for wind farms of direct drive permanent magnet wind turbines

Abstract

The invention relates to a dynamic equivalence method for a wind power station of direct drive permanent magnet wind turbine generators. The method is based on output time sequence data clustering of the wind turbine generators and comprises the following steps: selecting wind turbine generator clustering sample data; carrying out phase-space reconstruction on all sampling data; obtaining a distance matrix dn*n of every two wind turbine generators by use of a geometric template matching algorithm; clustering the wind turbine generators by use of an attribute threshold clustering algorithm; and carrying out equivalence on wind turbine generator parameters and network parameters in the wind turbine generator cluster. According to the dynamic equivalence method provided by the invention, the time sequence data of output power of the wind turbine generators are used as the cluster sample data, and the clustering is carried out by use of the sample data to reflect the operation properties of the wind turbine generators at various moments within the time period; the clustering is carried out by use of the geometric template matching algorithm and the attribute threshold clustering algorithm, the clustering method can reflect the operation properties of the wind turbine generators, the obtained clustering result is suitable for all moments within the time period, and multi-time clustering of the wind turbine generators can be avoided, so that the dynamic equivalence method has a certain engineering application value.

Description

technical field [0001] The invention relates to a wind farm equivalent method, in particular to a wind farm dynamic equivalent method for a direct drive permanent magnet wind turbine. Background technique [0002] In large-scale wind farm grid-connected simulation, if modeling each wind turbine, not only the workload is heavy, but also the speed, accuracy and convergence of the algorithm will be affected. Therefore, it is necessary to carry out dynamic equivalent modeling of wind farms. At present, the equivalent modeling methods of wind farms mainly include single-unit equivalent method, semi-equivalence method and multi-machine equivalent method. The single-machine equivalent method means that all the wind turbines in the wind farm are equivalent to one machine; the half-equivalence means that the wind turbine part of each wind turbine is retained, and the generator model is equivalent to one; the multi-machine equivalent means that The wind turbines are equivalent to m m...

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Problems solved by technology

However, the current grouping of wind turbines is mostly based on data at a fixed time, and the grouping method will change as time changes. If you want to make the grouping results of wind farms unique within a period of time, grouping by using time series is a feasible method. However, there are few studies on the use of time series data for grouping of wind turbines

There are literatures that extract indicators from the transient trajectory of machine terminal voltage to group wind turbines, but there are few failures in actual operation, and the adaptability is not very wide; there are also literatures that use spectral clustering algorithm based on diffusion mapping theory to target The measured active and reactive time series of wind turbines are grouped, and a feasible idea is proposed for the research of grouping using the time data measured by wind turbines

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