Online monitoring method of reactive power control capability of wind power plant

A wind farm and power control technology, applied in wind power generation, reactive power compensation, AC network voltage adjustment, etc.

Active Publication Date: 2020-05-08

ZHEJIANG UNIV +1

5 Cites 3 Cited by

AI-Extracted Technical Summary

Problems solved by technology

At present, most renewable new energy power generation bases maintain unit power factor operation, that is, they do not exchange reactive power with the outside world. Influenced by fluctuations, the voltage stability problem of the power system containing renewable ne...

Abstract

The invention discloses an online monitoring method of a reactive power control capability of a wind power plant. The method comprises the following steps: firstly, constructing a wind power plant reactive power control capability aggregation evaluation model in combination with wind power plant operation state information measured by a field measurement mutual inductor; then selecting a relatively small reference point to update a threshold parameter based on previous application experience to construct a wind power plant reactive power control capability online monitoring model; on the basisof the above model, according to related operation data accumulated step by step, fitting a quantitative relation of evaluation errors and evaluation time consumption in a daily operation scene of the wind power plant with respect to threshold parameters through a data driving method; and finally, constructing a reference point updating threshold parameter optimization model and solving to obtainan optimal value for evaluating a dynamic updating threshold of a reference point so as to realize an optimal operation effect of the wind power plant reactive power control capability online monitoring model.

Application Domain

Single network parallel feeding arrangementsWind energy generation +2

Technology Topic

Data-drivenReactive power control +5

Image

Examples

Experimental program(1)

Example Embodiment

[0083] Example

[0084] In the following, the present invention takes an actual wind farm in China as an example to specifically describe the application effect of the present invention. The wind farm consists of 33 doubly-fed wind turbines with a rated power of 1.5MW connected to a suitably modified IEEE-9 node power system. The measured data on May 18, 2019 is selected as the test data, including the measured wind speed of each wind turbine in the wind farm (one measurement point every 5 minutes, a total of 288 measurement points) and the real-time active power output of each wind turbine. .

[0085] figure 1 The implementation flow of the content of the present invention is shown. On the basis of the wind farm reactive power control capability aggregation evaluation model and the wind farm reactive power control capability online monitoring model, according to the relevant operating data accumulated gradually, the data-driven method is used to fit the evaluation error and the estimated consumption in the daily operation scenario of the wind farm. quantization relationship with respect to the threshold parameter. Among them, the nonlinear relationship between the evaluation error and the system disturbance is as follows figure 2 As shown, the approximate linear relationship between the evaluation time and the number of benchmark updates is as follows image 3 shown. Based on the above results, the threshold parameter optimization model is constructed and solved. Among them, the weight factors of the evaluation error and evaluation time in the optimization objective function are both set to 0.5, and the optimal value of the dynamic update threshold of the evaluation reference point is finally obtained as 0.4. Figure 4 The evaluation error E and evaluation time T are given with respect to the threshold parameter D T The actual simulation results of , in which several typical threshold parameters (0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 2.0) were selected for testing, it can be seen that 0.4 is a relatively optimal This conclusion verifies the correctness of the threshold parameter optimization model constructed by the present invention. Figure 5 The operation results of the online monitoring method for the reactive power control capability of the wind farm under the optimal parameters are given, and it can be seen that the method has achieved very good monitoring results.

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