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Power grid frequency intelligent control method based on empirical mode decomposition

An empirical mode decomposition, grid frequency technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve problems such as the inability to meet the high efficiency requirements of power systems, and achieve the effect of optimal grid frequency control.

Active Publication Date: 2021-02-23
GUANGXI UNIV
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Problems solved by technology

Therefore, the traditional single-layer frequency control framework cannot meet the power system's requirements for frequency control efficiency

Method used

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  • Power grid frequency intelligent control method based on empirical mode decomposition
  • Power grid frequency intelligent control method based on empirical mode decomposition
  • Power grid frequency intelligent control method based on empirical mode decomposition

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Embodiment Construction

[0051] A power grid frequency intelligent control method based on empirical mode decomposition proposed by the present invention is described in detail in conjunction with the accompanying drawings as follows:

[0052] figure 1 It is the implementation flowchart of the method of the present invention. In order to prove the superiority of the method of the present invention, an IEEE standard two-area load frequency control simulation example including a wind power generation model, a photovoltaic power generation model and an energy storage device model is used for verification, and the inventive method is further elaborated and illustrated. The implementation steps of this embodiment are as follows:

[0053] (1) Record the frequency deviation signal data of a certain area in the two areas of the previous day. The format of the data is to sample the frequency deviation every 1 second from 0:00 to 24:00 of the current day, with a total of 86401 time nodes Record the grid frequ...

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Abstract

The invention provides a power grid frequency intelligent control method based on empirical mode decomposition. The method comprises two modules: an empirical mode decomposition module based on a long-term and short-term memory artificial neural network, and a control module comprising a reinforcement learning method and a deep neural network. Meanwhile, a double-layer step-by-step frequency control framework is provided. Firstly, a decomposition module decomposes a power grid frequency signal into a plurality of modal components in real time, and the regularity of the signal is highlighted; secondly, the control module adjusts the output of the generator set according to the high-frequency signal and the low-frequency signal of the modal component to maintain the stability of the power grid frequency; and finally, in a power grid containing multiple units, the decomposition module and the control module can be effectively applied to a double-layer step-by-step frequency control framework to obtain optimal control performance.

Description

technical field [0001] The invention belongs to the field of power grid frequency control, and relates to a frequency control method based on artificial intelligence technology, which is suitable for secondary frequency regulation of power systems. Background technique [0002] Frequency stability is one of the important indicators to measure the power quality of the power grid. Frequency adjustment can generally be divided into primary frequency modulation, secondary frequency modulation and tertiary frequency modulation according to the frequency modulation cycle. Secondary frequency modulation is also called automatic power generation control. It maintains the frequency of the power grid within a stable range (50±0.2Hz in China) by adjusting the output of the generator set in real time, and evaluates the main indicators of control performance with frequency deviation and regional control error. . Proportional-integral-derivative control method is the main traditional con...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H02J3/24H02J3/46H02J3/28G06N3/04G06N3/08
CPCH02J3/241H02J3/466H02J3/28G06N3/049G06N3/08H02J2203/20H02J2203/10G06N3/044G06N3/045Y04S10/50
Inventor 殷林飞吴云智孙志响高放
Owner GUANGXI UNIV
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