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Power grid oscillation mode evaluation and safety active early warning method based on deep learning

A technology of deep learning and oscillation mode, applied in electrical digital data processing, instruments, computer-aided design, etc., can solve the problems of power grid volatility and time-varying enhancement, massive measurement data cannot be effectively used, and dynamic model dimension increase

Inactive Publication Date: 2021-02-02
ZHEJIANG UNIV
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Problems solved by technology

[0003] With the continuous development of the power system, the power system is facing many new challenges in the case of a large number of access to new energy sources: the increase in the dimension of the dynamic model, the increase in the volatility and time-varying nature of the power grid, and the ineffective use of massive measurement data, etc. problem

Method used

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  • Power grid oscillation mode evaluation and safety active early warning method based on deep learning
  • Power grid oscillation mode evaluation and safety active early warning method based on deep learning
  • Power grid oscillation mode evaluation and safety active early warning method based on deep learning

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Experimental program
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Embodiment

[0058] Taking the IEEE48 node power system as an example, the system includes 16 generators and 68 transmission lines.

[0059] (1) Test example of uniform load change

[0060] In order to evaluate the effect of the small disturbance stability warning of the system, it is assumed that each load changes at a uniform speed at a random speed between 0.005p.u. / s and 0.005p.u. / s, and the power balance is ensured through the rescheduling of the generator set processing, every 0.1 s samples the transmission power of each node and line in the system. Randomly select 3000 sets of data as the training data. The error of the proposed algorithm for the training set and the test set is shown in the following table. The schematic diagram of the running trend of a key feature value is shown in image 3 shown.

[0061] Table 2 The training process of the eigenvalue motion trend vector under the condition of uniform load change

[0062]

[0063] (2) The load changes according to the sinu...

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Abstract

The invention discloses a power grid oscillation mode evaluation and safety active early warning method based on deep learning. The method comprises the following steps: creating a double-current convolutional neural network specific structure and hyper-parameters for predicting a key characteristic value trend according to the scale and structure of a power system needing early warning, and determining an input and output data structure; training a model for predicting the change trend of the key characteristic value of the power grid according to the simulated mass data; and finally, performing small-interference stability early warning by using the trained model according to the online real-time measurement information. The method can track the current operation state of the power gridin real time, and meets the requirements of real-time small-interference stable safety early warning of a power system.

Description

technical field [0001] The invention belongs to the field of power system stability analysis, and in particular relates to a deep learning-based power grid oscillation mode evaluation and safety active warning method. Background technique [0002] As a national basic energy facility, the power industry is closely related to social development and people's lives. It is an important condition for the healthy, stable and sustainable development of the national economy and society. The safe and stable operation of the power grid is the guarantee for all walks of life to carry out production activities in a safe and orderly manner. In recent years, with the rapid development of new energy power generation, smart grid, and UHV AC / DC transmission, my country's power grid is becoming a super-large-scale power grid with gradually increasing interconnection across the country. At the same time, the concept and concept of energy Internet are gradually being Recognized by industry and ac...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/27G06F113/04G06F119/02
CPCG06F30/27G06F2113/04G06F2119/02
Inventor 江全元颜融李洋麟耿光超寸馨
Owner ZHEJIANG UNIV
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