Transfer learning-oriented power system fault sample generation and model construction method

A technology for power systems and fault samples, applied in electrical components, circuit devices, AC network circuits, etc., can solve problems such as low accuracy and poor applicability, and achieve the effects of improving efficiency, increasing applicability, and reducing training time.

Active Publication Date: 2020-03-31
BEIJING JIAOTONG UNIV +2
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Problems solved by technology

[0004] Therefore, the technical problem to be solved by the present invention is to overcome the shortcomings of low accuracy and poor applicability of the power system transient stability evaluation model

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  • Transfer learning-oriented power system fault sample generation and model construction method
  • Transfer learning-oriented power system fault sample generation and model construction method
  • Transfer learning-oriented power system fault sample generation and model construction method

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[0028] The technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Apparently, the described embodiments are some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0029]In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer" etc. The indicated orientation or positional relationship is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the referred device or element must have a specific orientation, o...

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Abstract

The invention provides a power system fault sample generation and model construction method for transfer learning. The method comprises the steps: obtaining an initial power system transient stabilityevaluation model; obtaining a power system fault sample; inputting the power system fault sample into an initial power system transient stability evaluation model to obtain an output result of a fullconnection layer corresponding to the initial power system transient stability evaluation model; taking an output result of the full connection layer as an input feature of a classification layer ofan initial power system transient stability evaluation model; and training a classification layer based on transient stability and transient instability samples in the power system fault samples to obtain a power system transient stability evaluation model meeting evaluation requirements. According to the method, the problems that when the operation mode and the topological structure of the powersystem are greatly changed, an original power system transient stability evaluation model is not suitable any more, and time and memory are consumed when one power system transient stability evaluation model is retrained are solved.

Description

technical field [0001] The invention relates to the field of smart grids, in particular to a transfer learning-oriented power system fault sample generation and model building method. Background technique [0002] Power system transient stability refers to the ability of each generator to maintain synchronous operation and transition to a new or restore the original stable operating state after the power system is subjected to a large disturbance. The evaluation of transient stability has always been a key issue related to the safe and stable operation of the power system. . In recent years, with the increasing scale of the power grid, the degree and impact of power system instability caused by various large disturbances will become more and more serious. [0003] The current power system transient stability assessment model based on machine learning and deep learning is only applicable to small changes in the system. The switching action of the line and the line will caus...

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Application Information

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IPC IPC(8): H02J3/00
CPCH02J3/00
Inventor 吴俊勇张若愚李宝琴邵美阳卢育梓段方维杨滢璇
Owner BEIJING JIAOTONG UNIV
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