Power system transient stability evaluation method based on short-time disturbed trajectory

A technique for transient stability and disturbed trajectories, applied to electrical components, circuit devices, AC network circuits, etc., can solve the problems of limited expression ability of input data features and constraints of generalization ability

Inactive Publication Date: 2018-12-07
NORTHEAST DIANLI UNIVERSITY +2
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Problems solved by technology

However, due to the limitations of the shallow structure of the machine learning method, the feature expression ability of the input data is limited, and the generalization ability is restricted when solving complex classification problems.

Method used

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  • Power system transient stability evaluation method based on short-time disturbed trajectory
  • Power system transient stability evaluation method based on short-time disturbed trajectory
  • Power system transient stability evaluation method based on short-time disturbed trajectory

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Embodiment Construction

[0045] The power system transient stability evaluation method based on short-term disturbed trajectory of the present invention, it comprises the following steps:

[0046]1) Determination of input features and output results of convolutional neural network:

[0047] ① Obtain disturbed trajectories of voltage amplitude, active power, rotational speed and power angle of a large number of generators through offline simulation, select four electrical quantity trajectories within 0.2s after the fault clearing time, sampling interval T=0.01s, each trajectory has a total of With 20 sampling points, the sampling sequences of the four short-term disturbed combined trajectories obtained by simulation constitute the input sample matrix set of the convolutional neural network, and the sampling sequences of the four electrical quantities of each generator are used as the input sample matrix set of each input sample matrix. column, the dimension of each sample matrix is ​​20×4n (n is the nu...

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Abstract

The invention relates to a power system transient stability evaluation method based on a short-time disturbed trajectory after fault removal. The method is characterized by comprising the following steps: firstly, selecting the short-time disturbed trajectory of the electric quantity at the end of the generator after the fault is removed, and secondly, searching the characteristic arrangement modeof the short-time trajectory when the evaluation accuracy is optimal, so that the local characteristic extracted by the model is more robust, and optimizing the parameters of the network window by taking the optimal comprehensive evaluation index of the model as the target, so that the generalization ability of the model is enhanced, and finally establishing the mapping relation between the short-time trajectory and the transient stability, so that rapid and accurate transient stability rapid evaluation of the power system can be realized. According to the method, misjudgment missing samplesof model evaluation can be effectively reduced, and compared with a traditional machine learning evaluation method, the method is more accurate and efficient, and the transient stability can be evaluated based on the short-time disturbed trajectory, and a certain time margin is reserved for a dispatcher to take a control measure.

Description

technical field [0001] The invention relates to the field of safe and stable operation of power systems, and relates to a method for evaluating transient stability of power systems based on short-term disturbed trajectories. Background technique [0002] Rapid assessment of transient stability is an important prerequisite for online security prevention and control of power systems, and is of great significance to the safe and stable operation of power systems. The existing rapid transient stability assessment technology uses the geometric characteristics of the response trajectory of the system to evaluate the transient stability of the power grid after a fault. It does not depend on the model and parameters of the system, and the analytical results are universal. However, this type of method requires a long time to acquire It is difficult to meet the requirements of "instant measurement, identification, and control". [0003] The machine learning method does not need to es...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H02J3/00
CPCH02J3/00H02J2203/20
Inventor 安军李宗翰艾士琪赵高尚李京杨少波刘道伟李柏青杨红英郑恒峰邵广惠徐兴伟阴宏民王震宇刘洋高德宾李泽宇
Owner NORTHEAST DIANLI UNIVERSITY
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