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Dynamic safety assessment method based on extreme gradient boosting decision tree

A dynamic security and decision tree technology, applied to instruments, character and pattern recognition, data processing applications, etc., can solve problems such as poor robustness and low efficiency of model training

Pending Publication Date: 2020-07-10
CHINA THREE GORGES UNIV
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, this technology shows the problem of low model training efficiency and poor robustness in transient stability assessment.

Method used

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  • Dynamic safety assessment method based on extreme gradient boosting decision tree
  • Dynamic safety assessment method based on extreme gradient boosting decision tree
  • Dynamic safety assessment method based on extreme gradient boosting decision tree

Examples

Experimental program
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Embodiment

[0102] The present invention is tested on an IEEE 39-node system, wherein the IEEE 39-node system includes 39 nodes and 10 generators. Simulate a three-phase short-circuit fault, the fault removal time is 0.3 seconds, 4000 samples are generated, 80% of the samples are used for training, and the remaining 20% ​​of the samples are used for testing. All tests were performed on a computer with an Intel Core i7 processor and 8GB of RAM.

[0103] Using the residual squared error (R 2 ) and Root Mean Squared Error (Root Mean Squared Error, RMSE) to evaluate the performance of the evaluation model, R 2 , RMSE are shown in formulas (8) and (9):

[0104]

[0105]

[0106] In the formula: Y i is the actual TSM value; Y i * is the predicted value of the model; for Y i The average value of ; n is the sample size.

[0107] The performance test results of the evaluation model in the present invention are shown in Table 1. Usually, R 2 The larger and the smaller the RMSE, the...

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Abstract

The invention discloses a dynamic safety assessment method based on an extreme gradient boosting decision tree. The method comprises the steps: 1, establishing an original sample set; 2, forming an intermediate sample set; 3, forming an efficient sample set; 4, performing offline training on the extreme gradient boosting decision tree by utilizing the efficient sample set, and constructing a dynamic safety evaluation model based on the XGBoost decision tree; 5, due to the influence of various operation factors of the power system, updating the efficient sample set through time domain simulation, and updating aDSA model by using the updated sample set; 6, based on the real-time measurement data of the synchronous phasor measurement unit, performing online DSA on the power system by using the DSA model. The invention aims to provide a dynamic safety assessment method based on an extreme gradient boosting decision tree. According to the method, online DSA can be efficiently and accuratelycarried out on the power system, the robustness of the evaluation model is good, and the method can adapt to the change of a network topology structure.

Description

technical field [0001] The invention relates to the field of dynamic security assessment of electric power systems, in particular to a dynamic security assessment method based on limit gradient lifting decision tree. Background technique [0002] Renewable energy sources such as wind energy and solar energy are increasingly integrated into modern power systems because they effectively reduce carbon emissions. With the widespread application of renewable energy power generation in the power system, the structure of the modern power system is becoming more and more complex and the scale is increasing. While achieving benefits, it also bears huge risks. A small disturbance may cause inestimable impact and loss. Therefore, it plays a very important role in the operation and planning of the power system to assess the safety level of the power system accurately and quickly. [0003] Traditionally, time-domain simulation method and transient energy function method are usually use...

Claims

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Application Information

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IPC IPC(8): G06Q10/06G06Q50/06G06Q50/26G06K9/62G06F30/27
CPCG06Q10/0639G06Q50/06G06Q50/265G06F18/24323G06F18/214
Inventor 刘颂凯晏光辉刘峻良刘炼张涛李文武李欣郭攀锋刁良涛邱立曹成王丰李丹
Owner CHINA THREE GORGES UNIV
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