Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

A data-driven approach for static voltage stability assessment of power systems

A static voltage stabilization, power system technology, applied in data processing applications, electrical components, circuit devices, etc., can solve the problems that cannot meet the requirements of high adaptability and high precision of voltage stability evaluation methods, cannot process sample data, and sample input space. Increase and other problems, to achieve the effect of reducing redundancy, overcoming large amount of calculation, and effective feature selection

Active Publication Date: 2022-04-08
CHINA THREE GORGES UNIV
View PDF4 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] However, literature 1 (Y.Fan, S.Liu, L.Qin, H.Li, H.Qiu, Anovel online estimation scheme for static voltage stability margin based on relationships exploration in a large data set, IEEE Trans Power Syst 30(3)( 2014) 1380–1393.) and literature 2 (B.Wang, B.Fang, Y.Wang, and Y.Liu, "Power system transient stability assessment based on big data and the core vector machine," IEEE Trans.SmartGrid, 7 (5) (2016) 2561-2570.) Both point out that ANN and SVM cannot handle a large amount of sample data, and literature 1 points out that dealing with data loss is also a problem for DT
At the same time, with the gradual expansion of the operation scale of modern power systems, the input space of samples will inevitably increase rapidly, resulting in the "curse of dimensionality"
[0006] In summary, the current static voltage evaluation method cannot meet the high adaptability and high precision requirements of the voltage stability evaluation method in the modern power system

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A data-driven approach for static voltage stability assessment of power systems
  • A data-driven approach for static voltage stability assessment of power systems
  • A data-driven approach for static voltage stability assessment of power systems

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0098] Embodiment 1: the embodiment 1 that the present invention uses is based on such as Figure 4 The shown IEEE 30-node system contains 30 nodes, 6 generators and 37 transmission lines. This test includes all steps described in the method of the present invention, by testing on a computer equipped with an Intel Core i7 processor and 8GB memory, and obtained the test results. In this test, 4270 initial samples and 526 initial features were obtained, and variables with a P value of 0 and ρ being the top 5% of all variables were selected as the features. 80% of the sample set is used for training, and the remaining 20% ​​is used for performance testing. After 5-fold cross-validation, stable results are obtained. Using R 2 and RMSE to evaluate the prediction performance, the calculation formula is as follows:

[0099]

[0100]

[0101] In the formula: Y i for the actual VSM i value; Y i * For evaluating model predictions; for Y i The average value; m is the numb...

Embodiment 2

[0114] Embodiment 2: Embodiment 2 used in the present invention is based on an actual power system with 7917 nodes, which includes 7917 nodes, 1325 generators, 5590 loads and 10796 transmission lines. The test hardware conditions are the same as in Example 1. This test obtained 10,710 initial samples and 161,109 initial features, and selected variables with a P value of 0 and ρ as the top 0.5% of all variables as the features. The test accuracy of the final model reaches R 2 = 0.9616, RMSE = 0.0163 (R 2 The closer it is to 1, the closer the RMSE is to 0, which means the higher the prediction accuracy of the model, the generally acceptable accuracy is R 2 ≥0.9, for IEEE 30-node systems, R 2 ≥0.9 corresponds to RMSE≤0.0263), it can be seen that the accuracy meets the actual needs and meets the purpose of the present invention.

[0115] The corresponding processing speed test results carried out on this system are shown in Table 1. It can be seen that the model proposed by t...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

A data-driven method for power system static voltage stability assessment, including the following steps: Step 1): Establish an initial data set; Step 2): Build a feature selection framework, and use this framework to select key features for the initial sample set, Establish a sample set composed of key features and corresponding VSM; step 3): train and update the random bit forest to form a voltage stability evaluation model based on RBF; step 4): based on the synchrophasor measurement unit and wide-area measurement system The power system operation data collected in real time, select the corresponding features, and use the trained voltage stability evaluation model to complete the real-time voltage stability evaluation. The purpose of the present invention is to establish an efficient feature selection framework and build a high-precision, highly adaptable power system static stability evaluation model, thereby providing a data-driven method for power system static voltage stability evaluation.

Description

technical field [0001] The invention relates to the field of power system static voltage stability evaluation, in particular to a data-driven method for power system static voltage stability evaluation. Background technique [0002] With the sustainable development of renewable energy and the wide-area interconnection of modern power systems, the safe operation of power systems is facing unprecedented challenges. Since voltage collapse may cause huge economic losses and adverse social impacts, the research on static voltage stability assessment has attracted more and more attention. [0003] Usually, research on static voltage stability evaluation uses Voltage Stability Margin (VSM) to measure the distance between a certain operating point and the voltage collapse point. In previous studies, it was mainly divided into two perspectives: mechanism research and data-driven. Among them, the methods based on mechanism research and used to predict VSM mainly include sensitivity ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): H02J3/46H02J3/06G06Q10/04G06K9/62G06Q50/06
CPCH02J3/466H02J3/06G06Q10/04H02J2203/20H02J2300/20H02J2203/10G06F18/214
Inventor 刘颂凯刘礼煌刘明怡陈浩薛田良张磊叶婧钟浩李世春杨苗陈云龙汪平陈星
Owner CHINA THREE GORGES UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com