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Power system anomaly prediction method based on machine learning and big data analysis

A power system and prediction method technology, applied in the field of big data analysis, can solve problems such as no power system diagnosis, and achieve the effect of real-time monitoring and abnormal prediction

Pending Publication Date: 2020-12-15
ZHONGSHAN POWER SUPPLY BUREAU OF GUANGDONG POWER GRID
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AI Technical Summary

Problems solved by technology

It can be seen that in the face of a large amount of complex data in the power system, there is currently no effective method for diagnosing, optimizing, and predicting the power system.

Method used

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  • Power system anomaly prediction method based on machine learning and big data analysis

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Embodiment Construction

[0029] The accompanying drawings are for illustrative purposes only and cannot be construed as limiting the patent;

[0030] For those skilled in the art, it is understandable that some well-known structures and descriptions thereof may be omitted in the drawings.

[0031] The technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0032] This embodiment proposes a power system anomaly prediction method based on machine learning and big data analysis, such as figure 1 As shown in FIG. 2 , it is a flow chart of the power system abnormality prediction method based on machine learning and big data analysis in this embodiment.

[0033] In the power system anomaly prediction method based on machine learning and big data analysis proposed in this embodiment, it specifically includes the following steps:

[0034] S1: Collect the original data of relevant power grid faults from the database, and pr...

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Abstract

The invention provides a power system abnormality prediction method based on machine learning and big data analysis, and the method comprises the following steps: collecting original data of a relatedpower grid fault from a database, and segmenting of the original data according to a time sequence to acquire a data sequence; preprocessing the data sequence to obtain a corresponding characteristicvalue and an abnormal degree value, and forming sample data by the data sequence, the characteristic value and the abnormal degree value; performing secondary processing, sampling, conversion and feature design and selection on the sample data, and then performing data classification to obtain feature data corresponding to the sample data; constructing a machine learning model, and inputting thefeature data into the machine learning model for training to obtain a trained machine learning model; and accessing the trained machine learning model to a database line to obtain real-time power griddetection data, inputting the real-time power grid detection data into the trained machine learning model, and outputting to obtain a power system anomaly prediction result.

Description

technical field [0001] The present invention relates to the technical field of big data analysis, and more specifically, to a power system abnormality prediction method based on machine learning and big data analysis. Background technique [0002] Smart grid is the development direction and trend of the power industry. With the construction of smart grid, a large amount of measurement and monitoring data are generated. How to process these data and tap its value is a problem faced by power companies. Modern life is very dependent on the power grid. The power grid must operate 24 hours a day with high efficiency. Common or even the most common system failures and defects in any mechanical system cannot be tolerated. [0003] At present, domestic and foreign universities and research institutions, IT companies, and power companies have carried out smart grid big data research and engineering applications. For example, IBM and C3-Energy have developed big data analysis systems ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/2458G06F40/216G06F40/284G06N20/00G06Q50/06G06K9/62
CPCG06F16/2465G06F40/216G06F40/284G06N20/00G06Q50/06G06F18/2323G06F18/23213G06F18/24
Inventor 张春梅
Owner ZHONGSHAN POWER SUPPLY BUREAU OF GUANGDONG POWER GRID
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