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

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 ...

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.

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