Key electric energy equipment fault diagnosis method based on deep learning

Abstract

The invention discloses a key electric energy equipment fault diagnosis method based on deep learning. The key electric energy equipment fault diagnosis method based on deep learning comprises the following steps: collecting data; segmenting tasks by a cloud computing platform; extracting and screening fault features, and then performing fault diagnosis analysis; producing a diagnosis result; and enabling a monitoring end to take countermeasures. Empirical mode decomposition has good time-frequency aggregation, and VMD can effectively avoid mode aliasing and noise interference when acquiring an intrinsic mode function (IMF); fault feature extraction of the circuit breaker is achieved through cooperation of the two vibration signal feature extraction methods; and a cloud computing technology is adopted, hardware nodes are increased or reduced according to actually needed computing resources, and the cost of equipment maintenance and data backup is reduced, so that intelligent power distribution and utilization typical service big data analysis of power saving, power utilization prediction, power distribution and utilization grid optimization and off-peak scheduling is realized.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis of key electric energy equipment, in particular to a fault diagnosis method for key electric energy equipment based on deep learning. Background technique [0002] With the development of sensing technology, automation control technology, communication network technology, distributed storage technology, big data analysis and artificial intelligence technology, as well as the huge promotion of social, economic and environmental needs, the power grid will also truly achieve deep integration with the Internet. The development of "smart grid" is an inevitable development trend of the power industry. [0003] With the increasing scale of the power system, the structure of the power grid is becoming more and more complex, and the consequences of system failures are becoming more and more serious. Many blackouts at home and abroad have caused huge economic losses and adverse social impacts. The p...

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Problems solved by technology

[0003] With the increasing scale of the power system, the structure of the power grid is becoming more and more complex, and the consequences of system failures are becoming more and more serious. Many blackouts at home and abroad have caused huge economic losses and adverse social impacts. The power grid equipment is widely distributed, with many management elements and information. The large amount of data leads to high management costs and low emergency response capabilities for emergencies. The existing equipment technology can no longer adapt to the rapid development of the power grid. It is urgent to develop efficient and reliable key technologies for power distribution systems to solve the problem of power grid accidents. At this stage, the performance of low-voltage distribution cabinets in terms of reliability, safety, intelligent control, and modularization needs to be improved. The safety and reliability of the operation of the power distribution room is only based on the uncertainty of the skills and sense of responsibility of the electrician on duty. based on the factors

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