UHV equipment monitoring system and method

Abstract

The invention discloses UHV equipment monitoring system and a UHV equipment monitoring method. The UHV equipment monitoring system comprises a platform resource layer, a basic service layer, a servicesupport layer and an application service layer, wherein the platform resource layer is configured to provide data and resource support, and the platform resource layer comprises a data base storing data generated during the operation and maintenance of UHV equipment, a knowledge base storing knowledge of the UHV equipment, a model library storing algorithm models and diagnostic rules, and an index library; the service support layer is configured to perform fault diagnosis and state monitoring on the UHV equipment, and the service support layer receives an operation instruction transmitted from the application service layer, accesses an infrastructure service layer, and performs corresponding business logic processing to generate a processing result in response to the operation instruction; and the service support layer comprises fault diagnosis module and a state monitoring module, and the fault diagnosis module comprises a dimensionality reduction unit which utilizes a non-linear manifold learning algorithm to directly extract low-dimensionality manifold in an original high-dimensional data space and a diagnostic unit for fault diagnosis based on a hybrid hidden Markov model.

Description

technical field [0001] The invention relates to the technical field of electric power equipment, in particular to a monitoring system and method for UHV equipment. Background technique [0002] The power system is moving in the direction of UHV, which has become a hot spot in power grid construction in recent years. Complex equipment such as UHV transformers and combined electrical appliances in UHV AC and DC substations have many components and multiple levels of structure. The relationship is complex and the coupling is strong. Compared with low-voltage power equipment, UHV equipment undertakes more important functions of power transmission and transformation. Once a failure occurs, it will often cause huge losses or even catastrophic consequences. Traditional "after-event maintenance" and "planned maintenance" It is difficult to adapt to the needs of UHV equipment fault diagnosis tasks. "Condition-based maintenance" and "predictive maintenance" that eliminate faults in t...

AI Technical Summary

Problems solved by technology

In addition, as fault diagnosis and state prediction technology are the core functions of equipment fault prediction and health management system, the existing algorithm models also have limitations and need to be improved

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