03-11-2022 дата публикации
Номер: US20220349957A1
Принадлежит:
The present disclosure provides a system for perceiving an operating state of a large power transformer based on vibro-acoustic integration, including a perception layer, a network layer, and a diagnostic layer, where the perception layer is used for monitoring, in real time, a state parameter for a coupling vibration signal and an acoustic signal of each of a transformer core, a winding, a clamp and a housing, a state parameter of each of a vibration signal and an acoustic signal during a gear position change of an on-load tap changer (OLTC), and preliminarily diagnosing and analyzing monitored data. The system can monitor the operating state of the OLTC online for a long time, flexibly configure the sensor channel and the sensor type according to different application requirements, automatically acquire and identify the gear position change, and correctly identify and process a gear position corresponding to the monitoring signal. 1. A system for perceiving an operating state of a large power transformer based on vibro-acoustic integration , comprising: a perception layer , a network layer and a diagnostic layer , whereinthe perception layer is used for monitoring, in real time, a state parameter for a coupling vibration signal and an acoustic signal of each of a transformer core, a winding, a clamp and a housing, a state parameter of each of a vibration signal and an acoustic signal during a gear position change of an on-load tap changer (OLTC), and preliminarily diagnosing and analyzing monitored data;the network layer is used for reliably transmitting a monitoring signal to a background; andthe diagnostic layer is used for managing basic information of a tested OLTC device; configuring a parameter for analysis of the perception layer; receiving the monitored data from the perception layer; analyzing and displaying a monitoring state and an advanced intelligent analysis result of the tested device in real time; performing fault data analysis, original graph ...
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