A 5G-based device-cloud collaborative wind power operation and maintenance diagnosis system

Abstract

The present invention relates to the technical field of remote operation and maintenance diagnosis of wind power generation equipment, and provides a 5G-based end-cloud collaborative wind power operation and maintenance diagnosis system, including: a data acquisition gateway, a 5G-based edge computing network, a wind field control center system, and a front-end Data collection service, operation and maintenance diagnosis cloud platform. Under the synergistic effect of the above-mentioned subsystems, the present invention realizes second-level collection of wind turbine data, remote operation and maintenance, and intelligent diagnosis, solves the problems of difficult inspection and monitoring of wind turbines, improves the centralized management and control level of wind turbines in different places, and reduces It reduces the failure rate of wind turbines, ensures the high-quality power generation efficiency of wind turbines, and creates considerable economic efficiency for wind farms.

Description

technical field [0001] The invention relates to the technical field of remote operation and maintenance diagnosis of wind power generation equipment, in particular to a 5G-based device-cloud collaborative wind power operation and maintenance diagnosis system. Background technique [0002] The wind turbines form a ring network through cables to realize equipment interconnection. The wind farm deploys scada system to realize the collection of wind turbine operating status, fault codes and key parameters. It is connected to the centralized control center of the wind farm through wired connections. The centralized control center of the wind farm builds an electronic kanban to realize data visualization. and centralized management and control, store key data through localized databases, and rely on manual inspections for equipment fault diagnosis. [0003] Deficiency 1: Equipment operation and maintenance is very dependent on manual regular inspections. The wind farm manually ins...

AI Technical Summary

Problems solved by technology

[0003] Deficiency 1: Equipment operation and maintenance is very dependent on manual regular inspections. The wind farm manually inspects the running status of the fans according to the operation and maintenance plan, and detects the health status of the fan equipment based on the work experience of the inspectors. The workload is heavy and the efficiency of equipment troubleshooting is low. If it is not timely, the fan failure rate is high

[0004] Defect 2: Acquisition of wind turbine operation data through the scada system, the data transmission volume is small, high-definition video cannot be transmitted, and there is a lack of intelligent fault diagnosis and prediction based on operating data

[0005] Defect 3: Usually, the wind turbines are installed in remote locations, widely distributed, and the geological conditions are complex. The wired network is easily affected by geological and climate disasters, and network failures occur, making maintenance difficult and costly.

[0006] Defect 4: The wind turbine control system cannot perform complex data calculations, and the data needs to be collected in the centralized control center of the wind farm for processing, and the identification and response of equipment abnormalities are not timely

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