Fan blade fault prediction method and system and storage medium

Abstract

The invention relates to the technical field of wind power generation, in particular to a fan blade fault prediction method and system based on deep learning and a storage medium. The method comprises the steps that a deep learning model is established, and the deep learning model is trained by using historical operation data of a fan blade; a blade operation normal judgment range is determined according to the historical operation data of the fan blade; data is collected from a fan SCADA system, and the collected data is input into the deep learning model to obtain a blade vibration equivalent; whether the blade vibration equivalent is within the blade operation normal judgment range or not is judged, and if the blade vibration equivalent is within the blade operation normal judgment range, it is judged that the blade operates normally; and if the blade vibration equivalent is beyond the blade operation normal judgment range, it is judged that the blade is in a pre-fault state, and the blade is maintained before faults occur so that the faults such as blade breakage can be avoided. According to the fan blade fault prediction method based on deep learning, the vibration condition of the existing fan blade can be accurately monitored without arranging additional structures, so that the blade faults are predicted, the operation maintenance of the fan blade is enhanced, and severe consequences such as fan blade breakage can be avoided.

Description

technical field [0001] The present invention relates to the technical field of wind power generation, and in particular to a method, system and storage medium for fault prediction of fan blades based on deep learning. Background technique [0002] The working principle of a wind turbine is that the wind rotor rotates under the action of the wind, converting the kinetic energy of the wind into the mechanical energy of the wind rotor shaft, and the generator rotates to generate electricity under the drive of the wind rotor shaft. Fan blades are the key components of wind turbines, but their working environment is complicated. If the fan blades vibrate abnormally, cracks may be formed on the blades. If the continuous vibration is abnormal, the blades may break, resulting in huge economic losses. Usually, the monitoring of blade vibration is to install sensors on the blades to collect vibration data, and then analyze the data collected by the sensors to monitor the vibration of ...

AI Technical Summary

Problems solved by technology

Existing wind turbines generally do not have sensors on the blades to collect the performance of the blades. The later installation of sensors is economical and requires a lot of manpower, and the fan blades work in an outdoor environment, the working environment is complicated, and the data collected by the sensors is noisy. It is relatively large and cannot accurately reflect the real condition of the blade; in addition, since the blade is always in a state of vibration during normal operation, occasional vibration abnormalities often cannot reflect the damage of the blade

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