A holographic identification method for shafting vibration faults of hydroelectric generating units

Abstract

The invention discloses a holographic identification method for shafting vibration faults of hydroelectric power units, which belongs to the field of fault identification of hydropower units. The bow swirl force, the bow swirl force of the runner, the oil film force and the eccentric force of the vortex belt of the runner are used as the external excitation vibration sources to establish the nonlinear vibration model of the hydroelectric group, and solve the vibration simulation information of the hydroelectric group. The holographic spectrum is analyzed and the corresponding relationship between each holographic spectrum feature and the shafting vibration fault is obtained; for the vibration measured signal of the guide bearing, the holographic spectrum is solved and the holographic spectrum feature is extracted. If the extraction fails, it is determined that no shafting vibration fault has occurred; otherwise, It is determined that a shafting vibration fault has occurred, and the shafting vibration fault corresponding to the holographic spectrum feature is searched, and the found shafting vibration fault is used as the fault identification result. The invention can accurately and effectively realize the fault identification of the shaft system vibration of the hydroelectric unit group.

Description

technical field [0001] The invention belongs to the field of fault identification of hydroelectric power units, and more particularly relates to a holographic identification method for shafting vibration faults of hydropower units. Background technique [0002] With the continuous penetration of conventional hydropower and pumped storage in the power system, hydropower, as a clean and renewable energy, has an increasing proportion of its capacity in the power system, and has become the backbone of the global dual-carbon goal. However, as a key component of energy conversion, shafting operates in complex and harsh hydroelectric-electromechanical coupling environments for a long time, which greatly reduces the safety and reliability of hydropower systems. [0003] In recent years, a large number of scholars in the world have done a lot of research on shafting vibration fault identification, which can be roughly divided into two categories. The first is to establish a vibratio...

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Problems solved by technology

Faulty shafting fault diagnosis method based on vibration mathematical model, a model can only diagnose a specific fault, but in the actual operating environment, there are still many faults in the shafting that cannot be described by mathematical theory

The shafting fault diagnosis method based on machine learning or deep learning, due to the complexity of the hydropower group system and its operating environment, cannot obtain enough fault history data for learning, and the accuracy of fault diagnosis cannot be guaranteed. , these methods will lose a lot of effective information in the process of signal processing, further reducing the effectiveness of diagnosis

[0005] In general, the existing methods cannot accurately and effectively realize the fault identification of the vibration of the shafting system of the hydropower group

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