Synchronous motor rotor winding turn-to-turn short circuit fault diagnosis method based on multi-source information

Abstract

The invention provides a synchronous motor rotor winding turn-to-turn short circuit fault diagnosis method based on multi-source information. The synchronous motor rotor winding turn-to-turn short circuit fault diagnosis method comprises the following steps: acquiring exciting current, rotor vibration and stator vibration signals of a synchronous motor working normally and under different rotor winding turn-to-turn short circuit working conditions in real time; converting the one-dimensional time sequence signal into a two-dimensional grayscale image by using a signal-image conversion method; respectively inputting the two-dimensional grayscale images of the excitation current signal, the rotor vibration and the stator vibration into the optimal two-dimensional CNN model for training to obtain a fault diagnosis loss function and diagnosis accuracy of a single signal, and determining basic trust distribution; and performing decision fusion on the fault diagnosis accuracy rates of the three evidence bodies by using a multi-source information fusion algorithm to realize final diagnosis. According to the method, the characteristics of the original signal are reserved to the greatest extent, the influence of manual characteristic extraction is eliminated, and the accuracy of synchronous generator rotor winding turn-to-turn short circuit fault diagnosis is remarkably improved.

Description

technical field [0001] The invention relates to the technical field of synchronous motor fault diagnosis, in particular to a method for diagnosing inter-turn short-circuit faults of synchronous motor rotor windings based on multi-source information. Background technique [0002] Synchronous motors frequently fail due to their complex structure and changing operating environment. Turn-to-turn short circuit of rotor windings is one of the most common faults in synchronous motors. When such faults occur in synchronous motors, the excitation current will increase, the temperature at the short-circuit point will rise, and the vibration of the shaft system will increase. If the degree of expansion is enlarged, it may cause the magnetization of the large shaft and the forced shutdown of the generator set due to ground faults at one or two points of the rotor, posing a threat to the safe operation of the generator and the entire power grid. Therefore, it is of great practical signi...

AI Technical Summary

Problems solved by technology

Deep learning can automatically extract the characteristics of the original signal, reducing the dependence on experience, and solving the problems of machine learning relying on expert experience, time-consuming, and poor generalization ability

However, at present, deep learning is mostly applied to motor bearing faults, and it is rarely used in the diagnosis of motor rotor inter-turn short circuit faults, and most of them rely on a single fault feature to achieve the purpose of diagnosis; the fault information reflected by a single signal is one-sided, and the acquisition of a single signal Vulnerable to factors such as weak fault characteristics, sensor faults, and changing environments, leading to misjudgments and missed judgments

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