On-load tap-changer mechanical fault diagnosis method

Abstract

The invention provides an on-load tap-changer mechanical fault diagnosis method. The method comprises the steps of collecting vibration signals of an on-load tap-changer device at at least two positions in an operation state through an acceleration sensor, and carrying out the preprocessing of the vibration signals; carrying out GA-VMD decomposition on the at least two preprocessed channel vibration signals, and extracting an energy entropy and a fuzzy entropy to construct a feature vector to serve as observation value matrix input of a coupling hidden Markov model; inputting the feature vector obtained in the step 2 into a coupling hidden Markov model for training, and establishing a coupling hidden Markov model operation state model; and inputting the test set into the operation state model obtained in the step 3, and judging the state and the fault type of the on-load tap-changer according to the maximum likelihood probability. According to the on-load tap-changer mechanical fault diagnosis method provided by the invention, the diagnosis result is high in accuracy, the structure is simple, and the operability is high.

Description

technical field [0001] The invention belongs to the technical field of electric equipment, in particular to a method for diagnosing a mechanical fault of an on-load tap changer. Background technique [0002] The importance of electric power to national economic and social development is self-evident, and power grid accidents caused by power equipment failures often cause huge economic losses and negative impacts. On-load tap changer (OLTC), as a key component of on-load tap changer, plays an important role in the power system to stabilize the grid voltage, adjust the reactive power flow, and increase the flexibility of the grid. However, on-load tap-changer failures occur more frequently. According to the statistics of China Electric Power Academy, from 1990 to May 1994, transformer accidents caused by on-load tap changer failures accounted for 18.5% of the total transformer accidents nationwide. According to foreign statistics, faults account for 41% of the faults of on-l...

AI Technical Summary

Problems solved by technology

Most of these methods are based on linear theory, however, studies have shown that the vibration signal during the switching process exhibits significant nonlinear behavior

Therefore, the time-frequency analysis method of assuming the vibration signal as a stationary or piecewise stationary signal is not very obvious.

EMD is a signal time-frequency analysis method proposed in recent years. It can effectively extract time-frequency features from data generated in noisy nonlinear and non-stationary processes, but it has the defects of mode mixing and endpoint effects.

The EEMD algorithm solves the modal aliasing effect existing in the EMD algorithm, but the calculation amount is too large, which affects the application efficiency in engineering

Variational Mode Decomposition (VMD) solves the above problems. It is an adaptive multi-resolution technology. Accuracy affects

[0004] In addition, the current fault judgment of OLTC only relies on a single vibration signal. Single-channel signals often cannot contain all fault features and have high requirements for signal feature extraction technology. The processing is difficult and the classification accuracy needs to be improved.

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