A Transformer Winding Working State Detection Method Based on Normalized Singular Spectral Entropy

Abstract

The invention discloses a transformer winding work state detection method based on normalization singular spectrum entropies. The method comprises the following steps: 1, performing frequency sweep excitation testing on transformer windings to obtain vibration frequency response curves of the transformer windings; 2, performing curve fusion on the vibration frequency response curves of transformer windings of M test points to obtain a vibration frequency response fusion curve of the transformer windings; 3, calculating the normalization singular spectrum entropies of the vibration frequency response fusion curves of the transformer windings; and 4, according to the change of the normalization singular spectrum entropies of the vibration frequency response fusion curves, determining the states of the windings: when the change of the normalization singular spectrum entropies exceeds 10%, determining that the windings of transformers are changed, wherein at the moment, maintenance processing needs to be performed timely so that severe faults can be avoided. According to the invention, diagnosis of the states of the transformer windings can be realized, and the change condition of the transformer windings is efficiently and flexibly detected; and at the same time, through fusion of the vibration frequency response curves of different test points, influences exerted by measuring errors on determining results are reduced to the minimum.

Description

technical field [0001] The invention relates to a signal monitoring method, in particular to a detection method for the working state of a transformer winding. Background technique [0002] As a primary device for power conversion, the power transformer plays a very important role in the power system and is called the heart of the power system. The safe and stable operation of the transformer is of great significance to the reliability and stability of the power grid. As the capacity of my country's power grid continues to increase, the short-circuit capacity increases accordingly. The huge electromagnetic force generated by the surge current formed by the short-circuit at the transformer outlet will pose a serious threat to the mechanical strength and dynamic stability of the transformer winding. If the faulty transformer is not repaired in time, it will not only damage the transformer, but also affect the normal operation of the power grid, and even cause the power system...

AI Technical Summary

Problems solved by technology

[0004] Transformer winding deformation detection is one of the routine test items of transformers at present. There are two most commonly used detection methods: one is the short-circuit impedance method, because the short-circuit impedance of the transformer mainly reflects the leakage reactance of the transformer winding, and the transformer leakage reactance is determined by The winding structure determines that once the transformer winding is loose or deformed, the leakage reactance of the transformer will also change accordingly. Therefore, the detection of the short-circuit impedance of the transformer can indirectly reflect whether the transformer winding is loose or deformed, but the sensitivity of this method is low , the fault detection rate is low, and more accurate diagnosis results can only be obtained when the overall deformation of the transformer coil is relatively serious

The second is the frequency response method. The transformer winding is regarded as a distributed parameter network, and its characteristics are described by the transfer function in the frequency domain. When the local mechanical deformation of the winding occurs, the distribution parameters change accordingly, thereby changing the network transfer function. Therefore, by Analyzing the network transfer function curve of the transformer winding can analyze the network electrical parameters, so as to deduce whether the transformer winding is loose or deformed. Quantitative criterion of

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