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Excitation inrush current identification method based on wavelet transformation and probabilistic neural network (PNN)

A technology of probabilistic neural network and excitation inrush current, applied in biological neural network models, special data processing applications, instruments, etc., can solve problems such as incoordination, achieve strong discrimination ability, and eliminate confusing judgments

Inactive Publication Date: 2012-06-20
SHANGHAI UNIVERSITY OF ELECTRIC POWER
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Problems solved by technology

However, with the development of power system and transformer manufacturing technology, just like the traditional second harmonic braking principle, various criteria using inrush current characteristics have encountered some uncoordinated contradictions in practice

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  • Excitation inrush current identification method based on wavelet transformation and probabilistic neural network (PNN)
  • Excitation inrush current identification method based on wavelet transformation and probabilistic neural network (PNN)
  • Excitation inrush current identification method based on wavelet transformation and probabilistic neural network (PNN)

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Embodiment Construction

[0021] The wavelet transform theory is developed from the short-time (fast) Fourier transform, and overcomes the related problems of time domain and frequency domain resolution. The short-time Fourier transform can only provide consistent resolution for all frequencies, but the wavelet transform is different. It uses higher frequency resolution in the low-frequency part of the signal, but the time resolution is low, and the frequency resolution can be reduced in the high-frequency part. to increase temporal resolution to obtain transient features. Because of these characteristics, wavelet is widely used in the fields of boundary processing and filtering, time-frequency analysis, signal-to-noise separation and weak signal extraction, fractal index calculation, signal identification and diagnosis, and multi-scale edge detection.

[0022] Discrete wavelet transform is a special wavelet transform that can discretely process signals in time domain and frequency domain. The definit...

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Abstract

The invention relates to an excitation inrush current identification method based on wavelet transformation and a probabilistic neural network (PNN), in particular to a novel method for identifying excitation inrush current by combining wavelet analysis and the PNN. Data of the excitation inrush current and fault current are acquired by simulation by a model constructed in Matlab / Simulink; and energy characteristic values of fault signals on different frequency bands are extracted by using the wavelet transformation, and are used as input parameters of the PNN, so that the excitation inrush current and short circuit current can be identified. The method is high in identification capacity; and confused judgment on a real short circuit fault and the excitation inrush current is avoided.

Description

technical field [0001] The invention relates to a power equipment protection technology, in particular to an excitation inrush current identification method based on wavelet transform and probability neural network. Background technique [0002] With the development of our country's national economy and the continuous improvement of the level of industrialization, the supply of electric energy has become an important issue restricting economic development. For this reason, the country has invested in the construction of many large-scale power plants and hydropower projects. Large-capacity units are continuously put into operation, which increases the system capacity, which in turn requires the expansion of the capacity of the power transmission and transformation link, resulting in a continuous increase in the proportion of large-capacity power transformers in the system. As an important equipment of the power system, the power transformer is of great significance to the saf...

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Application Information

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IPC IPC(8): H02H7/045G06F17/50G06N3/02
Inventor 杨旭红许行
Owner SHANGHAI UNIVERSITY OF ELECTRIC POWER
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