Electric power system harmonics analysis method based on multilayered feedforward neural network

A feedforward neural network and power system technology, applied in the field of power system harmonic analysis based on multi-layer feedforward neural network, can solve the problems of long calculation time, insufficient harmonic analysis accuracy, etc., and achieves fast harmonic analysis speed. The effect of reducing the number of times and time and improving the accuracy

Inactive Publication Date: 2012-02-15
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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Problems solved by technology

[0006] Aiming at the deficiencies mentioned in the above-mentioned background technology that the existing harmonic analysis accuracy is not enough, t...

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  • Electric power system harmonics analysis method based on multilayered feedforward neural network
  • Electric power system harmonics analysis method based on multilayered feedforward neural network
  • Electric power system harmonics analysis method based on multilayered feedforward neural network

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[0028] The preferred embodiments will be described in detail below in conjunction with the accompanying drawings. It should be emphasized that the following description is only exemplary and not intended to limit the scope of the invention and its application.

[0029] The steps of the present invention are as follows: firstly obtain the power system signal, add a Hanning window to it and perform discrete Fourier transform; then correct the signal obtained after performing discrete Fourier transform, and obtain the amplitude and cosine component of the sine component of each harmonic The amplitude and angular frequency of the neural network; use these data as the weight of the neural network, and use the specified algorithm to train the neural network; after the training, obtain the final amplitude and frequency of each harmonic according to the weight of the network. Specifically:

[0030] 1. Acquisition of the initial weight of the neural network

[0031] The initial weigh...

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Abstract

The invention discloses an electric power system harmonics analysis method based on a multilayered feedforward neural network in the electric power system signal testing technology field. In the invention, an electric power system voltage or current signal is obtained through an optical fiber voltage sensor or an optical fiber current sensor, single hidden layer of the multilayered feedforward neural network is established; an excitation function is a sine and a cosine function and a variable parameter is a harmonic amplitude and an angular frequency; a Hanning window is performed to an obtained electric power system signal; and then discrete fourier transform (DFT) is performed; the sine and cosine component amplitude of each corrected subharmonic and the harmonic angular frequency are taken as an initial weight value of the neural network; an RPROP algorithm training is employed on the basis of the initial weight value; the amplitude and the frequency of the each subharmonic can be acquired according to the trained weight value. By using the method of the invention, accuracy of a calculating result is high and a speed is fast. The harmonic wave analysis accuracy at short sampling time can be greatly raised. A principle is simple and the method is easy to be realized.

Description

technical field [0001] The invention belongs to the technical field of power system signal testing, and in particular relates to a power system harmonic analysis method based on a multilayer feedforward neural network. Background technique [0002] With the development of electronic technology, many electronic precision industries have put forward higher requirements for power quality. However, with the increasing use of power electronic devices in the industry, the problem of harmonics in the power system is becoming more and more serious, which seriously affects the power quality. Accurate analysis of harmonics is a prerequisite for harmonic governance. Therefore, harmonic analysis has always been a hot issue in power system research. [0003] After obtaining the voltage and current signals of the power system accurately, it is very important to obtain the amplitude and phase of each harmonic of the signal through the harmonic analysis method. Artificial neural network ...

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Application Information

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IPC IPC(8): G01R23/16G06N3/08
Inventor 赵丽娟李永倩
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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