Electric energy quality monitoring and electric appliance fault analysis system based on wavelet neural network and working method thereof

A technology of power quality monitoring and wavelet neural network, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve problems such as unsuccessful cases

Active Publication Date: 2020-06-19
山东卓文信息科技有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

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  • Electric energy quality monitoring and electric appliance fault analysis system based on wavelet neural network and working method thereof
  • Electric energy quality monitoring and electric appliance fault analysis system based on wavelet neural network and working method thereof
  • Electric energy quality monitoring and electric appliance fault analysis system based on wavelet neural network and working method thereof

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Embodiment 1

[0072] A power quality monitoring and electrical fault analysis system based on wavelet neural network, such as figure 1 As shown, including main control system, intelligent circuit breaker,

[0073] Such as image 3 As shown, the intelligent circuit breaker includes a first central processing unit and a first power supply module connected to the first central processing unit, a switch (air switch), a first bus module, a voltage and current sensor, and the first power supply module is connected to a switch; The sensor is used to obtain the current signal in the power grid, and the first central processing unit is used to send an instruction to the first bus module, ordering the first bus module to send the obtained current signal to the main control system; the first bus module is used to send the obtained current signal to the main control system; The signal is sent to the main control system, and the first power module is used to realize the power supply of the intelligent...

Embodiment 2

[0077] The working method of the power quality monitoring and electrical fault analysis system described in embodiment 1, such as Figure 5 shown, including the following steps:

[0078] (1) The smart circuit breaker obtains the current signal in the power grid through the voltage and current sensor, and sends the current signal to the main control system through the second bus module in real time;

[0079] (2) The main control system receives the current signal sent in step (1) through the second bus module, and obtains the harmonic information of the current signal through the trained wavelet neural network calculation. The harmonic information includes the harmonic order, harmonic The frequency and the time point of its appearance, the phase of the harmonic, and the amplitude of the harmonic;

[0080] The current signal obtained by the smart circuit breaker can be decomposed into a linear combination of wavelet functions of different scales and different time shifts, where...

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Abstract

The invention relates to an electric energy quality monitoring and electric appliance fault analysis system based on a wavelet neural network and a working method thereof, and the method comprises thesteps that (1) an intelligent circuit breaker obtains a current signal in a power grid, and transmits the current signal to a main control system; (2) the main control system receives the current signal, and obtains harmonic information of the current signal through calculation of the trained wavelet neural network; (3) electric energy quality monitoring: comparing the harmonic information with an electric energy quality model in a preset database one by one, calculating similarity, and judging a problem type; (4) electric appliance fault analysis: comparing the harmonic information with an electric appliance fault model one by one, calculating similarity, and judging a fault type; and (5) a judgment result is transmitted to a far-end server and is stored by the server, and a user can check information in real time through a web page end or a mobile phone APP end. Wavelet transform analysis is realized by adopting the three-layer neural network, the wavelet transform accuracy can be improved, and the harmonic analysis precision is further improved.

Description

technical field [0001] The invention relates to the technical field of power system maintenance, in particular to a wavelet neural network-based power quality monitoring and electrical fault analysis system and a working method thereof. Background technique [0002] With the continuous progress of the national economy and the continuous expansion of the fields supported by the power industry, the requirements for power supply and power quality in daily production and life are getting higher and higher. However, the introduction of large-scale nonlinear components in the power grid has caused a large pollute. At the same time, with the continuous expansion of the scale of the power system, how to use the electric energy itself to analyze electrical faults is also of great significance. Therefore, it is necessary to develop a high-precision, high-robust system for power quality monitoring and electrical fault analysis. [0003] At present, harmonic analysis methods are commo...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/00G06F17/14G06K9/00G06N3/04G06N3/08
CPCG01R31/00G06F17/14G06N3/084G06N3/045G06F2218/08Y04S10/52
Inventor 徐通通陈浩
Owner 山东卓文信息科技有限公司
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