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Intelligent electric energy meter failure rate prediction method based on deep neural network

A deep neural network and smart energy meter technology, applied in biological neural network models, neural architectures, measurement devices, etc., can solve the problems of huge amount of original training data and unsupervised learning, so as to improve the fault analysis ability and improve the whole machine. Quality, the effect of prolonging the service life

Pending Publication Date: 2021-04-16
国网天津市电力公司营销服务中心 +4
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Problems solved by technology

[0004] The above model is usually unsupervised learning and used to deal with classification problems. For the prediction of the failure rate of electric energy meters, it is a supervised regression problem, and the original training data is huge

Method used

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  • Intelligent electric energy meter failure rate prediction method based on deep neural network
  • Intelligent electric energy meter failure rate prediction method based on deep neural network
  • Intelligent electric energy meter failure rate prediction method based on deep neural network

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Embodiment Construction

[0029] The present invention will be further described in detail below through the specific examples, the following examples are only descriptive, not restrictive, and cannot limit the protection scope of the present invention with this.

[0030] A method for predicting the failure rate of a smart electric energy meter based on a deep neural network of the present invention, the specific content of the deep neural network prediction method is:

[0031] (1) Component failure rate model

[0032] According to IEC-TR-62380 "General Model for Reliability Prediction of Electronic Components, PCBs and Equipment", the stress model between the working failure rate and the basic failure rate of components is built, and the calculation formula is:

[0033]

[0034] where λ pc is the working failure rate of components, λ bc is the basic failure rate of components, π j It is the product of the adjustment coefficients of various influencing factors. The influencing factors include the...

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Abstract

The invention relates to an intelligent electric energy meter failure rate prediction method based on a deep neural network. The method comprises the following steps: 1, selecting a component reliability prediction manual; 2, obtaining working failure rates of the components through calculation; 3, solving the sum of the working failure rates of all the components, and then obtaining the working failure rates of all the modules; 4, calculating the field failure rate of the electric energy meter according to the fault data of the application field of the intelligent electric energy meter in combination with a formula; and 5, establishing a deep neural network model according to the solved working failure rate of each module and the field failure rate of the electric energy meter, and predicting the failure rate of the intelligent electric energy meter which does not fail yet. The method can improve the fault analysis capability of the intelligent electric energy meter, achieve the accurate prediction of the failure rate of the intelligent electric energy meter, obtain the corresponding measures for improving the reliability of the electric energy meter through analysis, further improves the overall quality of the intelligent electric energy meter, and prolongs the service life.

Description

technical field [0001] The invention belongs to the technical field of the application of artificial intelligence algorithms in power metering equipment, and relates to a method for predicting the failure rate of smart electric energy meters based on a deep neural network. Background technique [0002] The construction of my country's smart grid is accelerating. As the metering terminal equipment of smart grid, the reliability of smart energy meter directly affects the safe and stable economic operation of the grid, and is also directly related to the reliability and safety of power supply for thousands of households. [0003] In recent years, thanks to the development of big data and the improvement of computing performance, deep learning has been widely used in various industries. Compared with traditional machine learning algorithms, deep learning has stronger feature learning capabilities and more complex network representation capabilities. At the same time, deep learni...

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Application Information

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IPC IPC(8): G06F17/11G06N3/04G01R35/04
Inventor 张卫欣刘佳林刘卿李蓓王玥王季孟陈晓芳赵茜茹李祯祥吉杨王崇丁欣张永强
Owner 国网天津市电力公司营销服务中心
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