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Electric energy meter fault classification method and device based on MATLAB neural network

A neural network and fault classification technology, applied in the field of electric energy meters, can solve problems such as low detection efficiency and large task load

Inactive Publication Date: 2020-10-23
ANHUI ZENITH ELECTRICITY & ELECTRONICS +1
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] In order to solve the technical problems of huge task load and low detection efficiency in the existing electric energy meter fault classification, the present invention provides a method and device for electric energy meter fault classification based on MATLAB neural network

Method used

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  • Electric energy meter fault classification method and device based on MATLAB neural network
  • Electric energy meter fault classification method and device based on MATLAB neural network
  • Electric energy meter fault classification method and device based on MATLAB neural network

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Embodiment 1

[0050] see figure 1 as well as figure 2 , this embodiment provides a MATLAB neural network-based electric energy meter fault classification method, which is applied to software fault classification of smart electric energy meters, also to hardware fault classification, and also to fault classification combining software and hardware. The classification method classifies electric energy meter faults based on the MATLAB neural network, and includes the following steps, namely steps S1-S5. Among them, the neural network has a three-layer structure, and the three-layer structure is an input layer, a hidden layer, and an output layer. In other embodiments, the neural network may have more hidden layer structures, which can be selected according to actual needs.

[0051] Step S1: Extract common fault types of electric energy meters and their corresponding software and hardware features, and establish a fault type matrix and corresponding feature data matrix. In this embodiment, th...

Embodiment 2

[0061] This embodiment provides a kind of electric energy meter fault classification method based on MATLAB neural network, and this method is similar to the method of embodiment 1, difference is that this embodiment provides the calculation formula of hidden layer neuron number, and calculation formula is:

[0062]

[0063] In the formula, M is the number of neurons in the hidden layer, N is the number of neurons in the input layer, L is the number of neurons in the output layer; α is a constant, and its value ranges from 1 to 10.

[0064] For example, if the number of neurons in the input layer is 8, the number of neurons in the output layer is 6, and α is 2, then the number of neurons in the hidden layer M=4+2=6. However, this formula may calculate a fractional part, therefore, in some embodiments, after optimizing this formula, the following optimization formula is obtained:

[0065]

[0066] In the formula, INT() is an upward rounding function, so that no matter W...

Embodiment 3

[0072] This embodiment provides a fault classification method for electric energy meters based on MATLAB neural network, which is similar to the method in Embodiment 2, except that the calculation formula for the number of neurons in the hidden layer is different. In this embodiment, the calculation formula is:

[0073] M=log 2 N

[0074] Obviously, in this embodiment, the number of neurons in the hidden layer is only determined by the number of neurons in the input layer, and no information about the number of neurons in the output layer is required. For some embodiments, neurons in the hidden layer are only associated with neurons in the input layer In terms of , the calculation formula is more reasonable, and it is also in line with the actual construction environment of the neural network.

[0075] Of course, the formula can be optimized here, and the optimized formula is: M=INT(log 2 N).

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Abstract

The invention discloses an electric energy meter fault classification method and device based on an MATLAB neural network. The method comprises the following steps: extracting common fault types and corresponding software and hardware characteristics of the electric energy meter, and establishing a fault type matrix and a corresponding characteristic data matrix; taking the fault type as the expected output of the neural network, taking the corresponding software and hardware features as the input of the neural network, and respectively encoding to serve as each group of training samples of the neural network; calculating the number of neurons of the hidden layer according to the number of neurons of the input layer and the number of neurons of the output layer; constructing a neural network, configuring training parameters, and training the neural network by using each group of training samples; and judging fault types corresponding to the software and hardware characteristics of theelectric energy meter by using the trained neural network, and classifying the fault types. Manual classification is not needed, the fault classification efficiency of the electric energy meter is improved, the fault classification effect is good, huge task load is met, the detection efficiency is improved, and popularization, use and development of the intelligent electric energy meter are facilitated.

Description

technical field [0001] The invention relates to a fault classification method in the technical field of electric energy meters, in particular to a fault classification method for electric energy meters based on a MATLAB neural network, and also to a fault classification device for electric energy meters based on the MATLAB neural network using the method. Background technique [0002] The development of smart grid puts forward more and more requirements for the functional design of smart energy meters, and the scale and complexity of software increase accordingly. At the same time, the failure rate of equipment caused by software is gradually increasing. Due to the large number of external interfaces involved in software failures, the huge system itself, and the complexity of programming instructions, there are many types of failures. Troubleshooting by category will be very beneficial to the promotion, use and development of smart energy meters. [0003] At present, the so...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/06G06N3/08
CPCG06N3/08G06N3/061G06N3/045G06F18/2433G06F18/214
Inventor 满翠芳左勇朱若兰钱亮陈鹏杰
Owner ANHUI ZENITH ELECTRICITY & ELECTRONICS