Comprehensive energy metering working condition extraction method and system

A technology that integrates energy and working conditions. It is applied in neural learning methods, computing, computer components, etc. It can solve the problems of insufficient consideration of feature engineering, inability to explicitly extract feature extraction, and poor results. The effect of classification accuracy, fast operation speed and strong nonlinear ability

Pending Publication Date: 2022-04-26
STATE GRID ELECTRIC POWER RES INST +4
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  • Abstract
  • Description
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  • Application Information

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Problems solved by technology

However, due to the inevitable two-sidedness of research algorithms, research in these related fields generally only studies a certain problem in signal processing, feature selection and classification methods, and the consideration of feature engineering problems is not comprehensive enough.
For example, efficient classification methods generally use systematic models without explicit feature extraction. Traditional feature extraction methods do not link the feature extraction process with classification results, and are not effective in classification.

Method used

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  • Comprehensive energy metering working condition extraction method and system
  • Comprehensive energy metering working condition extraction method and system
  • Comprehensive energy metering working condition extraction method and system

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Embodiment 1

[0049] Such as Figure 1-6 As shown, a method for extracting comprehensive energy metering working conditions proposed by the present invention includes the following steps:

[0050] Step 1. Obtain the first eigenvector

[0051] The one-dimensional convolutional network model of the working condition is constructed based on the comprehensive energy metering working condition signal. The input layer of the network is the comprehensive energy measuring working condition signal, and the network output layer is the first eigenvector of the comprehensive energy measuring. figure 2 shown.

[0052] The one-dimensional convolutional network model of the working condition includes a layer of pooling layer, three convolutional layers and corresponding activation functions; the structure of the one-dimensional convolutional network model of the working condition is input layer-convolution layer-pooling layer -Convolutional layer-convolutional layer-output layer; each convolutional lay...

Embodiment 2

[0088] The present invention also provides a comprehensive energy metering working condition feature extraction system, including:

[0089] The first eigenvector acquisition module inputs the comprehensive energy measurement working condition signal into the working condition one-dimensional convolution network model to obtain the first eigenvector of the comprehensive energy measurement;

[0090] The second eigenvector acquisition module inputs the imaged comprehensive energy measurement working condition signal into the working condition one-dimensional convolution network model to obtain the second eigenvector of comprehensive energy measurement;

[0091] The working condition dimensionality reduction eigenvector acquisition module obtains the working condition dimensionality reduction eigenvector of comprehensive energy metering according to the first eigenvector and the second eigenvector.

[0092] Those skilled in the art should understand that the embodiments of the pre...

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Abstract

The invention discloses a comprehensive energy metering working condition extraction method. The method comprises the following steps: inputting a comprehensive energy metering working condition signal into a working condition one-dimensional convolutional network model to obtain a first feature vector of comprehensive energy metering; inputting the visualized comprehensive energy metering working condition signal into a working condition one-dimensional convolutional network model to obtain a second characteristic vector of comprehensive energy metering; and according to the first feature vector and the second feature vector, obtaining a working condition dimensionality reduction feature vector for comprehensive energy metering. The method has higher working condition classification accuracy. On one hand, the working condition feature extraction method can extract working condition signal features and working condition matrix features in a targeted manner, can optimize feature data through a classification algorithm, and has a stronger theoretical basis. And on the other hand, the method has very strong non-linear capability, can effectively extract features in various working condition scenes, is simple in operation steps and high in operation speed, and has a relatively good application prospect.

Description

technical field [0001] The invention belongs to the technical field of energy metering, and in particular relates to a method and system for extracting working conditions of comprehensive energy metering. Background technique [0002] With the access of new energy power generation systems and the expansion of power grid business, nonlinear loads and other loads in energy metering lead to more complex and variable metering conditions, and traditional feature extraction methods for working conditions are difficult to meet the needs of complex scenarios. . At present, the feature extraction of traditional energy metering conditions in my country mainly relies on wavelet transform, Fourier transform, S transform, Hilbert-Huang transform, singular value decomposition and other variant models. These traditional methods expand the way and method of working condition feature extraction through fitting or algorithm, but there are problems such as slow extraction speed and low accura...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06V10/764G06V10/82G06N3/04G06N3/08
CPCG06N3/084G06N3/047G06N3/048G06N3/045G06F2218/08G06F2218/12G06F18/2415
Inventor 朱庆韦思雅李雪明郑红娟张卫国余洋顾琳琳孙季泽陈嘉栋杨凤坤周材邵军军李化林慧婕
Owner STATE GRID ELECTRIC POWER RES INST
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