Electric appliance load type intelligent identification method and device

A load type, intelligent recognition technology, applied in the direction of measuring device, character and pattern recognition, and pattern recognition in signals, etc., can solve the problems of incomplete accurate recognition, single identification method, single characteristic nature, etc., to achieve rich feature information, Simple acquisition method and high recognition accuracy

Inactive Publication Date: 2016-08-31
HUNAN UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Various methods can realize the identification of electrical load properties to a certain extent, but due to the single feature nature and single identification means, there are generally problems of insufficient generalization ability and incomplete and accurate identification.

Method used

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  • Electric appliance load type intelligent identification method and device
  • Electric appliance load type intelligent identification method and device
  • Electric appliance load type intelligent identification method and device

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 2

[0085] Example 2 selects the NBC classifier as the auxiliary classifier. Naive Bayes classification is defined as follows:

[0086] ⑴ Let x={a 1 ,a 2 ,...,a m} is an item to be classified, and each a is a characteristic attribute of x;

[0087] ⑵There is a category set C={y 1 ,y 2 ,...,y n};

[0088] (3) Calculate P(y 1 |x),P(y 2 |x),...,P(y n |x);

[0089] ⑷If P(y k |x)=max{P(y 1 |x),P(y 2 |x),...,P(y n |x)}, then x∈y k .

[0090] The specific method of calculating each conditional probability in step (3) is:

[0091] ① Find a set of items to be classified with known classification as the training sample set;

[0092] ②Statistically obtain the conditional probability estimates of each feature attribute under each category;

[0093] P(a 1 |y 1 ),P(a 2 |y 1 ),…,P(a m |y 1 );

[0094] P(a 1 |y 2 ),P(a 2 |y 2 ),…,P(a m |y 2 );

[0095] ...;

[0096] P(a 1 |y n ),P(a 2 |y n ),…,P(a m |y n ).

[0097] ③According to Bayes' theorem, there are: ...

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Abstract

The invention discloses an electric appliance load type intelligent identification device, comprising an information collection module, an information processing module and a communication module. The electric appliance load type intelligent identification device simultaneously adopts a start-up current characteristic, a fundamental wave voltage current phase difference of the electric appliance load and a load current frequency spectrum characteristic as the electric appliance load identification characteristics, and the characteristic information is abundant; a combined classifier comprising a support vector machine classifier and a Bayes classifier is adopted to perform identification classification; the comprehensive identification is performed by giving consideration to features of the two classifiers and the identification accuracy is high; and the obtaining method for the fundamental wave voltage current phase difference, the start-up current characteristic and the load current spectrum characteristic is simple and reliable. The device provided by the invention can be applied to public occasions such as student dormitories, large-scale pedlars' markets and the like which require electric appliance load management, and can also be applied to other electric appliance management required occasions demanding electric appliance load type identification and statistics.

Description

technical field [0001] The invention relates to an equipment identification and classification device, in particular to a method and device for intelligent identification of electrical load types. Background technique [0002] At present, the mainstream electrical load properties or electrical type identification methods include the electrical load identification method based on the load power comprehensive coefficient algorithm, the electrical load identification method based on electromagnetic induction, the electrical load identification method based on neural network algorithm, and the periodic discrete transformation algorithm. Electrical load identification method, etc. Various methods can realize the identification of electrical load properties to a certain extent, but due to the single characteristic properties and single identification means, there are generally problems of insufficient generalization ability and incomplete and accurate identification. Contents of...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G01R31/00
CPCG01R31/00G06F2218/02G06F2218/12G06F18/285G06F18/24155G06F18/2411
Inventor 肖伸平练红海凌云陈刚
Owner HUNAN UNIV OF TECH
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