CN108181533A - A kind of electric load intelligent measurement of non-intrusion type and hierarchical classification method - Google Patents
A kind of electric load intelligent measurement of non-intrusion type and hierarchical classification method Download PDFInfo
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- CN108181533A CN108181533A CN201810048734.5A CN201810048734A CN108181533A CN 108181533 A CN108181533 A CN 108181533A CN 201810048734 A CN201810048734 A CN 201810048734A CN 108181533 A CN108181533 A CN 108181533A
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- 230000001052 transient effect Effects 0.000 claims description 26
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
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Abstract
The present invention relates to intelligent testing technology fields, a kind of electric load intelligent measurement of non-intrusion type and hierarchical classification method are provided, identify that two parts form by event detection and hierarchical classification, it is compared and judges out with the threshold value set by the difference of the current strength in former and later two periods of calculated current signal, pass event, classified in load identification using temporal signatures and frequency domain character using the SVM algorithm of layering, with the detection of existing electric load, identification, the method of classification is compared, the present invention solves to be sentenced often occur less, sentence more, the situation of erroneous judgement, improve the accuracy of stress event detection and the accuracy of load equipment classification.
Description
Technical field
The present invention relates to intelligent testing technology fields, and in particular to a kind of electric load intelligent measurement of non-intrusion type and point
Layer sorting technique.
Background technology
Non-intrusive electrical load detection method is mainly to utilize the letters such as voltage, electric current and the power of electric load inlet
Breath is analyzed and is studied to its ingredient, point including the analysis method based on load steady state characteristic and based on load transient characteristic
Two kinds of analysis method.Mainly have currently based on the analysis method of load transient characteristic and the transient state of voltage, electric current is sampled, extract
The characteristic value of electric current, such as the root mean square of current peak, current average and electric current, after the characteristic value of these electric currents has been obtained
Classified again using various methods to electric appliance.Sorting technique mainly has k nearest neighbor rule and back-propagation artificial neural network.K
Neighbour's rule classification method training time is shorter, the automatic classification for the class field for being suitble to sample size bigger, but calculation amount ratio
It is larger, to the predictablity rate of classification than relatively low；The self study of back-propagation artificial neural network method and adaptive ability and
Fault-tolerant ability is stronger, but convergence rate is slow, other classifications are easily judged to the classification of classification.Generally speaking, currently based on
The problem of analysis method of load transient characteristic is primarily present is as follows：
(1) there is the phenomenon that sentencing more, sentencing less and judge by accident in the detection of event；
(2) exist to the event detection of low power electric appliance and challenge, there is the phenomenon that can't detect；
(3) electrical equipment of similar characteristic has similar feature, and more difficult in classification, Classification and Identification rate is relatively low.
Invention content
For the detection of existing electric load, identification, classification method there are the problem of, when the present invention proposes a kind of
The hierarchical classification method that domain, frequency domain are combined improves the accuracy of stress event detection and the accuracy of load equipment classification of type.
To achieve the above object, the specific technical solution of the present invention is as follows：A kind of electric load of non-intrusion type is intelligently examined
Survey and hierarchical classification method, include the following steps：
1) system initialization, wherein, initialization content includes：Open threshold value, close threshold value, frequency domain character sample database, when
Characteristic of field sample database 1, temporal signatures sample database 2；
2) acquisition, conversion and the filtering of load signal carries out A/D to collected initial load signal and is converted to number
Then signal carries out Chebyshev's low-pass filtering；It goes to step 3)；
3) digital signal is detected, event, the generation of pass event is judged whether there is out, when such as event occurs, goes to step 4),
Otherwise it goes to step 3)；
4) transient process that interception event occurs, and the temporal signatures and frequency domain character of event transient process are extracted, turn step
It is rapid 5)；
5) classified using frequency domain grader to load, i.e., using the frequency domain character of event transient process as test set,
Using frequency domain character sample database as sample set, using SVM algorithm to load classification, if load matching degree is more than 90%, classify
9) success, goes to step, otherwise, goes to step 6)；
6) classified using time domain device 1 to load, i.e., using the temporal signatures of event transient process as test set,
Using temporal signatures sample database 1 as sample set, using SVM algorithm to load classification, if load matching degree is more than 90%, classify
9) success, goes to step, otherwise, goes to step 7)；
7) classified using time domain device 2 to load, i.e., using the temporal signatures of event transient process as test set,
Using temporal signatures sample database 2 as sample set, using SVM algorithm to load classification, if load matching degree is more than 90%, classify
9) success, goes to step, otherwise, goes to step 8)；
8) classify unsuccessful, report error message, go to step 3)；
9) load classification success.
Further, above-mentioned steps 1) in, the frequency domain character of frequency domain character sample database include frequency domain energy, harmonic wave vector,
Harmonic distortion；The temporal signatures of characteristic of field sample database 1 include：Current maxima, current average, electric current root mean square, transient state week
Phase, form factor, transient state energy；The temporal signatures of temporal signatures sample database 2 include crest factor, peak-to-peak value, peak factor,
Instantaneous power, low level ratio, high level ratio.
Further, above-mentioned steps 3) in, judge whether that the method that event occurs is：
3.1) difference of the current strength in former and later two periods of calculated current signal；
3.2) event is detected out if difference is more than and opens threshold value；
3.3) pass event is detected if difference is less than and closes threshold value.
Further, above-mentioned we intercept the transient process of event generation when detecting that event occurs, extract on frequency domain
Characteristic parameter, to load carry out first step classification, if load matching degree be higher than 90%, be considered as and identify successfully output load class
Type, if load matching degree be less than 90%, be considered as it is unfiled go out load, by it is unfiled go out load extraction time domain on one
Characteristic parameter is divided to carry out second step classification, if load matching degree is higher than 90%, is considered as and identifies successfully output load type, if load
Matching degree is less than 90%, then be considered as it is unfiled go out load, by it is unfiled go out load be then transferred to next step time domain device and exist
Other characteristic parameters extracted in time domain carry out final step classification.
The beneficial effects of the invention are as follows：With the detection of existing electric load, identification, classification method compared with, it is of the invention
A kind of electric load intelligent measurement of non-intrusion type and hierarchical classification method, solve often occur lack the feelings sentencing, sentence more, judging by accident
Condition improves the accuracy of stress event detection and the accuracy of load equipment classification.
Description of the drawings
Fig. 1 is non-intrusive electrical load intelligent measurement and hierarchical classification method flow diagram.
Specific embodiment
The invention will be further described with embodiment below in conjunction with the accompanying drawings.
It is as shown in Figure 1 the non-intrusive electrical load intelligent measurement of the present invention and hierarchical classification method flow diagram, it is a kind of
The intelligent measurement and hierarchical classification method of non-intrusion type include the following steps：
1) system initialization, initialization content include：
1.1) initialization opens threshold value, closes threshold value；
1.2) frequency domain character for extracting each electric appliances establishes frequency domain character sample database, wherein, frequency domain character includes frequency domain energy
Amount, harmonic wave vector, harmonic distortion；
1.3) temporal signatures for extracting each electric appliances establish temporal signatures sample database 1, wherein, temporal signatures sample database 1 is wrapped
The temporal signatures contained include：Current maxima, current average, electric current root mean square, transient period, form factor, transient state energy；
1.4) temporal signatures for extracting each electric appliances establish temporal signatures sample database 2, wherein, temporal signatures sample database 2 is wrapped
The temporal signatures contained include crest factor, peak-to-peak value, peak factor, instantaneous power, low level ratio, high level ratio；
2) acquisition, conversion and the filtering of load signal carries out collected initial load signal A/D conversions, conversion
For digital signal, Chebyshev's low-pass filtering is then carried out；Turn in next step；
3) digital signal is detected, event, the generation of pass event is judged whether there is out, when such as event occurs, goes to step 4),
Otherwise it goes to step 3)；Wherein, judge whether the method that event occurs be former and later two periods of calculated current signal electric current it is strong
The difference of degree, is compared with threshold value, then detects out event if more than threshold value is opened, pass is detected if less than threshold value is closed
Event；
4) transient process that interception event occurs, and the temporal signatures of event transient process and frequency desire feature are extracted, turn step
It is rapid 5)；
5) classified using frequency domain grader to load, i.e., using the frequency domain character of event transient process as test set,
Using frequency domain character sample database as sample set, using SVM algorithm to load classification, if load matching degree is higher than 90%, classify
9) success, goes to step, otherwise, goes to step 6)；
6) classified using time domain device 1 to load, i.e., using the temporal signatures of event transient process as test set,
Using temporal signatures sample database 1 as sample set, using SVM algorithm to load classification, if load matching degree is higher than 90%, classify
9) success, goes to step, otherwise, goes to step 7)；
7) classified using time domain device 2 to load, i.e., using the temporal signatures of event transient process as test set,
Using temporal signatures sample database 2 as sample set, using SVM algorithm to load classification, if load matching degree is higher than 90%, classify
9) success, goes to step, otherwise, goes to step 8)；
8) classify unsuccessful, provide error message, go to step 3)；
9) load classification success.
It in the specific embodiment of the invention, opens threshold value and is initialized as 0.15A, close threshold value and be initialized as -0.28A；Load
Including electric light, fan, insulating pot, micro-wave oven, display, host desktop, electric heater, washing machine, refrigerator, water dispenser, TV, sky
12 kinds of tune etc..Electric light, fan, insulating pot, electric heater, water dispenser, TV can accurately be sorted out by step 4), pass through step
5) refrigerator, micro-wave oven, air-conditioning can be sorted out, remaining electric appliance display, washing machine, host desktop can all be divided by step 6)
Class.
Claims (3)
1. a kind of electric load intelligent measurement of non-intrusion type and hierarchical classification method, which is characterized in that include the following steps：
1) system initialization, wherein, initialization content includes：It opens threshold value, close threshold value, frequency domain character sample database, time domain spy
Levy sample database 1, temporal signatures sample database 2；
2) acquisition, conversion and the filtering of load signal carries out A/D to collected initial load signal and is converted to digital letter
Number, then carry out Chebyshev's low-pass filtering；It goes to step 3)；
3) digital signal is detected, event, the generation of pass event is judged whether there is out, when such as event occurs, goes to step 4), otherwise turn
Step 3)；
4) transient process that interception event occurs, and the temporal signatures and frequency domain character of event transient process are extracted, it goes to step 5)；
5) classified using frequency domain grader to load, i.e., using the frequency domain character of event transient process as test set, with frequency
Characteristic of field sample database is as sample set, using SVM algorithm to load classification, if load matching degree is more than 90%, and success of classifying,
It goes to step 9), otherwise, goes to step 6)；
6) classified using time domain device 1 to load, i.e., using the temporal signatures of event transient process as test set, with when
Characteristic of field sample database 1 is used as sample set, using SVM algorithm to load classification, if load matching degree is more than 90%, is categorized into
9) work(is gone to step, otherwise, go to step 7)；
7) classified using time domain device 2 to load, i.e., using the temporal signatures of event transient process as test set, with when
Characteristic of field sample database 2 is used as sample set, using SVM algorithm to load classification, if load matching degree is more than 90%, is categorized into
9) work(is gone to step, otherwise, go to step 8)；
8) classify unsuccessful, report error message, go to step 3)；
9) load classification success.
2. the electric load intelligent measurement of non-intrusion type as described in claim 1 and hierarchical classification method, which is characterized in that institute
It states in step 1), the frequency domain character of frequency domain character sample database includes frequency domain energy, harmonic wave vector, harmonic distortion；Characteristic of field sample
The temporal signatures in library 1 include：Current maxima, current average, electric current root mean square, transient period, form factor, transient state energy
Amount；The temporal signatures of temporal signatures sample database 2 include crest factor, peak-to-peak value, peak factor, instantaneous power, low level ratio
Rate, high level ratio.
3. the electric load intelligent measurement of non-intrusion type as described in claim 1 and hierarchical classification method, which is characterized in that institute
It states in step 3), judges whether that the method that event occurs is：
3.1) difference of the current strength in former and later two periods of calculated current signal；
3.2) event is detected out if difference is more than and opens threshold value；
3.3) pass event is detected if difference is less than and closes threshold value.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109596912A (en) * | 2018-11-21 | 2019-04-09 | 河海大学 | A kind of decomposition method of non-intrusion type power load |
| CN112034281A (en) * | 2020-07-30 | 2020-12-04 | 河海大学 | Non-invasive load identification method in bedroom electricity environment |
| CN114184870A (en) * | 2021-12-14 | 2022-03-15 | 河北科技大学 | Non-invasive load identification method and equipment |
| CN115825634A (en) * | 2023-02-16 | 2023-03-21 | 上海红檀智能科技有限公司 | Malignant load identification method based on load complex impedance characteristics |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106646026A (en) * | 2016-11-11 | 2017-05-10 | 华北电力大学 | Non-intrusive household appliance load identification method |
| CN106802379A (en) * | 2017-03-06 | 2017-06-06 | 中国海洋大学 | A kind of load switch detection of adaptive threshold and recognition methods and system |
-
2018
- 2018-01-18 CN CN201810048734.5A patent/CN108181533B/en not_active Expired - Fee Related
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106646026A (en) * | 2016-11-11 | 2017-05-10 | 华北电力大学 | Non-intrusive household appliance load identification method |
| CN106802379A (en) * | 2017-03-06 | 2017-06-06 | 中国海洋大学 | A kind of load switch detection of adaptive threshold and recognition methods and system |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109596912A (en) * | 2018-11-21 | 2019-04-09 | 河海大学 | A kind of decomposition method of non-intrusion type power load |
| CN112034281A (en) * | 2020-07-30 | 2020-12-04 | 河海大学 | Non-invasive load identification method in bedroom electricity environment |
| CN114184870A (en) * | 2021-12-14 | 2022-03-15 | 河北科技大学 | Non-invasive load identification method and equipment |
| CN115825634A (en) * | 2023-02-16 | 2023-03-21 | 上海红檀智能科技有限公司 | Malignant load identification method based on load complex impedance characteristics |
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