CN110726870A - Load switch event detection method and system based on data purity - Google Patents

Load switch event detection method and system based on data purity Download PDF

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CN110726870A
CN110726870A CN201910997168.7A CN201910997168A CN110726870A CN 110726870 A CN110726870 A CN 110726870A CN 201910997168 A CN201910997168 A CN 201910997168A CN 110726870 A CN110726870 A CN 110726870A
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翟明岳
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Guangdong University of Petrochemical Technology
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Abstract

The embodiment of the invention discloses a load switch event detection method and a system based on data purity, wherein the method comprises the following steps: step 1, inputting an actually measured power signal sequence S; and 2, detecting the load switch event according to the data purity property. The method specifically comprises the following steps: if the K window data purity HKSatisfies the judgment condition | HK|≥e0Detecting a load switch event at the Kth point of the signal sequence S; otherwise, no load switch event is detected. Wherein e is0A threshold is determined for the load switch event.

Description

Load switch event detection method and system based on data purity
Technical Field
The invention relates to the field of electric power, in particular to a load switch event detection method and system.
Background
With the development of smart grids, the analysis of household electrical loads becomes more and more important. Through the analysis of the power load, a family user can obtain the power consumption information of each electric appliance and a refined list of the power charge in time; the power department can obtain more detailed user power utilization information, can improve the accuracy of power utilization load prediction, and provides a basis for overall planning for the power department. Meanwhile, the power utilization behavior of the user can be obtained by utilizing the power utilization information of each electric appliance, so that the method has guiding significance for the study of household energy consumption evaluation and energy-saving strategies.
The current electric load decomposition is mainly divided into an invasive load decomposition method and a non-invasive load decomposition method. The non-invasive load decomposition method does not need to install monitoring equipment on internal electric equipment of the load, and can obtain the load information of each electric equipment only according to the total information of the electric load. The non-invasive load decomposition method has the characteristics of less investment, convenience in use and the like, so that the method is suitable for decomposing household load electricity.
In the non-invasive load decomposition algorithm, the detection of the switching event of the electrical equipment is the most important link. The initial event detection takes the change value of the active power P as the judgment basis of the event detection, and is convenient and intuitive. This is because the power consumed by any one of the electric devices changes, and the change is reflected in the total power consumed by all the electric devices. Besides the need to set a reasonable threshold for the power variation value, this method also needs to solve the problem of the event detection method in practical application: a large peak (for example, a motor starting current is much larger than a rated current) appears in an instantaneous power value at the starting time of some electric appliances, so that an electric appliance steady-state power change value is inaccurate, and the judgment of a switching event is influenced, and the peak is actually pulse noise; moreover, the transient process of different household appliances is long or short (the duration and the occurrence frequency of impulse noise are different greatly), so that the determination of the power change value becomes difficult; due to the fact that the active power changes suddenly when the quality of the electric energy changes (such as voltage drop), misjudgment is likely to happen. The intensity of (impulse) noise is large and background noise has a large impact on the correct detection of switching events.
Load switching events that are now commonly used are often determined using changes in power data: when the power change value exceeds a preset threshold value, a load switch event is considered to occur. This approach, while simple and easy to implement, results in a significant drop in the accuracy of the switching event detection due to the impulse noise and the common use of non-linear loads.
Therefore, in the switching event detection process, how to improve the switching event detection accuracy is very important. Load switch event detection is the most important step in energy decomposition, and can detect the occurrence of an event and determine the occurrence time of the event. However, the accuracy of the detection of the switching event is greatly affected by noise in the power signal (power sequence), and particularly, impulse noise generally exists in the power signal, which further affects the detection accuracy. Therefore, it is currently a very important task to effectively improve the detection accuracy of the load switch event.
Load switching events that are now commonly used are often determined using changes in power data: when the power change value exceeds a preset threshold value, a load switch event is considered to occur. This approach, while simple and easy to implement, results in a significant drop in the accuracy of the switching event detection due to the impulse noise and the common use of non-linear loads.
Disclosure of Invention
The invention aims to provide a load switch event detection method and system based on data purity. The method has good switching event detection performance and is simple in calculation.
In order to achieve the purpose, the invention provides the following scheme:
a load switch event detection method based on data purity, comprising:
step 1, inputting actually measured power signal sequenceS；
And 2, detecting the load switch event according to the data purity property. The method specifically comprises the following steps: if the K window data purity HKSatisfies the judgment condition | HK|≥e0Detecting a load switch event at the Kth point of the signal sequence S; otherwise, no load switch event is detected. Wherein e is0A threshold is determined for the load switch event.
A load switch event detection system based on data purity, comprising:
an acquisition module for inputting the measured power signal sequenceS；
And the detection module detects the load switch event according to the data purity property. The method specifically comprises the following steps: if the K window data purity HKSatisfies the judgment condition | HK|≥e0Detecting a load switch event at the Kth point of the signal sequence S; otherwise, no load switch event is detected. Wherein e is0A threshold is determined for the load switch event.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
although the load switch event detection method based on power data transformation has wide application in load switch event detection and relatively mature technology, due to the wide application of the nonlinear load, the power signal is easily affected by the environmental noise and the nonlinear noise, so that the method often cannot obtain satisfactory results when applied in the actual working environment.
The invention aims to provide a load switch event detection method and system based on data purity. The method has good switching event detection performance and is simple in calculation.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic diagram of the system of the present invention;
FIG. 3 is a flow chart illustrating an embodiment of the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
FIG. 1 is a schematic flow chart of a load switch event detection method based on data purity
Fig. 1 is a schematic flow chart of a load switch event detection method based on data purity according to the present invention. As shown in fig. 1, the load switch event detection method based on data purity specifically includes the following steps:
step 1, inputting actually measured power signal sequenceS；
And 2, detecting the load switch event according to the data purity property. The method specifically comprises the following steps: if the K window data purity HKSatisfies the judgment condition | HK|≥e0Detecting a load switch event at the Kth point of the signal sequence S; otherwise, no load switch event is detected. Wherein e is0A threshold is determined for the load switch event.
Before the step 2, the method further comprises:
step 3, obtaining the data purity H of the Kth windowKAnd the load switch event judgment threshold e0。
The step 3 comprises the following steps:
step 301, generating the nth signal difference sequence Δ SnThe method specifically comprises the following steps:
ΔSn＝[ΔSn-D,ΔSn-D+1,…,ΔSn,ΔSn+1,…,ΔSn+D]
wherein:
ΔSi＝Si-Si-1: the nth signal differential sequence Delta SnThe ith element [ i ═ n-D, n-D +1, …, n + D]
Si: the ith element in the signal sequence S
S＝[S1，S2，…，SN]The length of the signal sequence is N
If the element SiSubscript i of>N or i<1, then Si＝0。
Figure BDA0002240139400000051
Length of window
Figure BDA0002240139400000052
Represents the lower rounding of
SNR: signal-to-noise ratio of the signal sequence S
Step 302, iteratively calculating the nth signal difference sequence Δ SnThe category center of (1) is specifically:
the first step is as follows: performing iterative initialization, specifically:
Figure BDA0002240139400000053
wherein:
maxΔSn: the nth signal differential sequence Delta SnMaximum element of (2)
minΔSn: the nth signal differential sequence Delta SnMinimum element of (2)
Initialization value of first class center
Figure BDA0002240139400000056
Initialization value of second class center
Figure BDA0002240139400000062
k＝0
Wherein:
Figure BDA0002240139400000063
initialization value of first class element set
Figure BDA0002240139400000064
Initialization value of second category element set
Figure BDA0002240139400000065
Initialization value of class judgment threshold value
k: iterative control parameter
And step two, iterative updating, which specifically comprises the following steps:
Figure BDA0002240139400000066
Figure BDA0002240139400000067
wherein:
Figure BDA0002240139400000068
step k-1 first class
Figure BDA0002240139400000069
The ith element in
Figure BDA00022401394000000610
Step k-1, second class
Figure BDA00022401394000000611
The j (th) element of (1)
N1: the first category
Figure BDA00022401394000000612
Middle element isNumber of
N2: the second class
Figure BDA00022401394000000613
Number of middle element
Figure BDA00022401394000000614
Step k first class
Figure BDA00022401394000000615
Class center of
Figure BDA00022401394000000616
Step k second class
Figure BDA00022401394000000617
Class center of
Figure BDA0002240139400000071
Figure BDA0002240139400000072
Wherein:
Figure BDA0002240139400000073
the kth step value of the first class element set
Step k value of the second class element set
Figure BDA0002240139400000075
The kth step value of the category judgment threshold value
k: iterative control parameter
Thirdly, iterative judgment, specifically comprising:
adding 1 to the iteration control parameter K, returning to the second step for re-iteration until the difference between two adjacent iteration values is less than one thousandth, wherein the iteration control parameter K is equal to K, and obtaining the nth signal difference sequence delta SnTwo classification centers of (2): class center of the first class
Figure BDA0002240139400000076
And a class center of the second class
Figure BDA0002240139400000077
Step 303, obtaining the nth signal difference sequence Δ SnThe optimal projection factor χ specifically is:
Figure BDA0002240139400000078
wherein:
W＝[ΔSn-μ1][ΔSn-μ2]T: the nth signal differential sequence Delta SnProjected value of
Step 304, calculating the data purity H of the Kth windowKThe method specifically comprises the following steps:
step 305, obtaining the operation state judgment threshold e0The method specifically comprises the following steps:
Figure BDA00022401394000000710
FIG. 2 structural intent of a data purity based load switch event detection system
Fig. 2 is a schematic structural diagram of a load switch event detection system based on data purity according to the present invention.
As shown in fig. 2, the load switch event detection system based on data purity includes the following structure:
an obtaining module 401 for inputting the measured power signal sequenceS；
A detection module 402 detects a load switch event based on the data purity property. The method specifically comprises the following steps: if the K window data purity HKSatisfies the judgment condition | HK|≥e0Detecting a load switch event at the Kth point of the signal sequence S; otherwise, no load switch event is detected. Wherein e is0A threshold is determined for the load switch event.
The system further comprises:
a calculating module 403, for obtaining the data purity H of the Kth windowKAnd the load switch event judgment threshold e0。
The calculation module 403 further includes the following units:
the first calculation unit 4031 generates an nth signal differential sequence Δ Sn, specifically:
ΔSn＝[ΔSn-D,ΔSn-D+1,…,ΔSn,ΔSn+1,…,ΔSn+D]
wherein:
ΔSi＝Si-Si-1: the nth signal differential sequence Delta SnThe ith element [ i ═ n-D, n-D +1, …, n + D]
Si: the ith element in the signal sequence S
S＝[S1，S2，…，SN]The length of the signal sequence is N
If the element SiSubscript i of>N or i<1, then Si＝0。
Figure BDA0002240139400000081
Length of window
Figure BDA0002240139400000082
Represents the lower rounding of
SNR: signal-to-noise ratio of the signal sequence S
An iteration unit 4032 for iteratively calculating the nth signal difference sequence Δ SnThe category center of (1) is specifically:
the first step is as follows: performing iterative initialization, specifically:
Figure BDA0002240139400000091
Figure BDA0002240139400000092
wherein:
maxΔSn: the nth signal differential sequence Delta SnMaximum element of (2)
minΔSn: the nth signal differential sequence Delta SnMinimum element of (2)
Figure BDA0002240139400000093
Initialization value of first class center
Figure BDA0002240139400000094
Initialization value of second class center
Figure BDA0002240139400000095
k＝0
Wherein:
Figure BDA0002240139400000097
initialization value of first class element set
Figure BDA0002240139400000098
Elements of the second categoryInitialization value of the set
Initialization value of class judgment threshold value
k: iterative control parameter
And step two, iterative updating, which specifically comprises the following steps:
Figure BDA0002240139400000101
Figure BDA0002240139400000102
wherein:
Figure BDA0002240139400000103
step k-1 first class
Figure BDA0002240139400000104
The ith element in
Figure BDA0002240139400000105
Step k-1, second class
Figure BDA0002240139400000106
The j (th) element of (1)
N1: the first category
Figure BDA0002240139400000107
Number of middle element
N2: the second class
Figure BDA0002240139400000108
Number of middle element
Figure BDA0002240139400000109
Step k first class
Figure BDA00022401394000001010
Class center of
Figure BDA00022401394000001011
Step k second class
Figure BDA00022401394000001012
Class center of
Figure BDA00022401394000001013
Figure BDA00022401394000001014
Wherein:
Figure BDA00022401394000001015
the kth step value of the first class element set
Step k value of the second class element set
Figure BDA00022401394000001017
The kth step value of the category judgment threshold value
k: iterative control parameter
Thirdly, iterative judgment, specifically comprising:
adding 1 to the iteration control parameter K, returning to the second step for re-iteration until the difference between two adjacent iteration values is less than one thousandth, wherein the iteration control parameter K is equal to K, and obtaining the nth signal difference sequence delta SnTwo classification centers of (2): class center of the first classAnd said secondClass center of classes
Figure BDA00022401394000001019
Second calculation unit 4033, which finds the nth signal difference sequence Δ SnThe optimal projection factor χ specifically is:
Figure BDA0002240139400000111
wherein:
W＝[ΔSn-μ1][ΔSn-μ2]T: the nth signal differential sequence Delta SnProjected value of
A third calculation unit 4034 for calculating the data purity H of the Kth windowKThe method specifically comprises the following steps:
Figure BDA0002240139400000112
a fourth calculation unit 4035 for obtaining the operating state determination threshold e0The method specifically comprises the following steps:
Figure BDA0002240139400000113
the following provides an embodiment for further illustrating the invention
FIG. 3 is a flow chart illustrating an embodiment of the present invention. As shown in fig. 3, the method specifically includes the following steps:
1. inputting a sequence of measured power signals
S＝[s1,s2,…,sN-1,sN]
Wherein:
s: a data sequence of measured power signal of length N
siI is 1,2, …, N is the measured power signal with serial number i
2. Generating a signal difference sequence
ΔSn＝[ΔSn-D,ΔSn-D+1,…,ΔSn,ΔSn+1,…,ΔSn+D]
Wherein:
ΔSi＝Si-Si-1: the nth signal differential sequence Delta SnThe ith element [ i ═ n-D, n-D +1, …, n + D]
Si: the ith element in the signal sequence S
S＝[S1，S2，…，SN]The length of the signal sequence is N
If the element SiSubscript i of>N or i<1, then Si＝0。
Figure BDA0002240139400000121
Length of window
Figure BDA0002240139400000122
Represents the lower rounding of
SNR: signal-to-noise ratio of the signal sequence S
3. Iterative determination of class centers of signal difference sequences
The first step is as follows: performing iterative initialization, specifically:
Figure BDA0002240139400000123
wherein:
maxΔSn: the nth signal differential sequence Delta SnMaximum element of (2)
minΔSn: the nth signal differential sequence Delta SnMinimum element of (2)
Figure BDA0002240139400000125
Initialization value of first class center
Figure BDA0002240139400000126
Initialization value of second class center
Figure BDA0002240139400000131
Figure BDA0002240139400000132
k＝0
Wherein:
Figure BDA0002240139400000133
initialization value of first class element set
Initialization value of second category element set
Figure BDA0002240139400000135
Initialization value of class judgment threshold value
k: iterative control parameter
And step two, iterative updating, which specifically comprises the following steps:
Figure BDA0002240139400000136
wherein:
Figure BDA0002240139400000138
step k-1 first class
Figure BDA0002240139400000139
The ith element in
Figure BDA00022401394000001310
Step k-1, second class
Figure BDA00022401394000001311
The j (th) element of (1)
N1: the first categoryNumber of middle element
N2: the second class
Figure BDA00022401394000001313
Number of middle element
Step k first classClass center of
Figure BDA00022401394000001316
Step k second class
Figure BDA00022401394000001317
Class center of
Figure BDA0002240139400000142
Wherein:
Figure BDA0002240139400000143
of said first set of category elementsStep k value
Figure BDA0002240139400000144
Step k value of the second class element set
Figure BDA0002240139400000145
The kth step value of the category judgment threshold value
k: iterative control parameter
Thirdly, iterative judgment, specifically comprising:
adding 1 to the iteration control parameter K, returning to the second step for re-iteration until the difference between two adjacent iteration values is less than one thousandth, wherein the iteration control parameter K is equal to K, and obtaining the nth signal difference sequence delta SnTwo classification centers of (2): class center of the first class
Figure BDA0002240139400000146
And a class center of the second class
Figure BDA0002240139400000147
4. Finding the best projection factor
Figure BDA0002240139400000148
Wherein:
W＝[ΔSn-μ1][ΔSn-μ2]T: the nth signal differential sequence Delta SnProjected value of
5. Calculating window data purity
Figure BDA0002240139400000149
6. Calculating a threshold for determining the operating state
Figure BDA00022401394000001410
7. Detecting a switching event
Load switch events are detected based on the data purity properties. The method specifically comprises the following steps: if the K window data purity HKSatisfies the judgment condition | HK|≥e0Detecting a load switch event at the Kth point of the signal sequence S; otherwise, no load switch event is detected. Wherein e is0A threshold is determined for the load switch event.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is simple because the system corresponds to the method disclosed by the embodiment, and the relevant part can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (5)

1. A load switch event detection method based on data purity, comprising:
step 1, inputting an actually measured power signal sequence S;
and 2, detecting the load switch event according to the data purity property. The method specifically comprises the following steps: if the K window data purity HKSatisfies the judgment condition | HK|≥e0Detecting a load switch event at the Kth point of the signal sequence S; otherwise, no load switch event is detected.
Wherein e is0A threshold is determined for the load switch event.
2. The method of claim 1, wherein prior to step 2, the method further comprises:
step 3, obtaining the data purity H of the Kth windowKAnd the load switch event judgment threshold e0。
3. The method of claim 2, wherein step 3 comprises:
step 301, generating the nth signal difference sequence Δ SnThe method specifically comprises the following steps:
ΔSn＝[ΔSn-D,ΔSn-D+1,…,ΔSn,ΔSn+1,…,ΔSn+D]
wherein:
ΔSi＝Si-Si-1: the nth signal differential sequence Delta SnThe ith element [ i ═ n-D, n-D +1, …, n + D]
Si: the ith element in the signal sequence S
S＝[S1，S2，…，SN]The length of the signal sequence is N
If the element SiSubscript i of>N or i<1, then Si＝0。
Figure FDA0002240139390000011
Length of window
Figure FDA0002240139390000012
Represents the lower rounding of
SNR: signal-to-noise ratio of the signal sequence S
Step 302, iteratively calculating the nth signal difference sequence Δ SnThe category center of (1) is specifically:
the first step is as follows: performing iterative initialization, specifically:
Figure FDA0002240139390000021
wherein:
maxΔSn: the nth signal differential sequence Delta SnMaximum element of (2)
minΔSn: the nth signal differential sequence Delta SnMinimum element of (2)
Figure FDA0002240139390000023
Initialization value of first class center
Figure FDA0002240139390000024
Initialization value of second class center
Figure FDA0002240139390000026
k＝0
Wherein:
Figure FDA0002240139390000027
initialization value of first class element set
Figure FDA0002240139390000028
Initialization value of second category element set
Figure FDA0002240139390000029
Initialization value of class judgment threshold value
k: iterative control parameter
And step two, iterative updating, which specifically comprises the following steps:
Figure FDA00022401393900000211
wherein:
Figure FDA00022401393900000212
step k-1 first class
Figure FDA00022401393900000213
The ith element in
Figure FDA00022401393900000214
Step k-1, second class
Figure FDA00022401393900000215
The j (th) element of (1)
N1: the first category
Figure FDA00022401393900000216
Number of middle element
N2: the second classNumber of middle element
Figure FDA00022401393900000218
Step k first class
Figure FDA00022401393900000219
Class center of
Figure FDA00022401393900000220
Step k second class
Figure FDA00022401393900000221
Class center of
Figure FDA0002240139390000032
Wherein:
Figure FDA0002240139390000033
the kth step value of the first class element set
Figure FDA0002240139390000034
Step k value of the second class element set
Figure FDA0002240139390000035
The kth step value of the category judgment threshold value
k: iterative control parameter
Thirdly, iterative judgment, specifically comprising:
adding 1 to the iteration control parameter K, returning to the second step for re-iteration until the difference between two adjacent iteration values is less than one thousandth, wherein the iteration control parameter K is equal to K, and obtaining the nth signal difference sequence delta SnTwo classification centers of (2): class center of the first class
Figure FDA0002240139390000036
And a class center of the second class
Figure FDA0002240139390000037
Step 303, obtaining the nth signal difference sequence Δ SnThe optimal projection factor χ specifically is:
Figure FDA0002240139390000038
wherein:
W＝[ΔSn-μ1][ΔSn-μ2]T: the nth signal differential sequence Delta SnProjected value of
Step 304, calculating the data purity H of the Kth windowKThe method specifically comprises the following steps:
Figure FDA0002240139390000039
step 305, obtaining the operation state judgment threshold e0The method specifically comprises the following steps:
Figure FDA00022401393900000310
4. a load switch event detection system based on data purity, comprising:
the acquisition module inputs an actually measured power signal sequence S;
and the detection module detects the load switch event according to the data purity property. The method specifically comprises the following steps: if the K window data purity HKSatisfies the judgment condition | HK|≥e0Detecting a load switch event at the Kth point of the signal sequence S; otherwise, no load switch event is detected. Wherein e is0A threshold is determined for the load switch event.
5. The system of claim 4, further comprising:
a calculation module for calculating the data purity H of the Kth windowKAnd stationThe load switch event judgment threshold e0。
CN201910997168.7A 2019-10-20 2019-10-20 Load switch event detection method and system based on data purity Withdrawn CN110726870A (en)

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Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2801792A2 (en) * 2013-05-08 2014-11-12 Samsung Electronics Co., Ltd. Non-intrusive load monitoring apparatus and method
CN105823948A (en) * 2016-05-18 2016-08-03 国网上海市电力公司 Non-invasive resident load identification method
WO2016141978A1 (en) * 2015-03-11 2016-09-15 You Know Watt Improved non-intrusive appliance load monitoring method and device
CN106124850A (en) * 2016-07-20 2016-11-16 国网江西省电力公司南昌供电分公司 A kind of load identification system for domestic intelligent electricity meter based on similarity algorithm and load recognition methods thereof
CN109307798A (en) * 2018-08-29 2019-02-05 广东石油化工学院 A kind of power signal filtering method for switch events detection
CN109324285A (en) * 2018-09-20 2019-02-12 广东石油化工学院 A kind of load switch event detecting method and system
CN109767113A (en) * 2019-01-04 2019-05-17 三峡大学 A kind of dynamic secure estimation and prevention and control integrated approach based on decision tree
CN110018369A (en) * 2019-03-05 2019-07-16 天津工业大学 A kind of household electrical appliances intelligent recognition and monitoring method based on non-intrusion type load decomposition
CN110133365A (en) * 2019-04-29 2019-08-16 广东石油化工学院 A kind of detection method and device of the switch events of load
CN110221121A (en) * 2019-06-28 2019-09-10 广东石油化工学院 A kind of on-load switch event detecting method and system based on Signal separator

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2801792A2 (en) * 2013-05-08 2014-11-12 Samsung Electronics Co., Ltd. Non-intrusive load monitoring apparatus and method
WO2016141978A1 (en) * 2015-03-11 2016-09-15 You Know Watt Improved non-intrusive appliance load monitoring method and device
CN105823948A (en) * 2016-05-18 2016-08-03 国网上海市电力公司 Non-invasive resident load identification method
CN106124850A (en) * 2016-07-20 2016-11-16 国网江西省电力公司南昌供电分公司 A kind of load identification system for domestic intelligent electricity meter based on similarity algorithm and load recognition methods thereof
CN109307798A (en) * 2018-08-29 2019-02-05 广东石油化工学院 A kind of power signal filtering method for switch events detection
CN109324285A (en) * 2018-09-20 2019-02-12 广东石油化工学院 A kind of load switch event detecting method and system
CN109767113A (en) * 2019-01-04 2019-05-17 三峡大学 A kind of dynamic secure estimation and prevention and control integrated approach based on decision tree
CN110018369A (en) * 2019-03-05 2019-07-16 天津工业大学 A kind of household electrical appliances intelligent recognition and monitoring method based on non-intrusion type load decomposition
CN110133365A (en) * 2019-04-29 2019-08-16 广东石油化工学院 A kind of detection method and device of the switch events of load
CN110221121A (en) * 2019-06-28 2019-09-10 广东石油化工学院 A kind of on-load switch event detecting method and system based on Signal separator

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
V.P.BORIN,ETC: "Vector Projection Classification for home appliances recognition: A load signature comparative analysis", 《2016 12TH IEEE INTERNATIONAL CONFERENCE ON INDUSTRY APPLICATIONS》 *
李静 等: "一种用于家用智能电表的负荷识别方法", 《现代电力》 *

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