CN110702980A - Load switch event detection method and system by using MUSIC classification - Google Patents

Load switch event detection method and system by using MUSIC classification Download PDF

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CN110702980A
CN110702980A CN201910946140.0A CN201910946140A CN110702980A CN 110702980 A CN110702980 A CN 110702980A CN 201910946140 A CN201910946140 A CN 201910946140A CN 110702980 A CN110702980 A CN 110702980A
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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 classified by MUSIC, wherein the method comprises the following steps: step 1, inputting an actually measured power signal sequence S; and 2, detecting a load switch event according to the MUSIC classification principle. The method specifically comprises the following steps: if the Kth normalized window score vector 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 detected for a load switch event.

Description

Load switch event detection method and system by using MUSIC classification
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 by using MUSIC classification. The method has good switching event detection performance and is very simple in calculation.
In order to achieve the purpose, the invention provides the following scheme:
a method of load switch event detection using MUSIC classification, comprising:
step 1, inputting an actually measured power signal sequence S;
and 2, detecting a load switch event according to the MUSIC classification principle. The method specifically comprises the following steps: if the Kth normalized window score vector 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 detected for a load switch event.
A load switch event detection system using MUSIC classification, comprising:
the acquisition module inputs an actually measured power signal sequence S;
and the detection module detects the load switch event according to the MUSIC classification principle. The method specifically comprises the following steps: if the Kth normalized window score vector 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 detected for a load switch event.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
although the transformer vibration and sound detection method is widely applied to load switch event monitoring and has a relatively mature technology, the vibration and sound detection method utilizes a vibration signal emitted by a transformer and is easily influenced by environmental noise, so that the method often cannot obtain a satisfactory result when applied in an actual working environment.
The invention aims to provide a load switch event detection method and system by using MUSIC classification. The method has good switching event detection performance and is very 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 method for detecting a load switch event by MUSIC classification
Fig. 1 is a schematic flow chart of a load switch event detection method using MUSIC classification according to the present invention. As shown in fig. 1, the method for detecting a load switch event by using MUSIC classification specifically includes the following steps:
step 1, inputting an actually measured power signal sequence S;
and 2, detecting a load switch event according to the MUSIC classification principle. The method specifically comprises the following steps: if the Kth normalized window score vector 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 detected for a load switch event.
Before the step 2, the method further comprises:
step 3, calculating the fraction vector H of the Kth normalization windowKAnd said load switch event detection threshold e0。
The step 3 comprises the following steps:
step 301, generating the nth signal first order difference sequence
Figure BDA0002224192240000041
The method specifically comprises the following steps:
Figure BDA0002224192240000042
wherein:
Figure BDA0002224192240000043
the nth signal first-order difference sequence [ N ═ 1,2]
Sn: the nth element in the signal sequence S
S＝[S1，S2，...，SN]The length of the signal sequence is N
If the element SjSubscript j of>N, then Sj＝0。
Step 302, generating the nth signal second order difference sequence
Figure BDA0002224192240000044
The method specifically comprises the following steps:
Figure BDA0002224192240000051
wherein:
Figure BDA0002224192240000052
the nth signal second order difference sequence [ N ═ 1,2]
If the element SjSubscript j of>N, then Sj＝0。
Step 303, calculating the n-th signal feature vector EnThe method specifically comprises the following steps:
Figure BDA0002224192240000053
wherein:
[En]i: the nth signal feature vector En1,2, n, i]
The nth signal first order difference sequence
Figure BDA0002224192240000055
The ith element of
Figure BDA0002224192240000056
The nth signal second order difference sequence
Figure BDA0002224192240000057
The ith element of
Figure BDA0002224192240000058
The nth signal first order difference sequence
Figure BDA0002224192240000059
Maximum value of all elements in
Figure BDA00022241922400000510
The nth signal second order difference sequence
Figure BDA00022241922400000511
Maximum value of all elements in
Step 304, calculating the K window fraction sequence hKThe method specifically comprises the following steps:
Figure BDA00022241922400000512
wherein:
Figure BDA00022241922400000513
matrix [ E ]K]TEKThe m-th feature vector of (2),
arranged according to the sequence of the characteristic values from big to small
[hK]j: the Kth window fraction vector hKThe jth element of (1)
K＝1,2，...，N
j＝1,2，...，K
Step 305, obtaining the K-th normalized window fraction vector HKThe method specifically comprises the following steps:
the first step is as follows: the score adjustment specifically comprises the following steps:
wherein:
σh: the Kth window fraction vector hKThe mean square error of (c).
The second step is that: rearranging the K window fraction sequence h in descending orderKObtaining the K descending window fraction sequence
Thirdly, calculating an AU parameter sequence a, specifically:
Figure BDA0002224192240000063
wherein:
[a]i: the ith element of the AU parameter sequence a
Figure BDA0002224192240000064
The Kth descending window fraction sequence
Figure BDA0002224192240000065
The ith element of
Figure BDA0002224192240000066
The Kth descending window fraction sequence
Figure BDA0002224192240000067
Maximum value of
Figure BDA0002224192240000068
The Kth descending window fraction sequence
Figure BDA0002224192240000069
Minimum value of (2)
Fourthly, calculating a weight sequence W, which specifically comprises the following steps:
Figure BDA00022241922400000610
wherein:
[W]i: the ith element of the weight sequence W
Fifthly, calculating the Kth normalized window fraction vector HKThe method specifically comprises the following steps:
[HK]i＝[W]i[hK]i
wherein:
[HK]i: the Kth normalized window score vector HKThe ith element of
Step 306, calculating the load switch event detection threshold e0The method specifically comprises the following steps:
Figure BDA0002224192240000071
wherein:
Figure BDA0002224192240000072
the nth signal primary differential sequence
Figure BDA0002224192240000073
Mean value of
The nth signal secondary difference sequence
Figure BDA0002224192240000075
Mean value of
Figure BDA0002224192240000076
Sequence of mean values
Figure BDA0002224192240000077
N is 1,2, mean value of N
Figure BDA0002224192240000078
Sequence of mean values
Figure BDA0002224192240000079
N is 1,2, mean value of N
Sequence of mean values
Figure BDA00022241922400000711
N1, 2, mean square error of N
Figure BDA00022241922400000712
Sequence of mean values
Figure BDA00022241922400000713
N1, 2, mean square error of N
Figure BDA00022241922400000714
Sequence of mean values
Figure BDA00022241922400000715
N is the maximum value of 1,2
Figure BDA00022241922400000716
Sequence of mean values
Figure BDA00022241922400000717
N is the maximum value of 1, 2.
FIG. 2 structural intent of a load switch event detection system using MUSIC classification
Fig. 2 is a schematic structural diagram of a load switch event detection system using MUSIC classification according to the present invention. As shown in fig. 2, the load switch event detection system using MUSIC classification includes the following structures:
the acquisition module 401 inputs an actually measured power signal sequence S;
the detection module 402 detects a load switch event according to the MUSIC classification principle. The method specifically comprises the following steps: if the Kth normalized window score vector 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 detected for a load switch event.
The system further comprises:
a calculation module 403 for calculating the Kth normalized window score vector HKAnd said load switch event detection threshold e0。
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
siN is the measured power signal with serial number i
2. Generating a first order difference sequence of signals
Figure BDA0002224192240000081
Wherein:
the nth signal first-order difference sequence [ N ═ 1,2]
Sn: the nth element in the signal sequence S
S＝[S1，S2，...，SN]The length of the signal sequence is N
If the element SjSubscript j of>N, then Sj＝0。
3. Generating a second order difference sequence of signals
Wherein:
Figure BDA0002224192240000084
the nth signal second order difference sequence [ N ═ 1,2]
If the element SjSubscript j of>N, then Sj＝0。
4. Determining a signal feature vector
Figure BDA0002224192240000091
Wherein:
[En]i: the nth signal feature vector En1,2, n, i]
Figure BDA0002224192240000092
The nth signal first order difference sequenceThe ith element of
Figure BDA0002224192240000094
The nth signal second order difference sequence
Figure BDA0002224192240000095
The ith element of
Figure BDA0002224192240000096
The nth signal first order difference sequence
Figure BDA0002224192240000097
Maximum value of all elements in
Figure BDA0002224192240000098
The nth signal second order difference sequence
Figure BDA0002224192240000099
Maximum value of all elements in
5. Finding a sequence of window scores
Figure BDA00022241922400000910
Wherein:
Figure BDA00022241922400000911
matrix [ E ]K]TEKThe m-th feature vector of (2),
arranged according to the sequence of the characteristic values from big to small
[hK]j: the Kth window fraction vector hKThe jth element of (1)
K＝1,2，...，N
j＝1,2，...，K
6. Calculating a normalized window score vector
The first step is as follows: the score adjustment specifically comprises the following steps:
Figure BDA00022241922400000912
wherein:
σh: the Kth window fraction vector hKThe mean square error of (c).
The second step is that: rearranging the K window fraction sequence h in descending orderKObtaining the K descending window fraction sequence
Thirdly, calculating an AU parameter sequence a, specifically:
Figure BDA0002224192240000101
wherein:
[a]i: the ith element of the AU parameter sequence a
The Kth descending window fraction sequence
Figure BDA0002224192240000103
The ith element of
Figure BDA0002224192240000104
The Kth descending window fraction sequence
Figure BDA0002224192240000105
Maximum value of
The Kth descending window fraction sequence
Figure BDA0002224192240000107
Minimum value of (2)
Fourthly, calculating a weight sequence W, which specifically comprises the following steps:
Figure BDA0002224192240000108
wherein:
[W]i: the ith element of the weight sequence W
Fifthly, calculating the Kth normalized window fraction vector HKThe method specifically comprises the following steps:
[HK]i＝[W]i[hK]i
wherein:
[HK]i: the Kth normalized window score vector HKThe ith element of
7. Determining load switch event detection threshold
Figure BDA0002224192240000111
Wherein:
Figure BDA0002224192240000112
the nth signal primary differential sequence
Figure BDA0002224192240000113
Mean value of
Figure BDA0002224192240000114
The nth signal secondary difference sequence
Figure BDA0002224192240000115
Mean value of
Figure BDA0002224192240000116
Sequence of mean values
Figure BDA0002224192240000117
N is 1,2, mean value of N
Sequence of mean values
Figure BDA0002224192240000119
N is 1,2, mean value of N
Figure BDA00022241922400001110
Sequence of mean valuesN1, 2, mean square error of N
Figure BDA00022241922400001112
Sequence of mean values
Figure BDA00022241922400001113
N1, 2, mean square error of N
Figure BDA00022241922400001114
Sequence of mean valuesN is the maximum value of 1,2
Figure BDA00022241922400001116
Sequence of mean values
Figure BDA00022241922400001117
N is the maximum value of 1, 2.
8. Detecting a switching event
Load switch events are detected according to the MUSIC classification principle. The method specifically comprises the following steps: if the Kth normalized window score vector 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 detected for a 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 method for load switch event detection using MUSIC classification, comprising:
step 1, inputting an actually measured power signal sequence S;
and 2, detecting a load switch event according to the MUSIC classification principle. The method specifically comprises the following steps: if the Kth normalized window score vector 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 detected for a load switch event.
2. The method of claim 1, wherein prior to step 2, the method further comprises:
step 3, calculating the K normalized window fraction vector HKAnd said load switch event detection threshold e0。
3. The method of claim 2, wherein step 3 comprises:
step 301, generating the nth signal first order difference sequence
Figure FDA0002224192230000011
The method specifically comprises the following steps:
Figure FDA0002224192230000012
wherein:
Figure FDA0002224192230000013
a step difference of the nth signalSubsequence [ N ═ 1,2, …, N]
Sn: the nth element in the signal sequence S
S＝[S1，S2，…，SN]The length of the signal sequence is N
If the element SjSubscript j of>N, then Sj＝0。
Step 302, generating the nth signal second order difference sequence
Figure FDA0002224192230000014
The method specifically comprises the following steps:
Figure FDA0002224192230000015
wherein:
Figure FDA0002224192230000016
the nth signal second order difference sequence [ N ═ 1,2, …, N]
If the element SjSubscript j of>N, then Sj＝0。
Step 303, calculating the n-th signal feature vector EnThe method specifically comprises the following steps:
wherein:
[En]i: the nth signal feature vector En1,2, …, n]
Figure FDA0002224192230000022
The nth signal first order difference sequence
Figure FDA0002224192230000023
The ith element of
The nth signal second order difference sequence
Figure FDA0002224192230000025
The ith element of
The nth signal first order difference sequence
Figure FDA0002224192230000027
Maximum value of all elements in
Figure FDA0002224192230000028
The nth signal second order difference sequence
Figure FDA0002224192230000029
Maximum value of all elements in
Step 304, calculating the K window fraction sequence hKThe method specifically comprises the following steps:
Figure FDA00022241922300000210
wherein:
matrix [ E ]K]TEKThe m-th feature vector of (2),
arranged according to the sequence of the characteristic values from big to small
[hK]j: the Kth window fraction vector hKThe jth element of (1)
K＝1,2，...，N
j＝1,2，…，K
Step 305, obtaining the K-th normalized window fraction vector HKTool for measuringThe body is as follows:
the first step is as follows: the score adjustment specifically comprises the following steps:
wherein:
σh: the Kth window fraction vector hKThe mean square error of (c).
The second step is that: rearranging the K window fraction sequence h in descending orderKObtaining the K descending window fraction sequence
Figure FDA00022241922300000213
Thirdly, calculating an AU parameter sequence a, specifically:
Figure FDA0002224192230000031
wherein:
[a]i: the ith element of the AU parameter sequence a
Figure FDA0002224192230000032
The Kth descending window fraction sequenceThe ith element of
Figure FDA0002224192230000034
The Kth descending window fraction sequence
Figure FDA0002224192230000035
Maximum value of
Figure FDA0002224192230000036
The Kth descending windowOral score sequence
Figure FDA0002224192230000037
Minimum value of (2)
Fourthly, calculating a weight sequence W, which specifically comprises the following steps:
Figure FDA0002224192230000038
wherein:
[W]i: the ith element of the weight sequence W
Fifthly, calculating the Kth normalized window fraction vector HKThe method specifically comprises the following steps:
[HK]i＝[W]i[hK]i
wherein:
[HK]i: the Kth normalized window score vector HKThe ith element of
Step 306, calculating the load switch event detection threshold e0The method specifically comprises the following steps:
wherein:
Figure FDA0002224192230000042
the nth signal primary differential sequenceMean value of
The nth signal secondary difference sequence
Figure FDA0002224192230000045
Mean value of
Sequence of mean values
Figure FDA0002224192230000047
N is the mean of 1,2, …, N
Figure FDA0002224192230000048
Sequence of mean values
Figure FDA0002224192230000049
N is the mean of 1,2, …, N
Figure FDA00022241922300000410
Sequence of mean values
Figure FDA00022241922300000411
N1, 2, …, mean square error of N
Figure FDA00022241922300000412
Sequence of mean values
Figure FDA00022241922300000413
N1, 2, …, mean square error of N
Figure FDA00022241922300000414
Sequence of mean values
Figure FDA00022241922300000415
N is 1,2, …, maximum value of N
Figure FDA00022241922300000416
Sequence of mean values
Figure FDA00022241922300000417
N is the maximum of 1,2, …, N.
4. A load switch event detection system using MUSIC classification, comprising:
the acquisition module inputs an actually measured power signal sequence S;
and the detection module detects the load switch event according to the MUSIC classification principle. The method specifically comprises the following steps: if the Kth normalized window score vector 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 detected for a load switch event.
5. The system of claim 4, further comprising:
a calculation module to calculate the Kth normalized window score vector HKAnd said load switch event detection threshold e0。
CN201910946140.0A 2019-10-03 2019-10-03 Load switch event detection method and system by using MUSIC classification Withdrawn CN110702980A (en)

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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

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