CN110749841A - Load switch event detection method and system by utilizing conversion space factor - Google Patents

Load switch event detection method and system by utilizing conversion space factor Download PDF

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CN110749841A
CN110749841A CN201911062088.9A CN201911062088A CN110749841A CN 110749841 A CN110749841 A CN 110749841A CN 201911062088 A CN201911062088 A CN 201911062088A CN 110749841 A CN110749841 A CN 110749841A
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翟明岳
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Abstract

The embodiment of the invention discloses a load switch event detection method and a system by utilizing conversion space factors, wherein the method comprises the following steps: step 1, inputting an actually measured signal sequence S; and 2, detecting a load switch event according to the property of the conversion space factor. The method specifically comprises the following steps: if the Kth window transforms the space factor 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 event.

Description

Load switch event detection method and system by utilizing conversion space factor
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.
Disclosure of Invention
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.
The invention aims to provide a load switch event detection method and system by utilizing conversion space factors. 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 using a transition space factor, comprising:
step 001 inputting an actually measured signal sequence S;
step 002 detects load switch events based on the transformed space factor properties. The method specifically comprises the following steps: if the Kth window transforms the space factor 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 event.
A load switch event detection system utilizing a transition space factor, comprising:
an acquisition module inputs an actually measured signal sequence S;
the judging module detects the load switch event according to the property of the conversion space factor. The method specifically comprises the following steps: if the Kth window transforms the space factor HKSatisfies the judgment condition | HK|≥e0At the Kth point of the signal sequence S, a load switch event is detected(ii) a Otherwise, no load switch event is detected. Wherein e is0A threshold is determined for the event.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
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.
The invention aims to provide a load switch event detection method and system by utilizing conversion space factors. The method has good switching event detection performance and is simple in calculation.
Drawings
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 flow chart 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 using space factor conversion
Fig. 1 is a flow chart illustrating a load switch event detection method using a transformed space factor according to the present invention. As shown in fig. 1, the load switch event detection method using space factor conversion specifically includes the following steps:
step 001 inputting an actually measured signal sequence S;
step 002 detects load switch events based on the transformed space factor properties. The method specifically comprises the following steps: if the Kth window transforms the space factor 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 event.
Prior to the step 002, the method further comprises:
step 003 of obtaining said transformed space factor HKAnd the event judgment threshold e0。
The step 003 further includes:
step 301 generates an nth signal first-order difference sequence, specifically:
Figure BDA0002258203360000031
wherein:
Figure BDA0002258203360000032
the nth signal first order difference sequence
sn: the nth element, N, of the signal sequence S is 1,2, N
N: length of the signal sequence S
n: subscripts of the elements, if n>N, element s corresponding ton＝0
Step 302 generates an nth signal second order difference sequence, specifically:
Figure BDA0002258203360000033
wherein:
Figure BDA0002258203360000034
the nth signal second order difference sequence
n: subscripts of the elements, if n>N, element s corresponding ton＝0
Step 303 finds the nth expected difference sequenceThe method specifically comprises the following steps:
Figure BDA0002258203360000036
wherein:
Wn: nth desired weight matrix
Figure BDA0002258203360000041
λ: correlation matrixMaximum eigenvalue of
Figure BDA00022582033600000416
The correlation matrix
Figure BDA0002258203360000043
The jth feature vector of
j: subscripts, j ═ 1,2, ·, N
ρ: the correlation matrix
Figure BDA0002258203360000044
Trace of
Step 304, calculating the kth window transform space factor, specifically:
Figure BDA0002258203360000045
wherein:
the nth signal first order difference sequence
Figure BDA0002258203360000047
Mean square error of
Figure BDA0002258203360000048
The nth signal second order difference sequence
Figure BDA0002258203360000049
Mean square error of
Step 305 of obtaining the state determination threshold e0The method specifically comprises the following steps:
Figure BDA00022582033600000410
wherein:
κj: j-th eigenvalue of correlation difference matrix C
Figure BDA00022582033600000411
j: subscripts, j ═ 1,2, ·, N
Figure BDA00022582033600000412
First order difference sequence of Nth signal
Figure BDA00022582033600000413
Mean square error of
Figure BDA00022582033600000414
Second order difference sequence of Nth signal
Figure BDA00022582033600000415
Mean square error of
FIG. 2 is a schematic diagram of a load switch event detection system using conversion space factor
Fig. 2 is a schematic diagram of a load switch event detection system using space factor conversion according to the present invention. As shown in fig. 2, the load switch event detection system using the transition space factor includes the following structure:
the acquisition module 401 inputs an actually measured signal sequence S;
the decision block 402 detects a load switch event based on the transformed space factor property. The method specifically comprises the following steps: if the Kth window transforms the space factor 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 event.
The system further comprises:
computing module 403 finds the transformed space factor HKAnd the event judgment threshold e0。
The calculation module 403 further includes the following units, which specifically include:
the calculating unit 4031 generates an nth signal first-order difference sequence, specifically:
Figure BDA0002258203360000051
wherein:
Figure BDA0002258203360000052
the nth signal first order difference sequence
sn: the nth element, N, of the signal sequence S is 1,2, N
N: length of the signal sequence S
n: subscripts of the elements, if n>N, element s corresponding ton＝0
The calculating unit 4032 generates an nth signal second-order difference sequence, specifically:
Figure BDA0002258203360000053
wherein:
Figure BDA0002258203360000054
the nth signal second order difference sequence
n: subscripts of the elements, if n>N, element s corresponding ton＝0
Calculation unit 4033 finds the nth expected difference sequence
Figure BDA0002258203360000055
The method specifically comprises the following steps:
wherein:
Wn: nth desired weight matrix
Figure BDA0002258203360000057
λ: correlation matrix
Figure BDA0002258203360000058
Maximum eigenvalue of
Figure BDA00022582033600000516
The correlation matrix
Figure BDA0002258203360000059
The jth feature vector of
j: subscripts, j ═ 1,2, ·, N
ρ: the correlation matrix
Figure BDA00022582033600000510
Trace of
The calculating unit 4034 calculates the kth window transformation space factor, specifically:
Figure BDA00022582033600000511
wherein:
Figure BDA00022582033600000512
the nth signal first order difference sequence
Figure BDA00022582033600000513
Mean square error of
Figure BDA00022582033600000514
The nth signal second order difference sequence
Figure BDA00022582033600000515
Mean square error of
Calculation unit 4035 obtains state determination threshold e0The method specifically comprises the following steps:
Figure BDA0002258203360000061
wherein:
κj: j-th eigenvalue of correlation difference matrix C
Figure BDA0002258203360000062
j: subscripts, j ═ 1,2, ·, N
Figure BDA0002258203360000063
First order difference sequence of Nth signal
Figure BDA0002258203360000064
Mean square error of
Figure BDA0002258203360000065
Second order difference sequence of Nth signal
Figure BDA0002258203360000066
Mean square error of
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:
0 start: inputting measured signal data sequence
S＝[s1,s2,···,sN-1,sN]
Wherein:
s: measured signal sequence of length N
sn: the nth element in the signal sequence S
n: subscript, N ═ 1,2,. cndot., N
1, generating an nth signal first-order difference sequence, specifically:
Figure BDA0002258203360000067
wherein:
Figure BDA0002258203360000068
the nth signal first order difference sequence
sn: the nth element, N, of the signal sequence S is 1,2, N
N: length of the signal sequence S
n: subscripts of the elements, if n>N, element s corresponding ton＝0
2, generating an nth signal second-order difference sequence, specifically:
Figure BDA0002258203360000069
wherein:
the nth signal second order difference sequence
n: subscripts of the elements, if n>N, element s corresponding ton＝0
3 obtaining the nth expected difference sequence
Figure BDA0002258203360000071
The method specifically comprises the following steps:
Figure BDA0002258203360000072
wherein:
Wn: nth desired weight matrix
λ: correlation matrix
Figure BDA0002258203360000074
Maximum eigenvalue of
Figure BDA00022582033600000718
The correlation matrix
Figure BDA0002258203360000075
The jth feature vector of
j: subscripts, j ═ 1,2, ·, N
ρ: the correlation matrixTrace of
4, solving the kth window conversion space factor, specifically:
Figure BDA0002258203360000077
wherein:
Figure BDA0002258203360000078
the nth signal first order difference sequence
Figure BDA0002258203360000079
Mean square error of
Figure BDA00022582033600000710
The nth signal second order difference sequenceMean square error of
5 obtaining the state judgment threshold e0The method specifically comprises the following steps:
Figure BDA00022582033600000712
wherein:
κj: j-th eigenvalue of correlation difference matrix C
Figure BDA00022582033600000713
j: subscripts, j ═ 1,2, ·, N
Figure BDA00022582033600000714
First order difference sequence of Nth signalMean square error of
Figure BDA00022582033600000716
Second order difference sequence of Nth signal
Figure BDA00022582033600000717
Mean square error of
And 6, finishing: determining an event
Load switch events are detected based on the transition space factor properties. The method specifically comprises the following steps: if the Kth window transforms the space factor 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 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 using a transition space factor, comprising:
step 001 inputting an actually measured signal sequence S;
step 002 detects load switch events based on the transformed space factor properties. In particular toComprises the following steps: if the Kth window transforms the space factor 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 event.
2. The method of claim 1, wherein prior to step 2, the method further comprises:
step 003 of obtaining said transformed space factor HKAnd the event judgment threshold e0。
3. The method of claim 2, wherein step 3 comprises:
step 301 generates an nth signal first-order difference sequence, specifically:
Figure FDA0002258203350000011
wherein:
Figure FDA0002258203350000012
the nth signal first order difference sequence
sn: the nth element, N ═ 1,2, …, N, of the signal sequence S
N: length of the signal sequence S
n: subscripts of the elements, if n>N, element s corresponding ton＝0
Step 302 generates an nth signal second order difference sequence, specifically:
Figure FDA0002258203350000013
wherein:
the nth signal second order difference sequence
n: subscripts of the elements, if n>N, element s corresponding ton＝0
Step 303 finds the nth expected difference sequence
Figure FDA0002258203350000015
The method specifically comprises the following steps:
Figure FDA0002258203350000016
wherein:
Wn: nth desired weight matrix
Figure FDA0002258203350000017
λ: correlation matrix
Figure FDA0002258203350000018
Maximum eigenvalue of
Figure FDA0002258203350000019
The correlation matrix
Figure FDA00022582033500000110
The jth feature vector of
j: subscript, j ═ 1,2, …, N
ρ: the correlation matrix
Figure FDA00022582033500000111
Trace of
Step 304, calculating the kth window transform space factor, specifically:
Figure FDA0002258203350000021
wherein:
Figure FDA0002258203350000022
the nth signal first order difference sequence
Figure FDA0002258203350000023
Mean square error of
Figure FDA0002258203350000024
The nth signal second order difference sequence
Figure FDA0002258203350000025
Mean square error of
Step 305 of obtaining the state determination threshold e0The method specifically comprises the following steps:
Figure FDA0002258203350000026
wherein:
κj: j-th eigenvalue of correlation difference matrix C
Figure FDA0002258203350000027
j: subscript, j ═ 1,2, …, N
First order difference sequence of Nth signalMean square error of
Figure FDA00022582033500000210
Second order difference sequence of Nth signal
Figure FDA00022582033500000211
The mean square error of (c).
4. A load switch event detection system utilizing a transition space factor, comprising:
an acquisition module inputs an actually measured signal sequence S;
the judging module detects the load switch event according to the property of the conversion space factor. The method specifically comprises the following steps: if the Kth window transforms the space factor 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 event.
5. The system of claim 4, further comprising:
calculating the conversion space factor H by a calculating moduleKAnd the event judgment threshold e0。
CN201911062088.9A 2019-11-02 2019-11-02 Load switch event detection method and system by utilizing conversion space factor Withdrawn CN110749841A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112257576A (en) * 2020-10-21 2021-01-22 华北电力大学 Load switch event detection method and system using Maha distance measure

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112257576A (en) * 2020-10-21 2021-01-22 华北电力大学 Load switch event detection method and system using Maha distance measure
CN112257576B (en) * 2020-10-21 2021-12-10 华北电力大学 Load switch event detection method and system using Maha distance measure

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