CN112307986B - Load switch event detection method and system by utilizing Gaussian gradient - Google Patents

Load switch event detection method and system by utilizing Gaussian gradient Download PDF

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CN112307986B
CN112307986B CN202011206199.5A CN202011206199A CN112307986B CN 112307986 B CN112307986 B CN 112307986B CN 202011206199 A CN202011206199 A CN 202011206199A CN 112307986 B CN112307986 B CN 112307986B
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CN112307986A (en
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翟明岳
罗永春
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North China Electric Power University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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Abstract

The embodiment of the invention discloses a load switch event detection method and a system by utilizing Gaussian gradient, wherein the method comprises the following steps: step 101, acquiring a signal sequence S acquired according to a time sequence; 102, solving a linear gradient matrix; step 103, solving a Gaussian gradient matrix; 104, generating N window signal matrixes; step 105, obtaining N Gaussian gradient values; step 106 detects a load switch event.

Description

Load switch event detection method and system by utilizing Gaussian gradient
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 Gaussian gradient. 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 method of load switch event detection using a gaussian gradient, comprising:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102, solving a linear gradient matrix, specifically: linear gradient matrix denoted G1The formula used is:
Figure BDA0002757186630000021
wherein:
σ represents the mean square error of the signal sequence S;
step 103, solving a gaussian gradient matrix, specifically: gauss gradient matrix is denoted as G2The ith row and the jth column are marked as
Figure BDA0002757186630000022
The solving formula is as follows:
Figure BDA0002757186630000023
wherein:
t is the sampling interval of the signal sequence S,
i is 1,2, 5 is a row number,
j 1,2, 5 is a column number;
step 104 generates N window signal matrices, specifically:
the kth window signal matrix is denoted as DkThe ith row and the jth column are marked as
Figure BDA0002757186630000024
The solving formula is as follows:
Figure BDA0002757186630000025
wherein:
s|i+j+1+5(k-1)|Nthe | i + j +1+5(k-1) | representing the signal sequence SNThe number of the elements is one,
s|i+j+5(k-1)|Nthe | i + j +5(k-1) | representing the signal sequence SNThe number of the elements is one,
|i+j+5(k-1)|Nindicating that the remainder is taken modulo N for i + j +5(k-1),
|i+j+1+5(k-1)|Nthe remainder is taken for i + j +1+5(k-1) by taking N as a modulus;
step 105, obtaining N gaussian gradient values, specifically:
the k-th Gaussian gradient value is recorded as HkThe formula used is:
Hk＝||(G1+G2)Dk||F
wherein: | | (G)1+G2)Dk||FIs represented by (G)1+G2)DkThe Frobenus moustache of (1);
step 106, detecting a load switch event, specifically: if the k-th Gaussian gradient value HkIs greater than or equal to
Figure BDA0002757186630000031
A load switch event is detected at the kth point of the signal sequence S; otherwise, no load switch event is detected; wherein max S represents the maximum value of the elements used in the signal sequence S; min S represents the minimum value of the elements used in the signal sequence S.
A load switch event detection system utilizing a gaussian gradient, comprising:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 finds a linear gradient matrix, specifically: linear gradient matrix denoted G1The formula used is:
Figure BDA0002757186630000032
wherein:
σ represents the mean square error of the signal sequence S;
the module 203 finds a gaussian gradient matrix, specifically: gauss gradient matrix is denoted as G2The ith row and the jth column are marked as
Figure BDA0002757186630000033
The solving formula is as follows:
Figure BDA0002757186630000034
wherein:
t is the sampling interval of the signal sequence S,
i is 1,2, 5 is a row number,
j 1,2, 5 is a column number;
the module 204 generates N window signal matrices, specifically:
the kth window signal matrix is denoted as DkThe ith row and the jth column are marked as
Figure BDA0002757186630000035
The solving formula is as follows:
Figure BDA0002757186630000036
wherein:
s|i+j+1+5(k-1)|Nthe | i + j +1+5(k-1) | representing the signal sequence SNAn element，
s|i+j+5(k-1)|NThe | i + j +5(k-1) | representing the signal sequence SNThe number of the elements is one,
|i+j+5(k-1)|Nindicating that the remainder is taken modulo N for i + j +5(k-1),
|i+j+1+5(k-1)|Nthe remainder is taken for i + j +1+5(k-1) by taking N as a modulus;
the module 205 calculates N gaussian gradient values, specifically:
the k-th Gaussian gradient value is recorded as HkThe formula used is:
Hk＝||(G1+G2)Dk||F
wherein: | | (G)1+G2)Dk||FIs represented by (G)1+G2)DkThe Frobenus moustache of (1);
the module 206 detects a load switch event, specifically: if the k-th Gaussian gradient value HkIs greater than or equal to
Figure BDA0002757186630000041
A load switch event is detected at the kth point of the signal sequence S; otherwise, no load switch event is detected; wherein max S represents the maximum value of the elements used in the signal sequence S; min S represents the minimum value of the elements used in the signal sequence S.
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 Gaussian gradient. 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 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 Gaussian gradients
FIG. 1 is a flow chart illustrating a method for detecting a load switch event using a Gaussian gradient according to the present invention. As shown in fig. 1, the method for detecting a load switch event by using a gaussian gradient specifically includes the following steps:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102, solving a linear gradient matrix, specifically: linear gradient matrix denoted G1All find publicThe formula is as follows:
Figure BDA0002757186630000051
wherein:
σ represents the mean square error of the signal sequence S;
step 103, solving a gaussian gradient matrix, specifically: gauss gradient matrix is denoted as G2The ith row and the jth column are marked as
Figure BDA0002757186630000052
The solving formula is as follows:
Figure BDA0002757186630000053
wherein:
t is the sampling interval of the signal sequence S,
i is 1,2, 5 is a row number,
j 1,2, 5 is a column number;
step 104 generates N window signal matrices, specifically:
the kth window signal matrix is denoted as DkThe ith row and the jth column are marked as
Figure BDA0002757186630000054
The solving formula is as follows:
Figure BDA0002757186630000055
wherein:
s|i+j+1+5(k-1)|Nthe | i + j +1+5(k-1) | representing the signal sequence SNThe number of the elements is one,
s|i+j+5(k-1)|Nthe | i + j +5(k-1) | representing the signal sequence SNThe number of the elements is one,
|i+j+5(k-1)|Nindicating that the remainder is taken modulo N for i + j +5(k-1),
|i+j+1+5(k-1)|Nthe remainder is taken for i + j +1+5(k-1) by taking N as a modulus;
step 105, obtaining N gaussian gradient values, specifically:
the k-th Gaussian gradient value is recorded as HkThe formula used is:
Hk＝||(G1+G2)Dk||F
wherein: | | (G)1+G2)Dk||FIs represented by (G)1+G2)DkThe Frobenus moustache of (1);
step 106, detecting a load switch event, specifically: if the k-th Gaussian gradient value HkIs greater than or equal to
Figure BDA0002757186630000061
A load switch event is detected at the kth point of the signal sequence S; otherwise, no load switch event is detected; wherein max S represents the maximum value of the elements used in the signal sequence S; min S represents the minimum value of the elements used in the signal sequence S.
FIG. 2 structural intent of a loadswitch event detection system using Gaussian gradients
FIG. 2 is a schematic diagram of a load switch event detection system using Gaussian gradient according to the present invention. As shown in fig. 2, the load switch event detection system using gaussian gradient includes the following structure:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 finds a linear gradient matrix, specifically: linear gradient matrix denoted G1The formula used is:
Figure BDA0002757186630000062
wherein:
σ represents the mean square error of the signal sequence S;
the module 203 finds a gaussian gradient matrix, specifically: gauss gradient matrix is denoted as G2I, j, column elements thereofIs marked as
Figure BDA0002757186630000063
The solving formula is as follows:
Figure BDA0002757186630000064
wherein:
t is the sampling interval of the signal sequence S,
i is 1,2, 5 is a row number,
j 1,2, 5 is a column number;
the module 204 generates N window signal matrices, specifically:
the kth window signal matrix is denoted as DkThe ith row and the jth column are marked as
Figure BDA0002757186630000065
The solving formula is as follows:
Figure BDA0002757186630000071
wherein:
s|i+j+1+5(k-1)|Nthe | i + j +1+5(k-1) | representing the signal sequence SNThe number of the elements is one,
s|i+j+5(k-1)|Nthe | i + j +5(k-1) | representing the signal sequence SNThe number of the elements is one,
|i+j+5(k-1)|Nindicating that the remainder is taken modulo N for i + j +5(k-1),
|i+j+1+5(k-1)|Nthe remainder is taken for i + j +1+5(k-1) by taking N as a modulus;
the module 205 calculates N gaussian gradient values, specifically:
the k-th Gaussian gradient value is recorded as HkThe formula used is:
Hk＝||(G1+G2)Dk||F
wherein: | | (G)1+G2)Dk||FIs represented by (G)1+G2)DkThe Frobenus moustache of (1);
the module 206 detects a load switch event, specifically: if the k-th Gaussian gradient value HkIs greater than or equal to
Figure BDA0002757186630000072
A load switch event is detected at the kth point of the signal sequence S; otherwise, no load switch event is detected; wherein max S represents the maximum value of the elements used in the signal sequence S; min S represents the minimum value of the elements used in the signal sequence S.
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:
step 301, acquiring a signal sequence S acquired according to a time sequence;
step 302, solving a linear gradient matrix, specifically: linear gradient matrix denoted G1The formula used is:
Figure BDA0002757186630000073
wherein:
σ represents the mean square error of the signal sequence S;
step 303, obtaining a gaussian gradient matrix, specifically: gauss gradient matrix is denoted as G2The ith row and the jth column are marked as
Figure BDA0002757186630000074
The solving formula is as follows:
Figure BDA0002757186630000075
wherein:
t is the sampling interval of the signal sequence S,
i is 1,2, 5 is a row number,
j 1,2, 5 is a column number;
step 304 generates N window signal matrices, specifically:
the kth window signal matrix is denoted as DkThe ith row and the jth column are marked as
Figure BDA0002757186630000081
The solving formula is as follows:
Figure BDA0002757186630000082
wherein:
s|i+j+1+5(k-1)|Nthe | i + j +1+5(k-1) | representing the signal sequence SNThe number of the elements is one,
s|i+j+5(k-1)|Nthe | i + j +5(k-1) | representing the signal sequence SNThe number of the elements is one,
|i+j+5(k-1)|Nindicating that the remainder is taken modulo N for i + j +5(k-1),
|i+j+1+5(k-1)|Nthe remainder is taken for i + j +1+5(k-1) by taking N as a modulus;
step 305 finds N gaussian gradient values, specifically:
the k-th Gaussian gradient value is recorded as HkThe formula used is:
Hk＝||(G1+G2)Dk||F
wherein: | | (G)1+G2)Dk||FIs represented by (G)1+G2)DkThe Frobenus moustache of (1);
step 306, detecting a load switch event, specifically: if the k-th Gaussian gradient value HkIs greater than or equal to
Figure BDA0002757186630000083
A load switch event is detected at the kth point of the signal sequence S; otherwise, no load switch event is detected; wherein max S represents the maximum value of the elements used in the signal sequence S; min S represents the minimum value of the elements used in the signal sequence S.
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 (2)

1. A method of load switch event detection using a gaussian gradient, comprising:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102, solving a linear gradient matrix, specifically: linear gradient matrix denoted G1The formula used is:
Figure FDA0002757186620000011
wherein:
σ represents the mean square error of the signal sequence S;
step 103, solving a gaussian gradient matrix, specifically: gauss gradient matrix is denoted as G2The ith row and the jth column are marked as
Figure FDA0002757186620000012
The solving formula is as follows:
Figure FDA0002757186620000013
wherein:
t is the sampling interval of the signal sequence S,
i is 1,2, 5 is a row number,
j 1,2, 5 is a column number;
step 104 generates N window signal matrices, specifically:
the kth window signal matrix is denoted as DkThe ith row and the jth column are marked as
Figure FDA0002757186620000014
The solving formula is as follows:
Figure FDA0002757186620000015
wherein:
Figure FDA0002757186620000016
the | i + j +1+5(k-1) | representing the signal sequence SNThe number of the elements is one,
Figure FDA0002757186620000017
the | i + j +5(k-1) | representing the signal sequence SNThe number of the elements is one,
|i+j+5(k-1)|Ndenotes taking the remainder of i + j +5(k-1) modulo N, | i + j +1+5(k-1) | survivalNThe remainder is taken for i + j +1+5(k-1) by taking N as a modulus;
step 105, obtaining N gaussian gradient values, specifically:
the k-th Gaussian gradient value is recorded as HkThe formula used is:
Hk＝||(G1+G2)Dk||F
wherein: | | (G)1+G2)Dk||FIs represented by (G)1+G2)DkThe Frobenus moustache of (1);
step 106 detects load onThe events are specifically: if the k-th Gaussian gradient value HkIs greater than or equal to
Figure FDA0002757186620000021
A load switch event is detected at the kth point of the signal sequence S; otherwise, no load switch event is detected; wherein max S represents the maximum value of the elements used in the signal sequence S; min S represents the minimum value of the elements used in the signal sequence S.
2. A load switch event detection system using a gaussian gradient, comprising:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 finds a linear gradient matrix, specifically: linear gradient matrix denoted G1The formula used is:
Figure FDA0002757186620000022
wherein:
σ represents the mean square error of the signal sequence S;
the module 203 finds a gaussian gradient matrix, specifically: gauss gradient matrix is denoted as G2The ith row and the jth column are marked as
Figure FDA0002757186620000023
The solving formula is as follows:
Figure FDA0002757186620000024
wherein:
t is the sampling interval of the signal sequence S,
i is 1,2, 5 is a row number,
j 1,2, 5 is a column number;
the module 204 generates N window signal matrices, specifically:
the kth window signal matrix is denoted as DkThe ith row and the jth column are marked as
Figure FDA0002757186620000025
The solving formula is as follows:
Figure FDA0002757186620000026
wherein:
Figure FDA0002757186620000027
the | i + j +1+5(k-1) | representing the signal sequence SNThe number of the elements is one,
Figure FDA0002757186620000028
the | i + j +5(k-1) | representing the signal sequence SNThe number of the elements is one,
|i+j+5(k-1)|Nindicating that the remainder is taken modulo N for i + j +5(k-1),
|i+j+1+5(k-1)|Nthe remainder is taken for i + j +1+5(k-1) by taking N as a modulus;
the module 205 calculates N gaussian gradient values, specifically:
the k-th Gaussian gradient value is recorded as HkThe formula used is:
Hk＝||(G1+G2)Dk||F
wherein: | | (G)1+G2)Dk||FIs represented by (G)1+G2)DkThe Frobenus moustache of (1);
the module 206 detects a load switch event, specifically: if the k-th Gaussian gradient value HkIs greater than or equal to
Figure FDA0002757186620000031
A load switch event is detected at the kth point of the signal sequence S; otherwise, no load switch event is detected;wherein max S represents the maximum value of the elements used in the signal sequence S; min S represents the minimum value of the elements used in the signal sequence S.
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