CN110704802A - Power signal reconstruction method and system by utilizing global regularization - Google Patents

Power signal reconstruction method and system by utilizing global regularization Download PDF

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CN110704802A
CN110704802A CN201910928097.5A CN201910928097A CN110704802A CN 110704802 A CN110704802 A CN 110704802A CN 201910928097 A CN201910928097 A CN 201910928097A CN 110704802 A CN110704802 A CN 110704802A
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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 power signal reconstruction method and a system based on a prediction matrix, wherein the method comprises the following steps: step 1, inputting an actually measured power signal sequence S; step 2, carrying out data reconstruction on the power signal sequence S, wherein the reconstructed power signal sequence is SNEW. The method specifically comprises the following steps:
Figure DDA0002219457910000011
wherein the content of the first and second substances,
Figure DDA0002219457910000012
called sparsity vectors;
Figure DDA0002219457910000013
referred to as a penalty degree vector, mu is a sparsity factor; g is a sparse matrix; r is a sparse reflection vector; kappa is a sparse adjustment factor; λ is a penalty factor; d is WA gram matrix; p is an intermediate parameter matrix; dxIs a direct difference of the signal sequence S; dyIs an indirect difference of the signal sequence S.

Description

Power signal reconstruction method and system by utilizing global regularization
Technical Field
The present invention relates to the field of power, and in particular, to a method and a system for reconstructing a power signal.
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 switch event detection takes the change value of the active power P as the judgment basis of the switch 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. The method needs to set a reasonable threshold value of the power change value, and also needs to solve the problems existing in the practical application of the event detection method, for example, a large peak appears in the instantaneous power value at the starting time of some electric appliances (the starting current of a motor is far larger than the rated current), which causes the inaccurate steady-state power change value of the electric appliances, thereby influencing the judgment of the detection of the switching event; 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. Meanwhile, in the process of acquiring and transmitting the power signal, the operation state of the related instrument and equipment may be temporarily in an abnormal state, which often causes the loss of the power signal.
Therefore, the actual measurement power signal used in the switching event detection process is often incomplete, and the switching event detection cannot be performed correctly by using the incomplete power signal. Therefore, how to effectively reconstruct the incomplete power signal is the key to the success of this method. The existing common method has insufficient attention to the problem, and no effective measure is taken to solve the problem.
Disclosure of Invention
The invention aims to provide a power signal reconstruction method and a power signal reconstruction system by utilizing global regularization. The method has the advantages of good robustness and simple calculation.
In order to achieve the purpose, the invention provides the following scheme:
a method of power signal reconstruction with global regularization, comprising:
step 1, inputting an actually measured power signal sequence S;
step 2, carrying out data reconstruction on the power signal sequence S, wherein the reconstructed power signal sequence is SNEW. The method specifically comprises the following steps:
Figure BDA0002219457890000021
wherein,
Figure BDA0002219457890000022
called sparsity vectors;
Figure BDA0002219457890000023
referred to as a penalty degree vector, mu is a sparsity factor; g is a sparse matrix; r is a sparse reflection vector; kappa is a sparse adjustment factor; λ is a penalty factor; d is a Wolk matrix; p is an intermediate parameter matrix; dxIs a direct difference of the signal sequence S; dyIs an indirect difference of the signal sequence S.
A power signal reconstruction system with global regularization, comprising:
the acquisition module inputs an actually measured power signal sequence S;
a reconstruction module for performing data reconstruction on the power signal sequence S, wherein the reconstructed power signal sequence is SNEW. The method specifically comprises the following steps:wherein,
Figure BDA0002219457890000025
called sparsity vectors;
Figure BDA0002219457890000026
referred to as a penalty degree vector, mu is a sparsity factor; g is a sparse matrix; r is a sparse reflection vector; kappa is a sparse adjustment factor; λ is a penalty factor; d is a Wolk matrix; p is an intermediate parameter matrix; dxIs a direct difference of the signal sequence S; dyIs an indirect difference of the signal sequence S.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
although the switching event detection method has wide application in non-invasive load decomposition and is relatively mature in technology, the power signal is often lost in the acquisition and transmission process and is often submerged in pulse noise with strong amplitude, and the switching event detection cannot be correctly performed by using the incomplete power signal. Therefore, how to effectively reconstruct the incomplete power signal is the key to the success of this method. The existing common method has insufficient attention to the problem, and no effective measure is taken to solve the problem.
The invention aims to provide a power signal reconstruction method and a power signal reconstruction system by utilizing global regularization. The method has the advantages of good robustness and simple 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 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 clear that the described embodiments are only some of the embodiments of the invention, and not all + embodiments. 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 flow chart of a power signal reconstruction method using global regularization
Fig. 1 is a schematic flow chart of a power signal reconstruction method using global regularization according to the present invention. As shown in fig. 1, the power signal reconstruction method using global regularization specifically includes the following steps:
step 1, inputting an actually measured power signal sequence S;
step 2, carrying out data reconstruction on the power signal sequence S, wherein the reconstructed power signal sequence is SNEW. The method specifically comprises the following steps:
Figure BDA0002219457890000041
wherein,
called sparsity vectors;
Figure BDA0002219457890000043
called penalty vector, μ isA sparsity factor; g is a sparse matrix; r is a sparse reflection vector; kappa is a sparse adjustment factor; λ is a penalty factor; d is a Wolk matrix; p is an intermediate parameter matrix; dxIs a direct difference of the signal sequence S; dyIs an indirect difference of the signal sequence S.
Before the step 2, the method further comprises:
step 3, solving the sparsity factor mu, the sparse matrix G, the sparse reflection vector R, the sparse adjustment factor kappa, the Wolk matrix D and the direct difference DxAnd indirect difference dy。
The step 3 comprises the following steps:
step 301, obtaining a cyclic delay matrixThe method specifically comprises the following steps:
Figure BDA0002219457890000051
wherein:
sn: the nth element [ N ═ 1,2, …, N of the signal sequence S]
N: length of the signal sequence S
Circulation parameter
Figure BDA0002219457890000053
Rounding under N as a modulus
SNR: signal-to-noise ratio of the signal sequence S
Step 302, obtaining the sparsity factor μ, specifically:
Figure BDA0002219457890000054
wherein:
Figure BDA0002219457890000055
matrix array
Figure BDA0002219457890000056
Inverse matrix of
I: unit matrix
Step 303, obtaining the sparse matrix G, specifically:
wherein:
Figure BDA0002219457890000058
matrix array
Figure BDA0002219457890000059
Kronecker multiplication of
Step 304, solving the sparse reflection vector R, specifically:
Figure BDA0002219457890000061
wherein:
Figure BDA0002219457890000062
selection matrix
Step 305, obtaining the sparse adjustment factor κ, specifically:
Figure BDA0002219457890000063
wherein:
Figure BDA0002219457890000064
transforming vectors
Figure BDA0002219457890000065
The conversion vector
Figure BDA0002219457890000066
N is 1,2, …, N]
mS: mean value of the signal sequence S
σS: mean square error of the signal sequence S
Step 306, obtaining the wacker matrix D, specifically:
Figure BDA0002219457890000067
wherein
Figure BDA0002219457890000068
Figure BDA0002219457890000069
Δ＝max[|s1-s2|,|s2-s3|,…,|sN-s1|]-min[|s1-s2|,|s2-s3|,…,|sN-s1|]
Step 307, obtaining the direct difference dxThe method specifically comprises the following steps:
Figure BDA0002219457890000071
step 308, obtaining the indirect difference dyThe method specifically comprises the following steps:
Figure BDA0002219457890000072
FIG. 2 structural intent of a power signal reconstruction system using global regularization
Fig. 2 is a schematic structural diagram of a power signal reconstruction system using global regularization according to the present invention. As shown in fig. 2, the power signal reconstruction system using global regularization includes the following structures:
the acquisition module 401 inputs an actually measured power signal sequence S;
a reconstruction module 402, configured to perform data reconstruction on the power signal sequence S, where the reconstructed power signal sequence is SNEW. The method specifically comprises the following steps:
Figure BDA0002219457890000073
wherein,
called sparsity vectors;
referred to as a penalty degree vector, mu is a sparsity factor; g is a sparse matrix; r is a sparse reflection vector; kappa is a sparse adjustment factor; λ is a penalty factor; d is a Wolk matrix; p is an intermediate parameter matrix; dxIs a direct difference of the signal sequence S; dyIs an indirect difference of the signal sequence S.
The system further comprises:
a calculating module 403, for obtaining the sparsity factor μ, the sparse matrix G, the sparse reflection vector R, the sparse adjustment factor κ, the wacker matrix D, and the direct difference DxAnd indirect difference dy。
The calculation module 403 further includes the following units:
a round-robin unit 4031 for determining a round-robin delay matrix
Figure BDA0002219457890000076
The method specifically comprises the following steps:
Figure BDA0002219457890000081
wherein:
sn: the nth element [ N ═ 1,2, …, N of the signal sequence S]
N: length of the signal sequence S
Figure BDA0002219457890000082
Circulation parameter
Rounding under N as a modulus
SNR: signal-to-noise ratio of the signal sequence S
The first calculation unit 4032 calculates the sparsity factor μ, specifically:
wherein:
Figure BDA0002219457890000085
matrix arrayInverse matrix of
I: unit matrix
The second calculation unit 4033, which calculates the sparse matrix G, specifically is:
Figure BDA0002219457890000087
wherein:
Figure BDA0002219457890000088
matrix arrayKronecker multiplication of
The third calculation unit 4034, which calculates the sparse reflection vector R, specifically is:
Figure BDA0002219457890000091
wherein:
Figure BDA0002219457890000092
selection matrix
The fourth calculating unit 4035, which calculates the sparse adjustment factor κ specifically is:
Figure BDA0002219457890000093
wherein:
Figure BDA0002219457890000094
transforming vectors
Figure BDA0002219457890000095
The conversion vector
Figure BDA0002219457890000096
N is 1,2, …, N]
mS: mean value of the signal sequence S
σS: mean square error of the signal sequence S
The fifth calculation unit 4036, which calculates the wacker matrix D, specifically is:
Figure BDA0002219457890000097
wherein
Figure BDA0002219457890000098
Figure BDA0002219457890000099
Δ＝max[|s1-s2|,|s2-s3|,…,|sN-s1|]-min[|s1-s2|,|s2-s3|,…,|sN-s1|]
Sixth calculation section 4037 for calculating direct difference dxThe method specifically comprises the following steps:
Figure BDA0002219457890000101
seventh calculation section 4038, finds the indirect difference dyThe method specifically comprises the following steps:
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: real vibration and sound signal data sequence with length N
siI is 1,2, …, N is measured vibration sound signal with serial number i
2. Determining a cyclic delay matrix
Wherein:
sn: the nth element [ N ═ 1,2, …, N of the signal sequence S]
N: length of the signal sequence S
Figure BDA0002219457890000104
Circulation parameter
Rounding under N as a modulus
SNR: signal-to-noise ratio of the signal sequence S
3. Calculating a sparsity factor
Figure BDA0002219457890000111
Wherein:
Figure BDA0002219457890000112
matrix arrayInverse matrix of
I: unit matrix
4. Obtaining sparse matrices
Figure BDA0002219457890000114
Wherein:
Figure BDA0002219457890000115
matrix arrayKronecker multiplication of
5. Finding sparse reflection vectors
Figure BDA0002219457890000117
Wherein:
Figure BDA0002219457890000118
selection matrix
6. Finding sparse adjustment factors
Figure BDA0002219457890000119
Wherein:
Figure BDA00022194578900001110
transforming vectors
Figure BDA00022194578900001111
The conversion vector
Figure BDA00022194578900001112
N is 1,2, …, N]
mS: mean value of the signal sequence S
σS: mean square error of the signal sequence S
7. Obtaining Wolk matrix
Wherein
Figure BDA0002219457890000122
Figure BDA0002219457890000128
Δ＝max[|s1-s2|,|s2-s3|,…,|sN-s1|]-min[|s1-s2|,|s2-s3|,…,|sN-s1|]
8. Calculating a direct difference
Figure BDA0002219457890000123
9. Calculating the indirect difference
Figure BDA0002219457890000124
10. Data reconstruction
Carrying out data reconstruction on the power signal sequence S, wherein the reconstructed power signal sequence is SNEW. The method specifically comprises the following steps:wherein,called sparsity vectors;
Figure BDA0002219457890000127
referred to as a penalty degree vector, mu is a sparsity factor; g is a sparse matrix; r is a sparse reflection vector; kappa is a sparse adjustment factor; λ is a penalty factor; d is a Wolk matrix; p is an intermediate parameter matrix; dxIs a direct difference of the signal sequence S; dyIs an indirect difference of 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 (5)

1. A method for power signal reconstruction with global regularization, comprising:
step 1, inputting an actually measured power signal sequence S;
step 2, carrying out data reconstruction on the power signal sequence S, wherein the reconstructed power signal sequence is SNEW. The method specifically comprises the following steps:
Figure FDA0002219457880000011
wherein,
Figure FDA0002219457880000012
called sparsity vectors;
Figure FDA0002219457880000013
referred to as a penalty degree vector, mu is a sparsity factor; g is a sparse matrix; r is a sparse reflection vector; kappa is a sparse adjustment factor; λ is a penalty factor; d is a Wolk matrix; p is an intermediate parameter matrix; dxIs a direct difference of the signal sequence S; dyIs an indirect difference of the signal sequence S.
2. The method of claim 1, wherein prior to step 2, the method further comprises:
step 3, solving the sparsity factor mu, the sparse matrix G, the sparse reflection vector R, the sparse adjustment factor kappa, the Wolk matrix D and the direct difference DxAnd indirect difference dy。
3. The method of claim 2, wherein step 3 comprises:
step 301, obtaining a cyclic delay matrix
Figure FDA0002219457880000014
The method specifically comprises the following steps:
Figure FDA0002219457880000015
wherein:
sn: the signal sequenceN-th element of S [ N ═ 1,2, …, N]
N: length of the signal sequence S
Figure FDA0002219457880000016
Circulation parameter
Figure FDA0002219457880000017
Rounding under N as a modulus
SNR: signal-to-noise ratio of the signal sequence S
Step 302, obtaining the sparsity factor μ, specifically:
Figure FDA0002219457880000021
wherein:
Figure FDA0002219457880000022
matrix array
Figure FDA0002219457880000023
Inverse matrix of
I: unit matrix
Step 303, obtaining the sparse matrix G, specifically:
Figure FDA0002219457880000024
wherein:
Figure FDA0002219457880000025
matrix array
Figure FDA0002219457880000026
And
Figure FDA0002219457880000027
kronecker multiplication of
Step 304, solving the sparse reflection vector R, specifically:
Figure FDA0002219457880000028
wherein:
Figure FDA0002219457880000029
selection matrix
Step 305, obtaining the sparse adjustment factor κ, specifically:
wherein:
Figure FDA00022194578800000211
transforming vectors
Figure FDA00022194578800000212
The conversion vectorN is 1,2, …, N]
mS: mean value of the signal sequence S
σS: mean square error of the signal sequence S
Step 306, obtaining the wacker matrix D, specifically:
Figure FDA0002219457880000031
wherein
Figure FDA0002219457880000032
Figure FDA0002219457880000033
Δ＝max[|s1-s2|,|s2-s3|,…,|sN-s1|]-min[|s1-s2|,|s2-s3|,…,|sN-s1|]
Step 307, obtaining the direct difference dxThe method specifically comprises the following steps:
Figure FDA0002219457880000034
step 308, obtaining the indirect difference dyThe method specifically comprises the following steps:
Figure FDA0002219457880000035
。
4. a system for power signal reconstruction with global regularization, comprising:
the acquisition module inputs an actually measured power signal sequence S;
a reconstruction module for performing data reconstruction on the power signal sequence S, wherein the reconstructed power signal sequence is SNEW. The method specifically comprises the following steps:
Figure FDA0002219457880000036
wherein,
Figure FDA0002219457880000037
called sparsity vectors;
Figure FDA0002219457880000038
referred to as a penalty degree vector, mu is a sparsity factor; g is a sparse matrix; r is a sparse reflection vector; kappa is a sparse adjustment factor; λ is a penalty factor; d isA Wolk matrix; p is an intermediate parameter matrix; dxIs a direct difference of the signal sequence S; dyIs an indirect difference of the signal sequence S.
5. The system of claim 4, further comprising:
a calculation module for calculating the sparsity factor mu, the sparse matrix G, the sparse reflection vector R, the sparse adjustment factor kappa, the Wolk matrix D and the direct difference DxAnd indirect difference dy。
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