CN110611310A - Power signal reconstruction method and system by using periodic boundary conditions - Google Patents

Power signal reconstruction method and system by using periodic boundary conditions Download PDF

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Publication number
CN110611310A
CN110611310A CN201910901360.1A CN201910901360A CN110611310A CN 110611310 A CN110611310 A CN 110611310A CN 201910901360 A CN201910901360 A CN 201910901360A CN 110611310 A CN110611310 A CN 110611310A
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China
Prior art keywords
boundary
factor
signal sequence
power signal
matrix
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CN201910901360.1A
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Chinese (zh)
Inventor
翟明岳
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Guangdong University of Petrochemical Technology
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Guangdong University of Petrochemical Technology
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Priority to CN201910901360.1A priority Critical patent/CN110611310A/en
Publication of CN110611310A publication Critical patent/CN110611310A/en
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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
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Soundproofing, Sound Blocking, And Sound Damping (AREA)

Abstract

The embodiment of the invention discloses a power signal reconstruction method and a system by using periodic boundary conditions, 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:wherein, alpha is a boundary factor; wOPTIs the optimal boundary vector; d is a boundary matrix; mu is a boundary repair factor; gamma is a boundary curvature factor; p is the intermediate vector.

Description

Power signal reconstruction method and system by using periodic boundary conditions
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 using a periodic boundary condition. 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 periodic boundary conditions, 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:
wherein, alpha is a boundary factor; wOPTIs the optimal boundary vector; d is a boundary matrix; mu is a boundary repair factor; gamma is a boundary curvature factor; p is the intermediate vector.
A power signal reconstruction system utilizing periodic boundary conditions, 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 SN. The method specifically comprises the following steps:
wherein, alpha is a boundary factor; wOPTIs the optimal boundary vector; d is a boundary matrix; mu is a boundary repair factor;
gamma is a boundary curvature factor; p is the intermediate vector.
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 using a periodic boundary condition. 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 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 flow chart of a power signal reconstruction method using periodic boundary conditions
Fig. 1 is a flow chart illustrating a power signal reconstruction method using periodic boundary conditions according to the present invention. As shown in fig. 1, the method for reconstructing a power signal using a periodic boundary condition 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:
wherein, alpha is a boundary factor; wOPTIs the optimal boundary vector; d is a boundary matrix; mu is a boundary repair factor; gamma is a boundary curvature factor; p is the intermediate vector.
Before the step 2, the method further comprises:
step 3, obtaining the boundary factor alpha and the optimal boundary vector WOPTA boundary matrix D, a boundary repair factor μ, and a boundary curvature factor γ.
The step 3 comprises the following steps:
step 301, obtaining a cyclic delay matrixThe method specifically comprises the following steps:
wherein:
sn: the nth element [ N ═ 1,2, …, N of the signal sequence S]
N: length of the signal sequence S
Circulation parameter
Rounding under N as a modulus
SNR: signal-to-noise ratio of the signal sequence S
Step 302, obtaining the boundary factor α, specifically:
step 303, obtaining the boundary matrix D, specifically:
wherein:
matrix arrayAndkronecker multiplication of
Matrix arrayEigenvalue matrix of
Step 304, calculating the boundary repairing factor μ, specifically:
wherein:
selection matrix
Step 305, obtaining the boundary curvature factor γ, specifically:
wherein:
transforming vectors
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, iteratively calculating the optimal boundary vector WOPTThe method specifically comprises the following steps:
the method comprises the following steps of firstly, initializing:
W1as S: initialization vector
Iterative control parameter k 1
And step two, iterative updating, which specifically comprises the following steps:
thirdly, iterative judgment, specifically comprising:
adding 1 to the iteration control parameter K, returning to the second step, and continuing to perform iteration updating until the difference between two adjacent iteration results is less than one thousandth, wherein the iteration control parameter K is K, and W isOPT＝Wk+1。
FIG. 2 structural intent of a power signal reconstruction system using periodic boundary conditions
Fig. 2 is a schematic structural diagram of a power signal reconstruction system using periodic boundary conditions according to the present invention. As shown in fig. 2, the power signal reconstruction system using the periodic boundary condition 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:
wherein, alpha is a boundary factor; wOPTIs the optimal boundary vector; d is a boundary matrix; mu is a boundary repair factor; gamma is a boundary curvature factor; p is the intermediate vector.
The system further comprises:
a calculation module 403 for obtaining the boundary factor α and the optimal boundary vector WOPTA boundary matrix D, a boundary repair factor μ, and a boundary curvature factor γ.
The calculation module 403 further includes the following units:
a round-robin unit 4031 for determining a round-robin delay matrixThe method specifically comprises the following steps:
wherein:
sn: the nth element [ N ═ 1,2, …, N of the signal sequence S]
N: length of the signal sequence S
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 boundary factor α, specifically:
the second calculation unit 4033, which calculates the boundary matrix D, specifically is:
wherein:
matrix arrayAndkronecker multiplication of
Matrix arrayEigenvalue matrix of
The third calculation unit 4034, which calculates the boundary repair factor μ, specifically:
wherein:
selection matrix
The fourth calculation unit 4035 calculates the boundary curvature factor γ, specifically:
wherein:
transforming vectors
The conversion vectorN is 1,2, …, N]
mS: mean value of the signal sequence S
σS: mean square error of the signal sequence S
An iteration unit 4036 for iteratively calculating the optimal boundary vector WOPTThe method specifically comprises the following steps:
the method comprises the following steps of firstly, initializing:
W1as S: initialization vector
Iterative control parameter k 1
And step two, iterative updating, which specifically comprises the following steps:
thirdly, iterative judgment, specifically comprising:
adding 1 to the iteration control parameter K, returning to the second step, and continuing to perform iteration updating until the difference between two adjacent iteration results is less than one thousandth, wherein the iteration control parameter K is K, and W isOPT＝Wk+1。
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
Circulation parameter
Rounding under N as a modulus
SNR: signal-to-noise ratio of the signal sequence S
3. Determining boundary factors
4. Determining a boundary matrix
Wherein:
matrix arrayAndkronecker multiplication of
Matrix arrayEigenvalue matrix of
5. Calculating boundary repair factor
Wherein:
selection matrix
6. Determining boundary curvature factor
Wherein:
transforming vectors
The conversion vectorN is 1,2, …, N]
mS: mean value of the signal sequence S
σS: mean square error of the signal sequence S
7. Finding the optimal boundary vector
The method comprises the following steps of firstly, initializing:
W1as S: initialization vector
Iterative control parameter k 1
And step two, iterative updating, which specifically comprises the following steps:
thirdly, iterative judgment, specifically comprising:
adding 1 to the iteration control parameter K, returning to the second step, and continuing to perform iteration updating until the difference between two adjacent iteration results is less than one thousandth, wherein the iteration control parameter K is K, and W isOPT＝Wk+1。
8. 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, alpha is a boundary factor; wOPTIs the optimal boundary vector; d is a boundary matrix; mu is a boundary repair factor; gamma is a boundary curvature factor; p is the intermediate vector.
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 using periodic boundary conditions, 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:wherein, alpha is a boundary factor; wOPTIs the optimal boundary vector; d is a boundary matrix; mu is a boundary repair factor; gamma is a boundary curvature factor; p is the intermediate vector.
2. The method of claim 1, wherein prior to step 2, the method further comprises:
step 3, obtaining the boundary factor alpha and the optimal boundary vector WOPTA boundary matrix D, a boundary repair factor μ, and a boundary curvature factor γ.
3. The method of claim 2, wherein step 3 comprises:
step 301, obtaining a cyclic delay matrixThe method specifically comprises the following steps:
wherein:
sn: the nth element [ N ═ 1,2, …, N of the signal sequence S]
N: length of the signal sequence S
Circulation parameter
Rounding under N as a modulus
SNR: signal-to-noise ratio of the signal sequence S
Step 302, obtaining the boundary factor α, specifically:
step 303, obtaining the boundary matrix D, specifically:
wherein:
matrix arrayAndkronecker multiplication of
Matrix arrayEigenvalue matrix of
Step 304, calculating the boundary repairing factor μ, specifically:
wherein:
selection matrix
Step 305, obtaining the boundary curvature factor γ, specifically:
wherein:
transforming vectors
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, iteratively calculating the optimal boundary vector WOPTThe method specifically comprises the following steps:
the method comprises the following steps of firstly, initializing:
W1as S: initialization vector
Iterative control parameter k 1
And step two, iterative updating, which specifically comprises the following steps:
thirdly, iterative judgment, specifically comprising:
adding 1 to the iteration control parameter K, returning to the second step, and continuing to perform iteration updating until the difference between two adjacent iteration results is less than one thousandth, wherein the iteration control parameter K is K, and W isOPT＝Wk+1。
4. A power signal reconstruction system that utilizes periodic boundary conditions, 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, alpha is a boundary factor; wOPTIs the optimal boundary vector; d is a boundary matrix; mu is a boundary repair factor; gamma is a boundary curvature factor; p is the intermediate vector.
5. The system of claim 4, further comprising:
a calculation module for obtaining the boundary factor alpha and the optimal boundary vector WOPTA boundary matrix D, a boundary repair factor μ, and a boundary curvature factor γ.
CN201910901360.1A 2019-09-24 2019-09-24 Power signal reconstruction method and system by using periodic boundary conditions Withdrawn CN110611310A (en)

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Application Number Priority Date Filing Date Title
CN201910901360.1A CN110611310A (en) 2019-09-24 2019-09-24 Power signal reconstruction method and system by using periodic boundary conditions

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910901360.1A CN110611310A (en) 2019-09-24 2019-09-24 Power signal reconstruction method and system by using periodic boundary conditions

Publications (1)

Publication Number Publication Date
CN110611310A true CN110611310A (en) 2019-12-24

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Application publication date: 20191224