CN110704795A - Power signal reconstruction method and system by using shrinkage operator - Google Patents

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 S_NEW. The method specifically comprises the following steps:

wherein α is a contractile factor; mu is a correction factor; z_OPTIs the best prediction vector; x_OPTIs the best correction vector.

Description

Power signal reconstruction method and system by using shrinkage operator

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 system by using a contraction operator. 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 using a puncturing operator, 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 S_NEW. The method specifically comprises the following steps:

wherein α is a contractile factor; mu is a correction factor; z_OPTIs the best prediction vector; x_OPTIs the best correction vector.

A power signal reconstruction system using a puncturing operator, 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 S_NEW. The method specifically comprises the following steps:

wherein α is a contractile factor; mu is a correction factor; z_OPTIs the best prediction vector; x_OPTIs the best correction 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 system by using a contraction operator. 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 schematic flow chart of a power signal reconstruction method using a contraction operator

Fig. 1 is a schematic flow chart of a power signal reconstruction method using a contraction operator according to the present invention. As shown in fig. 1, the method for reconstructing a power signal by using a shrinking operator 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 S_NEW. The method specifically comprises the following steps:

wherein α is a contractile factor; mu is a correction factor; z_OPTIs the best prediction vector; x_OPTIs the best correction vector.

Before the step 2, the method further comprises:

step 3, obtaining the shrinkage factor alpha, the correction factor mu and the optimal prediction vector Z_OPTAnd the optimum correction vector X_OPT。

The step 3 comprises the following steps:

step 301, obtaining the shrinkage factor α, specifically as

Wherein

s_i: the ith element [ i ═ 1,2, …, N of the signal sequence S]

m_S: mean value of the signal sequence S

σ_S: mean square error of the signal sequence S

N: length of the signal sequence S

Step 302, obtaining the correction factor μ, specifically:

wherein

S: the signal sequence

Correction operator

First correction matrix

Second correction matrix

Step 303, iteratively calculating the optimal prediction vector Z_OPTAnd the optimum correction vector X_OPTThe method specifically comprises the following steps:

a first step of initialization, in particular

Z₁As S: initialized prediction vectors

X₁When the ratio is 0: initialized correction vector

n is 1: iterative control parameter

Wherein

0: all zero vector

Second step, update, specifically

X_n+1＝Γ[H(X_n)]+X_n

Wherein

Projection operation

Scaling operations

The contraction operator is used to perform a contraction operation,

representing a puncturing operation on each element of the matrix B

B: a parameter matrix for representing a contraction operator expression

Thirdly, iterative judgment, specifically comprising

And (4) turning the iteration control parameter n plus 1 to a second step to continuously perform iteration updating until the difference between the iteration results of two adjacent times is less than one thousandth. At this time, the iterative control parameter n is K, and the optimal prediction vector Z is obtained_OPT＝Z_K+1And the optimum correction vector X_OPT＝X_K+1。

FIG. 2 structural intent of a power signal reconstruction system using a shrink operator

FIG. 2 is a schematic diagram of a power signal reconstruction system using a contraction operator according to the present invention. As shown in fig. 2, the power signal reconstruction system using the puncturing operator includes the following structure:

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 S_NEW. The method specifically comprises the following steps:

wherein α is a contractile factor; mu is a correction factor; z_OPTIs the best prediction vector; x_OPTIs the best correction vector.

The system further comprises:

a calculating module 403 for obtaining the shrinkage factor α, the correction factor μ, and the optimal prediction vector Z_OPTAnd the optimum correction vector X_OPT。

The calculation module 403 further includes the following units:

a first calculation unit 4031 for calculating the shrinkage factor α, specifically

Wherein

s_i: the ith element [ i ═ 1,2, …, N of the signal sequence S]

m_S: mean value of the signal sequence S

σ_S: mean square error of the signal sequence S

N: length of the signal sequence S

The second calculation unit 4032 calculates the correction factor μ, specifically:

wherein

S: the signal sequence

Correction operator

First correction matrix

Second correction matrix

An iteration unit 4033 for iteratively calculating the optimal prediction vector Z_OPTAnd the optimum correction vector X_OPT，

The method specifically comprises the following steps:

a first step of initialization, in particular

Z₁As S: initialized prediction vectors

X₁When the ratio is 0: initialized correction vector

n is 1: iterative control parameter

Wherein

0: all zero vector

Second step, update, specifically

X_n+1＝Γ[H(X_n)]+X_n

Wherein

Projection operation

Scaling operations

The contraction operator is used to perform a contraction operation,

representing a puncturing operation on each element of the matrix B

B: a parameter matrix for representing a contraction operator expression

Thirdly, iterative judgment, specifically comprising

And (4) turning the iteration control parameter n plus 1 to a second step to continuously perform iteration updating until the difference between the iteration results of two adjacent times is less than one thousandth. At this time, the iterative control parameter n is K, and the optimal prediction vector Z is obtained_OPT＝Z_K+1And the optimum correction vector X_OPT＝X_K+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,…,s_N-1,s_N]

Wherein:

s: real vibration and sound signal data sequence with length N

s_iI is 1,2, …, N is measured vibration sound signal with serial number i

2. Determining a shrinkage factor

Wherein

s_i: the ith element [ i ═ 1,2, …, N of the signal sequence S]

m_S: mean value of the signal sequence S

σ_S: mean square error of the signal sequence S

N: length of the signal sequence S

3. Calculating a correction factor

Wherein

S: the signal sequence

Correction operator

First correction matrix

Second correction matrix

4. Obtaining an optimal prediction vector and an optimal correction vector

First, initialization

Z₁As S: initialized prediction vectors

X₁When the ratio is 0: initialized correction vector

n is 1: iterative control parameter

Wherein

0: all zero vector

Second, update

X_n+1＝Γ[H(X_n)]+X_n

Wherein

Projection operation

Scaling operations

The contraction operator is used to perform a contraction operation,

representing a puncturing operation on each element of the matrix B

B: a parameter matrix for representing a contraction operator expression

Third, iterative judgment

And (4) turning the iteration control parameter n plus 1 to a second step to continuously perform iteration updating until the difference between the iteration results of two adjacent times is less than one thousandth. At this time, the iterative control parameter n is K, and the optimal prediction vector Z is obtained_OPT＝Z_KAnd the optimum correction vector X_OPT＝X_K。₊₁

5. Data reconstruction

Carrying out data reconstruction on the power signal sequence S, wherein the reconstructed power signal sequence is S_NEW. The method specifically comprises the following steps:wherein α is a contractile factor; mu is a correction factor; z_OPTIs the best prediction vector; x_OPTIs the best correction 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 a puncturing operator, 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 S_NEW. The method specifically comprises the following steps:

wherein α is a contractile factor; mu is a correction factor; z_OPTIs the best prediction vector; x_OPTIs the best correction vector.

2. The method of claim 1, wherein prior to step 2, the method further comprises:

step 3, obtaining the shrinkage factor alpha, the correction factor mu and the optimal prediction vector Z_OPTAnd the optimum correction vector X_OPT。

3. The method of claim 2, wherein step 3 comprises:

step 301, obtaining the shrinkage factor α, specifically as

Wherein

s_i: the ith element [ i ═ 1,2, …, N of the signal sequence S]

m_S: mean value of the signal sequence S

σ_S: mean square error of the signal sequence S

N: length of the signal sequence S

Step 302, obtaining the correction factor μ, specifically:

wherein

S: the signal sequence

Correction operator

First correction matrix

Second correction matrix

Step 303, iteratively calculating the optimal prediction vector Z_OPTAnd the optimum correction vector X_OPTThe method specifically comprises the following steps:

a first step of initialization, in particular

Z₁As S: initialized prediction vectors

X₁When the ratio is 0: initialized correction vector

n is 1: iterative control parameter

Wherein

0: all zero vector

Second step, update, specifically

X_n+1＝Γ[H(X_n)]+X_n

Wherein

Projection operation

Scaling operations

The contraction operator is used to perform a contraction operation,

representing a puncturing operation on each element of the matrix B

B: a parameter matrix for representing a contraction operator expression

And thirdly, performing iterative judgment, specifically, turning the iterative control parameter n plus 1 to the second step for continuous iterative updating until the difference between the two adjacent iterative results is less than one per thousand. At this time, the iterative control parameter n is K, and the optimal prediction vector Z is obtained_OPT＝Z_K+1And the optimum correction vector X_OPT＝X_K+1。

4. A system for power signal reconstruction using a puncturing operator, 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 S_NEW. The method specifically comprises the following steps:

wherein α is a contractile factor; mu is a correction factor; z_OPTIs the best prediction vector; x_OPTIs the best correction vector.

5. The system of claim 4, further comprising:

a calculation module for obtaining the contraction factor alpha, the correction factor mu and the optimal prediction vector Z_OPTAnd the optimum correction vector X_OPT。

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