CN110783921A - Power signal reconstruction method and system by utilizing dictionary prediction matrix - Google Patents

Abstract

The embodiment of the invention discloses a power signal reconstruction method and a system by utilizing a dictionary prediction matrix, wherein the method comprises the following steps: step 1, inputting an actually measured signal sequence S; step 2, reconstructing the signal sequence S according to the dictionary prediction matrix, wherein the reconstructed signal sequence is S _NEW. The method specifically comprises the following steps: s _NEW＝W _OPTS+R _OPTg _OPT. Wherein, W _OPTPredicting a matrix for the dictionary; r _OPTA best recovery matrix; g _OPTIs the best matching atom.

Description

Power signal reconstruction method and system by utilizing dictionary prediction matrix

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

As mentioned above, during the switching event detection process, the used measured power signals are often incomplete, and the switching event detection cannot be correctly performed by using the incomplete power signals. 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 utilizing a dictionary prediction matrix. The method has better signal reconstruction performance and simpler calculation.

In order to achieve the purpose, the invention provides the following scheme:

a method of power signal reconstruction using a dictionary prediction matrix, comprising:

step 001 inputting an actually measured signal sequence S;

step 002, reconstructing the signal sequence S according to the dictionary prediction matrix, wherein the reconstructed signal sequence is S _NEW. The method specifically comprises the following steps: s _NEW＝W _OPTS+R _OPTg _OPT. Wherein, W _OPTPredicting a matrix for the dictionary; r _OPTA best recovery matrix; g _OPTIs the best matching atom.

A system for power signal reconstruction using a dictionary prediction matrix, comprising:

an acquisition module inputs an actually measured signal sequence S;

the reconstruction module reconstructs the signal sequence S according to the dictionary prediction matrix, and the reconstructed signal sequence is S _NEW. The method specifically comprises the following steps: s _NEW＝W _OPTS+R _OPTg _OPT. Wherein, W _OPTPredicting a matrix for the dictionary; r _OPTA best recovery matrix; g _OPTIs the best matching atom.

According to the specific embodiment provided by the invention, the invention discloses the following technical effects:

as mentioned above, during the switching event detection process, the used measured power signals are often incomplete, and the switching event detection cannot be correctly performed by using the incomplete power signals. 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 utilizing a dictionary prediction matrix. The method has better signal reconstruction performance and simpler 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 flow chart of a power signal reconstruction method using a dictionary prediction matrix

Fig. 1 is a flowchart illustrating a power signal reconstruction method using a dictionary prediction matrix according to the present invention. As shown in fig. 1, the method for reconstructing a power signal by using a dictionary prediction matrix specifically includes the following steps:

step 001 inputting an actually measured signal sequence S;

step 002, reconstructing the signal sequence S according to the dictionary prediction matrix, and reconstructing the signal sequence SHas a signal sequence S _NEW. The method specifically comprises the following steps: s _NEW＝W _OPTS+R _OPTg _OPT. Wherein, W _OPTPredicting a matrix for the dictionary; r _OPTA best recovery matrix; g _OPTIs the best matching atom.

Prior to the step 002, the method further comprises:

step 003 of obtaining the dictionary prediction matrix W _OPTThe best recovery matrix R _OPTAnd the best matching atom g _OPT。

The step 003 further includes:

step 301 generates a signal difference sequence Δ S, specifically:

ΔS＝[0,s ₂-s ₁,s ₃-s ₂,···,s _N-s _N-1]

wherein:

s _n: the nth element of the signal sequence S

N: length of the signal sequence S

Step 302 iteratively finds the best matching atom g _OPTAnd an optimal recovery matrix R _OPTThe method specifically comprises the following steps:

iteration step 1, iteration initialization, specifically:

R ⁰＝I

k＝1

wherein:

R ⁰: initialized recovery matrix

I: unit matrix

Δ ⁰: initialized radicals

k: iterative control parameter

Iteration step 2, iteration updating, specifically:

R ^k＝[R ^k-1S ^TΔS-<R ^k-1S,g ^k>g ^kΔS]

Δ ^k＝Δ ^k-1∪g ^k

wherein:

g ^k: work atom of step k

x: intermediate vector

R ^k: the recovery matrix of the k step

Δ ^k: radical of step k

And 3, ending iteration in the iteration step, specifically:

adding 1 to the value of the iteration control parameter k and returning to the iteration

Updating the second iteration until the results of two adjacent iterations

Until the difference is less than 0.001. At this time, the iterative control parameter

K is K, and the recovery matrix is R ^KObtaining the optimum

Matching atom g _OPT＝g ^KThe best recovery matrix R _OPT＝

R ^K

Step 303 of obtaining said dictionary prediction matrix W _OPTThe method specifically comprises the following steps:

W _OPT＝[g _OPT-S ^T][g _OPT-S ^T] ^TR ^K。

FIG. 2 structural intent of a power signal reconstruction system using a dictionary prediction matrix

Fig. 2 is a schematic structural diagram of a power signal reconstruction system using a dictionary prediction matrix according to the present invention. As shown in fig. 2, the power signal reconstruction system using the dictionary prediction matrix includes the following structures:

the acquisition module 401 inputs an actually measured signal sequence S;

the reconstruction module 402 reconstructs the signal sequence S according to the dictionary prediction matrix, where the reconstructed signal sequence is S _NEW. The method specifically comprises the following steps: s _NEW＝W _OPTS+R _OPTg _OPT. Wherein，W _OPTPredicting a matrix for the dictionary; r _OPTA best recovery matrix; g _OPTIs the best matching atom.

The system further comprises:

calculation module 403 finds the dictionary prediction matrix W _OPTThe best recovery matrix R _OPTAnd the best matching atom g _OPT。

The calculation module 403 further includes the following units, which specifically include:

the calculating unit 4031 generates a signal difference sequence Δ S, specifically:

ΔS＝[0,s ₂-s ₁,s ₃-s ₂,···,s _N-s _N-1]

wherein:

s _n: the nth element of the signal sequence S

N: length of the signal sequence S

The calculation unit 4032 iteratively finds the best matching atom g _OPTAnd an optimal recovery matrix R _OPT，

The method specifically comprises the following steps:

iteration step 1, iteration initialization, specifically:

R ⁰＝I

k＝1

wherein:

R ⁰: initialized recovery matrix

I: unit matrix

Δ ⁰: initialized radicals

k: iterative control parameter

Iteration step 2, iteration updating, specifically:

R ^k＝[R ^k-1S ^TΔS-<R ^k-1S,g ^k>g ^kΔS]

Δ ^k＝Δ ^k-1∪g ^k

wherein:

g ^k: work atom of step k

x: intermediate vector

R ^k: the recovery matrix of the k step

Δ ^k: radical of step k

And 3, ending iteration in the iteration step, specifically:

and adding 1 to the value of the iteration control parameter k, and returning to the iteration second step for iteration updating until the difference between the results of two adjacent iterations is less than 0.001. In this case, the iteration control parameter K is K, and the recovery matrix is R ^KObtaining the best matching atom g _OPT＝g ^KThe best recovery matrix R _OPT＝R ^K

Calculation unit 4033 finds the dictionary prediction matrix W _OPTThe method specifically comprises the following steps:

W _OPT＝[g _OPT-S ^T][g _OPT-S ^T] ^TR ^K。

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＝[s ₁,s ₂,···,s _N-1,s _N]

Wherein:

s: measured signal sequence of length N

s _n: the nth element in the signal sequence S

n: subscript, N ═ 1,2,. cndot., N

1, generating a signal difference sequence Δ S, specifically:

ΔS＝[0,s ₂-s ₁,s ₃-s ₂,···,s _N-s _N-1]

wherein:

s _n: the nth element of the signal sequence S

N: length of the signal sequence S

2 iteratively solving for the best matching atom g _OPTAnd an optimal recovery matrix R _OPTThe method specifically comprises the following steps:

iteration step 1, iteration initialization, specifically:

R ⁰＝I

k＝1

wherein:

R ⁰: initialized recovery matrix

I: unit matrix

Δ ⁰: initialized radicals

k: iterative control parameter

Iteration step 2, iteration updating, specifically:

R ^k＝[R ^k-1S ^TΔS-<R ^k-1S,g ^k>g ^kΔS]

Δ ^k＝Δ ^k-1∪g ^k

wherein:

g ^k: work atom of step k

x: intermediate vector

R ^k: the recovery matrix of the k step

Δ ^k: radical of step k

And 3, ending iteration in the iteration step, specifically:

and adding 1 to the value of the iteration control parameter k, and returning to the iteration second step for iteration updating until the difference between the results of two adjacent iterations is less than 0.001. At this timeThe iterative control parameter K is K, and the recovery matrix is R ^KObtaining the best matching atom g _OPT＝g ^KThe best recovery matrix R _OPT＝R ^K

3 solving the dictionary prediction matrix W _OPTThe method specifically comprises the following steps:

W _OPT＝[g _OPT-S ^T][g _OPT-S ^T] ^TR ^K。

and 4, finishing: reconstruction

Reconstructing the signal sequence S according to a dictionary prediction matrix, wherein the reconstructed signal sequence is S _NEW. The method specifically comprises the following steps: s _NEW＝W _OPTS+R _OPTg _OPT. Wherein, W _OPTPredicting a matrix for the dictionary; r _OPTA best recovery matrix; g _OPTIs the best matching atom.

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 dictionary prediction matrix, comprising:

step 001 inputting an actually measured signal sequence S;

step 002, reconstructing the signal sequence S according to the dictionary prediction matrix, wherein the reconstructed signal sequence is S _NEW. The method specifically comprises the following steps:S _NEW＝W _OPTS+R _OPTg _OPT. Wherein, W _OPTPredicting a matrix for the dictionary; r _OPTA best recovery matrix; g _OPTIs the best matching atom.

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

step 003 of obtaining the dictionary prediction matrix W _OPTThe best recovery matrix R _OPTAnd the best matching atom g _OPT。

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

step 301 generates a signal difference sequence Δ S, specifically:

ΔS＝[0,s ₂-s ₁,s ₃-s ₂,…,s _N-s _N-1]

wherein:

s _n: the nth element of the signal sequence S

N: length of the signal sequence S

Step 302 iteratively finds the best matching atom g _OPTAnd an optimal recovery matrix R _OPTThe method specifically comprises the following steps:

iteration step 1, iteration initialization, specifically:

R ⁰＝I

k＝1

wherein:

R ⁰: initialized recovery matrix

I: unit matrix

Δ ⁰: initialized radicals

k: iterative control parameter

Iteration step 2, iteration updating, specifically:

R ^k＝[R ^k-1S ^TΔS-＜R ^k-1S,g ^k＞g ^kΔS]

Δ ^k＝Δ ^k-1∪g ^k

wherein:

g ^k: work atom of step k

x: intermediate vector

R ^k: the recovery matrix of the k step

Δ ^k: radical of step k

And 3, ending iteration in the iteration step, specifically:

and adding 1 to the value of the iteration control parameter k, and returning to the iteration second step for iteration updating until the difference between the results of two adjacent iterations is less than 0.001. In this case, the iteration control parameter K is K, and the recovery matrix is R ^KObtaining the best matching atom g _OPT＝g ^KThe best recovery matrix R _OPT＝R ^K

Step 303 of obtaining said dictionary prediction matrix W _OPTThe method specifically comprises the following steps:

W _OPT＝[g _OPT-S ^T][g _OPT-S ^T] ^TR ^K。

4. a system for power signal reconstruction using a dictionary prediction matrix, comprising:

an acquisition module inputs an actually measured signal sequence S;

the reconstruction module reconstructs the signal sequence S according to the dictionary prediction matrix, and the reconstructed signal sequence is S _NEW. The method specifically comprises the following steps: s _NEW＝W _OPTS+R _OPTg _OPT. Wherein, W _OPTPredicting a matrix for the dictionary; r _OPTA best recovery matrix; g _OPTIs the best matching atom.

5. The system of claim 4, further comprising:

the calculation module calculates the dictionary prediction matrix W _OPTThe best recovery matrix R _OPTAnd the best matching atom g _OPT。

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