阅读说明：本技术 一种基于波形正则化的功率信号滤波方法和系统 (A kind of power signal filtering method and system based on waveform regularization ) 是由 翟明岳 于 2019-08-31 设计创作，主要内容包括：本发明的实施例公开一种基于波形正则化的功率信号滤波方法和系统,所述方法包括：步骤1,输入实测的功率信号序列S；步骤2,对所述功率信号序列S进行滤除噪声处理,滤除噪声后的功率信号序列为S<Sub>NEW</Sub>。具体为：S<Sub>NEW</Sub>＝m<Sub>OPT</Sub>+B(S-Lm<Sub>OPT</Sub>)；其中m<Sub>OPT</Sub>为最佳预测矢量；B为修正矩阵；L表示系统矩阵。(The embodiment of the present invention discloses a kind of power signal filtering method and system based on waveform regularization, which comprises step 1, inputs the power signal sequence S of actual measurement；Step 2, the power signal sequence S is carried out filtering out noise processed, the power signal sequence after filtering out noise is S NEW .Specifically: S NEW =m OPT +B(S‑Lm OPT )；Wherein m OPT For optimum prediction vector；B is correction matrix；L indicates sytem matrix.)

1. a kind of power signal filtering method based on waveform regularization characterized by comprising

Step 1, the power signal sequence S of actual measurement is inputted；

Step 2, the power signal sequence S is carried out filtering out noise processed, the power signal sequence after filtering out noise is S_NEW。 Specifically: S_NEW=m_OPT+B(S-Lm_OPT)；Wherein m_OPTFor optimum prediction vector；B is correction matrix；L indicates sytem matrix.

2. the method according to claim 1, wherein before the step 2, the method also includes:

Step 3, the optimum prediction vector m is sought_OPT, correction matrix B and sytem matrix L.

3. according to the method described in claim 2, it is characterized in that, the step 3 includes:

Step 301, delay vector S is generated_D, specifically:

S_D=[s_K+1, s_K+2..., s_N, s₁, s₂..., s_K]

Wherein

Retardation

N: the length of the signal sequence S

SNR: the signal-to-noise ratio of the signal sequence S

Step 302, signal incidence matrix C is sought, specifically:

C=S^TS_D

*^T: it indicates to carry out transposition to *

Step 303, Compression Vector S is sought_Q, specifically:

Step 304, compression incidence matrix C is sought_Q, specifically:

C_Q=S^TS_Q

*^T: it indicates to carry out transposition to *

Step 305, the correction matrix B is sought, specifically:

B=C^-TC_Q

*^-T: expression seeks transposition to the * inverse matrix carried out

Step 306, the sytem matrix L is sought, specifically:

L=C_QC

Step 307, iteration seeks the optimum prediction vector m_OPT, specifically:

Step 1: initialization, specifically: m₁=S, k=1 is iteration control parameter.

Step 2: update, specifically:

m_k+1=m_k+B[S-Lm_k]

Step 3: iteration control parameter k adds 1, second step is repeated.Until iteration result twice difference less than 0.001 until, obtain Optimum prediction vector m_OPT=m_K, m_KThe result updated for last time.

4. a kind of power signal filtering system based on waveform regularization characterized by comprising

Module is obtained, the power signal sequence S of actual measurement is inputted；

Reconstructed module, carries out filtering out noise processed to the power signal sequence S, and the power signal sequence after filtering out noise is S_NEW.Specifically: S_NEW=m_OPT+B(S-Lm_OPT)；Wherein m_OPTFor optimum prediction vector；B is correction matrix；L indicates system square Battle array.

5. system according to claim 4, which is characterized in that further include:

Computing module seeks the optimum prediction vector m_OPT, correction matrix B and sytem matrix L.

Technical field

The present invention relates to power domain more particularly to the reconstructing methods and system of a kind of power signal.

Background technique

With the development of smart grid, the analysis of household electricity load is become more and more important.Pass through point of power load Analysis, domestic consumer can obtain the power information of each electric appliance and the fining inventory of the electricity charge in time；Power department can obtain More detailed user power utilization information is obtained, and the accuracy of electro-load forecast can be improved, provides overall planning for power department Foundation.Meanwhile using the power information of each electric appliance, would know that the electricity consumption behavior of user, this for family's energy consumption assessment and The research of Energy Saving Strategy has directive significance.

Current power load decomposition is broadly divided into two methods of intrusive load decomposition and non-intrusion type load decomposition.It is non-to invade Enter formula load decomposition method not needing that monitoring device is installed in the power inside equipment of load, it is only necessary to total according to power load Information can be obtained the information on load of each electrical equipment.Non-intrusion type load decomposition method has less investment, convenient to use etc. Feature, therefore, this method are suitable for the decomposition of family's load electricity consumption.

In non-intrusion type load decomposition algorithm, the switch events detection of electrical equipment is most important one link.Initially Switch events detect the judgment basis that detect using the changing value of active-power P as switch events, facilitate and intuitively.This be because It changes for the operating status of any one electrical equipment, consumed performance number also necessarily changes, and this changes Change will also embody in the general power consumed by all electric appliances.Conjunction of this method in addition to needing to be arranged power change values Manage threshold value, it is also necessary to solve the problems, such as that event detecting method exists in practical applications, such as the wink at certain appliance starting moment When performance number will appear biggish spike (motor start-up current be much larger than rated current), will cause electric appliance steady state power changing value Inaccuracy, to influence the judgement to switch event detection；And the transient process or length of different household electrical appliance or short (pulse is made an uproar The duration of sound and occurrence frequency difference are larger), therefore the determination of power change values becomes more difficult；Due to power quality Variation (such as voltage die) active power the case where will appear mutation, be likely to judge by accident in this way.

Therefore, in switch events detection process, used measured power signal is frequently subjected to the influence of noise, utilizes this A little incomplete power signals cannot correctly carry out switch events detection.Therefore how incomplete function is effectively reconstructed Rate signal filters out the influence of noise, is the key that the method success.Existing frequently-used method payes attention to not enough this problem, It does not take effective measures also and solves the problems, such as this.

Summary of the invention

The object of the present invention is to provide a kind of power signal filtering methods and system based on waveform regularization, are proposed The difference of power signal and noise in terms of waveform is utilized in method, and the filter of power signal is realized according to waveform regularization theory Wave.The method proposed has preferable robustness, calculates simple.

To achieve the above object, the present invention provides following schemes:

A kind of power signal filtering method based on waveform regularization, comprising:

Step 1, the power signal sequence S of actual measurement is inputted；

Step 2, the power signal sequence S is carried out filtering out noise processed, the power signal sequence after filtering out noise is S_NEW.Specifically: S_NEW=m_OPT+B(S-Lm_OPT)；Wherein m_OPTFor optimum prediction vector；B is correction matrix；L indicates system square Battle array.

A kind of power signal filtering system based on waveform regularization, comprising:

Module is obtained, the power signal sequence S of actual measurement is inputted；

Filter module carries out filtering out noise processed, the power signal sequence after filtering out noise to the power signal sequence S For S_NEW.Specifically: S_NEW=m_OPT+B(S-Lm_OPT)；Wherein m_OPTFor optimum prediction vector；B is correction matrix；L indicates system square Battle array.

The specific embodiment provided according to the present invention, the invention discloses following technical effects:

Although switch events detection method has a wide range of applications in non-intrusion type load decomposition, and technology relative at It is ripe, but power signal is usually submerged in the stronger impulsive noise of amplitude in collection and transmission, it is endless using these Kind power signal cannot correctly carry out switch events detection.Therefore incomplete power letter how is effectively reconstructed Number, the influence of noise is filtered out, is the key that the method success.Existing frequently-used method payes attention to not enough, also not this problem It takes effective measures and solves the problems, such as this.

The object of the present invention is to provide a kind of power signal filtering methods and system based on waveform regularization, are proposed The difference of power signal and noise in terms of waveform is utilized in method, and the filter of power signal is realized according to waveform regularization theory Wave.The method proposed has preferable robustness, calculates simple.

Detailed description of the invention

It in order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, below will be to institute in embodiment Attached drawing to be used is needed to be briefly described.It is clear that drawings in the following description are only some embodiments of the invention, For those of ordinary skill in the art, without creative efforts, it can also be obtained according to these attached drawings Other attached drawings.

Fig. 1 is method flow schematic diagram of the invention；

Fig. 2 is system structure diagram of the invention；

Fig. 3 is the flow diagram of present invention specific implementation case.

Specific embodiment

Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete Site preparation description.Obviously, the described embodiment is only a part of the embodiment of the present invention, instead of all the embodiments.Based on this Embodiment in invention, every other reality obtained by those of ordinary skill in the art without making creative efforts Example is applied, shall fall within the protection scope of the present invention.

In order to make the foregoing objectives, features and advantages of the present invention clearer and more comprehensible, with reference to the accompanying drawing and specific real Applying mode, the present invention is described in further detail.

A kind of flow diagram of the power signal filtering method based on waveform regularization of Fig. 1

Fig. 1 is a kind of flow diagram of the power signal filtering method based on waveform regularization of the present invention.Such as Fig. 1 institute Show, a kind of power signal filtering method based on waveform regularization specifically includes the following steps:

Step 1, the power signal sequence S of actual measurement is inputted；

Step 2, the power signal sequence S is carried out filtering out noise processed, the power signal sequence after filtering out noise is S_NEW.Specifically: S_NEW=m_OPT+B(S-Lm_OPT)；Wherein m_OPTFor optimum prediction vector；B is correction matrix；L indicates system square Battle array.

Before the step 2, the method also includes:

Step 3, the optimum prediction vector m is sought_OPT, correction matrix B and sytem matrix L.

The step 3 includes:

Step 301, delay vector S is generated_D, specifically:

S_D=[s_K+1, s_K+2..., s_N, s₁, s₂..., s_K]

Wherein

Retardation

N: the length of the signal sequence S

SNR: the signal-to-noise ratio of the signal sequence S

Step 302, signal incidence matrix C is sought, specifically:

C=S^TS_D

*^T: it indicates to carry out transposition to *

Step 303, Compression Vector S is sought_Q, specifically:

Step 304, compression incidence matrix C is sought_Q, specifically:

C_Q=S^TS_Q

*^T: it indicates to carry out transposition to *

Step 305, the correction matrix B is sought, specifically:

B=C^-TC_Q

*^-T: expression seeks transposition to the * inverse matrix carried out

Step 306, the sytem matrix L is sought, specifically:

L=C_QC

Step 307, iteration seeks the optimum prediction vector m_OPT, specifically:

Step 1: initialization, specifically: m₁=S, k=1, k are iteration control parameter.

Step 2: update, specifically:

m_k+1=m_k+B[S-Lm_k]。

Step 3: iteration control parameter k adds 1, second step is repeated.Until iteration result twice difference less than 0.001 until, Obtain optimum prediction vector m_OPT=m_K, m_KThe result updated for last time.

A kind of structure of power signal filtering system based on waveform regularization of Fig. 2 is intended to

Fig. 2 is a kind of structural schematic diagram of the power signal filtering system based on waveform regularization of the present invention.Such as Fig. 2 institute Show, a kind of power signal filtering system based on waveform regularization includes with flowering structure:

Module 401 is obtained, the power signal sequence S of actual measurement is inputted；

Reconstructed module 402 carries out filtering out noise processed, the power signal after filtering out noise to the power signal sequence S Sequence is S_NEW.Specifically: S_NEW=m_OPT+B(S-Lm_OPT)；Wherein m_OPTFor optimum prediction vector；B is correction matrix；L indicates system System matrix.

The system, further includes:

Computing module 403 seeks the optimum prediction vector m_OPT, correction matrix B and sytem matrix L.

The computing module 403 includes following units:

Delay cell 4301 generates delay vector S_D, specifically:

S_D=[s_K+1, s_K+2..., s_N, s₁, s₂..., s_K]

Wherein

Retardation

N: the length of the signal sequence S

SNR: the signal-to-noise ratio of the signal sequence S

First computing unit 4302 seeks signal incidence matrix C, specifically:

C=S^TS_D

*^T: it indicates to carry out transposition to *

Second computing unit 4303, seeks Compression Vector S_Q, specifically:

Third computing unit 4304 seeks compression incidence matrix C_Q, specifically:

C_Q=S^TS_Q

*^T: it indicates to carry out transposition to *

4th computing unit 4305 seeks the correction matrix B, specifically:

B=C^-TC_Q

*^-T: expression seeks transposition to the * inverse matrix carried out

5th computing unit 4306 seeks the sytem matrix L, specifically:

L=C_QC

Iteration unit 4307, iteration seek the optimum prediction vector m_OPT, specifically:

Step 1: initialization, specifically: m₁=S, k=1 are iteration control parameter.

Step 2: update, specifically:

m_k+1=m_k+B[S-Lm_k]。

Step 3: iteration control parameter k adds 1, second step is repeated.Until iteration result twice difference less than 0.001 until, Obtain optimum prediction vector m_OPT=m_K, m_KThe result updated for last time.

A specific implementation case is provided below, further illustrates the solution of the present invention

Fig. 3 is the flow diagram of present invention specific implementation case.As shown in figure 3, specifically includes the following steps:

1. inputting the power signal sequence of actual measurement

S=[s₁,s₂,…,s_N-1,s_N]

Wherein:

S: actual measurement acoustic signal data sequence, length N

s_i, i=1,2 ..., N: serial number i actual measurement acoustic signal

2. generating delay vector S_D

S_D=[s_K+1, s_K+2..., s_N, s₁, s₂..., s_K]

Wherein

Retardation

N: the length of the signal sequence S

SNR: the signal-to-noise ratio of the signal sequence S

3. seeking signal incidence matrix

C=S^TS_D

*^T: it indicates to carry out transposition to *.

4. seeking Compression Vector

5. seeking compression incidence matrix

C_Q=S^TS_Q

*^T: it indicates to carry out transposition to *

6. seeking the correction matrix

B=C^-TC_Q

*^-T: expression seeks transposition to the * inverse matrix carried out

7. seeking sytem matrix

L=C_QC

8. iteration seeks optimum prediction vector

Step 1: initialization, specifically: m₁=S, k=1 are iteration control parameter.

Step 2: update, specifically:

m_k+1=m_k+B[S-Lm_k]。

Step 3: iteration control parameter k adds 1, second step is repeated.Until iteration result twice difference less than 0.001 until, Obtain optimum prediction vector m_OPT=m_K, m_KThe result updated for last time.

9. filtering

S_NEW=m_OPT+B(S-Lm_OPT)

Each embodiment in this specification is described in a progressive manner, the highlights of each of the examples are with other The difference of embodiment, the same or similar parts in each embodiment may refer to each other.For system disclosed in embodiment For, since it is corresponded to the methods disclosed in the examples, so description is relatively simple, related place is referring to method part illustration .

Used herein a specific example illustrates the principle and implementation of the invention, and above embodiments are said It is bright to be merely used to help understand method and its core concept of the invention；At the same time, for those skilled in the art, foundation Thought of the invention, there will be changes in the specific implementation manner and application range.In conclusion the content of the present specification is not It is interpreted as limitation of the present invention.