CN110531149B

CN110531149B - Power signal filtering method and system based on waveform regularization

Info

Publication number: CN110531149B
Application number: CN201910818165.2A
Authority: CN
Inventors: 翟明岳
Original assignee: Guangdong University of Petrochemical Technology
Current assignee: Guangdong University of Petrochemical Technology
Priority date: 2019-08-31
Filing date: 2019-08-31
Publication date: 2021-06-18
Anticipated expiration: 2039-08-31
Also published as: CN110531149A

Abstract

The embodiment of the invention discloses a power signal filtering method and a system based on waveform regularization, wherein the method comprises the following steps: step 1, inputting an actually measured power signal sequence S; step 2, carrying out noise filtering processing on the power signal sequence S, wherein the power signal sequence after noise filtering is S_NEW. The method specifically comprises the following steps: s_NEW＝m_OPT+B(S‑Lm_OPT) (ii) a Wherein m is_OPTIs the best prediction vector; b is a correction matrix; l denotes a system matrix.

Description

Power signal filtering method and system based on waveform 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.

Therefore, in the switching event detection process, the actually measured power signal used is often affected by noise, and the switching event detection cannot be performed correctly by using the imperfect power signal. Therefore, how to effectively reconstruct the incomplete power signal and filter the influence of noise is the key to the success of the 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 filtering method and a system based on waveform 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 filtering a power signal based on waveform regularization, comprising:

step 1, inputting an actually measured power signal sequence S;

step 2, carrying out noise filtering processing on the power signal sequence S, wherein the power signal sequence after noise filtering is S_NEW. The method specifically comprises the following steps: s_NEW＝m_OPT+B(S-Lm_OPT) (ii) a Wherein m is_OPTIs the best prediction vector; b is a correction matrix; l denotes a system matrix.

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

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

the filtering module is used for carrying out noise filtering processing on the power signal sequence S, and the power signal sequence after noise filtering is S_NEW. The method specifically comprises the following steps: s_NEW＝m_OPT+B(S-Lm_OPT) (ii) a Wherein m is_OPTIs the best prediction vector; b is a correction matrix; l denotes a system matrix.

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 and relatively mature technology in non-invasive load decomposition, the power signal is often submerged in the pulse noise with strong amplitude during the acquisition and transmission process, and the switching event detection cannot be correctly performed by using the imperfect power signal. Therefore, how to effectively reconstruct the incomplete power signal and filter the influence of noise is the key to the success of the method. The existing common method has insufficient attention to the problem, and no effective measure is taken to solve the problem.

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 filtering method based on waveform regularization

Fig. 1 is a schematic flow chart of a power signal filtering method based on waveform regularization according to the present invention. As shown in fig. 1, the method for filtering a power signal based on waveform regularization specifically includes the following steps:

step 1, inputting an actually measured power signal sequence S;

Before the step 2, the method further comprises:

step 3, obtaining the optimal prediction vector m_OPTA correction matrix B and a system matrix L.

The step 3 comprises the following steps:

step 301, generating a delay vector S_DThe method specifically comprises the following steps:

S_D＝[s_K+1，s_K+2，…，s_N，s₁，s₂，…，s_K]

wherein

Amount of delay

N: length of the signal sequence S

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

Step 302, obtaining a signal correlation matrix C, specifically:

C＝S^TS_D

*^T: transposing representation pairs

Step (ii) of303, obtaining a compressed vector S_QThe method specifically comprises the following steps:

step 304, obtaining a compressed incidence matrix C_QThe method specifically comprises the following steps:

C_Q＝S^TS_Q

*^T: transposing representation pairs

Step 305, obtaining the correction matrix B, specifically:

B＝C^-TC_Q

*^-T: inverse matrix transposition of pairs

Step 306, obtaining the system matrix L, specifically:

L＝C_QC

step 307, iteratively calculating the optimal prediction vector m_OPTThe method specifically comprises the following steps:

the first step is as follows: initializing specifically as follows: m is₁And k is 1 and is an iteration control parameter.

The second step is that: updating, specifically:

m_k+1＝m_k+B[S-Lm_k]。

the third step: and (5) adding 1 to the iteration control parameter k, and repeating the second step. Until the difference between the two iteration results is less than 0.001, obtaining the optimal prediction vector m_OPT＝m_K，m_KIs the result of the last update.

FIG. 2 is a structural intent of a power signal filtering system based on waveform regularization

Fig. 2 is a schematic structural diagram of a power signal filtering system based on waveform regularization according to the present invention. As shown in fig. 2, the power signal filtering system based on waveform regularization includes the following structures:

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

a reconstruction module 402 for performing noise filtering processing on the power signal sequence SThe power signal sequence after noise filtering is S_NEW. The method specifically comprises the following steps: s_NEW＝m_OPT+B(S-Lm_OPT) (ii) a Wherein m is_OPTIs the best prediction vector; b is a correction matrix; l denotes a system matrix.

The system further comprises:

a calculating module 403 for obtaining the optimal prediction vector m_OPTA correction matrix B and a system matrix L.

The calculation module 403 includes the following units:

delay section 4301 generates delay vector S_DThe method specifically comprises the following steps:

S_D＝[s_K+1，s_K+2，…，s_N，s₁，s₂，…，s_K]

wherein

Amount of delay

N: length of the signal sequence S

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

The first calculation unit 4302 obtains a signal correlation matrix C, which specifically is:

C＝S^TS_D

*^T: transposing representation pairs

Second calculation section 4303 obtains compressed vector S_QThe method specifically comprises the following steps:

third calculation unit 4304, find compressed correlation matrix C_QThe method specifically comprises the following steps:

C_Q＝S^TS_Q

*^T: transposing representation pairs

The fourth calculation unit 4305 obtains the correction matrix B, specifically:

B＝C^-TC_Q

*^-T: inverse matrix transposition of pairs

The fifth calculation unit 4306 obtains the system matrix L, which specifically is:

L＝C_QC

an iteration unit 4307 for iteratively calculating the optimal prediction vector m_OPTThe method specifically comprises the following steps:

the first step is as follows: initializing specifically as follows: m is₁And (5) setting k to 1 as an iteration control parameter.

The second step is that: updating, specifically:

m_k+1＝m_k+B[S-Lm_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:

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. Generating a delay vector S_D

S_D＝[s_K+1，s_K+2，…，s_N，s₁，s₂，…，s_K]

Wherein

Amount of delay

N: length of the signal sequence S

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

3. Determining a signal correlation matrix

C＝S^TS_D

*^T: the representation transposes.

4. Finding compressed vectors

5. Determining a compressed correlation matrix

C_Q＝S^TS_Q

*^T: transposing representation pairs

6. Obtaining the correction matrix

B＝C^-TC_Q

*^-T: inverse matrix transposition of pairs

7. Determining a system matrix

L＝C_QC

8. Iterative calculation of optimal prediction vector

The second step is that: updating, specifically:

m_k+1＝m_k+B[S-Lm_k]。

9. Filtering

S_NEW＝m_OPT+B(S-Lm_OPT)

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

1. A power signal filtering method based on waveform regularization is characterized by comprising the following steps:

step 1, inputting an actually measured power signal sequence S;

step 2, generating a delay vector S_DThe method specifically comprises the following steps:

S_D＝[s_K+1，s_K+2，…，s_N，s₁，s₂，…，s_K]；

wherein:

a delay amount;

n: the length of the signal sequence S;

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

step 3, solving a signal correlation matrix C, specifically:

C＝S^TS_D；

*^T: representing transposing of pairs;

step 4, obtaining a compressed vector S_QThe method specifically comprises the following steps:

step 5, solving a compressed incidence matrix C_QThe method specifically comprises the following steps:

C_Q＝S^TS_Q；

*^T: representing transposing of pairs;

step 6, obtaining a correction matrix B, specifically:

B＝C^-TC_Q；

*^-T: the inversion of the inverse matrix performed on the x is expressed;

step 7, solving a system matrix L, specifically:

L＝C_QC；

step 8, iteratively solving the optimal prediction vector m_OPTThe method specifically comprises the following steps:

the first step is as follows: initializing specifically as follows: m is₁S, k 1, which is an iteration control parameter;

the second step is that: updating, specifically:

m_k+1＝m_k+B[S-Lm_k]；

the third step: adding 1 to the iteration control parameter k, repeating the second step until the difference between the two iteration results is less than 0.001 to obtain the optimal prediction vector m_OPT＝m_K，m_KIs the result of the last update;

step 9, carrying out noise filtering processing on the power signal sequence S, wherein the power signal sequence after noise filtering is S_NEW(ii) a The method specifically comprises the following steps: s_NEW＝m_OPT+B(S-Lm_OPT)。