CN111665389A - Power signal filtering method and system by utilizing random projection - Google Patents

Abstract

The embodiment of the invention discloses a power signal filtering method and a system by utilizing random projection, wherein the method comprises the following steps: step 101, acquiring a signal sequence S acquired according to a time sequence; step 102 of obtaining a geometric random projection matrix F_e(ii) a Step 103 of obtaining a Gaussian random projection matrix F_g(ii) a Step 104 is to obtain the signal sequence S with noise filtered_new。

Description

Power signal filtering method and system by utilizing random projection

Technical Field

The present invention relates to the field of power, and in particular, to a method and a system for filtering 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

In the process of detecting the switching event, the actually measured power signal used is often affected by noise, and the detection of the switching event 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.

The invention aims to provide a power signal filtering method and a system by utilizing random projection. The method has better robustness and simpler calculation.

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

a method of filtering a power signal using stochastic projection, comprising:

step 101, acquiring a signal sequence S acquired according to a time sequence;

step 102 of obtaining a geometric random projection matrix F_eThe method specifically comprises the following steps: the geometric random projection matrix F_eRow i and column j elements of

Is calculated by the formula

Wherein i is a row serial number, and the value range of the row serial number i is 1,2, …, N; j is a column serial number, and the value range of the row serial number j is 1,2, …, N; n is the length of the signal sequence S; y is_ijThe random variables are independently and uniformly distributed;

step 103 of obtaining a Gaussian random projection matrix F_gThe method specifically comprises the following steps: the Gaussian random projection matrix F_gIth row and jth column of element g_ijIs composed of

Wherein, X_ijIs a mean value of m₀The mean square error is sigma₀Independent and identically distributed Gaussian random variables; m is₀Is the mean of the signal sequence S; sigma₀Is the mean square error of the signal sequence S;

step 104 is to obtain the signal sequence S with noise filtered_newThe method specifically comprises the following steps: of all intermediate parameter vectors x, choose to

The smallest intermediate parameter vector x is used as the noise-filtered signal sequence S_newThe value of (c).

A power signal filtering system using stochastic projection, comprising:

the module 201 acquires a signal sequence S acquired in time sequence;

module 202 finds a geometric random projection matrix F_eThe method specifically comprises the following steps: the geometric random projection matrix F_eRow i and column j elements of

Is calculated by the formula

Wherein i is a row serial number, and the value range of the row serial number i is 1,2, …, N; j is a column serial number, and the value range of the row serial number j is 1,2, …, N; n is the length of the signal sequence S; y is_ijThe random variables are independently and uniformly distributed;

module 203 finds a Gaussian random projection matrix F_gThe method specifically comprises the following steps: the Gaussian random projection matrix F_gIth row and jth column of element g_ijIs composed of

Wherein, X_ijIs a mean value of m₀The mean square error is sigma₀Independent and identically distributed Gaussian random variables; m is₀Is the mean of the signal sequence S; sigma₀Is the mean square error of the signal sequence S;

module 204 finds the noise-filtered signal sequence S_newThe method specifically comprises the following steps: of all intermediate parameter vectors x, choose to

The smallest intermediate parameter vector x is used as the noise-filtered signal sequence S_newThe value of (c).

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

in the process of detecting the switching event, the actually measured power signal used is often affected by noise, and the detection of the switching event 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.

The invention aims to provide a power signal filtering method and a system by utilizing random projection. The method has better robustness 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 schematic flow chart of a power signal filtering method using stochastic projection

FIG. 1 is a flow chart illustrating a method for filtering a power signal using stochastic projection according to the present invention. As shown in fig. 1, the method for filtering a power signal by using stochastic projection specifically includes the following steps:

step 101, acquiring a signal sequence S acquired according to a time sequence;

step 102 of obtaining a geometric random projection matrix F_eThe method specifically comprises the following steps: the geometric random projection matrix F_eRow i and column j elements of

Is calculated by the formula

Wherein i is a row serial number, and the value range of the row serial number i is 1,2, …, N; j is a column serial number, and the value range of the row serial number j is 1,2, …, N; n is the length of the signal sequence S; y is_ijThe random variables are independently and uniformly distributed;

step 103 of obtaining a Gaussian random projection matrix F_gThe method specifically comprises the following steps: the Gaussian random projection matrix F_gIth row and jth column of element g_ijIs composed of

Wherein, X_ijIs a mean value of m₀The mean square error is sigma₀Independent and identically distributed Gaussian random variables; m is₀Is the mean of the signal sequence S; sigma₀Is the mean square error of the signal sequence S;

step 104 is to obtain the signal sequence S with noise filtered_newThe method specifically comprises the following steps: of all intermediate parameter vectors x, choose to

The smallest intermediate parameter vector x is used as the noise-filtered signal sequence S_newThe value of (c).

FIG. 2 is a schematic diagram of a power signal filtering system using stochastic projection

FIG. 2 is a schematic diagram of a power signal filtering system using stochastic projection according to the present invention. As shown in fig. 2, the power signal filtering system using stochastic projection includes the following structure:

the module 201 acquires a signal sequence S acquired in time sequence;

module 202 finds a geometric random projection matrix F_eThe method specifically comprises the following steps: the geometric random projection matrix F_eRow i and column j elements of

Is calculated by the formula

Wherein i is a row serial number, and the value range of the row serial number i is 1,2, …, N; j is a column serial number, and the value range of the row serial number j is 1,2, …, N; n is the length of the signal sequence S; y is_ijThe random variables are independently and uniformly distributed;

module 203 finds a Gaussian random projection matrix F_gThe method specifically comprises the following steps: the Gaussian random projection matrix F_gIth row and jth column of element g_ijIs composed of

Wherein, X_ijIs a mean value of m₀The mean square error is sigma₀Independent and identically distributed Gaussian random variables; m is₀Is the mean of the signal sequence S; sigma₀Is the mean square error of the signal sequence S;

module 204 finds the noise-filtered signal sequence S_newThe method specifically comprises the following steps: of all intermediate parameter vectors x, choose to

The smallest intermediate parameter vector x is used as the noise-filtered signal sequence S_newThe value of (c).

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:

step 301, acquiring a signal sequence S acquired according to a time sequence;

step 302 of obtaining a geometric random projection matrix F_eThe method specifically comprises the following steps: the geometric random projection matrix F_eRow i and column j elements of

Is calculated by the formula

Wherein i is a row serial number, and the value range of the row serial number i is 1,2, …, N; j is a column serial number, and the value range of the row serial number j is 1,2, …, N; n is the length of the signal sequence S; y is_ijThe random variables are independently and uniformly distributed;

step 303 of obtaining a Gaussian random projection matrix F_gThe method specifically comprises the following steps: the Gaussian random projection matrix F_gIth row and jth column of element g_ijIs composed of

Wherein, X_ijIs a mean value of m₀The mean square error is sigma₀Independent and identically distributed Gaussian random variables; m is₀Is the mean of the signal sequence S; sigma₀Is the mean square error of the signal sequence S;

step 304 finds a noise-filtered signal sequence S_newThe method specifically comprises the following steps: of all intermediate parameter vectors x, choose to

The smallest intermediate parameter vector x is used as the noise-filtered signal sequence S_newThe value of (c).

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 (2)

1. The method for filtering the power signal by using the random projection is characterized by comprising the following steps:

step 101, acquiring a signal sequence S acquired according to a time sequence;

step 102 of obtaining a geometric random projection matrix F_eThe method specifically comprises the following steps: the geometric random projection matrix F_eRow i and column j elements of

Is calculated by the formula

Wherein i is a row serial number, and the value range of the row serial number i is i ═ 1,2, ·, N; j is a column serial number, and the value range of the row serial number j is 1,2, ·, N; n is the length of the signal sequence S; y is_ijThe random variables are independently and uniformly distributed;

step 103 of obtaining a Gaussian random projection matrix F_gThe method specifically comprises the following steps: the Gaussian random projection matrix F_gIth row and jth column of element g_ijIs composed of

Wherein, X_ijIs a mean value of m₀The mean square error is sigma₀Independent and identically distributed Gaussian random variables; m is₀Is the mean of the signal sequence S; sigma₀Is the mean square error of the signal sequence S;

step 104 is to obtain the signal sequence S with noise filtered_newThe method specifically comprises the following steps: of all intermediate parameter vectors x, choose to

The smallest intermediate parameter vector x is used as the noise-filtered signal sequence S_newThe value of (c).

2. The power signal filtering system using stochastic projection is characterized by comprising:

the module 201 acquires a signal sequence S acquired in time sequence;

module 202 finds a geometric random projection matrix F_eThe method specifically comprises the following steps: the geometric random projection matrix F_eRow i and column j elements of

Is calculated by the formula

Wherein i is a row serial number, and the value range of the row serial number i is i ═ 1,2, ·, N; j is a column serial number, and the value range of the row serial number j is 1,2, ·, N; n is the length of the signal sequence S; y is_ijThe random variables are independently and uniformly distributed;

module 203 finds a Gaussian random projection matrix F_gThe method specifically comprises the following steps: the Gaussian random projection matrix F_gIth row and jth column of element g_ijIs composed of

Wherein, X_ijIs a mean value of m₀The mean square error is sigma₀Independent and identically distributed Gaussian random variables; m is₀Is the mean of the signal sequence S; sigma₀Is the mean square error of the signal sequence S;

module 204 finds the noise-filtered signal sequence S_newThe method specifically comprises the following steps: of all intermediate parameter vectors x, choose to

The smallest intermediate parameter vector x is used as the noise-filtered signal sequence S_newThe value of (c).

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