CN111679119A - Power signal reconstruction method and system by using near-end matching mapping - Google Patents

Power signal reconstruction method and system by using near-end matching mapping Download PDF

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CN111679119A
CN111679119A CN202010478803.3A CN202010478803A CN111679119A CN 111679119 A CN111679119 A CN 111679119A CN 202010478803 A CN202010478803 A CN 202010478803A CN 111679119 A CN111679119 A CN 111679119A
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signal sequence
finds
end matching
normalized average
matching mapping
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翟明岳
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Guangdong University of Petrochemical Technology
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Guangdong University of Petrochemical Technology
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R21/00—Arrangements for measuring electric power or power factor
    • G01R21/001—Measuring real or reactive component; Measuring apparent energy
    • G01R21/002—Measuring real component
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere

Abstract

The invention discloses a power signal reconstruction method and a system by using near-end matching mapping, wherein the method comprises the following steps: step 101, acquiring a signal sequence S acquired according to a time sequence; step 102, obtaining a normalized average matrix B; step 103 for obtaining step length t0(ii) a 104, solving a near-end matching mapping vector f; step 105 finds the reconstructed signal sequence SNEW。

Description

Power signal reconstruction method and system by using near-end matching mapping
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 using near-end matching mapping. 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 with near-end matching mapping, comprising:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102 finds the normalized average matrix B. In particular to
Figure BDA0002516690260000021
Wherein m is0Represents the mean value of the signal sequence S; sigma0Represents the mean square error of the signal sequence S;
step 103 for obtaining step length t0Is concretely provided with
Figure BDA0002516690260000022
Wherein λ isjIs the jth eigenvalue of the normalized average matrix B; j is a characteristic value serial number, and the value range of j is 1,2, ·, N; n is the length of the signal sequence S; lambda [ alpha ]maxThe maximum eigenvalue of the normalized average matrix B;
step 104 of obtaining a near-end matching mapping vector f, specifically
Figure BDA0002516690260000023
Wherein h is a non-smooth vector, and the 1 st element h thereof1Is 0, i-th element hiIs hi＝si-si-1(ii) a i is the element serial number, and the value range is i ═ 2,3, ·, N; siIs the ith element of the signal sequence S; si-1Is the i-1 th element of the signal sequence S;
step 105 finds the reconstructed signal sequence SNEWThe method specifically comprises the following steps:
Figure BDA0002516690260000024
a power signal reconstruction system utilizing near-end matching mapping, comprising:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 finds the normalized average matrix B. In particular to
Figure BDA0002516690260000025
Wherein m is0Represents the mean value of the signal sequence S; sigma0Represents the mean square error of the signal sequence S;
module 203 finds the step t0Is concretely provided with
Figure BDA0002516690260000026
Wherein λ isjIs the jth eigenvalue of the normalized average matrix B; j is a characteristic value serial number, and the value range of j is 1,2, ·, N; n is the length of the signal sequence S; lambda [ alpha ]maxThe maximum eigenvalue of the normalized average matrix B;
module 204 finds the near-end matching mapping vector f, specifically
Figure BDA0002516690260000027
Wherein h is a non-smooth vector, and the 1 st element h thereof1Is 0, i-th element hiIs hi＝si-si-1(ii) a i is the element serial number, and the value range is i ═ 2,3, ·, N; siIs the ith element of the signal sequence S; si-1Is the i-1 th element of the signal sequence S;
the module 205 finds the reconstructed signal sequence SNEWThe method specifically comprises the following steps:
Figure BDA0002516690260000028
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 using near-end matching mapping. 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 near-end matching mapping
Fig. 1 is a flowchart illustrating a power signal reconstruction method using near-end matching mapping according to the present invention. As shown in fig. 1, the method for reconstructing a power signal by using near-end matching mapping specifically includes the following steps:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102 finds the normalized average matrix B. In particular to
Figure BDA0002516690260000031
Wherein m is0Represents the mean value of the signal sequence S; sigma0Represents the mean square error of the signal sequence S;
step 103 for obtaining step length t0Is concretely provided with
Figure BDA0002516690260000032
Wherein λ isjIs the jth eigenvalue of the normalized average matrix B; j is a characteristic value serial number, and the value range of j is 1,2, ·, N; n is the length of the signal sequence S; lambda [ alpha ]maxThe maximum eigenvalue of the normalized average matrix B;
step 104 of obtaining a near-end matching mapping vector f, specifically
Figure BDA0002516690260000033
Wherein h is a non-smooth vector, and the 1 st element h thereof1Is 0, i-th element hiIs hi＝si-si-1(ii) a i is the element serial number, and the value range is i ═ 2,3, ·, N; siIs the ith element of the signal sequence S; si-1Is the i-1 th element of the signal sequence S;
step 105 finds the reconstructed signal sequence SNEWThe method specifically comprises the following steps:
Figure BDA0002516690260000034
FIG. 2 is a schematic diagram of a power signal reconstruction system using near-end matching mapping
Fig. 2 is a schematic structural diagram of a power signal reconstruction system using near-end matching mapping according to the present invention. As shown in fig. 2, the power signal reconstruction system using near-end matching mapping includes the following structures:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 finds the normalized average matrix B. In particular to
Figure BDA0002516690260000035
Wherein m is0Represents the mean value of the signal sequence S; sigma0Represents the mean square error of the signal sequence S;
module 203 finds the step t0Is concretely provided with
Figure BDA0002516690260000041
Wherein λ isjIs the jth eigenvalue of the normalized average matrix B; j is a characteristic value serial number, and the value range of j is 1,2, ·, N; n is the length of the signal sequence S; lambda [ alpha ]maxThe maximum eigenvalue of the normalized average matrix B;
module 204 finds the near-end matching mapping vector f, specifically
Figure BDA0002516690260000042
Wherein h is a non-smooth vector, and the 1 st element h thereof1Is 0, i-th element hiIs hi＝si-si-1(ii) a i is the element serial number, and the value range is i ═ 2,3, ·, N; siIs the ith element of the signal sequence S; si-1Is the i-1 th element of the signal sequence S;
the module 205 finds the reconstructed signal sequence SNEWThe method specifically comprises the following steps:
Figure BDA0002516690260000043
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 finds the normalized average matrix B. In particular to
Figure BDA0002516690260000044
Wherein m is0Represents the mean value of the signal sequence S; sigma0Represents the mean square error of the signal sequence S;
step 303 finds the step length t0Is concretely provided with
Figure BDA0002516690260000045
Wherein λ isjIs the jth eigenvalue of the normalized average matrix B; j is a characteristic value serial number, and the value range of j is 1,2, ·, N; n is the length of the signal sequence S; lambda [ alpha ]maxThe maximum eigenvalue of the normalized average matrix B;
step 304 finds a near-end matching mapping vector f, specifically
Figure BDA0002516690260000046
Wherein h is a non-smooth vector, and the 1 st element h thereof1Is 0, i-th element hiIs hi＝si-si-1(ii) a i is the element serial number, and the value range is i ═ 2,3, ·, N; siIs the ith element of the signal sequence S; si-1Is the i-1 th element of the signal sequence S;
step 305 finds a reconstructed signal sequence SNEWThe method specifically comprises the following steps:
Figure BDA0002516690260000047
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. A method for power signal reconstruction using near-end matching mapping, comprising:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102 finds the normalized average matrix B. In particular to
Figure FDA0002516690250000011
Wherein m is0Represents the mean value of the signal sequence S; sigma0Represents the mean square error of the signal sequence S;
step 103 for obtaining step length t0Is concretely provided with
Figure 1
Wherein λ isjIs the jth eigenvalue of the normalized average matrix B; j is a characteristic value serial number, and the value range of j is 1,2, ·, N; n is the length of the signal sequence S; lambda [ alpha ]maxThe maximum eigenvalue of the normalized average matrix B;
step 104 of obtaining a near-end matching mapping vector f, specifically
Figure 3
. Wherein h is a non-smooth vector, and the 1 st element h thereof1Is 0, i-th element hiIs hi＝si-si-1(ii) a i is the element serial number, and the value range is i ═ 2,3, ·, N; siIs the ith element of the signal sequence S; si-1Is the i-1 th element of the signal sequence S;
step 105 of obtaining a reconstructed signal sequence SNEWThe method specifically comprises the following steps:
Figure FDA0002516690250000015
2. a system for power signal reconstruction using near-end matching mapping, comprising:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 finds the normalized average matrix B. In particular to
Figure FDA0002516690250000016
Wherein m is0Represents the mean value of the signal sequence S; sigma0Represents the mean square error of the signal sequence S;
module 203 finds the step t0Is concretely provided with
Figure 2
Wherein λ isjIs the jth eigenvalue of the normalized average matrix B; j is a characteristic value serial number, and the value range of j is 1,2, ·, N; n is the length of the signal sequence S; lambda [ alpha ]maxThe maximum eigenvalue of the normalized average matrix B;
module 204 finds the near-end matching mapping vector f, specifically
Figure 6
. Wherein h is a non-smooth vector, and the 1 st element h thereof1Is 0, i-th element hiIs hi＝si-si-1(ii) a i is the element serial number, and the value range is i ═ 2,3, ·, N; siIs the ith element of the signal sequence S; si-1Is the i-1 th element of the signal sequence S;
the module 205 finds the reconstructed signal sequence SNEWThe method specifically comprises the following steps:
Figure FDA00025166902500000110
CN202010478803.3A 2020-05-30 2020-05-30 Power signal reconstruction method and system by using near-end matching mapping Withdrawn CN111679119A (en)

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