CN108918930B - Power signal self-adaptive reconstruction method in load decomposition - Google Patents

Power signal self-adaptive reconstruction method in load decomposition Download PDF

Info

Publication number
CN108918930B
CN108918930B CN201811055290.4A CN201811055290A CN108918930B CN 108918930 B CN108918930 B CN 108918930B CN 201811055290 A CN201811055290 A CN 201811055290A CN 108918930 B CN108918930 B CN 108918930B
Authority
CN
China
Prior art keywords
matrix
power
signal sequence
data
power signal
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Expired - Fee Related
Application number
CN201811055290.4A
Other languages
Chinese (zh)
Other versions
CN108918930A (en
Inventor
翟明岳
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangdong University of Petrochemical Technology
Original Assignee
Guangdong University of Petrochemical Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Guangdong University of Petrochemical Technology filed Critical Guangdong University of Petrochemical Technology
Priority to CN201811055290.4A priority Critical patent/CN108918930B/en
Publication of CN108918930A publication Critical patent/CN108918930A/en
Application granted granted Critical
Publication of CN108918930B publication Critical patent/CN108918930B/en
Expired - Fee Related legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R11/00—Electromechanical arrangements for measuring time integral of electric power or current, e.g. of consumption

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention provides a power signal self-adaptive reconstruction method in load decomposition, which can reconstruct and generate a power signal sequence without missing data. The method comprises the following steps: collecting a power signal sequence, and converting the power signal sequence into a power matrix; constructing a transformation operator matrix according to the power matrix obtained by conversion; constructing a measurement matrix; determining reconstruction weight, and iteratively updating a power matrix according to the obtained transformation operator matrix, the reconstruction weight and the constructed measurement matrix until the current iteration number is equal to the length of the power signal sequence; and converting the currently obtained power matrix to generate a power signal sequence without missing data. The invention relates to the field of electric power.

Description

Power signal self-adaptive reconstruction method in load decomposition
Technical Field
The invention relates to the field of electric power, in particular to a power signal self-adaptive reconstruction method in load decomposition.
Background
Load splitting (which may also be referred to as energy splitting) is the splitting of the power value read at the meter into the power values consumed by the individual loads, as shown in fig. 1, where the data in fig. 1 is analog data and non-measured data.
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.
The power data used by the non-intrusive load splitting method is derived from readings from a smart meter, which is typically installed at the home power utility line to detect the total amount of power used by the home user. The completeness of data is particularly important in load splitting, since load splitting is typically an underdetermined problem (i.e., the number of equations is much smaller than the number of unknown variables). However, actual data from the smart meter may be missing (for example, a failure of the smart meter, a failure of communication, a failure of a data receiving device, etc. all cause data missing), and the missing data may further deteriorate the solution of the underdetermined problem, so that the load decomposition result has a large error.
Disclosure of Invention
The invention aims to provide a power signal self-adaptive reconstruction method in load decomposition to solve the problem of data loss of an intelligent electric meter in the prior art.
To solve the foregoing technical problem, an embodiment of the present invention provides a power signal adaptive reconstruction method in load decomposition, including:
collecting a power signal sequence, and converting the power signal sequence into a power matrix;
constructing a transformation operator matrix according to the power matrix obtained by conversion;
constructing a measurement matrix;
determining reconstruction weight, and iteratively updating a power matrix according to the obtained transformation operator matrix, the reconstruction weight and the constructed measurement matrix until the current iteration number is equal to the length of the power signal sequence;
and converting the currently obtained power matrix to generate a power signal sequence without missing data.
Further, the acquiring the power signal sequence and converting it into a power matrix includes:
collecting a power signal sequence pori＝[P1,P2,…,PN]Wherein, N is the length of the power signal sequence;
according to the power signalSequencing the sequence, dividing the power signal sequence into NRSegments, each segment containing NCThe number of the data is one,
Figure BDA0001795622910000021
wherein, the symbol
Figure BDA0001795622910000022
Representing upper rounding;
if N is present<NR×NCZero-filling the deficient part of the last section;
rearranging the segmented data into a matrix form, wherein one segment of data is one row to obtain a power matrix
Figure BDA0001795622910000023
Further, the constructing a transform operator matrix according to the converted power matrix includes:
to power matrix
Figure BDA0001795622910000024
Converting into a two-dimensional signal;
determining a signal transformation operator of the two-dimensional signal;
and converting the signal transformation operator into a matrix form to obtain a transformation operator matrix.
Further, the two-dimensional signal obtained after conversion is:
Figure BDA0001795622910000025
nr＝1,2,…,NR
nc＝1,2,…,NC
wherein the content of the first and second substances,
Figure BDA0001795622910000026
a two-dimensional signal is represented by,
Figure BDA0001795622910000027
representing a power matrix
Figure BDA0001795622910000028
N of (2)rLine, n-thcColumn elements.
Further, the signal transformation operator is represented as:
Figure BDA0001795622910000031
wherein the content of the first and second substances,
Figure BDA0001795622910000032
a representation of the signal transformation operator is shown,
Figure BDA0001795622910000033
representing a parameter;
Figure BDA0001795622910000034
is composed of
Figure BDA0001795622910000035
Weight function in the domain, argument being
Figure BDA0001795622910000036
Is composed of
Figure BDA0001795622910000037
Weight function in the domain, argument being
Figure BDA0001795622910000038
Superscript i denotes imaginary units.
Further, the transform operator matrix is represented as:
Figure BDA0001795622910000039
wherein D represents a transformation operator matrix; formula (II)
Figure BDA00017956229100000310
Representing the nth of the transformation operator matrix DrLine, n-thcThe elements of the column are
Figure BDA00017956229100000311
D is NR×NCA dimension matrix.
Further, the measurement matrix is constructed in the form of:
Figure BDA00017956229100000312
wherein R represents a measurement matrix; i is an identity matrix; 0 is a zero matrix.
Further, determining the reconstruction weight, and iteratively updating the power matrix according to the obtained transform operator matrix, the reconstruction weight and the constructed measurement matrix until the current iteration number is equal to the length of the power signal sequence includes:
iteratively updating the power matrix through a power matrix iteration formula until the current iteration times are equal to the length N of the power signal sequence, and obtaining the power matrix without missing data
Figure BDA00017956229100000313
Wherein the power matrix iterative formula is represented as:
Figure BDA00017956229100000314
Figure BDA00017956229100000315
Figure BDA00017956229100000316
Figure BDA00017956229100000317
Figure BDA0001795622910000041
Figure BDA0001795622910000042
Figure BDA0001795622910000043
Figure BDA0001795622910000044
wherein the content of the first and second substances,
Figure BDA0001795622910000045
representing a power matrix obtained by the k iteration;
Figure BDA0001795622910000046
representing a power matrix obtained by the k-1 iteration; α represents a reconstruction weight;
Figure BDA0001795622910000047
representing a threshold operator;
Figure BDA0001795622910000048
representation pair matrix
Figure BDA0001795622910000049
Performing threshold operation on all elements in the sequence; x is the number ofijRepresentation matrix
Figure BDA00017956229100000410
Row i, column j elements; sigmamaxTo represent
Figure BDA00017956229100000411
Maximum value of absolute value of all elements in the list; sigmaminTo represent
Figure BDA00017956229100000412
The minimum of the absolute values of all elements in (c).
Further, the converting the currently obtained power matrix to generate the power signal sequence without missing data includes:
the obtained matrix PrecThe first line of data is used as a first section, the second line of data is used as a second section, and so on, the last line of data is used as a last section, the sections are connected in sequence, the first N data are intercepted to form a data sequence, and the data sequence is a power signal sequence without missing data.
The technical scheme of the invention has the following beneficial effects:
in the scheme, a power signal sequence is collected and converted into a power matrix; constructing a transformation operator matrix according to the power matrix obtained by conversion; constructing a measurement matrix; determining reconstruction weight, and iteratively updating a power matrix according to the obtained transformation operator matrix, the reconstruction weight and the constructed measurement matrix until the current iteration number is equal to the length of the power signal sequence; and converting the currently obtained power matrix to generate a power signal sequence without missing data, so that the data of the intelligent electric meter is quickly reconstructed, and the problem of data missing of the intelligent electric meter is solved.
Drawings
FIG. 1 is a schematic view of load decomposition;
fig. 2 is a schematic flowchart of a power signal adaptive reconstruction method in load decomposition according to an embodiment of the present invention;
fig. 3 is a detailed flowchart of a power signal adaptive reconstruction method in load decomposition according to an embodiment of the present invention;
fig. 4 is a schematic diagram of data segmentation and matrix arrangement provided in the embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments.
The invention provides a power signal self-adaptive reconstruction method in load decomposition, aiming at the problem of data loss of the existing intelligent electric meter.
As shown in fig. 2, a method for adaptively reconstructing a power signal in load splitting according to an embodiment of the present invention includes:
s101, collecting a power signal sequence and converting the power signal sequence into a power matrix;
s102, constructing a transformation operator matrix according to the power matrix obtained by conversion;
s103, constructing a measurement matrix;
s104, determining reconstruction weights, and iteratively updating a power matrix according to the obtained transformation operator matrix, the reconstruction weights and the constructed measurement matrix until the current iteration times are equal to the length of the power signal sequence;
and S105, converting the currently obtained power matrix to generate a power signal sequence without missing data.
The power signal self-adaptive reconstruction method in load decomposition, provided by the embodiment of the invention, comprises the steps of collecting a power signal sequence and converting the power signal sequence into a power matrix; constructing a transformation operator matrix according to the power matrix obtained by conversion; constructing a measurement matrix; determining reconstruction weight, and iteratively updating a power matrix according to the obtained transformation operator matrix, the reconstruction weight and the constructed measurement matrix until the current iteration number is equal to the length of the power signal sequence; and converting the currently obtained power matrix to generate a power signal sequence without missing data, so that the data of the intelligent electric meter is quickly reconstructed, and the problem of data missing of the intelligent electric meter is solved.
For better understanding of the method for adaptively reconstructing a power signal in load splitting according to the embodiment of the present invention, details thereof are described, and as shown in fig. 3, the method for adaptively reconstructing a power signal in load splitting may specifically include the following steps:
a1, collecting power signal sequence
Collecting a power signal sequence pori＝[P1,P2,…,PN]Wherein N is the length of the power signal sequence, whereinThe rate signal sequence may also be referred to as a power data sequence.
A2, converting the power signal sequence pori＝[P1,P2,…,PN]Segmentation is performed and the segmented data is rearranged into a power matrix P, the data segmentation and matrix arrangement being shown in fig. 4.
A21, dividing the power signal sequence into N according to the sequence of the power signal sequenceRSegments, each segment containing NCThe number of the data is one,
Figure BDA0001795622910000061
wherein, the symbol
Figure BDA0001795622910000062
Meaning that the upper rounding, for example,
Figure BDA00017956229100000625
the purpose of this is that all data is involved in the operation and not discarded.
In general, N isR256 or 512 or 1024, in practical applications, NRThe value of (a) is determined by the actual application scenario.
A22 if N<NR×NCThe insufficient part of the last segment is zero-filled.
A23, rearranging the segmented data into matrix form, one segment of data is one row, so that the power matrix P has N in totalRLine, NCColumn, power matrix P can be expressed as
Figure BDA0001795622910000063
A3, mixing power matrix
Figure BDA0001795622910000064
Conversion to two-dimensional signals
Figure BDA0001795622910000065
Figure BDA0001795622910000066
nr＝1,2,…,NR
nc＝1,2,…,NC
Wherein the content of the first and second substances,
Figure BDA0001795622910000067
a two-dimensional signal is represented by,
Figure BDA0001795622910000068
representing a power matrix
Figure BDA0001795622910000069
N of (2)rLine, n-thcColumn elements.
A4, determining a two-dimensional signal
Figure BDA00017956229100000610
Signal transformation operator of
Figure BDA00017956229100000611
Signal transformation operator
Figure BDA00017956229100000612
Expressed as:
Figure BDA00017956229100000613
wherein the content of the first and second substances,
Figure BDA00017956229100000614
representing a parameter;
Figure BDA00017956229100000615
is composed of
Figure BDA00017956229100000616
Weight function in the domain, argument being
Figure BDA00017956229100000617
A gaussian function may be selected in general;
Figure BDA00017956229100000618
is composed of
Figure BDA00017956229100000619
Weight function in the domain, argument being
Figure BDA00017956229100000620
Superscript i denotes imaginary units.
A5, constructing a transformation operator matrix D
Transforming a signal into an operator
Figure BDA00017956229100000621
Conversion to matrix form:
Figure BDA00017956229100000622
wherein the content of the first and second substances,
Figure BDA00017956229100000623
representing the nth of the transformation operator matrix DrLine, n-thcThe elements of the column are
Figure BDA00017956229100000624
Thus, the matrix D is NR×NCA dimension matrix.
A6, constructing a measurement matrix R
The general form of the measurement matrix R can be expressed as:
Figure BDA0001795622910000071
wherein, I is an identity matrix which represents a segment without data loss; 0 is a zero matrix, representing a segment with a missing data.
In this embodiment, the value of the measurement matrix is determined by the power matrix, and if data in the 2 nd row and the 3 rd column in the power matrix is missing, the element in the 2 nd row and the 3 rd column in the measurement matrix is 0, otherwise, 1.
A7, iterative operation
Assuming that the kth iteration is currently performed, the power matrix obtained in the k iterations is
Figure BDA0001795622910000072
The power matrix obtained at the last time (i.e., k-1 time) is
Figure BDA0001795622910000073
k is 1,2, …, N, and let the matrix
Figure BDA0001795622910000074
A71, determining a power matrix
Figure BDA0001795622910000075
Determining reconstruction weight, updating power matrix according to the obtained transformation operator matrix, reconstruction weight and constructed measurement matrix
Figure BDA0001795622910000076
Comprises the following steps:
Figure BDA0001795622910000077
Figure BDA0001795622910000078
Figure BDA0001795622910000079
Figure BDA00017956229100000710
Figure BDA00017956229100000711
Figure BDA00017956229100000712
Figure BDA00017956229100000713
Figure BDA00017956229100000714
where, alpha represents the reconstruction weight,
Figure BDA0001795622910000081
a representation threshold operator for performing a threshold operation on the data in parentheses;
Figure BDA0001795622910000082
representation pair matrix
Figure BDA0001795622910000083
(wherein, the product of
Figure BDA0001795622910000084
Is a matrix) is subjected to a threshold operation, which is a thresholding operation on the matrix
Figure BDA0001795622910000085
One for each element in (a); x is the number ofijRepresentation matrix
Figure BDA0001795622910000086
Row i, column j elements; sigmamaxTo represent
Figure BDA0001795622910000087
Maximum value of absolute value of all elements in the list; sigmaminTo represent
Figure BDA0001795622910000088
The minimum of the absolute values of all elements in (c).
A72, judging whether the current iteration number is equal to the length N of the power signal sequence, if k is equal to N, terminating the iteration and obtaining the power matrix without missing data
Figure BDA0001795622910000089
Entering step A8; otherwise, k +1 returns to step a71 to continue the iteration.
A8, rearranging data, obtaining the power matrix P without missing datarecConverting the signal sequence into a power signal sequence to obtain a power signal sequence without missing data
The obtained matrix PrecThe first line of data is used as a first section, the second line of data is used as a second section, and so on, the last line of data is used as a last section, the sections are connected in sequence, the previous N data are intercepted to form a data sequence, and the data sequence is a power signal sequence without missing data, namely the data sequence is obtained.
The power signal self-adaptive reconstruction method in load decomposition can effectively recover the missing power signal. If the acquired power signal sequence is missing by no more than 20% of the total data, the error of the signal sequence recovered by the algorithm is no more than 3.5%. In addition, the power signal self-adaptive reconstruction method in the load decomposition adopts an iteration mode, so that the calculation is simple and quick.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (2)

1. A power signal adaptive reconstruction method in load decomposition is characterized by comprising the following steps:
collecting a power signal sequence, and converting the power signal sequence into a power matrix;
constructing a transformation operator matrix according to the power matrix obtained by conversion;
constructing a measurement matrix;
determining reconstruction weight, and iteratively updating a power matrix according to the obtained transformation operator matrix, the reconstruction weight and the constructed measurement matrix until the current iteration number is equal to the length of the power signal sequence;
converting the currently obtained power matrix to generate a power signal sequence without missing data;
wherein, the acquiring the power signal sequence and converting it into a power matrix comprises:
collecting a power signal sequence pori＝[P1,P2,…,PN]Wherein, N is the length of the power signal sequence;
dividing the power signal sequence into N according to the sequence of the power signal sequenceRSegments, each segment containing NCThe number of the data is one,
Figure FDA0002665778500000011
wherein, the symbol
Figure FDA0002665778500000012
Representing upper rounding;
if N is present<NR×NCZero-filling the deficient part of the last section;
rearranging the segmented data into a matrix form, wherein one segment of data is one row to obtain a power matrix
Figure FDA0002665778500000013
Wherein, the constructing a transform operator matrix according to the power matrix obtained by conversion comprises:
to power matrix
Figure FDA0002665778500000014
Converting into a two-dimensional signal;
determining a signal transformation operator of the two-dimensional signal;
converting the signal transformation operator into a matrix form to obtain a transformation operator matrix;
wherein, the two-dimensional signal obtained after conversion is:
Figure FDA0002665778500000015
wherein the content of the first and second substances,
Figure FDA0002665778500000016
a two-dimensional signal is represented by,
Figure FDA0002665778500000017
representing a power matrix
Figure FDA0002665778500000018
N of (2)rLine, n-thcA column element;
wherein the signal transformation operator is represented as:
Figure FDA0002665778500000019
wherein the content of the first and second substances,
Figure FDA0002665778500000021
a representation of the signal transformation operator is shown,
Figure FDA00026657785000000218
representing a parameter;
Figure FDA0002665778500000022
is composed of
Figure FDA0002665778500000023
Weight function in the domain, argument being
Figure FDA0002665778500000024
Figure FDA0002665778500000025
Is composed of
Figure FDA0002665778500000026
Weight function in the domain, argument being
Figure FDA0002665778500000027
Superscript i represents the imaginary unit;
wherein the transform operator matrix is represented as:
wherein D represents a transformation operator matrix; formula (II)
Figure FDA0002665778500000029
Representing the nth of the transformation operator matrix DrLine, n-thcThe elements of the column are
Figure FDA00026657785000000210
D is NR×NCA dimension matrix;
wherein the form of the constructed measurement matrix is as follows:
Figure FDA00026657785000000211
wherein R represents a measurement matrix; i is an identity matrix; 0 is a zero matrix;
determining the reconstruction weight, and iteratively updating the power matrix according to the obtained transformation operator matrix, the reconstruction weight and the constructed measurement matrix until the current iteration number is equal to the length of the power signal sequence comprises the following steps:
iteratively updating the power matrix through a power matrix iteration formula until the current iteration times are equal to the length N of the power signal sequence, and obtaining the power matrix without missing data
Figure FDA00026657785000000212
Wherein the power matrix iterative formula is represented as:
Figure FDA00026657785000000213
Figure FDA00026657785000000214
Figure FDA00026657785000000215
Figure FDA00026657785000000216
Figure FDA00026657785000000217
Figure FDA0002665778500000031
Figure FDA0002665778500000032
Figure FDA0002665778500000033
wherein the content of the first and second substances,
Figure FDA0002665778500000034
representing a power matrix obtained by the k iteration;
Figure FDA0002665778500000035
representing a power matrix obtained by the k-1 iteration; α represents a reconstruction weight;
Figure FDA0002665778500000036
representing a threshold operator;
Figure FDA0002665778500000037
representation pair matrix
Figure FDA0002665778500000038
Performing threshold operation on all elements in the sequence; x is the number ofijRepresentation matrix
Figure FDA0002665778500000039
Row i, column j elements; sigmamaxTo represent
Figure FDA00026657785000000310
Maximum value of absolute value of all elements in the list; sigmaminTo represent
Figure FDA00026657785000000311
The minimum of the absolute values of all elements in (c).
2. The method of claim 1, wherein the converting the currently obtained power matrix to generate the power signal sequence without missing data comprises:
the obtained matrix PrecThe first line of data is used as a first section, the second line of data is used as a second section, and so on, the last line of data is used as a last section, the sections are connected in sequence, the first N data are intercepted to form a data sequence, and the data sequence is a power signal sequence without missing data.
CN201811055290.4A 2018-09-11 2018-09-11 Power signal self-adaptive reconstruction method in load decomposition Expired - Fee Related CN108918930B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201811055290.4A CN108918930B (en) 2018-09-11 2018-09-11 Power signal self-adaptive reconstruction method in load decomposition

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201811055290.4A CN108918930B (en) 2018-09-11 2018-09-11 Power signal self-adaptive reconstruction method in load decomposition

Publications (2)

Publication Number Publication Date
CN108918930A CN108918930A (en) 2018-11-30
CN108918930B true CN108918930B (en) 2020-12-22

Family

ID=64408487

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201811055290.4A Expired - Fee Related CN108918930B (en) 2018-09-11 2018-09-11 Power signal self-adaptive reconstruction method in load decomposition

Country Status (1)

Country Link
CN (1) CN108918930B (en)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111580450A (en) * 2019-02-15 2020-08-25 广东石油化工学院 PLC signal reconstruction method and system
CN113158134B (en) * 2021-04-07 2024-02-27 国网江苏省电力有限公司信息通信分公司 Method, device and storage medium for constructing non-invasive load identification model
CN114721266B (en) * 2022-03-30 2023-05-05 大连理工大学 Self-adaptive reconstruction control method under condition of structural failure of control surface of airplane
CN116341616B (en) * 2022-11-11 2023-10-17 南京工程学院 Electric load information acquisition method based on matrix reconstruction two-dimensional convolution network

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6041423A (en) * 1996-11-08 2000-03-21 Oracle Corporation Method and apparatus for using undo/redo logging to perform asynchronous updates of parity and data pages in a redundant array data storage environment
EP2134018A1 (en) * 2008-05-23 2009-12-16 Deutsches Zentrum für Luft- und Raumfahrt e.V. Method for recovery of lost and/ or corrupted data
CN102624399A (en) * 2012-03-30 2012-08-01 北京邮电大学 Reconfiguration method for compression sensing signal
CN102938649A (en) * 2012-09-27 2013-02-20 江苏大学 Self-adaptive reconstruction and uncompressing method for power quality data based on compressive sensing theory
CN103888145A (en) * 2014-03-28 2014-06-25 电子科技大学 Method for reconstructing signals
CN103983850A (en) * 2014-05-13 2014-08-13 天津大学 Power system harmonious wave compressed signal reconstruction and detection method based on compressed sensing
CN104539293A (en) * 2014-12-31 2015-04-22 昆明理工大学 Electricity travelling wave signal reconstructing method based on compressed sensing
CN104779960A (en) * 2015-03-20 2015-07-15 南京邮电大学 A signal reconstruction method based on block compressed sensing
CN107615089A (en) * 2014-01-03 2018-01-19 朱宇东 Compressed sensing and MRI modeling and verification method

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6400150B1 (en) * 2001-04-02 2002-06-04 Regents Of The University Of Minnesota NMR spectroscopy data recovery method and apparatus
US7620786B2 (en) * 2003-09-12 2009-11-17 Lsi Corporation Storage recovery using a delta log

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6041423A (en) * 1996-11-08 2000-03-21 Oracle Corporation Method and apparatus for using undo/redo logging to perform asynchronous updates of parity and data pages in a redundant array data storage environment
EP2134018A1 (en) * 2008-05-23 2009-12-16 Deutsches Zentrum für Luft- und Raumfahrt e.V. Method for recovery of lost and/ or corrupted data
CN102624399A (en) * 2012-03-30 2012-08-01 北京邮电大学 Reconfiguration method for compression sensing signal
CN102938649A (en) * 2012-09-27 2013-02-20 江苏大学 Self-adaptive reconstruction and uncompressing method for power quality data based on compressive sensing theory
CN107615089A (en) * 2014-01-03 2018-01-19 朱宇东 Compressed sensing and MRI modeling and verification method
CN103888145A (en) * 2014-03-28 2014-06-25 电子科技大学 Method for reconstructing signals
CN103983850A (en) * 2014-05-13 2014-08-13 天津大学 Power system harmonious wave compressed signal reconstruction and detection method based on compressed sensing
CN104539293A (en) * 2014-12-31 2015-04-22 昆明理工大学 Electricity travelling wave signal reconstructing method based on compressed sensing
CN104779960A (en) * 2015-03-20 2015-07-15 南京邮电大学 A signal reconstruction method based on block compressed sensing

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
A SIMPLIFIED DATA ASSIMILATION METHOD FOR RECONSTRUCTING TIME-SERIES MODIS NDVI DATA;Juan Gu等;《GARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium》;20080711;全文 *
Data Compression of Power Quality Events Using the Slantlet Transform;G. Panda等;《IEEE TRANSACTIONS ON POWER DELIVERY》;20020430;第17卷(第2期);全文 *
基于信号方向信息的二维压缩感知重构研究;程涛等;《半导体光电》;20150430;第36卷(第2期);全文 *

Also Published As

Publication number Publication date
CN108918930A (en) 2018-11-30

Similar Documents

Publication Publication Date Title
CN108918930B (en) Power signal self-adaptive reconstruction method in load decomposition
CN108918931B (en) Power signal self-adaptive filtering method in load decomposition
CN108918928B (en) Power signal self-adaptive reconstruction method in load decomposition
CN108918932B (en) Adaptive filtering method for power signal in load decomposition
CN111428816A (en) Non-invasive load decomposition method
CN109193635B (en) Power distribution network topological structure reconstruction method based on self-adaptive sparse regression method
CN109462404A (en) Adaptive Wave data compression method based on similarity segmentation
Wang et al. Regional nonintrusive load monitoring for low voltage substations and distributed energy resources
CN112215405A (en) Non-invasive type residential electricity load decomposition method based on DANN domain adaptive learning
CN114037178B (en) Non-invasive load decomposition method based on unsupervised pretrained neural network
Chen et al. Mining appliance usage patterns in smart home environment
CN102901855B (en) De-noising method for ultra-high-voltage direct-current corona current signal
CN108918929B (en) Power signal self-adaptive filtering method in load decomposition
CN109194367B (en) Power signal reconstruction method in energy decomposition
CN109038577B (en) Power signal self-adaptive reconstruction method in load decomposition
CN111199014A (en) Time sequence based seq2point NILM method and device
CN115563583A (en) Non-invasive load monitoring method based on multi-physical quantity fusion
CN115932428A (en) Iterative unsupervised non-invasive load identification method and system
CN110133396B (en) Method for detecting household electrical appliance switch event based on binary recursive SVD decomposition
CN110531149B (en) Power signal filtering method and system based on waveform regularization
CN110783921A (en) Power signal reconstruction method and system by utilizing dictionary prediction matrix
CN110705426B (en) Power signal filtering method and system by using deblurring operator
Yu et al. Nonintrusive load disaggregation method based on graph signal processing
CN110611310A (en) Power signal reconstruction method and system by using periodic boundary conditions
Zhang et al. Non-intrusive load decomposition of stacked hybrid recurrent neural networks

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant
CF01 Termination of patent right due to non-payment of annual fee

Granted publication date: 20201222

Termination date: 20210911

CF01 Termination of patent right due to non-payment of annual fee