CN109307798B - Power signal filtering method for switch event detection - Google Patents

Power signal filtering method for switch event detection Download PDF

Info

Publication number
CN109307798B
CN109307798B CN201810995998.1A CN201810995998A CN109307798B CN 109307798 B CN109307798 B CN 109307798B CN 201810995998 A CN201810995998 A CN 201810995998A CN 109307798 B CN109307798 B CN 109307798B
Authority
CN
China
Prior art keywords
power signal
matrix
filtering
function
calculating
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
CN201810995998.1A
Other languages
Chinese (zh)
Other versions
CN109307798A (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 CN201810995998.1A priority Critical patent/CN109307798B/en
Publication of CN109307798A publication Critical patent/CN109307798A/en
Application granted granted Critical
Publication of CN109307798B publication Critical patent/CN109307798B/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
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Complex Calculations (AREA)
  • Remote Monitoring And Control Of Power-Distribution Networks (AREA)

Abstract

The invention provides a power signal filtering method for detecting a switching event, which is used for solving the problems that the background noise with obvious non-stationarity and non-Gaussian characteristic cannot be effectively filtered for a power signal and the switching event is difficult to be accurately detected in the prior art. The method comprises the following steps: and summing all scales of the obtained filtered power signals to obtain a filtered power matrix. According to the invention, through carrying out multi-scale summation after filtering the power signals, noise with non-stationarity and non-Gaussian characteristic is effectively filtered, the power signals are effectively filtered, accurate switching event detection is carried out, the filtering method is simple, and the calculation speed is high, so that accurate decomposition of the household electrical load is completed.

Description

Power signal filtering method for switch event detection
Technical Field
The invention belongs to the field of electric power, and particularly relates to a power signal filtering method for switch event detection.
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. 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 the internal electric equipment of the load, can obtain the load information of each electric equipment only according to the total information of the electric load, has less investment and convenient use, and is suitable for the decomposition of 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 operating state of any one of the electric devices changes, the consumed power value of any one of the electric devices also changes inevitably, and the change is reflected in the total power consumed by all the electric devices, and the commonly used switch event detection takes the change value Δ P of the active power P as the judgment basis of the event detection. Fig. 1 is a schematic diagram showing a simulation of energy decomposition of a household electrical load in the prior art.
This method requires setting a reasonable threshold for the power variation value, and has the following problems: the instantaneous power value at the starting time of some electric appliances has larger peak, namely pulse noise (for example, the starting current of a motor is far larger than the rated current), which causes the change value of the steady-state power of the electric appliances to be inaccurate, thereby influencing the judgment of a switching event; the transient processes of different household appliances are either long or short (the duration and the frequency of occurrence of impulse noise are very different), so that the determination of the power variation 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. Fig. 2 shows a power signal (also referred to as a power data sequence) measured by a switching event detection device and a conventional detection algorithm in the prior art, and the distribution of noise (including impulse noise) in the signal can be seen, where the intensity of the (impulse) noise in the power sequence is very large, and 3 switching events are detected although only one true switching event exists. Therefore, filtering the power signal is an important step in the switching event detection process.
Noise cancellation devices commonly used in the prior art are low pass filters and median filters. The low-pass filter has a good effect of eliminating stationarity and noise conforming to the Gaussian law, but has a poor effect of eliminating noise with non-stationarity and non-Gaussian characteristics. In daily life, nonlinear electrical appliances such as a juicer, a coffee maker, a soymilk maker and the like using a motor are increasingly applied, and more impulse noise appears in background noise. The noise has high instantaneous power and presents more obvious non-stationarity and non-Gaussian characteristics, and the common noise elimination equipment in the prior art cannot effectively filter power signals and is difficult to accurately detect switching events.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a power signal filtering method for detecting a switching event, aiming at non-stationary and non-Gaussian power signals in the prior art, the method can also effectively filter signals and accurately detect the switching event, and is simple in filtering method and high in calculation speed, so that the household electrical load is accurately decomposed.
To solve the above technical problem, an embodiment of the present invention provides a power signal filtering method for switching event detection, where the method includes: and summing all the scales to obtain a filtered power signal matrix.
Wherein the method further comprises the steps of:
before solving the filtered power signal matrix:
step S1, inputting measured power signals;
step S2, segmenting the actually measured power signal, and rearranging the segmented data into a power signal matrix;
step S3, calculating the discrete Fourier transform function of the basis function;
step S4, calculating the discrete value of the basis function according to the Fourier transform function of the basis function;
step S5, calculating the discrete value of the mother function according to the discrete value of the basis function;
step S6, calculating the projection of the power signal under the mother function according to the discrete value of the mother function to obtain a transformed power signal matrix;
step S7, filtering the transformed power signal matrix;
after obtaining the filtered power signal matrix, the method further includes:
and step S9, converting the filtered power signal matrix into a data sequence, rearranging the data, and obtaining the power signal with the noise filtered.
Wherein the step S2 further includes:
step S21, setting the length of the input actual measurement power signal as N, and dividing all data into NRA segment;
step S22, segmenting according to data sequence, wherein each segment contains NCThe number of the data is one,
Figure BDA0001781839330000031
wherein, the symbol
Figure BDA0001781839330000032
Is shown onGetting the whole;
step S23, if N < NR×NCZero-filling the insufficient part;
step S24, rearranges the segmented data into a matrix form.
Wherein, the step S3 is to calculate the basis function
Figure BDA0001781839330000033
Fourier transform function of
Figure BDA0001781839330000034
The method specifically comprises the following steps:
Figure BDA0001781839330000035
wherein:
Figure BDA0001781839330000036
Figure BDA0001781839330000037
Figure BDA0001781839330000038
Figure BDA0001781839330000039
Figure BDA00017818393300000310
z represents all integers
Figure BDA0001781839330000041
Wherein, in the step S4, discrete values of the basis functions are obtained
Figure BDA0001781839330000042
Comprises the following steps:
Figure BDA0001781839330000043
wherein, the discrete value ψ of the mother function is obtained in the step S5lmijComprises the following steps:
Figure BDA0001781839330000044
l＝1,2,L,10
m＝1,2,L,10
i＝1,2,L,NR
j＝1,2,L,NC
wherein the content of the first and second substances,
Figure BDA0001781839330000045
wherein, the step S6 of obtaining the projection of the power signal under the mother function further includes:
step S61, dividing the discrete value psi of the mother functionlmijConversion to a matrix of mother functions:
Figure BDA0001781839330000046
wherein psilmijIs a matrixlmRow i and column j elements of (1);
step S62, the power signal matrix and the mother function matrix are subjected to point multiplication to obtain the transformed power signal
Number matrix:
Figure BDA0001781839330000047
wherein, L is 1,2, L, 10; m is 1,2, L, 10.
Wherein, the filtering the transformed power signal matrix in step S7 further includes:
step S71, calculating the threshold lambda by the following equationlm：
Figure BDA0001781839330000048
Wherein, mean [ psi ]lmij]Indicating that the variables l and m are fixed, psi in the case where i, j take all valueslmijThe median value of (d);
step S72, according to the threshold lambdalmThe value of each element of the new matrix is determined.
Wherein the content of the first and second substances,
in the step S71, by
Figure BDA0001781839330000051
Calculating a threshold lambdalm；
Wherein, mean [ psi ]lmij]Indicating that the variables l and m are fixed, psi in the case where i, j take all valueslmijThe median value of (d);
the step S72 further includes:
if it is not
Figure BDA0001781839330000052
If any element in (b) is smaller than the threshold λ, making this element 0; after the processing, a new matrix is obtained
Figure BDA0001781839330000053
The ith row and jth column elements of the matrix are denoted as
Figure BDA0001781839330000054
Step S8, where the summation of all the scales is performed to obtain a filtered power signal matrix, and specifically, the filtered power signal matrix is:
P1＝[Pij](6)
wherein the content of the first and second substances,
Figure BDA0001781839330000055
i, j represents a scale, and all i, j are summed, i.e., all scales are summed.
The technical scheme of the invention has the following beneficial effects:
according to the invention, through carrying out multi-scale summation after filtering the power signal, noise with non-stationarity and non-Gaussian characteristic is effectively filtered, and the power signal is effectively filtered. The signal-to-noise ratio of the signal after noise filtering is improved by about 5dB, and a good foundation is laid for subsequent switch event detection. The filtering method is simple and the calculation speed is high.
Drawings
In order to more clearly illustrate the embodiments of the present invention and the prior art, the following technical scheme description figures of the present invention are briefly introduced, and it is obvious that other figures can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic diagram of a prior art energy-resolved simulation of a domestic electrical load;
FIG. 2 is a graph of power signals measured by a prior art switching event detection device and a conventional detection algorithm;
FIG. 3 is a flow chart illustrating a filtering method for detecting a switching event according to an embodiment of the present invention;
fig. 4 is a schematic diagram of data segmentation and matrix arrangement.
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 novel filtering method aiming at the noise characteristic in a power signal, aiming at the problems that the common noise elimination equipment in the prior art can not effectively filter the noise with non-stationarity and non-Gaussian characteristic and is difficult to accurately detect the switching event.
The present invention will be described in further detail below with reference to specific embodiments and drawings.
Examples
The present embodiment provides a power signal filtering method for detecting a switching event, and fig. 3 illustrates the power signal filtering method according to the present embodiment. As shown in fig. 3, the power signal filtering method for switching event detection includes the steps of:
in step S1, the measured power signal is input, and the signal length is set to N.
Specifically, the measured power signal sequences P (1), P (2), L, P (N) are input, and N is the length of the power signal sequence.
Step S2, segmenting the measured power signal and rearranging the segmented data into a power signal matrix P.
Specifically, the method comprises the following steps:
step S21, dividing all data into NRAnd (4) section.
Preferably, said N isR256 or 512 or 1024.
Step S22, segmenting according to data sequence, wherein each segment contains NCAnd (4) data.
Wherein the content of the first and second substances,
Figure BDA0001781839330000061
symbol
Figure BDA0001781839330000062
Meaning rounding up. For example
Figure BDA0001781839330000063
The purpose of this is that all data is involved in the operation and not discarded.
Step S23, if N < NR×NCThe insufficient portion is zero-padded.
Step S24, rearranges the segmented data into a matrix form.
Fig. 4 is a schematic diagram of data segmentation and matrix arrangement. As shown in FIG. 4, a segment of data is a row, and thus, the power signal matrix P has N in totalRLine, NCAnd (4) columns.
Step S3, calculating a basis function
Figure BDA0001781839330000071
Discrete fourier transform function of
Figure BDA0001781839330000072
Specifically, the transformed function is:
Figure BDA0001781839330000073
wherein:
Figure BDA0001781839330000074
Figure BDA0001781839330000075
Figure BDA0001781839330000076
Figure BDA0001781839330000077
Figure BDA0001781839330000078
z represents all integers
Figure BDA0001781839330000079
Step S4, calculating discrete value of basis function according to Fourier transform of basis function
Figure BDA00017818393300000710
Figure BDA00017818393300000711
Wherein, i is 1,2, L, NR；j＝1,2,L,NC
Step S5, discrete value according to basis function
Figure BDA00017818393300000712
Calculating the discrete value psi of the mother functionlmij：
Figure BDA0001781839330000081
Wherein, L is 1,2, L, 10; m is 1,2, L, 10; i is 1,2, L, NR；j＝1,2,L,NC；
Figure BDA0001781839330000082
Step S6, discrete value psi according to mother functionlmijAnd solving the projection of the power signal under the mother function to obtain a transformed power signal matrix.
Further, the method specifically comprises the following steps:
step S61, dividing the discrete value psi of the mother functionlmijConversion to a matrix of mother functions:
Figure BDA0001781839330000083
phi in formula (3)lmijRepresentation matrixlmThe ith row and the jth column of the matrix, the matrix having N in totalRLine, NCAnd (4) columns.
Step S62, the power signal matrix and the mother function matrix are subjected to point multiplication to obtain the transformed power signal
Number matrix:
Figure BDA0001781839330000084
wherein, L is 1,2, L, 10; m is 1,2, L, 10.
In step S7, the transformed power signal matrix is filtered.
Specifically, the filtering process includes the following steps:
step S71, calculating the threshold lambda by the following equationlm：
Figure BDA0001781839330000085
Wherein, mean [ psi ]lmij]Indicating that the variables l and m are fixed, psi in the case where i, j take all valueslmijThe median value of (d);
in step S72, the value of each element of the new matrix is determined based on the threshold value.
In particular, if
Figure BDA0001781839330000086
Is smaller than a threshold value lambdalmLet this element be 0; after the processing, a new matrix is obtained
Figure BDA0001781839330000087
The ith row and jth column elements of the matrix are denoted as
Figure BDA0001781839330000088
Step S8, summing all scales, and solving a filtered power signal matrix:
P1＝[Pij](6)
wherein the content of the first and second substances,
Figure BDA0001781839330000089
in this step, i, j represents a scale, and the sum of all i, j is the sum of all scales.
And step S9, converting the filtered power signal matrix into a data sequence, rearranging the data, and obtaining the power signal with the noise filtered.
Specifically, the method comprises the following steps: will matrix P1The first line of data is used as the first section, the 2 nd line of data is used as the second section, and so on, the last line of data is used as the last section, the sections are connected in sequence, and before interception is carried outThe N data of the surface form a data sequence, which is the power signal after filtering out the background noise (especially the strong impulse noise).
According to the technical scheme, the power signal filtering method for detecting the switching event effectively filters noise with non-stationarity and non-Gaussian characteristics through multi-scale filtering, effectively filters the power signal, accurately detects the switching event, is simple in filtering method and high in calculation speed, and accordingly achieves accurate decomposition of the household power load.
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 (9)

1. A method of filtering a power signal for switching event detection, the method comprising:
step S1, inputting measured power signals;
step S2, segmenting the actually measured power signal, and rearranging the segmented data into a power signal matrix;
step S3, calculating the discrete Fourier transform function of the basis function;
step S4, calculating the discrete value of the basis function according to the Fourier transform function of the basis function;
step S5, calculating the discrete value of the mother function according to the discrete value of the basis function;
step S6, calculating the projection of the power signal under the mother function according to the discrete value of the mother function to obtain a transformed power signal matrix;
step S7, filtering the transformed power signal matrix;
step S8, summing all scales, and solving a filtered power signal matrix;
and step S9, converting the filtered power signal matrix into a data sequence, rearranging the data, and obtaining the power signal with the noise filtered.
2. The power signal filtering method according to claim 1, wherein the step S2 further comprises:
step S21, setting the length of the input actual measurement power signal as N, and dividing all data into NRA segment;
step S22, segmenting according to data sequence, wherein each segment contains NCThe number of the data is one,
Figure FDA0002545483360000011
wherein, the symbol
Figure FDA0002545483360000012
Representing upper rounding;
step S23, if N < NR×NCZero-filling the insufficient part;
step S24, rearranges the segmented data into a matrix form.
3. The method for filtering a power signal according to claim 1, wherein the step S3 comprises calculating a basis function
Figure FDA0002545483360000013
Fourier transform function of
Figure FDA0002545483360000014
The method specifically comprises the following steps:
Figure FDA0002545483360000021
wherein:
Figure FDA0002545483360000022
Figure FDA0002545483360000023
Figure FDA0002545483360000024
Figure FDA0002545483360000025
Figure FDA0002545483360000026
z represents all integers
Figure FDA0002545483360000027
4. The method for filtering a power signal according to claim 1, wherein the discrete value of the basis function is obtained in step S4
Figure FDA0002545483360000028
Comprises the following steps:
Figure FDA0002545483360000029
5. the power signal filtering method according to claim 1, wherein the discrete value ψ of the mother function in said step S5 is obtainedlmijComprises the following steps:
Figure FDA00025454833600000210
wherein the content of the first and second substances,
Figure FDA0002545483360000031
6. the method for filtering a power signal according to claim 1, wherein the step S6 of calculating the projection of the power signal under the mother function further comprises:
step S61, dividing the discrete value psi of the mother functionlmijConversion to a matrix of mother functions:
Figure FDA0002545483360000032
wherein psilmijIs a matrixlmRow i and column j elements of (1);
step S62, performing dot product operation on the power signal matrix and the mother function matrix to obtain a transformed power signal matrix:
Figure FDA0002545483360000033
wherein, l is 1,2, …, 10; m is 1,2, …, 10.
7. The method for filtering a power signal according to claim 1, wherein the step S7 of matrix filtering the transformed power signal further comprises:
step S71, calculating the threshold lambda by the following equationlm：
Figure FDA0002545483360000034
Wherein the content of the first and second substances,
Figure FDA0002545483360000035
indicating that the variables l and m are fixed, psi in the case where i, j take all valueslmijThe median value of (d);
step S72, according to the threshold lambdalmThe value of each element of the new matrix is determined.
8. The power signal filtering method according to claim 7,
in the step S71, by
Figure FDA0002545483360000036
Calculating a threshold lambdalm；
Wherein, mean [ psi ]lmij]Indicating that the variables l and m are fixed, psi in the case where i, j take all valueslmijThe median value of (d);
the step S72 further includes:
if it is not
Figure FDA0002545483360000037
If any element in (b) is smaller than the threshold λ, making this element 0; after the processing, a new matrix is obtained
Figure FDA0002545483360000038
The ith row and jth column elements of the matrix are denoted as
Figure FDA0002545483360000039
9. The method of claim 1, wherein the filtered power signal matrix is:
P1＝[Pij](6)
wherein the content of the first and second substances,
Figure FDA0002545483360000041
i, j represents a scale, and all i, j are summed, i.e., all scales are summed.
CN201810995998.1A 2018-08-29 2018-08-29 Power signal filtering method for switch event detection Expired - Fee Related CN109307798B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810995998.1A CN109307798B (en) 2018-08-29 2018-08-29 Power signal filtering method for switch event detection

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201810995998.1A CN109307798B (en) 2018-08-29 2018-08-29 Power signal filtering method for switch event detection

Publications (2)

Publication Number Publication Date
CN109307798A CN109307798A (en) 2019-02-05
CN109307798B true CN109307798B (en) 2020-08-28

Family

ID=65223912

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201810995998.1A Expired - Fee Related CN109307798B (en) 2018-08-29 2018-08-29 Power signal filtering method for switch event detection

Country Status (1)

Country Link
CN (1) CN109307798B (en)

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109902961A (en) * 2019-03-04 2019-06-18 广东石油化工学院 The multi-scale filtering method and system filtered for power signal in Energy Decomposition
CN110196354B (en) * 2019-04-23 2021-09-17 广东石油化工学院 Method and device for detecting switching event of load
CN110221121A (en) * 2019-06-28 2019-09-10 广东石油化工学院 A kind of on-load switch event detecting method and system based on Signal separator
CN110531149B (en) * 2019-08-31 2021-06-18 广东石油化工学院 Power signal filtering method and system based on waveform regularization
CN110542855B (en) * 2019-09-08 2021-09-21 广东石油化工学院 Load switch event detection method and system based on discrete cosine transform
CN110726870A (en) * 2019-10-20 2020-01-24 广东石油化工学院 Load switch event detection method and system based on data purity

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1610144A2 (en) * 2004-06-23 2005-12-28 Whirlpool Corporation Method for calibrating offset from DC bus
JP4765180B2 (en) * 2001-03-07 2011-09-07 パナソニック株式会社 Method for measuring output power of inductive load of electric powered vehicle
CN102288285A (en) * 2011-05-24 2011-12-21 南京航空航天大学 Blind source separation method for single-channel vibration signals
CN102841341A (en) * 2012-09-03 2012-12-26 深圳先进技术研究院 Pulse radar moving-target detection method
CN106908657A (en) * 2017-03-06 2017-06-30 中国海洋大学 A kind of load switch detection means and method based on power

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP4765180B2 (en) * 2001-03-07 2011-09-07 パナソニック株式会社 Method for measuring output power of inductive load of electric powered vehicle
EP1610144A2 (en) * 2004-06-23 2005-12-28 Whirlpool Corporation Method for calibrating offset from DC bus
CN102288285A (en) * 2011-05-24 2011-12-21 南京航空航天大学 Blind source separation method for single-channel vibration signals
CN102841341A (en) * 2012-09-03 2012-12-26 深圳先进技术研究院 Pulse radar moving-target detection method
CN106908657A (en) * 2017-03-06 2017-06-30 中国海洋大学 A kind of load switch detection means and method based on power

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
Research of Transmission Parameters of Four-Conductor Cables for Power Line Communication;Mengcong Tang 等;《2008 International Conference on Computer Science and Software Engineering》;20081214;全文 *
基于稀疏矩阵变换的电网故障电流快速算法;陈志光 等;《广东电力》;20151009;第28卷(第9期);全文 *
矩阵稀疏化中的最优子波滤波器组设计;彭志威 等;《西安电子科技大学学报》;19990220(第1期);全文 *

Also Published As

Publication number Publication date
CN109307798A (en) 2019-02-05

Similar Documents

Publication Publication Date Title
CN109307798B (en) Power signal filtering method for switch event detection
CN109188069B (en) Pulse noise filtering method for load switch event detection
CN108918932B (en) Adaptive filtering method for power signal in load decomposition
CN108918931B (en) Power signal self-adaptive filtering method in load decomposition
CN111680590A (en) Power signal filtering method and system by using contraction gradient
CN108918927B (en) Power signal filtering method and system in energy decomposition
CN108918929B (en) Power signal self-adaptive filtering method in load decomposition
CN109241874B (en) Power signal filtering method in energy decomposition
CN111666870A (en) Power signal reconstruction method and system by utilizing quadratic constraint
CN110187167B (en) Method and device for detecting load switch event based on manifold classification
CN110196354B (en) Method and device for detecting switching event of load
CN110542855B (en) Load switch event detection method and system based on discrete cosine transform
CN109740582B (en) Power signal noise filtering method and system for energy decomposition
CN112434567B (en) Power signal filtering method and system by using noise jitter property
CN111830405A (en) Load switch event detection method and system by using frequency difference
CN111832474A (en) Power signal filtering method and system by using energy scale
CN111585544A (en) Method and filter for filtering power signal impulse noise
CN110221119B (en) Load switch event detection method and system based on power and akie fusion information
CN109194367B (en) Power signal reconstruction method in energy decomposition
CN111639606A (en) Power signal filtering method and system utilizing Dantzig total gradient minimization
CN112307986B (en) Load switch event detection method and system by utilizing Gaussian gradient
CN111737645A (en) Power signal reconstruction method and system by using prediction matrix
CN111400649A (en) Harmonic responsibility quantification method and device, computer equipment and storage medium
CN110514884B (en) Power signal filtering method and system based on delay vector
CN110705426B (en) Power signal filtering method and system by using deblurring operator

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

Granted publication date: 20200828

Termination date: 20210829