[go: up one dir, main page]

CN110542855B - Load switch event detection method and system based on discrete cosine transform - Google Patents

Load switch event detection method and system based on discrete cosine transform Download PDF

Info

Publication number
CN110542855B
CN110542855B CN201910845479.1A CN201910845479A CN110542855B CN 110542855 B CN110542855 B CN 110542855B CN 201910845479 A CN201910845479 A CN 201910845479A CN 110542855 B CN110542855 B CN 110542855B
Authority
CN
China
Prior art keywords
load switch
data
switch event
following
steps
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
CN201910845479.1A
Other languages
Chinese (zh)
Other versions
CN110542855A (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 CN201910845479.1A priority Critical patent/CN110542855B/en
Publication of CN110542855A publication Critical patent/CN110542855A/en
Application granted granted Critical
Publication of CN110542855B publication Critical patent/CN110542855B/en
Expired - Fee Related legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
    • 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
    • G01R31/327—Testing of circuit interrupters, switches or circuit-breakers

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Complex Calculations (AREA)

Abstract

本发明的实施例公开一种基于离散余弦变换的负荷开关事件检测方法和系统，所述方法包括：步骤1，输入实测的智能电表数据序列S；步骤2，根据所述智能电表数据序列S的余弦变换系数判断负荷开关事件；具体为：如果

Figure DDA0002195000230000011
则在第n点存在负荷开关事件；如果
Figure DDA0002195000230000012
则在第n点不存在负荷开关事件；其中cn为所述智能电表数据序列S在第n点的余弦变换系数；π为圆周率；N为所述智能电表数据序列的长度。

Figure 201910845479

The embodiment of the present invention discloses a method and system for detecting a load switch event based on discrete cosine transform. The method includes: step 1, inputting the measured data sequence S of the smart meter; step 2, according to the data sequence S of the smart meter The cosine transformation coefficient judges the load switching event; specifically: if

Figure DDA0002195000230000011
Then there is a load switch event at point n; if
Figure DDA0002195000230000012
Then there is no load switch event at the nth point; where cn is the cosine transformation coefficient of the smart meter data sequence S at the nth point; π is the pi; N is the length of the smart meter data sequence.

Figure 201910845479

Description

Load switch event detection method and system based on discrete cosine transform
Technical Field
The invention relates to the field of electric power, in particular to a method and a system for detecting vibration and sound of a load switch event.
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 event detection takes the change value of the active power P as the judgment basis of the 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. Besides the need to set a reasonable threshold for the power variation value, this method also needs to solve the problem of the event detection method in practical application: a large peak (for example, a motor starting current is much larger than a rated current) appears in an instantaneous power value at the starting time of some electric appliances, so that an electric appliance steady-state power change value is inaccurate, and the judgment of a switching event is influenced, and the peak is actually pulse noise; 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. The intensity of (impulse) noise is large and background noise has a large impact on the correct detection of switching events.
Load switching events that are now commonly used are often determined using changes in power data: when the power change value exceeds a preset threshold value, a load switch event is considered to occur. This approach, while simple and easy to implement, results in a significant drop in the accuracy of the switching event detection due to the impulse noise and the common use of non-linear loads.
Therefore, in the switching event detection process, how to improve the switching event detection accuracy is very important. Load switch event detection is the most important step in energy decomposition, and can detect the occurrence of an event and determine the occurrence time of the event. However, the accuracy of the detection of the switching event is greatly affected by noise in the power signal (power sequence), and particularly, impulse noise generally exists in the power signal, which further affects the detection accuracy. Therefore, it is currently a very important task to effectively improve the detection accuracy of the load switch event.
Load switching events that are now commonly used are often determined using changes in power data: when the power change value exceeds a preset threshold value, a load switch event is considered to occur. This approach, while simple and easy to implement, results in a significant drop in the accuracy of the switching event detection due to the impulse noise and the common use of non-linear loads.
Disclosure of Invention
The invention aims to provide a load switch event detection method and system based on discrete cosine transform, the proposed method utilizes the cosine signal nature of power reading change caused by the load switch event, and utilizes the difference between the signal and noise in the cosine transform domain to filter, and further detect the load switch event. The method has good switching event detection performance and is very simple in calculation.
In order to achieve the purpose, the invention provides the following scheme:
the load switch event detection method based on discrete cosine transform comprises the following steps:
step 1, inputting an actually measured data sequence S of the intelligent electric meter;
step 2, judging a load switch event according to a cosine transform coefficient of the intelligent electric meter data sequence S; the method specifically comprises the following steps: if it is not
Figure BDA0002195000210000021
Then there is a load switch event at point n; if it is not
Figure BDA0002195000210000022
Then there is no load switch event at point n; wherein c isnThe cosine transform coefficient of the intelligent electric meter data sequence S at the nth point is obtained; pi is the circumference ratio; and N is the length of the data sequence of the intelligent electric meter.
A load switch event detection system based on discrete cosine transform comprises:
the acquisition module is used for inputting an actually measured data sequence S of the intelligent electric meter;
the filtering module is used for judging a load switch event according to the cosine transform coefficient of the intelligent electric meter data sequence S; the method specifically comprises the following steps: if it is not
Figure BDA0002195000210000031
Then there is a load switch event at point n; if it is not
Figure BDA0002195000210000032
Then there is no load switch event at point n; wherein c isnThe cosine transform coefficient of the intelligent electric meter data sequence S at the nth point is obtained; pi is the circumference ratio; n is the data sequence of the intelligent ammeterLength of (d).
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
although the transformer vibration and sound detection method is widely applied to load switch event monitoring and has a relatively mature technology, the vibration and sound detection method utilizes a vibration signal emitted by a transformer and is easily influenced by environmental noise, so that the method often cannot obtain a satisfactory result when applied in an actual working environment. The invention aims to provide a load switch event detection method and system based on discrete cosine transform, the proposed method utilizes the cosine signal nature of power reading change caused by the load switch event, and utilizes the difference between the signal and noise in the cosine transform domain to filter, and further detect the load switch event. The method has good switching event detection performance and is very simple in 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 present invention;
FIG. 2 is a schematic structural view 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 method for detecting a load switch event based on discrete cosine transform
Fig. 1 is a schematic flow chart of a discrete cosine transform-based load switch event detection method according to the present invention. As shown in fig. 1, the method for detecting a load switch event based on discrete cosine transform specifically includes the following steps:
step 1, inputting an actually measured data sequence S of the intelligent electric meter;
step 2, judging a load switch event according to a cosine transform coefficient of the intelligent electric meter data sequence S; the method specifically comprises the following steps: if it is not
Figure BDA0002195000210000041
Then there is a load switch event at point n; if it is not
Figure BDA0002195000210000042
Then there is no load switch event at point n; wherein c isnThe cosine transform coefficient of the intelligent electric meter data sequence S at the nth point is obtained; pi is the circumference ratio; and N is the length of the data sequence of the intelligent electric meter.
Before the step 2, the method further comprises:
step 3, calculating the cosine transform coefficient cn
The step 3 comprises the following steps:
301, performing data transformation on the data sequence of the intelligent electric meter to obtain transformation data xnThe method specifically comprises the following steps:
xn＝sn-0.918sn-1
n＝2,3,…,N
wherein:
xn: transformed nth point data
sn: the nth point data in the vibro-acoustic signal sequence S
sn-1: the vibration and sound signal sequenceData of n-1 point in S
N: the length of the vibro-acoustic signal sequence S
Step 302, performing windowing on the transformed data to obtain windowed data ynThe method specifically comprises the following steps:
Figure BDA0002195000210000051
n＝2,3,…,N
step 303, performing fourier transform on the windowed data to obtain a fourier transform coefficient YkThe method specifically comprises the following steps:
Figure BDA0002195000210000052
k＝2,3,…,N
step 304, filtering the Fourier transform coefficient to obtain a spectrum index MqThe method specifically comprises the following steps:
Figure BDA0002195000210000053
1≤q≤N
Figure BDA0002195000210000054
step 305, obtaining the cosine transform coefficient cnThe method comprises the following steps:
Figure BDA0002195000210000055
FIG. 2 structural intent of a discrete cosine transform based load switch event detection system
Fig. 2 is a schematic structural diagram of a load switch event detection system based on discrete cosine transform according to the present invention. As shown in fig. 2, the system for detecting a load switch event based on discrete cosine transform includes the following structures:
the acquisition module 401 inputs an actually measured data sequence S of the intelligent electric meter;
the filtering module 402 is used for judging a load switch event according to the cosine transform coefficient of the intelligent electric meter data sequence S; the method specifically comprises the following steps: if it is not
Figure BDA0002195000210000061
Then there is a load switch event at point n; if it is not
Figure BDA0002195000210000062
Then there is no load switch event at point n; wherein c isnThe cosine transform coefficient of the intelligent electric meter data sequence S at the nth point is obtained; pi is the circumference ratio; and N is the length of the data sequence of the intelligent electric meter.
The system further comprises:
a calculation module for calculating the cosine transform coefficient cn。
The following provides an embodiment for further illustrating the invention
FIG. 3 is a flow chart illustrating an embodiment of the present invention. As shown in fig. 3, the method specifically includes the following steps:
1. inputting measured vibration and sound detection signal sequence
S＝[S1,S2,…,SN]
Wherein
S: data sequence
Sn: the nth data, N-1, 2, …, N in the data sequence
2. Data transformation
Performing data transformation on the data sequence of the intelligent electric meter to obtain transformation data xnThe method specifically comprises the following steps:
xn＝sn-0.918sn-1
n＝2,3,…,N
wherein:
xn: transformed nth point data
sn: the vibration sound signal sequence SThe nth point data in (1)
sn-1: the n-1 point data in the vibro-acoustic signal sequence S
N: the length of the vibro-acoustic signal sequence S
3. Windowing
Windowing the transformed data to obtain windowed data ynThe method specifically comprises the following steps:
Figure BDA0002195000210000071
n＝2,3,…,N
4. fourier transform
Fourier transform is carried out on the windowed data to obtain a Fourier transform coefficient YkThe method specifically comprises the following steps:
Figure BDA0002195000210000072
5. filtering
Filtering the Fourier transform coefficient to obtain a spectrum index MqThe method specifically comprises the following steps:
Figure BDA0002195000210000073
1≤q≤N
Figure BDA0002195000210000074
6. calculating cosine transform coefficients
Determining cosine transform coefficient cnThe method comprises the following steps:
Figure BDA0002195000210000081
7. determining load switch events
According to the number of the intelligent electric metersJudging a load switch event according to a cosine transform coefficient of the sequence S; the method specifically comprises the following steps: if it is not
Figure BDA0002195000210000082
Then there is a load switch event at point n; if it is not
Figure BDA0002195000210000083
Then there is no load switch event at point n; wherein c isnThe cosine transform coefficient of the intelligent electric meter data sequence S at the nth point is obtained; pi is the circumference ratio; and N is the length of the data sequence of the intelligent electric meter.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is simple because the system corresponds to the method disclosed by the embodiment, and the relevant part can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (1)

1. The load switch event detection method based on discrete cosine transform is characterized by comprising the following steps:
step 1, inputting an actually measured data sequence S of the intelligent electric meter;
step 2, performing data transformation on the data sequence of the intelligent electric meter to obtain transformation data xnThe method specifically comprises the following steps:
xn＝sn-0.918sn-1；
n＝2，3，…，N；
wherein:
xn: n th after conversionPoint data;
sn: the nth point data in the sequence S;
sn-1: the (n-1) th point data in the sequence S;
n: the length of the sequence S;
step 3, windowing the transformed data to obtain windowed data ynThe method specifically comprises the following steps:
Figure FDA0003102269000000011
n＝2,3,…,N；
step 4, carrying out Fourier transform on the windowed data to obtain a Fourier transform coefficient YkThe method specifically comprises the following steps:
Figure FDA0003102269000000012
k＝2,3,…,N；
step 5, filtering the Fourier transform coefficient to obtain a spectrum index MqThe method specifically comprises the following steps:
Figure FDA0003102269000000013
1≤q≤N；
Figure FDA0003102269000000014
step 6, calculating cosine transform coefficient cnThe method comprises the following steps:
Figure FDA0003102269000000021
step 7, judging according to the cosine transform coefficient of the data sequence S of the intelligent electric meterA load switch event; the method specifically comprises the following steps: if it is not
Figure FDA0003102269000000022
Then there is a load switch event at point n; if it is not
Figure FDA0003102269000000023
Then there is no load switch event at point n; where pi is the circumferential ratio.
CN201910845479.1A 2019-09-08 2019-09-08 Load switch event detection method and system based on discrete cosine transform Expired - Fee Related CN110542855B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910845479.1A CN110542855B (en) 2019-09-08 2019-09-08 Load switch event detection method and system based on discrete cosine transform

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910845479.1A CN110542855B (en) 2019-09-08 2019-09-08 Load switch event detection method and system based on discrete cosine transform

Publications (2)

Publication Number Publication Date
CN110542855A CN110542855A (en) 2019-12-06
CN110542855B true CN110542855B (en) 2021-09-21

Family

ID=68712886

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910845479.1A Expired - Fee Related CN110542855B (en) 2019-09-08 2019-09-08 Load switch event detection method and system based on discrete cosine transform

Country Status (1)

Country Link
CN (1) CN110542855B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112257576B (en) * 2020-10-21 2021-12-10 华北电力大学 A load switch event detection method and system using Maha distance measurement

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102290047A (en) * 2011-09-22 2011-12-21 哈尔滨工业大学 Robust speech characteristic extraction method based on sparse decomposition and reconfiguration
CN103001230A (en) * 2012-11-16 2013-03-27 天津大学 Non-invasive power load monitoring and decomposing current mode matching method
CN103347189A (en) * 2013-08-03 2013-10-09 程涛 Bidimensional compressed sensing image acquisition and reconstruction method based on discrete cosine transformation (DCT) and discrete Fourier transformation (DFT)
US9612292B1 (en) * 2014-05-06 2017-04-04 The Florida State University Research Foundation, Inc. Method for small-signal stability assessment of power systems using source side and load side perturbations and unit circle criterion
CN109307798A (en) * 2018-08-29 2019-02-05 广东石油化工学院 A power signal filtering method for switching event detection

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102290047A (en) * 2011-09-22 2011-12-21 哈尔滨工业大学 Robust speech characteristic extraction method based on sparse decomposition and reconfiguration
CN103001230A (en) * 2012-11-16 2013-03-27 天津大学 Non-invasive power load monitoring and decomposing current mode matching method
CN103347189A (en) * 2013-08-03 2013-10-09 程涛 Bidimensional compressed sensing image acquisition and reconstruction method based on discrete cosine transformation (DCT) and discrete Fourier transformation (DFT)
US9612292B1 (en) * 2014-05-06 2017-04-04 The Florida State University Research Foundation, Inc. Method for small-signal stability assessment of power systems using source side and load side perturbations and unit circle criterion
CN109307798A (en) * 2018-08-29 2019-02-05 广东石油化工学院 A power signal filtering method for switching event detection

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
"基于离散余弦变换和小波变换的电能质量扰动信号检测方法";刘安定等;《电网技术》;20050531;第29卷(第10期);第70-74页 *

Also Published As

Publication number Publication date
CN110542855A (en) 2019-12-06

Similar Documents

Publication Publication Date Title
CN108021736B (en) A Load Switching Action Monitoring Method Based on Sliding Window Residual Model
CN109145825B (en) A kind of coherent noise filtering method and system
CN110542855B (en) Load switch event detection method and system based on discrete cosine transform
CN111680590A (en) A method and system for filtering power signals using shrinkage gradients
CN110221119B (en) Load switching event detection method and system based on power and akie fusion information
CN110244115B (en) A method and system for detecting load switch events based on signal connectivity
CN110196354B (en) Method and device for detecting switching event of load
CN112329637B (en) A load switch event detection method and system using pattern features
CN111830405A (en) A load switch event detection method and system using frequency difference
CN112257576B (en) A load switch event detection method and system using Maha distance measurement
CN112307986B (en) Load switch event detection method and system by utilizing Gaussian gradient
CN110749841A (en) Load switch event detection method and system by utilizing conversion space factor
CN110702980A (en) Load switch event detection method and system by using MUSIC classification
CN110514884B (en) Power signal filtering method and system based on delay vector
CN111737645A (en) A method and system for power signal reconstruction using prediction matrix
CN110702981A (en) A load switch event detection method and system using classification tree
CN112270282B (en) A power signal filtering method and system using matrix spectral mode
CN110531149B (en) Power signal filtering method and system based on waveform regularization
CN112307997B (en) Power signal reconstruction method and system by using main mode decomposition
CN112347922B (en) Power signal filtering method and system by using Hankerl matrix
CN112180153A (en) Load switch event detection method and system by using KULLBACK-Leibler distance
CN110276275A (en) A method and device for filtering power signals based on hyperplanes in energy decomposition
CN110705426B (en) Power signal filtering method and system by using deblurring operator
CN112180155A (en) A method and system for detecting load switch events using compact supports
CN109902961A (en) Multiscale filtering method and system for power signal filtering in energy decomposition

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: 20210921

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