CN112488373A - Capacitive device online monitoring data processing method and terminal device - Google Patents

Capacitive device online monitoring data processing method and terminal device Download PDF

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CN112488373A
CN112488373A CN202011330655.7A CN202011330655A CN112488373A CN 112488373 A CN112488373 A CN 112488373A CN 202011330655 A CN202011330655 A CN 202011330655A CN 112488373 A CN112488373 A CN 112488373A
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monitoring data
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王宁
张菁
王颖
张倩茅
齐晓光
吴鹏
习朋
张红梅
张丽洁
徐田丰
胡源
朱天曈
陈宇
田家辉
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State Grid Corp of China SGCC
Economic and Technological Research Institute of State Grid Hebei Electric Power Co Ltd
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Economic and Technological Research Institute of State Grid Hebei Electric Power Co Ltd
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Abstract

The invention is suitable for the technical field of electric power, and discloses a capacitive device online monitoring data processing method and terminal equipment, wherein the method comprises the following steps: acquiring a training sample set, and constructing a regression function according to the training sample set; obtaining a test input sample and a test output sample, and inputting the test input sample into a regression function to obtain a predicted value of the dielectric loss factor tan delta; wherein the test output sample comprises a test tan δ; and obtaining the variable quantity of the tan delta without the influence of the environmental factors according to the predicted value of the dielectric loss factor tan delta and the test tan delta. The method can obtain the actual variable quantity of tan delta under the complex environment condition, can eliminate the influence of environmental factors, effectively remove the deviation of online monitoring data caused by the environmental influence, improve the accuracy of the online monitoring data, and ensure that the processed online monitoring data has better stability and practical value.

Description

Capacitive device online monitoring data processing method and terminal device
Technical Field
The invention belongs to the technical field of electric power, and particularly relates to a capacitive device online monitoring data processing method and terminal equipment.
Background
Capacitive equipment is commonly used in open-type substations, and the actual online monitoring environment is complex. In the prior art, the on-line monitoring of the dielectric loss factor tan δ is easily affected by voltage fluctuation, temperature and humidity changes, dirt on the outer surface of the insulation of equipment and other factors, so that fluctuation deviating from an actual value occurs, the on-line monitoring result of the dielectric loss factor tan δ is inaccurate, and the on-line monitoring device sends out wrong alarm information, and phenomena such as misjudgment and missing judgment occur.
Disclosure of Invention
In view of this, the embodiment of the present invention provides a method for processing online monitoring data of a capacitive device and a terminal device, so as to solve the problem that an online monitoring result of a dielectric loss factor tan δ in the prior art is inaccurate.
The first aspect of the embodiments of the present invention provides a method for processing online monitoring data of a capacitive device, including:
acquiring a training sample set, and constructing a regression function according to the training sample set;
obtaining a test input sample and a test output sample, and inputting the test input sample into a regression function to obtain a predicted value of the dielectric loss factor tan delta; wherein the test output sample comprises a test tan δ;
and obtaining the variable quantity of the tan delta without the influence of the environmental factors according to the predicted value of the dielectric loss factor tan delta and the test tan delta.
A second aspect of the embodiments of the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method for processing the on-line monitoring data of the capacitive device according to the first aspect when executing the computer program.
A third aspect of embodiments of the present invention provides a computer-readable storage medium, which stores a computer program, and the computer program, when executed by one or more processors, implements the steps of the method for processing on-line monitoring data of a capacitive device according to the first aspect.
Compared with the prior art, the embodiment of the invention has the following beneficial effects: according to the embodiment of the invention, the regression function is constructed according to the training sample set, the predicted value of tan delta is obtained according to the regression function, the variable quantity of tan delta without the influence of environmental factors is obtained according to the predicted value of tan delta and the tan delta test, the actual variable quantity of tan delta under the complex environmental condition can be obtained, the influence of the environmental factors can be eliminated, the deviation of online monitoring data caused by the environmental influence is effectively eliminated, the accuracy of the online monitoring data is improved, the processed online monitoring data has better stability and practical value, and the working personnel can conveniently identify the equipment operation condition according to the online monitoring data.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic flow chart illustrating an implementation of a capacitive device online monitoring data processing method according to an embodiment of the present invention;
FIG. 2 is a schematic block diagram of a capacitive device online monitoring data processing system according to an embodiment of the present invention;
fig. 3 is a schematic block diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
Fig. 1 is a schematic flow chart of an implementation of a method for processing online monitoring data of a capacitive device according to an embodiment of the present invention, and for convenience of description, only a portion related to the embodiment of the present invention is shown. The execution main body of the embodiment of the invention can be terminal equipment. As shown in fig. 1, the method may include the steps of:
s101: and acquiring a training sample set, and constructing a regression function according to the training sample set.
In an embodiment of the present invention, the "constructing a regression function according to a training sample set" in S101 above may include:
and constructing a regression function according to the training sample set based on the Lagrangian function.
In one embodiment of the invention, the training sample set comprises a training input sample subset and a training output sample subset;
the above constructing a regression function according to the training sample set based on the lagrangian function includes:
for pre-constructed Lagrange function
Figure BDA0002795679020000031
Figure BDA0002795679020000032
Respectively calculating partial derivatives of the variable omega and the variable b, respectively setting the partial derivatives of the variable omega and the partial derivatives of the variable b to be 0, and obtaining an equation set
Figure BDA0002795679020000033
Wherein N is the number of training samples in the training sample set; omega is a weight matrix; alpha is alphaiA Lagrange multiplier corresponding to the ith training sample; b is a constant deviation; y isiFor the i-th training output sample, y, of the subset of training output samplesi∈R；xiIs the ith training input sample in the subset of training input samples;
substituting the equation set into the Lagrangian function to obtain the processed Lagrangian function
Figure BDA0002795679020000034
Figure BDA0002795679020000035
Constructing an objective function according to the processed Lagrange function, and solving the objective function to obtain an expression of a variable omega and an expression of a variable b;
and substituting the expression of the variable omega and the expression of the variable b into the pre-constructed Lagrangian function to obtain a regression function.
In an embodiment of the present invention, the objective function is:
Figure BDA0002795679020000041
Figure BDA0002795679020000042
αi≥0。
in one embodiment of the present invention, the variable ω is expressed as
Figure BDA0002795679020000043
The expression of the variable b is
Figure BDA0002795679020000044
In an embodiment of the present invention, the online monitoring data may include ambient temperature, ambient humidity, bus voltage, and dielectric loss tangent tan δ. The training sample set is historical online monitoring data, can be online monitoring data of a capacitive device continuously acquired in a past period, and can comprise a training input sample subset and a training output sample subset. The subset of training input samples may include a plurality of training input samples, which may include a training ambient temperature, a training ambient humidity, and a training bus voltage. The training output sample subset may include a plurality of training output samples, which may include training tan δ. The training input samples and the training output samples are in a one-to-one correspondence. That is, in the online monitoring data, the ambient temperature, the ambient humidity, and the bus voltage are input data, and tan δ is output data. The training input samples and the corresponding training output samples form training samples, and the training sample set comprises a plurality of training samples. The number of training input samples is the same as the number of training output samples, and is N.
Specifically, obtaining the training sample set may include: acquiring online monitoring data of the capacitive equipment continuously acquired within a period of time in the past as an initial sample set, and removing initial samples including blank values, "-" values and Chinese characters in the initial sample set to obtain a training sample set.
Wherein the partial derivative of the variable ω and the partial derivative of the variable b are respectively made 0, i.e.
Figure BDA0002795679020000045
Obtain a system of equations
Figure BDA0002795679020000046
In an embodiment of the present invention, before constructing the regression function according to the training sample set, the method for processing online monitoring data of the capacitive device further includes:
carrying out normalization processing on the training sample set to obtain a normalized training sample set;
correspondingly, a regression function is constructed according to the training sample set, and the regression function comprises the following steps:
and constructing a regression function according to the normalized training sample set.
Specifically, the data in the training sample set are all changed into decimal between (0,1) through the normalization operation, so that the convergence is accelerated when the subsequent steps are executed.
S102: obtaining a test input sample and a test output sample, and inputting the test input sample into a regression function to obtain a predicted value of the dielectric loss factor tan delta; wherein the test output sample comprises a test tan δ.
In an embodiment of the present invention, the online monitoring data corresponding to the test input sample and the test output sample and the online monitoring data in the training sample set are online monitoring data of the same time period in different years.
In an embodiment of the present invention, one test sample may include a test input sample and a test output sample, the test sample may be one or more, the test sample may be data that is monitored online for capacitive devices that have been collected continuously in the near future, and data including blank values, "-" values, and chinese characters have been removed.
The test sample and the training sample are online monitoring data of the same capacitive equipment in the same time period in different years, so that the environmental influence conditions are consistent, and the regression functions have the same identity. Illustratively, the test sample is online monitoring data of capacitive devices in month 7 of this year, the training sample is online monitoring data of capacitive devices in month 7 of the last year, and so on.
In one embodiment of the invention, the test input samples include test ambient temperature, test ambient humidity, and test bus voltage.
In an embodiment of the present invention, before inputting the test input sample into the regression function to obtain the predicted value of the dielectric loss factor tan δ, the method for processing the online monitoring data of the capacitive device further includes:
carrying out normalization processing on the test input sample to obtain a normalized test input sample;
carrying out normalization processing on the test output sample to obtain a normalized test output sample;
correspondingly, inputting the test input sample into the regression function to obtain the predicted value of the dielectric loss factor tan δ, including:
and inputting the normalized test input sample into a regression function to obtain a predicted value of the dielectric loss factor tan delta.
In the embodiment of the present invention, the regression function is input to each test sample, so as to obtain the predicted value of tan δ corresponding to each test sample.
S103: and obtaining the variable quantity of the tan delta without the influence of the environmental factors according to the predicted value of the dielectric loss factor tan delta and the test tan delta.
Specifically, the predicted value of the dielectric loss factor tan δ and the test tan δ are subtracted to obtain the variation of tan δ without the influence of environmental factors.
Optionally, comparing the variation of tan δ with a preset variation threshold, and determining whether the variation of tan δ exceeds the preset variation threshold;
and if the variation of the tan delta exceeds a preset variation threshold value, alarming.
The preset change threshold value can be determined according to the requirements of regulations and the like.
As can be seen from the above description, in the embodiment of the present invention, a regression function is constructed according to a training sample set, a predicted value of tan δ is obtained according to the regression function, and a variable quantity of tan δ without an influence of an environmental factor is obtained according to the predicted value of tan δ and a test tan δ, so that an actual variable quantity of tan δ under a complex environmental condition can be obtained, the influence of the environmental factor can be eliminated, a deviation of online monitoring data due to the environmental influence is effectively eliminated, accuracy of online monitoring data is improved, the processed online monitoring data has better stability and practical value, and a worker can conveniently identify an operation state of equipment according to the online monitoring data.
In addition, determining whether the variation amount of tan δ exceeds a preset variation threshold by comparing the variation amount of tan δ with the preset variation threshold; if the variable quantity of the tan delta exceeds a preset variable threshold value, an alarm is given, and the phenomena of false alarm and missed alarm can be avoided.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Fig. 2 is a schematic block diagram of a capacitive device online monitoring data processing system according to an embodiment of the present invention, and for convenience of description, only the portions related to the embodiment of the present invention are shown.
In an embodiment of the present invention, the capacitive device online monitoring data processing system 30 may include a regression function construction module 301, a predicted value determination module 302, and a variation determination module 303.
The regression function constructing module 301 is configured to obtain a training sample set, and construct a regression function according to the training sample set;
a predicted value determining module 302, configured to obtain a test input sample and a test output sample, and input the test input sample into a regression function to obtain a predicted value of the dielectric loss factor tan δ; wherein the test output sample comprises a test tan δ;
and a variation determining module 303, configured to obtain a variation of tan δ without an influence of an environmental factor according to the predicted value of the dielectric loss factor tan δ and a test tan δ.
Optionally, the regression function building module 301 is further configured to:
and constructing a regression function according to the training sample set based on the Lagrangian function.
Optionally, the training sample set comprises a training input sample subset and a training output sample subset;
the regression function building module 301 is further configured to:
for pre-constructed Lagrange function
Figure BDA0002795679020000071
Figure BDA0002795679020000072
Respectively calculating partial derivatives of the variable omega and the variable b, respectively setting the partial derivatives of the variable omega and the partial derivatives of the variable b to be 0, and obtaining an equation set
Figure BDA0002795679020000073
Wherein N is the number of training samples in the training sample set; omega is a weight matrix; alpha is alphaiA Lagrange multiplier corresponding to the ith training sample; b is a constant deviation; y isiFor the i-th training output sample, y, of the subset of training output samplesi∈R；xiIs the ith training input sample in the subset of training input samples;
substituting the equation set into the Lagrangian function to obtain the processed Lagrangian function
Figure BDA0002795679020000074
Figure BDA0002795679020000075
Constructing an objective function according to the processed Lagrange function, and solving the objective function to obtain an expression of a variable omega and an expression of a variable b;
and substituting the expression of the variable omega and the expression of the variable b into the pre-constructed Lagrangian function to obtain a regression function.
Optionally, the objective function is:
Figure BDA0002795679020000081
Figure BDA0002795679020000082
αi≥0。
optionally, the variable ω is expressed as
Figure BDA0002795679020000083
The expression of the variable b is
Figure BDA0002795679020000084
Optionally, the online monitoring data corresponding to the test input sample and the test output sample and the online monitoring data in the training sample set are online monitoring data of the same time period in different years.
Optionally, the capacitive device online monitoring data processing system 30 may further include a first normalization module and a second normalization module;
the first normalization module is used for performing normalization processing on the training sample set to obtain a normalized training sample set;
accordingly, the regression function building module 301 is further configured to:
constructing a regression function according to the normalized training sample set;
the second normalization module is used for performing normalization processing on the test input sample to obtain a normalized test input sample;
carrying out normalization processing on the test output sample to obtain a normalized test output sample;
accordingly, the predictor determination module 302 is further configured to:
and inputting the normalized test input sample into a regression function to obtain a predicted value of the dielectric loss factor tan delta.
Optionally, the test input samples include a test ambient temperature, a test ambient humidity, and a test bus voltage.
It is obvious to those skilled in the art that, for convenience and simplicity of description, the above-mentioned division of the functional units and modules is merely used as an example, in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the capacitive device online monitoring data processing system is divided into different functional units or modules to perform all or part of the above-described functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the above-mentioned apparatus may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Fig. 3 is a schematic block diagram of a terminal device according to an embodiment of the present invention. As shown in fig. 3, the terminal device 40 of this embodiment includes: one or more processors 401, a memory 402, and a computer program 403 stored in the memory 402 and executable on the processors 401. The processor 401, when executing the computer program 403, implements the steps in each of the above embodiments of the capacitive device online monitoring data processing method, such as steps S101 to S104 shown in fig. 1. Alternatively, the processor 401, when executing the computer program 403, implements the functions of the modules/units in the above capacitive device online monitoring data processing system embodiment, such as the functions of the modules 301 to 304 shown in fig. 2.
Illustratively, the computer program 403 may be partitioned into one or more modules/units that are stored in the memory 402 and executed by the processor 401 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used for describing the execution process of the computer program 403 in the terminal device 40. For example, the computer program 403 may be divided into a regression function building module, a predicted value determining module and a variation determining module, and each module has the following specific functions:
the regression function building module is used for obtaining a training sample set and building a regression function according to the training sample set;
the predicted value determining module is used for obtaining a test input sample and a test output sample, and inputting the test input sample into a regression function to obtain a predicted value of the dielectric loss factor tan delta; wherein the test output sample comprises a test tan δ;
and the variable quantity determining module is used for obtaining the variable quantity of the tan delta without the influence of the environmental factors according to the predicted value of the dielectric loss factor tan delta and the test tan delta.
Other modules or units can refer to the description of the embodiment shown in fig. 2, and are not described again here.
The terminal device 40 may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal device 40 includes, but is not limited to, a processor 401 and a memory 402. Those skilled in the art will appreciate that fig. 3 is only one example of a terminal device 40, and does not constitute a limitation to the terminal device 40, and may include more or less components than those shown, or combine some components, or different components, for example, the terminal device 40 may further include an input device, an output device, a network access device, a bus, etc.
The Processor 401 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 402 may be an internal storage unit of the terminal device 40, such as a hard disk or a memory of the terminal device 40. The memory 402 may also be an external storage device of the terminal device 40, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal device 40. Further, the memory 402 may also include both an internal storage unit of the terminal device 40 and an external storage device. The memory 402 is used for storing the computer program 403 and other programs and data required by the terminal device 40. The memory 402 may also be used to temporarily store data that has been output or is to be output.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed capacitive device online monitoring data processing system and method may be implemented in other ways. For example, the above-described capacitive device on-line monitoring data processing system embodiments are merely illustrative, and for example, the division of the modules or units is only one logical function division, and there may be other division ways in actual implementation, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media which may not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.

Claims (10)

1. A capacitive device online monitoring data processing method is characterized by comprising the following steps:
acquiring a training sample set, and constructing a regression function according to the training sample set;
obtaining a test input sample and a test output sample, and inputting the test input sample into the regression function to obtain a predicted value of the dielectric loss factor tan delta; wherein the test output sample comprises a test tan δ;
and obtaining the variable quantity of the tan delta without the influence of environmental factors according to the predicted value of the dielectric loss factor tan delta and the test tan delta.
2. The method for processing online monitoring data of capacitive devices according to claim 1, wherein the constructing a regression function from the training sample set includes:
and constructing a regression function according to the training sample set based on a Lagrangian function.
3. The capacitive device online monitoring data processing method of claim 2, wherein the training sample set comprises a training input sample subset and a training output sample subset;
the constructing a regression function according to the training sample set based on the Lagrangian function comprises the following steps:
for pre-constructed Lagrange function
Figure FDA0002795679010000011
Figure FDA0002795679010000012
Respectively calculating partial derivatives of the variable omega and the variable b, respectively setting the partial derivatives of the variable omega and the partial derivatives of the variable b to be 0, and obtaining an equation set
Figure FDA0002795679010000013
Wherein N is the number of training samples in the training sample set; omega is a weight matrix; alpha is alphaiA Lagrange multiplier corresponding to the ith training sample; b is a constant deviation; y isiFor the i-th training output sample, y, of the subset of training output samplesi∈R；xiFor the ith training input sample in the subset of training input samples;
substituting the equation set into the Lagrangian function to obtain a processed Lagrangian function
Figure FDA0002795679010000014
Constructing an objective function according to the processed Lagrangian function, and solving the objective function to obtain an expression of the variable omega and an expression of the variable b;
and substituting the expression of the variable omega and the expression of the variable b into the pre-constructed Lagrangian function to obtain a regression function.
4. The capacitive device on-line monitoring data processing method as claimed in claim 3, wherein the objective function is:
Figure FDA0002795679010000021
Figure FDA0002795679010000022
αi≥0。
5. the capacitive device on-line monitoring data processing method as claimed in claim 3, wherein the variable ω is expressed by
Figure FDA0002795679010000023
The expression of the variable b is
Figure FDA0002795679010000024
6. The method for processing the online monitoring data of the capacitive device according to any one of claims 1 to 5, wherein the online monitoring data corresponding to the test input sample and the test output sample and the online monitoring data in the training sample set are online monitoring data of the same time period in different years.
7. The method for processing online monitoring data of a capacitive device according to any one of claims 1 to 5, wherein before the constructing a regression function from the training sample set, the method further comprises:
carrying out normalization processing on the training sample set to obtain a normalized training sample set;
correspondingly, the constructing a regression function according to the training sample set includes:
constructing a regression function according to the normalized training sample set;
before the inputting the test input sample into the regression function to obtain the predicted value of the dielectric loss factor tan δ, the method for processing the online monitoring data of the capacitive device further comprises the following steps:
carrying out normalization processing on the test input sample to obtain a normalized test input sample;
carrying out normalization processing on the test output sample to obtain a normalized test output sample;
correspondingly, the inputting the test input sample into the regression function to obtain a predicted value of the dielectric loss factor tan δ includes:
and inputting the normalized test input sample into the regression function to obtain a predicted value of the dielectric loss factor tan delta.
8. The capacitive device on-line monitoring data processing method as claimed in any one of claims 1 to 5, wherein the test input samples include a test environment temperature, a test environment humidity and a test bus voltage.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for processing on-line monitoring data of a capacitive device according to any one of claims 1 to 8 when executing the computer program.
10. A computer-readable storage medium, storing a computer program which, when executed by one or more processors, implements the steps of the capacitive device online monitoring data processing method according to any one of claims 1 to 8.
CN202011330655.7A 2020-11-24 2020-11-24 Capacitive device online monitoring data processing method and terminal device Pending CN112488373A (en)

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1793990A (en) * 2005-12-29 2006-06-28 西安电子科技大学 On-line monitoring system for capacitor type equipment dielectricless
CN103440761A (en) * 2013-09-10 2013-12-11 国家电网公司 Online monitoring method and system for dielectric loss of capacitive type device
CN105868770A (en) * 2016-03-23 2016-08-17 国网山东省电力公司电力科学研究院 High-voltage circuit breaker fault diagnosis method based on unsupervised learning model
US20180157965A1 (en) * 2016-12-01 2018-06-07 Fujitsu Limited Device and method for determining convolutional neural network model for database
CN109596894A (en) * 2019-01-02 2019-04-09 厦门科华恒盛股份有限公司 Ac capacitor on-line monitoring method and device

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1793990A (en) * 2005-12-29 2006-06-28 西安电子科技大学 On-line monitoring system for capacitor type equipment dielectricless
CN103440761A (en) * 2013-09-10 2013-12-11 国家电网公司 Online monitoring method and system for dielectric loss of capacitive type device
CN105868770A (en) * 2016-03-23 2016-08-17 国网山东省电力公司电力科学研究院 High-voltage circuit breaker fault diagnosis method based on unsupervised learning model
US20180157965A1 (en) * 2016-12-01 2018-06-07 Fujitsu Limited Device and method for determining convolutional neural network model for database
CN109596894A (en) * 2019-01-02 2019-04-09 厦门科华恒盛股份有限公司 Ac capacitor on-line monitoring method and device

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
OLDBIBI: "SVM-支持向量机理解（拉格朗日乘子法）", 《HTTPS://BLOG.CSDN.NET/WEIXIN_43909872/ARTICLE/DETAILS/85172749》 *
WANG NING等: "Study of Influence of Environmental Factor on Capacitive Equipment On-line Monitoring Based on the Grey Relational Analysis", 《IOP CONFERENCE SERIES:MATERIALS SCIENCE AND ENGINEERING 》 *
王永强等: "采用支持向量机和遗传算法的电容型设备介质损耗因数修正方法", 《中国电机工程学报》 *

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