CN117250449A - Transformer insulation state evaluation method and device, electronic equipment and storage medium - Google Patents
Transformer insulation state evaluation method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention provides a transformer insulation state evaluation method, a device, electronic equipment and a storage medium, wherein the transformer insulation state evaluation method comprises the following steps: according to the wind-light output data and the load data, determining a load coefficient of a target transformer; determining the insulation state characteristic quantity of the target transformer by adopting core principal component analysis; determining an insulation state index of the target transformer according to the abnormal state statistical data, and determining a threshold value of the insulation state index of the target transformer according to the historical data; and acquiring real-time monitoring data of the target transformer, and further determining the insulation state of the target transformer. The beneficial effects of the invention are as follows: the transformer insulation state evaluation of wind power, photovoltaic, load and operation environment influence is considered, and the accuracy of the transformer insulation state evaluation is improved; and the multi-dimensional operation data of the transformer, the redundancy analysis and the correlation analysis of the monitoring data are completed by adopting the analysis of the nuclear principal components, so that risk prediction is provided for the faults of the transformer.
Description
Technical Field
The present invention relates to the field of power equipment monitoring and computer technologies, and in particular, to a method and apparatus for evaluating an insulation state of a transformer, an electronic device, and a storage medium.
Background
With the increasing permeability of new energy sources in an electric power system, intermittent power generation of new energy sources such as wind power, photovoltaic and the like and load variation can cause tiny change of the running temperature of a transformer, tiny change of the running temperature of the transformer, meteorological (such as temperature, humidity and the like) parameter change of the running environment of the transformer and the like can cause change of dissolved gas in insulating oil, so that an inaccurate evaluation result of the insulating state of the transformer based on the dissolved gas in the oil is caused.
Analysis of dissolved gas in transformer insulating oil is one of the most effective methods for judging early latent faults of transformers. However, the existing transformer running state analysis methods based on dissolved gas in oil all adopt deterministic methods, and the influence of wind-light output, load uncertainty and environmental temperature change on the running of the transformer is not considered, so that the running state evaluation of the transformer is inaccurate. Only experience and single parameter are adopted, so that the transformer insulation state evaluation accuracy is low, and the problems of over repair or no repair of the transformer and the like occur.
Disclosure of Invention
The embodiment of the invention mainly aims to provide a transformer insulation state evaluation method, a device, electronic equipment and a storage medium, which improve the accuracy of transformer insulation state evaluation and provide risk prediction for transformer faults.
One aspect of the present invention provides a transformer insulation state evaluation method, including:
acquiring wind-light output data and load data accessed by a target transformer according to a transformer insulation state evaluation request, and determining a load factor of the target transformer according to the wind-light output data and the load data;
collecting environment parameters and insulation monitoring data of the target transformer, and determining insulation state characteristic quantity of the target transformer by adopting core principal component analysis according to the load coefficient, the environment parameters and the insulation monitoring data;
acquiring historical data and abnormal state statistical data, determining an insulation state index of the target transformer according to the abnormal state statistical data, and determining a threshold value of the insulation state index of the target transformer according to the historical data;
and acquiring real-time monitoring data of the target transformer, and determining the insulation state of the target transformer according to the real-time monitoring data, the insulation state index and the threshold value of the insulation state index.
According to the transformer insulation state evaluation method, wind-light output data and load data accessed by a target transformer are obtained according to a transformer insulation state evaluation request, and a load coefficient of the target transformer is determined according to the wind-light output data and the load data, comprising the following steps:
performing per unit processing on the wind-light output data and the load data by taking the rated capacity of the target transformer as a reference value to obtain a wind-power output probability density function, a photovoltaic output probability density function and a load output probability density function, wherein the wind-light output data comprises wind-power output data and photovoltaic output data;
constructing a wind-solar-load multi-dimensional joint probability density distribution function f (p) by adopting a normal Copula function according to the wind power output probability density function, the photovoltaic output probability density function and the load output probability density function w ,p p ,p l ) Is that
Wherein I is an identity matrix, ζ' = [ Φ ] -1 (a),Φ -1 (b),Φ -1 (c)]A, b and c sequentially represent a wind power cumulative distribution function, a photovoltaic cumulative distribution function and a load cumulative distribution function, phi -1 The method comprises the steps that an inverse function of a cumulative distribution function of standard normal distribution is represented, R is a product moment correlation coefficient matrix of a normal Copula function, and the product moment correlation coefficient matrix of the normal Copula function is calculated by adopting at least one mode of maximum information coefficient, kendall rank correlation coefficient and Spearman phase relation;
according to the wind-light-load multidimensional joint probability density distribution function, determining the transformer load factor f (k) as
Wherein p is w 、p p 、p l And respectively representing the per unit values of wind power output, photovoltaic output and load.
The transformer insulation state evaluation method according to the present invention, wherein the method further comprises:
the environmental parameters are collected through a first sensor, and the environmental data collected by the first sensor comprise at least one of environmental temperature, rainfall, humidity and wind speed;
the insulation monitoring data are collected through a second sensor, and the insulation monitoring data collected by the second sensor comprise at least one of oil temperature, micro water, carbon monoxide, carbon dioxide, methane, ethane, ethylene, acetylene and hydrogen, wherein the collected insulation monitoring data can be adjusted in a self-defined mode.
The transformer insulation state evaluation method according to the present invention, wherein the collecting environmental parameters and insulation monitoring data of the target transformer, and determining insulation state feature values of the target transformer by using a core principal component analysis according to the load factor, the environmental parameters and the insulation monitoring data, includes:
according to the transformer load factor f (k), the environmental parameter and the insulation monitoring data of the target transformer, obtaining a sample data matrix S as
S＝{X 1 ,X 2 ...X i ,...X n }
Wherein i is the number identifier of the transformer, X i Represents the i-th transformer characteristic quantity, and X i Represents an m-dimensional vector, n is the total number of samples, and;
passing a sample data matrix through a nonlinear mapping functionMapping to a high-dimensional feature space, wherein the dimension of the high-dimensional feature space is d, and then a d multiplied by n nonlinear mapping matrix phi (X) is obtained
{φ(X 1 ),φ(X 2 ),…,φ(X i )…,φ(X n )}
Wherein the nonlinear mapping functionThe mapping mode of (2) is phi (X) to R m →R d D > m, wherein the nonlinear mapping function +.>One of a radial basis kernel function, a gaussian kernel function and a polynomial kernel function can also be used;
determining the kernel matrix K as based on the nonlinear mapping matrix
[φ(X) T φ(X)]
Symmetric semi-positive definite matrix with K being n x n, core matrix centeringIs that
Wherein I is n Indicating that each element isN x n matrix of (c);
determining a matrix eigenvector alpha, eigenvalue lambda from the kernel matrix, wherein kα=λα, performing eigenvector normalizationIs that
Determining the reserved principal component according to the characteristic value exceeding the preset accumulated contribution quantity, and selecting a normalized characteristic vector exceeding the preset accumulated contribution quantity for reconstructionIs that
The transformer insulation state evaluation method according to the present invention, wherein the step of obtaining historical data and abnormal state statistics, determining an insulation state index of the target transformer according to the abnormal state statistics, and determining a threshold value of the insulation state index of the target transformer according to the historical data, comprises:
based on the historical data and the abnormal state statistics, hotelling-T is adopted 2 Calculate T 2 Statistics, and, using the square prediction error Q statistics, rootAccording to T 2 Statistics and Q statistics determine the insulation state index as
Wherein,is a nonlinear mapping matrix +.>Is the principal component feature vector->The method is characterized in that the method is a sample mapping mean value, I is a unit matrix, and lambda is a characteristic value matrix;
according to the normal state T in the history data 2 And Q statistics, calculate T 2 Threshold of statistics and Q statisticsAnd Q m Is that
Wherein l represents the number of reserved principal components, and a is the confidence; f (F) a (l, n-l) is F distribution critical value under the conditions of degrees of freedom of l and n-l, mu is sample Q statistic mean value and standard deviation of rho sample Q statisticIndicating a degree of freedom of h=2μ 2 And/ρ and confidence is the chi-square distribution of a.
The transformer insulation state evaluation method according to the present invention, wherein acquiring real-time monitoring data of the target transformer, determining an insulation state of the target transformer according to the real-time monitoring data, the insulation state index, and a threshold value of the insulation state index, includes:
calculating the transformer through the real-time monitoring dataStatistics and Q r Statistics according to threshold->And Q m Calculating an outlier index ψ as +.>
And determining the insulation state of the target transformer according to the value of the abnormal value index psi.
The transformer insulation state evaluation method according to the present invention, wherein the method further comprises:
and executing alarm prompt of corresponding level according to the alarm interval range and the insulation state of the value of the abnormal value index psi.
Another aspect of the embodiments of the present invention provides a transformer insulation state evaluation device, including:
the first module is used for acquiring wind-light output data and load data accessed by a target transformer according to a transformer insulation state evaluation request, and determining a load coefficient of the target transformer according to the wind-light output data and the load data;
the second module is used for collecting the environmental parameters and insulation monitoring data of the target transformer and determining the insulation state characteristic quantity of the target transformer by adopting core principal component analysis according to the load coefficient, the environmental parameters and the insulation monitoring data;
a third module, configured to obtain historical data and abnormal state statistics, determine an insulation state index of the target transformer according to the abnormal state statistics, and determine a threshold value of the insulation state index of the target transformer according to the historical data;
and a fourth module, configured to obtain real-time monitoring data of the target transformer, and determine an insulation state of the target transformer according to the real-time monitoring data, the insulation state index, and a threshold value of the insulation state index.
Another aspect of an embodiment of the present invention provides an electronic device, including a processor and a memory;
the memory is used for storing programs;
the processor executes the program to implement the method as described above.
Embodiments of the present invention also disclose a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions may be read from a computer-readable storage medium by a processor of a computer device, and executed by the processor, cause the computer device to perform the method described previously.
The beneficial effects of the invention are as follows: the transformer insulation state evaluation of wind power, photovoltaic, load and operation environment influence is considered, and the accuracy of the transformer insulation state evaluation is improved; the multi-dimensional operation data of the transformer, the redundancy analysis and the correlation analysis of the monitoring data are completed by adopting the analysis of the nuclear main components, the insulation state of the transformer can be rapidly identified according to Hotelling-T2 and SquarendPrectionError statistics, abnormal states can be quantified, different precautionary measures are acquired according to the severity of the abnormal states, so that technical support is provided for the reliable operation of the transformer, and risk prediction is provided for the faults of the transformer.
Drawings
The foregoing and/or additional aspects and advantages of the invention will become apparent and may be better understood from the following description of embodiments taken in conjunction with the accompanying drawings in which:
fig. 1 is a schematic diagram of a transformer insulation state evaluation system according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a transformer insulation state evaluation flow according to an embodiment of the invention.
Fig. 3 is a schematic diagram of a transformer load factor calculation flow according to an embodiment of the invention.
Fig. 4 is a schematic diagram of a transformer feature extraction flow according to an embodiment of the invention.
Fig. 5 is a flowchart of calculating the insulation state index and the limit value of the transformer according to the embodiment of the invention.
Fig. 6 is a schematic diagram of an apparatus for evaluating insulation state of a transformer according to an embodiment of the present invention.
Detailed Description
The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention.
Referring to fig. 1, fig. 1 is a schematic diagram of a system for transformer insulation state evaluation. It comprises a transformer 300, a server 400 and a client 500, wherein the transformer 300 is used for receiving generated power by a wind-solar power generation device 100 through a power grid 200, wherein the wind-solar power generation device comprises a wind power generation device and a photovoltaic power generation device. The transformer 300 is further provided with a first sensor (not shown) and a second sensor (not shown), wherein the first sensor is used for collecting environmental data of the transformer, such as environmental temperature, rainfall, humidity, wind speed and the like, the first sensor is used for collecting insulation monitoring data, the second sensor is used for collecting environmental data, and the environmental data collected by the second sensor comprises oil temperature, micro water, carbon monoxide, carbon dioxide, methane, ethane, ethylene, acetylene, hydrogen and the like), and the types and the quantity of the first sensor and the second sensor can be set in a self-defined mode according to requirements. The server 400 also stores historical data and abnormal state data of the transformer 300, wherein the historical data represents that the transformer comprises environment data, insulation detection data, wind power output, photovoltaic output, load output data and the like in a normal state, and the abnormal state data represents environment data, insulation detection data, wind power output, photovoltaic output, load output data and the like when the transformer is in fault or abnormal.
The server 400 is configured to obtain wind-light output data and load data of the transformer 300 according to an insulation state evaluation request of the transformer 300, and determine a load factor of the transformer 300 according to the wind-light output data and the load data; collecting environment parameters and insulation monitoring data of the transformer 300, and determining insulation state characteristic quantity of the transformer 300 by adopting nuclear principal component analysis according to the load coefficient, the environment parameters and the insulation monitoring data; acquiring historical data and abnormal state statistical data, determining an insulation state index of the transformer 300 according to the abnormal state statistical data, and determining a threshold value of the insulation state index of the transformer 300 according to the historical data; real-time monitoring data of the transformer 300 is acquired, the insulation state of the transformer 300 is determined according to the real-time monitoring data, the insulation state index and the threshold value of the insulation state index, and the server 400 sends the state evaluation result of the transformer 300 to the client 500.
In some embodiments, the wind-solar power generation device may be other non-stationary power generation devices, such as tidal power generation device, waste heat recovery power generation device, etc.
Referring to fig. 2, fig. 2 is a schematic diagram of a transformer insulation state evaluation flow, which includes, but is not limited to, steps S100 to S400:
s100, acquiring wind-light output data and load data accessed by a target transformer according to a transformer insulation state evaluation request, and determining a load factor of the target transformer according to the wind-light output data and the load data.
In some embodiments, referring to the transformer load factor calculation flow diagram shown in fig. 3, it includes, but is not limited to, steps S110 to S130:
s110, carrying out per unit processing on wind-light output data and load data by taking rated capacity of a target transformer as a reference value to obtain a wind-power output probability density function, a photovoltaic output probability density function and a load output probability density function, wherein the wind-light output data comprises wind-power output data and photovoltaic output data;
s120, constructing a wind-solar-load multi-dimensional joint probability density distribution function f (p) by adopting a normal Copula function according to the wind power output probability density function, the photovoltaic output probability density function and the load output probability density function w ,p p ,p l ) Is that
Wherein I is an identity matrix, ζ' = [ Φ ] -1 (a),Φ -1 (b),Φ -1 (c)]A, b and c sequentially represent a wind power cumulative distribution function, a photovoltaic cumulative distribution function and a load cumulative distribution function, phi -1 The method comprises the steps that an inverse function of a cumulative distribution function of standard normal distribution is represented, R is a product moment correlation coefficient matrix of a normal Copula function, and the product moment correlation coefficient matrix of the normal Copula function is calculated by adopting at least one mode of maximum information coefficient, kendall rank correlation coefficient and Spearman phase relation;
s130, determining the transformer load factor f (k) as according to the wind-light-load multidimensional joint probability density distribution function
Wherein p is w 、p p 、p l And respectively representing the per unit values of wind power output, photovoltaic output and load.
S200, acquiring environment parameters and insulation monitoring data of the target transformer, and determining insulation state characteristic quantity of the target transformer by adopting nuclear principal component analysis according to the load coefficient, the environment parameters and the insulation monitoring data.
In some embodiments, reference is made to the transformer feature extraction flow diagram shown in fig. 4, which includes, but is not limited to, the steps of:
s210, obtaining a sample data matrix S as according to the transformer load factor f (k), the environmental parameter and the insulation monitoring data of the target transformer
S＝{X 1 ,X 2 ...X i ,...X n }
Wherein i is the number identifier of the transformer, X i Represents the i-th transformer characteristic quantity, and X i Represents an m-dimensional vector, n is the total number of samples, and;
s220, passing the sample data matrix through a nonlinear mapping functionMapping to a high-dimensional feature space with dimension d, andobtain d X n nonlinear mapping matrix phi (X) as
{φ(X 1 ),φ(X 2 ),…,φ(X i )…,φ(X n )}
Wherein the nonlinear mapping functionThe mapping mode of (2) is phi (X) to R m →R d D > m, wherein the nonlinear mapping function +.>One of a radial basis kernel function, a gaussian kernel function and a polynomial kernel function can also be used;
s230, determining the kernel matrix K as according to the nonlinear mapping matrix
[φ(X) T φ(X)]
Symmetric semi-positive definite matrix with K being n x n, core matrix centeringIs that
Wherein I is n Indicating that each element isN x n matrix of (c);
s240, determining a matrix eigenvector alpha and an eigenvalue lambda according to the kernel matrix, wherein K alpha=lambda alpha, and performing eigenvector normalizationIs that
S250, determining the reserved principal components according to the characteristic values exceeding the preset accumulated contribution, and selecting normalized characteristic vectors exceeding the preset accumulated contribution for reconstructionIs that
S300, historical data and abnormal state statistical data are acquired, insulation state indexes of the target transformer are determined according to the abnormal state statistical data, and threshold values of the insulation state indexes of the target transformer are determined according to the historical data.
In some embodiments, referring to the transformer insulation state index and limit value calculation flowchart shown in fig. 5, it includes, but is not limited to, steps S310 to S320:
s310, adopting Hotelling-T according to historical data and abnormal state statistical data 2 Calculate T 2 Statistics, and, using the square prediction error Q statistics, according to T 2 Statistics and Q statistics determine an insulation state index as
Wherein,is a nonlinear mapping matrix +.>Is the principal component feature vector->The method is characterized in that the method is a sample mapping mean value, I is a unit matrix, and lambda is a characteristic value matrix;
s320, according to the normal state T in the history data 2 And Q statistics, calculate T 2 Threshold of statisticsAnd calculating a threshold value of Q statistic as Q m Is that
Wherein l represents the number of reserved principal components, and a is the confidence; f (F) a (l, n-l) is F distribution critical value under the conditions of degrees of freedom of l and n-l, mu is sample Q statistic mean value and standard deviation of rho sample Q statisticIndicating a degree of freedom of h=2μ 2 And/ρ and confidence is the chi-square distribution of a.
S400, acquiring real-time monitoring data of the target transformer, and determining the insulation state of the target transformer according to the real-time monitoring data, the insulation state index and the threshold value of the insulation state index.
In some embodiments, T2r and Q of the transformer are calculated from real-time monitoring data of the transformer r Statistics;
calculating an outlier index ψ, which can be expressed asWhen psi is<And 0, judging that the insulating state of the transformer is normal, otherwise, judging that the abnormality occurs, and indicating that the abnormality is more serious as the value of ψ is larger.
In some embodiments, the abnormal value index ψ is divided into a plurality of levels, and is divided into 1-4 levels according to the verification from small to large, so as to identify the severity of the insulation state of the transformer in the abnormal state, and the risk levels obtained after real-time collection and analysis by the above embodiments are judged and corresponding processing is performed, for example, when the risk level of the insulation state of the transformer is detected to be 1, the abnormal level is lower, early warning report is performed, and when the risk level of the insulation state of the transformer is detected to be 3, the transformer has corresponding faults, which may cause the risk of power distribution faults, equipment replacement and maintenance are required.
Fig. 6 is a schematic diagram of an insulation state alarm flow of a transformer according to an embodiment of the present invention.
In some embodiments, referring to the schematic diagram of the transformer insulation state evaluation device shown in fig. 6, the transformer insulation state evaluation device includes a first module 610, a second module 620, a third module 630, and a fourth module 640.
The first module is used for acquiring wind-light output data and load data accessed by a target transformer according to the transformer insulation state evaluation request, and determining the load coefficient of the target transformer according to the wind-light output data and the load data; the second module is used for collecting the environmental parameters and insulation monitoring data of the target transformer and determining the insulation state characteristic quantity of the target transformer by adopting the analysis of the nuclear main component according to the load coefficient, the environmental parameters and the insulation monitoring data; the third module is used for acquiring historical data and abnormal state statistical data, determining an insulation state index of the target transformer according to the abnormal state statistical data, and determining a threshold value of the insulation state index of the target transformer according to the historical data; and the fourth module is used for acquiring real-time monitoring data of the target transformer and determining the insulation state of the target transformer according to the real-time monitoring data, the insulation state index and the threshold value of the insulation state index.
By way of example, under the cooperation of the first module, the second module, the third module and the fourth module in the device, the device of the embodiment can realize any one of the transformer insulation state evaluation methods described above, that is, according to the transformer insulation state evaluation request, wind-light output data and load data of the target transformer access are obtained, and according to the wind-light output data and the load data, the load coefficient of the target transformer is determined; collecting environment parameters and insulation monitoring data of a target transformer, and determining insulation state characteristic quantity of the target transformer by adopting nuclear principal component analysis according to the load coefficient, the environment parameters and the insulation monitoring data; acquiring historical data and abnormal state statistical data, determining an insulation state index of the target transformer according to the abnormal state statistical data, and determining a threshold value of the insulation state index of the target transformer according to the historical data; and acquiring real-time monitoring data of the target transformer, and determining the insulation state of the target transformer according to the real-time monitoring data, the insulation state index and the threshold value of the insulation state index. The beneficial effects of the invention are as follows: the transformer insulation state evaluation which is influenced by wind power, photovoltaic, load and operation environment is considered, so that the accuracy of the transformer insulation state evaluation is improved; the multi-dimensional operation data of the transformer, the redundancy analysis and the correlation analysis of the monitoring data are completed by adopting the analysis of the nuclear main components, the insulation state of the transformer can be rapidly identified according to Hotelling-T2 and SquarendPrectionError statistics, abnormal states can be quantified, different precautionary measures are acquired according to the severity of the abnormal states, so that technical support is provided for the reliable operation of the transformer, and risk prediction is provided for the faults of the transformer.
The embodiment of the invention also provides electronic equipment, which comprises a processor and a memory;
the memory stores a program;
the processor executes a program to execute the transformer insulation state evaluation method; the electronic equipment has the function of carrying and running the software system for evaluating the insulation state of the transformer.
The embodiment of the invention also provides a computer-readable storage medium storing a program that is executed by a processor to implement the transformer insulation state evaluation method as described above.
In some alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flowcharts of the present invention are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented by embodiments of the invention. Alternative embodiments are contemplated in which the order of various operations is changed, and in which sub-operations described as part of a larger operation are performed independently.
Embodiments of the present invention also disclose a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions may be read from a computer-readable storage medium by a processor of a computer device, and executed by the processor, cause the computer device to perform the transformer insulation state evaluation method described previously.
Furthermore, while the invention is described in the context of functional modules, it should be appreciated that, unless otherwise indicated, one or more of the described functions and/or features may be integrated in a single physical device and/or software module or one or more functions and/or features may be implemented in separate physical devices or software modules. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary to an understanding of the present invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed in the embodiments of the present invention will be understood within the ordinary skill of the engineer in view of their attributes, functions and internal relationships. Accordingly, one of ordinary skill in the art can implement the invention as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative and are not intended to be limiting upon the scope of the invention, which is to be defined in the appended claims and their full scope of equivalents.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the invention, the scope of which is defined by the claims and their equivalents.
While the preferred embodiment of the present invention has been described in detail, the present invention is not limited to the embodiments described above, and those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention, and these equivalent modifications or substitutions are included in the scope of the present invention as defined in the appended claims.
Claims (10)
1. A transformer insulation state evaluation method, comprising:
acquiring wind-light output data and load data accessed by a target transformer according to a transformer insulation state evaluation request, and determining a load factor of the target transformer according to the wind-light output data and the load data;
collecting environment parameters and insulation monitoring data of the target transformer, and determining insulation state characteristic quantity of the target transformer by adopting core principal component analysis according to the load coefficient, the environment parameters and the insulation monitoring data;
acquiring historical data and abnormal state statistical data, determining an insulation state index of the target transformer according to the abnormal state statistical data, and determining a threshold value of the insulation state index of the target transformer according to the historical data;
and acquiring real-time monitoring data of the target transformer, and determining the insulation state of the target transformer according to the real-time monitoring data, the insulation state index and the threshold value of the insulation state index.
2. The method for evaluating the insulation state of a transformer according to claim 1, wherein the step of obtaining wind-light output data and load data of a target transformer according to the insulation state evaluation request of the transformer, and determining the load factor of the target transformer according to the wind-light output data and the load data comprises the steps of:
performing per unit processing on the wind-light output data and the load data by taking the rated capacity of the target transformer as a reference value to obtain a wind-power output probability density function, a photovoltaic output probability density function and a load output probability density function, wherein the wind-light output data comprises wind-power output data and photovoltaic output data;
constructing a wind-solar-load multi-dimensional joint probability density distribution function f (p) by adopting a normal Copula function according to the wind power output probability density function, the photovoltaic output probability density function and the load output probability density function w ,p p ,p l ) Is that
Wherein I is an identity matrix, ζ' = [ Φ ] -1 (a),Φ -1 (b),Φ -1 (c)]A, b and c sequentially represent a wind power cumulative distribution function, a photovoltaic cumulative distribution function and a load cumulative distribution function, phi -1 An inverse function of a cumulative distribution function representing a standard normal distribution, R being a matrix of product moment correlation coefficients of a normal Copula function, wherein the normal CopulaThe matrix of the correlation coefficient of the moment of the la function is calculated by adopting at least one mode of the maximum information coefficient, kendall rank correlation coefficient and Spearman phase relation;
according to the wind-light-load multidimensional joint probability density distribution function, determining the transformer load factor f (k) as
Wherein p is w 、p p 、p l And respectively representing the per unit values of wind power output, photovoltaic output and load.
3. The transformer insulation state evaluation method according to claim 1, characterized in that the method further comprises:
the environmental parameters are collected through a first sensor, and the environmental data collected by the first sensor comprise at least one of environmental temperature, rainfall, humidity and wind speed;
the insulation monitoring data are collected through a second sensor, and the insulation monitoring data collected by the second sensor comprise at least one of oil temperature, micro water, carbon monoxide, carbon dioxide, methane, ethane, ethylene, acetylene and hydrogen, wherein the collected insulation monitoring data can be adjusted in a self-defined mode.
4. The transformer insulation state evaluation method according to claim 2, wherein the collecting the environmental parameter and insulation monitoring data of the target transformer, and determining the insulation state feature quantity of the target transformer by using a core principal component analysis according to the load factor, the environmental parameter and the insulation monitoring data, comprises:
according to the transformer load factor f (k), the environmental parameter and the insulation monitoring data of the target transformer, obtaining a sample data matrix S as
S＝{X 1 ,X 2 ...X i ,...X n }
Wherein i is the number identifier of the transformer, X i Represents the i-th transformer characteristic quantity, and X i Represents an m-dimensional vector, n is the total number of samples, and;
passing a sample data matrix through a nonlinear mapping functionMapping to a high-dimensional feature space, wherein the dimension of the high-dimensional feature space is d, and further obtaining a d multiplied by n nonlinear mapping matrix phi (X) as { phi (X) 1 ),φ(X 2 ),…,φ(X i )…,φ(X n )}
Wherein the nonlinear mapping functionThe mapping mode of (C) is phi (X): R m →R d D > m, wherein the nonlinear mapping function +.>One of a radial basis kernel function, a gaussian kernel function and a polynomial kernel function can also be used;
determining the kernel matrix K as [ phi (X) based on the nonlinear mapping matrix T φ(X)]
Symmetric semi-positive definite matrix with K being n x n, core matrix centeringIs that
Wherein I is n Indicating that each element isN x n matrix of (c);
determining a matrix eigenvector alpha, eigenvalue lambda from the kernel matrix, wherein kα=λα, performing eigenvector normalizationIs that
Determining the reserved principal component according to the characteristic value exceeding the preset accumulated contribution quantity, and selecting a normalized characteristic vector exceeding the preset accumulated contribution quantity for reconstructionIs that
5. The transformer insulation state evaluation method according to claim 4, wherein the acquiring the history data and the abnormal state statistics, determining the insulation state index of the target transformer according to the abnormal state statistics, determining the threshold value of the insulation state index of the target transformer according to the history data, comprises:
based on the historical data and the abnormal state statistics, hotelling-T is adopted 2 Calculate T 2 Statistics, and, using the square prediction error Q statistics, according to T 2 Statistics and Q statistics determine the insulation state index as
Wherein,is a nonlinear mapping matrix +.>Is the principal component feature vector->Mapping the mean for a sampleI is an identity matrix, and lambda is a eigenvalue matrix;
according to the normal state T in the history data 2 And Q statistics, calculate T 2 Threshold of statistics and Q statisticsAnd Q m Is that
Wherein l represents the number of reserved principal components, and a is the confidence; f (F) a (l, n-l) is F distribution critical value under the conditions of degrees of freedom of l and n-l, mu is sample Q statistic mean value and standard deviation of rho sample Q statisticIndicating a degree of freedom of h=2μ 2 And/ρ and confidence is the chi-square distribution of a.
6. The method of claim 5, wherein the acquiring real-time monitoring data of the target transformer and determining the insulation state of the target transformer according to the real-time monitoring data, the insulation state index and the threshold value of the insulation state index comprise:
calculating the transformer through the real-time monitoring dataStatistics and Q r Statistics according to threshold->And Q m Calculating an outlier index ψ as +.>
And determining the insulation state of the target transformer according to the value of the abnormal value index psi.
7. The transformer insulation state evaluation method according to claim 6, further comprising:
and executing alarm prompt of corresponding level according to the alarm interval range and the insulation state of the value of the abnormal value index psi.
8. A transformer insulation state evaluation device, characterized by comprising:
the first module is used for acquiring wind-light output data and load data accessed by a target transformer according to a transformer insulation state evaluation request, and determining a load coefficient of the target transformer according to the wind-light output data and the load data;
the second module is used for collecting the environmental parameters and insulation monitoring data of the target transformer and determining the insulation state characteristic quantity of the target transformer by adopting core principal component analysis according to the load coefficient, the environmental parameters and the insulation monitoring data;
a third module, configured to obtain historical data and abnormal state statistics, determine an insulation state index of the target transformer according to the abnormal state statistics, and determine a threshold value of the insulation state index of the target transformer according to the historical data;
and a fourth module, configured to obtain real-time monitoring data of the target transformer, and determine an insulation state of the target transformer according to the real-time monitoring data, the insulation state index, and a threshold value of the insulation state index.
9. An electronic device comprising a processor and a memory;
the memory is used for storing programs;
the processor executing the program implements the transformer insulation state evaluation method according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that the storage medium stores a program that is executed by a processor to implement the transformer insulation state evaluation method according to any one of claims 1 to 7.
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