CN112000831A - Abnormal data identification optimization method based on transformer substation graph transformation - Google Patents
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Abstract
The invention discloses an abnormal data identification optimization method based on transformer substation graph transformation, which comprises the steps of collecting basic information of relevant equipment of a transformer substation and adding private attributes to perform graph transformation; constructing an anomaly identification model based on a least square strategy, and carrying out anomaly identification on processing data in the graph conversion process; and if the abnormal data is identified, marking the abnormal data, removing the marked abnormal data by combining three-parameter distribution and a gray prediction strategy, and outputting the optimized graph conversion result. According to the method, the abnormal data in the graph transformation process is identified through the established abnormal identification model, the marked abnormal data is eliminated by combining three-parameter distribution and a gray prediction strategy, the optimized graph transformation result is output, the graph transformation quality and efficiency are improved, the abnormal data is accurately positioned and eliminated, the repeated work and the error and the leakage of operation and maintenance personnel are avoided, and the risk coefficient of safe operation of equipment is reduced.
Description
Technical Field
The invention relates to the technical field of transformer substation and graph transformation, in particular to an abnormal data identification optimization method based on transformer substation graph transformation.
Background
The secondary system devices in the transformer substation are numerous and various cables are also various, and the secondary system devices comprise relay protection devices, safety automatic devices, fault recording devices, relay protection fault information system substations, merging unit devices, network switches, intelligent terminal devices and the like. The secondary wiring in the transformer substation is very complex, and the accuracy of the secondary wiring is related to the operation safety of the power grid, so that the secondary wiring has a very important position. Such complex external wiring also presents significant challenges to installation and maintenance. In the routine work of secondary overhaul of power transformation, in order to ensure the safety of a power grid, equipment and personnel, secondary safety measures are often required to be taken before work. Aiming at the important protective screens such as a main transformer protective screen and a bus differential protective screen, the following safety measures are required to be correspondingly taken: opening the wiring at the inner side of the terminal block, making safety isolation measures, and timely recovering after the work is finished. Because the secondary wiring of these protection screens is relatively more, often can meet the difficult condition of going down to the hand when resumeing terminal row medial line, or can't see the condition of wiring hole site. In actual execution, the number of people is required to repeatedly check, and sometimes the terminal plug is gripped by a tool such as a nipper pliers and is connected. This easily produces and connects potential safety hazards such as wrong position, causes this operation degree of difficulty to be big, work efficiency low grade a great deal of problem.
At present, most of electric circuits in a transformer substation are stored in a drawing form, an electric graph cannot be associated with actual equipment (such as a device, electric equipment and the like), a transportation and inspection debugging worker needs to browse a large amount of drawing information during field maintenance, and the electric circuit information related to certain equipment (such as the device, the electric equipment and the like) cannot be quickly searched, so that the working efficiency of the field transportation and inspection debugging worker is greatly reduced, and the working difficulty and the learning cost are increased; meanwhile, data errors are prone to occur in the graph conversion process, operation and maintenance personnel cannot find the data errors easily, potential safety hazards to substation equipment exist, accuracy of graph conversion is not high, and uniqueness positioning and identification of equipment information cannot be achieved.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above-mentioned conventional problems.
Therefore, the invention provides an abnormal data identification and optimization method based on transformer substation graph transformation, which can solve the problem that the normal operation of equipment is influenced because abnormal data cannot be found due to an abnormal data error in the existing graph transformation process.
In order to solve the technical problems, the invention provides the following technical scheme: acquiring basic information of relevant equipment of a transformer substation and adding a private attribute to perform graph transformation; constructing an anomaly identification model based on a least square strategy, and carrying out anomaly identification on processing data in the graph conversion process; and if the abnormal data is identified, marking the abnormal data, removing the marked abnormal data by combining three-parameter distribution and a gray prediction strategy, and outputting the optimized graph conversion result.
As an optimal scheme of the abnormal data identification optimization method based on the transformer substation graph transformation, the method comprises the following steps: the graph transformation comprises the steps of collecting the basic information of the relevant equipment of the transformer substation and storing the basic information into a database according to the actual physical level relation of the transformer substation; an analysis model is established based on a linear programming criterion, and an electric graph and a topological relation graph of the electric circuit in the basic information are read for analysis to obtain an electric graph object; extracting text information of the electrical graphic object by using a random forest strategy, and matching and binding the text information with data in the database; associating the matched information with the electrical graphic object to construct a secondary topological relation graph; and extracting the information of the electrical graphic object and the secondary topological relation graph by using the random forest strategy, adding private attributes into an SVG text, and generating a new SVG text.
As an optimal scheme of the abnormal data identification optimization method based on the transformer substation graph transformation, the method comprises the following steps: the database needs to establish a basic information model in advance, including a cubicle information model, a screen cabinet information model, a device information model and an electrical equipment information model in the transformer substation.
As an optimal scheme of the abnormal data identification optimization method based on the transformer substation graph transformation, the method comprises the following steps: establishing the cubicle information model, wherein the cubicle information model comprises the unique number of a cubicle in a database and the name of the cubicle in the transformer substation; establishing the screen cabinet information model, wherein the screen cabinet is uniquely numbered in the database, the name of the screen cabinet in the transformer substation and the serial number of the small chamber where the screen cabinet is located are included; establishing the device information model comprises the unique number of the device in the database, the name of the device in the transformer substation, the type of the device and the number of a screen cabinet where the device is located; the establishing of the electrical equipment information model comprises the uniqueness number of the electrical equipment in the database, the name of the electrical equipment in the transformer substation, the type of the electrical equipment and the number of the screen cabinet where the electrical equipment is located.
As an optimal scheme of the abnormal data identification optimization method based on the transformer substation graph transformation, the method comprises the following steps: constructing the analytical model includes establishing an objective function using the linear programming criterion, as follows,
where x is the device for input storage, y is the device for identification output,
to evaluate the linear combination coefficient of DMU, b+As a relaxation variable, b-And alpha is the analysis optimal solution of the objective function for the residual variable.
As an optimal scheme of the abnormal data identification optimization method based on the transformer substation graph transformation, the method comprises the following steps: constructing the anomaly recognition model includes selecting a radial basis function as a target function of the LSSVM, as follows
Wherein x ═ { x ═ x1；x2；…；x14}: a characteristic matrix formed by historical data amplitude-frequency characteristic vectors influencing identification factors in the basic information, wherein y: the amplitude-frequency characteristic vector influencing the identification factor in the basic information, sigma:
target vector, i.e. the distribution or range characteristic of the basic information.
As an optimal scheme of the abnormal data identification optimization method based on the transformer substation graph transformation, the method comprises the following steps: the identification model needs to be trained in advance, and the identification model comprises initializing punishment parameters and the target vector, training the LSSVM by using the basic information, and testing by using the generated SVG file; if the identification model does not meet the requirement of the precision threshold, carrying out assignment optimization on the punishment parameters and the target vectors according to errors; and forming the recognition model until the precision threshold requirement is met, and outputting a recognition result.
As an optimal scheme of the abnormal data identification optimization method based on the transformer substation graph transformation, the method comprises the following steps: the identification result comprises normal data and abnormal data; the normal data comprises standard attributes, types, formats and characters; the exception data includes the attributes, the type, the format, and the characters that exceed or fail the criteria.
As an optimal scheme of the abnormal data identification optimization method based on the transformer substation graph transformation, the method comprises the following steps: the three-parameter distribution and gray prediction strategy includes calculating an optimization index using a three-parameter cumulative distribution function, as follows,
wherein, sigma is a scale parameter, alpha is a shape parameter, and theta is a shape parameter.
As an optimal scheme of the abnormal data identification optimization method based on the transformer substation graph transformation, the method comprises the following steps: also comprises the following steps of (1) preparing,
and y and z are marked abnormal data prediction rejection parameters, and o is a solving factor.
The invention has the beneficial effects that: according to the method, the abnormal data in the graph transformation process is identified through the established abnormal identification model, the marked abnormal data is eliminated by combining three-parameter distribution and a gray prediction strategy, the optimized graph transformation result is output, the graph transformation quality and efficiency are improved, the abnormal data is accurately positioned and eliminated, the repeated work and the error and the leakage of operation and maintenance personnel are avoided, and the risk coefficient of safe operation of equipment is reduced.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced 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. Wherein:
fig. 1 is a schematic flow chart of an abnormal data identification and optimization method based on substation graph transformation according to an embodiment of the present invention;
fig. 2 is an electrical graphic object schematic diagram of an abnormal data identification optimization method based on substation graphic conversion according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, specific embodiments accompanied with figures are described in detail below, and it is apparent that the described embodiments are a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present invention, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
The present invention will be described in detail with reference to the drawings, wherein the cross-sectional views illustrating the structure of the device are not enlarged partially in general scale for convenience of illustration, and the drawings are only exemplary and should not be construed as limiting the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
Meanwhile, in the description of the present invention, it should be noted that the terms "upper, lower, inner and outer" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and operate, and thus, cannot be construed as limiting the present invention. Furthermore, the terms first, second, or third are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected and connected" in the present invention are to be understood broadly, unless otherwise explicitly specified or limited, for example: can be fixedly connected, detachably connected or integrally connected; they may be mechanically, electrically, or directly connected, or indirectly connected through intervening media, or may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Example 1
Referring to fig. 1 and 2, for a first embodiment of the present invention, a method for identifying and optimizing abnormal data based on substation graph transformation is provided, including:
s1: and acquiring basic information of relevant equipment of the transformer substation and adding private attributes to perform graph transformation. Wherein, the graph transformation includes:
acquiring basic information of relevant equipment of the transformer substation and storing the basic information into a database according to the actual physical level relation of the transformer substation;
an analysis model is established based on a linear programming criterion, and an electric graph and a topological relation graph of the electric circuit in the basic information are read for analysis to obtain an electric graph object;
extracting text information of the electrical graphic object by using a random forest strategy, and matching and binding the text information with data in a database;
associating the matched information with the electrical graphic object to construct a secondary topological relation graph;
and extracting information of the electrical graphic object and the secondary topological relation graph by using a random forest strategy, adding a private attribute into the SVG text, and generating a new SVG text.
Specifically, the database needs to establish a basic information model in advance, which includes:
building a cubicle information model, a screen cabinet information model, a device information model and an electrical equipment information model in the transformer substation;
establishing a cubicle information model, wherein the cubicle information model comprises the unique number of a cubicle in a database and the name of the cubicle in a transformer substation;
establishing a screen cabinet information model, wherein the screen cabinet is uniquely numbered in a database, the name of the screen cabinet in a transformer substation and the number of a small chamber where the screen cabinet is located are established;
establishing a device information model, wherein the device information model comprises a unique number of the device in a database, a name of the device in a transformer substation, a device type and a number of a screen cabinet where the device is located;
the establishment of the electrical equipment information model comprises the unique serial number of the electrical equipment in the database, the name of the electrical equipment in the transformer substation, the type of the electrical equipment and the serial number of a screen cabinet where the electrical equipment is located.
Further, constructing the analytical model includes:
the objective function is established using linear programming criteria, as follows,
where x is the device for input storage, y is the device for identification output,
to evaluate the linear combination coefficient of DMU, b+As a relaxation variable, b-For the residual variables, α is the analytical optimal solution of the objective function.
Still further, referring to fig. 2, the electrical graphic object includes:
the primitive types comprise text and vector graphics;
the vector graphics comprise basic graphics and combined graphics;
basic figures include, straight lines, rectangles, circles, ellipses, and arcs;
the combined graph comprises a screen cabinet, a device, an electric port, a terminal strip, a node and a mutual inductor.
S2: and constructing an anomaly identification model based on a least square strategy, and carrying out anomaly identification on the processing data in the graph conversion process. It should be noted that, the constructing of the anomaly identification model includes:
the radial basis function is selected as the objective function of the LSSVM as follows
Wherein x ═ { x ═ x1；x2；…；x14}: a characteristic matrix formed by historical data amplitude-frequency characteristic vectors influencing identification factors in the basic information, wherein y: the amplitude-frequency characteristic vector affecting the identification factor in the basic information, sigma: target vector, i.e. the distribution or range characteristic of the basic information.
Preferably, the recognition model needs to be trained in advance, including:
initializing punishment parameters and target vectors, training the LSSVM by using basic information, and testing by using the generated SVG file;
if the identification model does not meet the requirement of the precision threshold, carrying out assignment optimization on the punishment parameters and the target vectors according to errors;
and forming an identification model until the requirement of the precision threshold is met, and outputting an identification result.
Specifically, the recognition result includes:
normal data and abnormal data;
normal data includes, standard attributes, types, formats, and characters;
the exception data includes attributes, types, formats, and characters that exceed or fall short of the criteria.
S3: and if the abnormal data is identified, marking the abnormal data, removing the marked abnormal data by combining the three-parameter distribution and the gray prediction strategy, and outputting an optimized graph conversion result. It should be further noted that the three-parameter distribution and gray prediction strategy includes:
the optimization index is calculated using a three-parameter cumulative distribution function, as follows,
wherein, sigma is a scale parameter, alpha is a shape parameter, and theta is a shape parameter.
Specifically, still include:
wherein y and z are marked abnormal data prediction elimination parameters, and o is a solving factor.
Generally speaking, the digitization degree of a transformer substation is higher and higher, a main medium of information exchange in the transformer substation is changed into an optical fiber from a cable, a transmitted signal is also changed into a digital quantity from an analog quantity, an electronic transformer and a merging unit are applied to the digital transformer substation in a large quantity, a primary voltage signal and a primary current signal are collected by an electronic book transformer and are converted into digital signals, and the digital signals are collected and synchronized by the merging unit and then are transmitted to a subsequent measuring and protecting device for processing; in the process, due to interference of an external electromagnetic environment and instability of the electronic equipment, the transmitted electrical quantity signal may be distorted, specifically, sudden change of one or more data points is/are called abnormal data points, the abnormal data is not a correct reflection of the electrical signal, but the quality in a data frame is normal due to the fact that the digits are normal, and the measurement and protection device processes the data as normal data, so that a result is greatly influenced, and protection misoperation can be caused in a serious case.
Preferably, in this embodiment, it should be further noted that, in the existing method for identifying abnormal data based on three-point continuous and effective discrimination of sampling values, it is indicated that any other points except for a plurality of discontinuities in a waveform are continuously conductive and derivatives are also continuously segmented, and whether sampling values are abnormal is determined by using this characteristic, but the method has insufficient sensitivity when the deviation between abnormal data and normal data is small, and cannot identify continuous abnormal data with small fluctuation amplitude; the existing transformer substation flying spot data identification method judges whether the data is abnormal or not by comparing the absolute value of the target sampling point data with the absolute value of the adjacent two sampling point data, and processes abnormal data by using a curve fitting method, wherein the method is invalid when the absolute value of the abnormal data is small or continuous abnormal data appears; thirdly, the existing sampling data effectiveness identification method calculates the rapid amplitude of the fundamental component current quantity through continuous three-point sampling values, judges whether the data is abnormal or not by utilizing the mutual comparison of the rapid amplitudes at the calculation positions of different sampling points and the comparison of the amplitude and a fixed threshold value, cannot determine the size of abnormal data, and cannot identify the absolute value of the abnormal data and the absolute value of an abnormal sampling point; it should be noted that the method of the present invention is not only used for identifying abnormal data in the graph transformation process, but also used for identifying abnormal data of each sampling point of the related equipment of the substation, and can solve the problems of the existing method that the identification of continuous multipoint abnormal data is difficult, the identification is difficult depending on the threshold value, and the identification of abnormal data with small value is difficult.
Example 2
In order to better verify and explain the technical effects adopted in the method, the conventional sampling value-based three-point continuous effective abnormal data identification method, the conventional substation flying spot data identification method and the conventional sampling data effectiveness identification method are respectively compared with the method for testing, and the test results are compared by means of scientific demonstration to verify the real effects of the method.
In order to verify that the method has higher identification sensitivity and identification efficiency compared with the traditional method, the three traditional methods and the method are adopted to carry out real-time measurement and comparison on the operation of relevant equipment of a certain transformer substation in the southern region respectively.
And (3) testing conditions are as follows: (1) selecting the same operation data of the same equipment in the same time period for carrying out abnormal recognition;
(2) collecting part of relevant equipment basic information of the transformer substation, starting automatic test equipment and performing system simulation by using MATLAB;
(3) and configuring operation calculation parameters of each method, and respectively acquiring output accuracy result data of each method.
Table 1: and testing a comparison data table.
In the embodiment, four types of substation operation equipment are used for testing, abnormal data in the same data transmission time are identified and tested by three traditional methods and the method, and referring to table 1, it can be seen visually that the accuracy of identifying the abnormal data is lower in the three traditional methods, but the accuracy of the method is improved by 10% compared with the traditional method, and based on the accuracy, the method provided by the invention verifies the true effect.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (10)
1. An abnormal data identification optimization method based on transformer substation graph transformation is characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
acquiring basic information of relevant equipment of the transformer substation and adding a private attribute to perform graph transformation;
constructing an anomaly identification model based on a least square strategy, and carrying out anomaly identification on processing data in the graph conversion process;
and if the abnormal data is identified, marking the abnormal data, removing the marked abnormal data by combining three-parameter distribution and a gray prediction strategy, and outputting the optimized graph conversion result.
2. The substation graph transformation-based abnormal data identification optimization method of claim 1, wherein: the graphic conversion comprises the steps of converting a graphic into a graphic,
acquiring the basic information of the relevant equipment of the transformer substation and storing the basic information into a database according to the actual physical level relation of the transformer substation;
an analysis model is established based on a linear programming criterion, and an electric graph and a topological relation graph of the electric circuit in the basic information are read for analysis to obtain an electric graph object;
extracting text information of the electrical graphic object by using a random forest strategy, and matching and binding the text information with data in the database;
associating the matched information with the electrical graphic object to construct a secondary topological relation graph;
and extracting the information of the electrical graphic object and the secondary topological relation graph by using the random forest strategy, adding private attributes into an SVG text, and generating a new SVG text.
3. The substation graph transformation-based abnormal data identification optimization method of claim 2, wherein: the database needs to establish basic information models in advance, including,
and establishing a cubicle information model, a screen cabinet information model, a device information model and an electrical equipment information model in the transformer substation.
4. The substation graph transformation-based abnormal data identification optimization method of claim 3, wherein: establishing the cubicle information model, wherein the cubicle information model comprises the unique number of a cubicle in a database and the name of the cubicle in the transformer substation;
establishing the screen cabinet information model, wherein the screen cabinet is uniquely numbered in the database, the name of the screen cabinet in the transformer substation and the serial number of the small chamber where the screen cabinet is located are included;
establishing the device information model comprises the unique number of the device in the database, the name of the device in the transformer substation, the type of the device and the number of a screen cabinet where the device is located;
the establishing of the electrical equipment information model comprises the uniqueness number of the electrical equipment in the database, the name of the electrical equipment in the transformer substation, the type of the electrical equipment and the number of the screen cabinet where the electrical equipment is located.
5. The substation graph transformation-based abnormal data identification optimization method of claim 4, wherein: the construction of the analytical model includes the steps of,
the linear programming criterion is used to establish an objective function that, as follows,
where x is the device for input storage, y is the device for identification output,
to evaluate the linear combination coefficient of DMU, b+As a relaxation variable, b-And alpha is the analysis optimal solution of the objective function for the residual variable.
6. The abnormal data identification and optimization method based on transformer substation graph transformation as claimed in any one of claims 1-5, characterized in that: constructing the anomaly identification model includes constructing the anomaly identification model,
the radial basis function is selected as the objective function of the LSSVM as follows
Wherein x ═ { x ═ x1；x2；…；x14}: a characteristic matrix formed by historical data amplitude-frequency characteristic vectors influencing identification factors in the basic information, wherein y: the amplitude-frequency characteristic vector influencing the identification factor in the basic information, sigma: target vector, i.e. the distribution or range characteristic of the basic information.
7. The substation graph transformation-based abnormal data identification optimization method of claim 6, wherein: the recognition model needs to be trained in advance, including,
initializing punishment parameters and the target vector, training the LSSVM by using the basic information, and testing by using the generated SVG file;
if the identification model does not meet the requirement of the precision threshold, carrying out assignment optimization on the punishment parameters and the target vectors according to errors;
and forming the recognition model until the precision threshold requirement is met, and outputting a recognition result.
8. The substation graph transformation-based abnormal data identification optimization method of claim 7, wherein: the identification result comprises normal data and abnormal data;
the normal data comprises standard attributes, types, formats and characters;
the exception data includes the attributes, the type, the format, and the characters that exceed or fail the criteria.
9. The substation graph transformation-based abnormal data identification optimization method of claim 8, wherein: the three-parameter distribution and gray prediction strategy includes,
the optimization index is calculated using a three-parameter cumulative distribution function, as follows,
wherein, sigma is a scale parameter, alpha is a shape parameter, and theta is a shape parameter.
10. The substation graph transformation-based anomaly data identification optimization method of claim 9, wherein: also comprises the following steps of (1) preparing,
and y and z are marked abnormal data prediction rejection parameters, and o is a solving factor.
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