CN117291479A - UPFC-based equipment portrait generation method and system - Google Patents
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Abstract
A device portrait generation method and system based on UPFC. The method comprises the steps of obtaining operation data of UPFC equipment, and cleaning and preprocessing the operation data; setting basic static information, running dynamic information and performance evaluation information of UPFC equipment as a first-level label; dividing the primary labels according to UPFC management and analysis dimensions to obtain secondary labels of UPFC equipment; subdividing the secondary label to obtain a tertiary label of UPFC equipment; and acquiring the numerical value of each three-level label to form the equipment portrait of the UPFC equipment. The scheme of the invention builds the equipment image framework of the whole UPFC, clearly and comprehensively displays the multidimensional operation characteristics of the equipment, and realizes the comprehensive analysis of UPFC operation data and the multidirectional management and control of UPFC equipment.
Description
Technical Field
The invention belongs to the technical field of electrical engineering, and particularly relates to a UPFC-based equipment portrait generation method and system.
Background
As the demand for electrical energy increases, the voltage class and structure of the power grid become more complex, and the problems faced by the power grid become more serious. In terms of improving the power grid transmission capacity, installing a flexible ac power transmission system (Flexible Alternative Current Transmission Systems, FACTS) device in an existing power grid becomes a more advantageous main means than the conventional way of newly creating a power transmission line. The unified power flow controller (UnifiedPower Flow Controller, UPFC) in the FACTS device has comprehensive regulation capability and is also recognized as the most advanced power flow controller, while the UPFC based on the modular multilevel converter is gradually developed and mature, and has been practically applied to power grids with different voltage grades, such as the UPFC with the voltage grade of 500kV currently built in south of sulce.
Along with the progress of UPFC management and control task, various data can be accumulated and generated in different departments and professions in the UPFC operation process, and higher requirements are set for UPFC management. How to generate equipment portraits to accurately control the running state of equipment becomes an aim of realizing urgent need.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a device portrait generation method based on UPFC (unified power flow controller) so as to solve the technical problems of comprehensive analysis of UPFC operation data and omnibearing control of UPFC devices.
In order to solve the technical problems, the invention adopts the following technical scheme.
The invention firstly discloses a UPFC-based equipment portrait generation method, which comprises the following steps:
step 1: obtaining operation data of UPFC equipment, and cleaning and preprocessing the operation data;
step 2: setting basic static information, running dynamic information and performance evaluation information of the UPFC equipment as primary labels;
step 3: dividing the primary label according to UPFC management and analysis dimensions to obtain equipment information, application information, running state information, running fault information, equipment maintenance information, quality evaluation information and function evaluation information, wherein the equipment information, the application information, the running state information, the running fault information, the equipment maintenance information, the quality evaluation information and the function evaluation information are used as secondary labels of UPFC equipment;
step 4: subdividing the secondary label to obtain rated parameter information, equipment specification information, operational age information, equipment state information, electrical data information, equipment fault information, overhaul stop time information, performance test information, running environment information, equipment quality grade information, equipment performance effect information, family quality history information, equipment technical indexes, equipment bearing capacity indexes and scheduling management indexes, and using the secondary label as a tertiary label of UPFC equipment;
step 5: and excavating data generated by UPFC operation of the three-level label by adopting a cluster analysis algorithm to obtain the numerical value of the three-level label, and forming the equipment portrait of the UPFC equipment.
The invention further comprises the following preferable schemes:
in step 1, the obtaining operation data of the UPFC device further includes:
step 1.1: obtaining basic information of equipment, including equipment specifications, rated parameters, models and quality grades, through an enterprise resource planning system (ERP);
step 1.2: obtaining operation information, equipment basic information, performance evaluation information and UPFC fault information through an equipment operation and maintenance management system (PMS);
step 1.3: obtaining UPFC overhaul information and system operation scheduling information through a scheduling management system (OMS);
step 1.4: obtaining transformer load rate and transformer conversion rate, and current and power through a UPFC engineering control device;
step 1.5: and obtaining the environment information where the UPFC operates by installing a sensor.
The basic static information label in the primary label is a long-term inherent attribute of UPFC; the running dynamic information label comprises running information and maintenance, and the label is updated in time along with time change; the performance evaluation information label records information with evaluation function which is summarized and obtained in the UPFC operation process.
The second-level tag in the basic static information tag comprises equipment information and application information; the secondary labels in the operation information comprise UPFC daily operation time, UPFC accumulated operation time, operation fault equipment quantity, operation fault equipment category and operation fault processing time; the second-level label in the maintenance service comprises a service stop time, a service duration, the number of maintenance fault devices, the types of the maintenance fault devices and a maintenance processing duration; the secondary labels in the performance evaluation information labels comprise overall evaluation, important attention, quality evaluation and functional evaluation.
The third-level label in the equipment information second-level label comprises rated parameters and equipment specifications; the third-level label in the second-level label of the operation information comprises a service life and a device state; the three-level tag in the two-level tag in the running state comprises electric data such as current, power and the like; the third-level label in the operation fault second-level label comprises equipment fault information; the third-level label in the equipment overhaul second-level label comprises overhaul stop time and performance test; the third-level label in the quality evaluation second-level label comprises equipment quality grade, equipment performance effect and family history quality; the three-level label in the function evaluation two-level label comprises equipment technical indexes, equipment bearing capacity indexes and scheduling management indexes.
In step 5, the cluster analysis further includes:
step 5.1: aiming at the secondary labels needing to mine and induce data in different periods, carrying out dimension reduction treatment before cluster analysis to obtain a one-dimensional data set D; by setting up the collectedmOf individual periodsnThe individual tag data set isW；N-th tag data for the m-th period;
，
defining a datasetWMean value of (1)Standard deviation isSWill beWSubtracting the average value of the column data from each column data of the column data to perform decentration, and then performing standardization; obtaining a standardized matrixV；
Calculating a matrixVCovariance matrix of (2)I is [1, m ]]：
，
Determination ofVMaximum eigenvalue and corresponding normalized eigenvector thereofαSubstituting and calculating to obtain a dimensionality-reduced data setD：
，
Step 5.2: for not to doTwo-level tags requiring mining generalization for data of different periods, for data setsDPerforming K-means cluster analysis to obtain the numerical value of the three-level label; k-means cluster analysis relies on the selection of cluster centers and the determination of the number K of clusters, wherein the selection of the cluster centers is divided into initial cluster center selection and other cluster center selection; the method specifically comprises the following steps:
step 5.21: selecting an initial clustering center, and setting a collected sample data setD=（x 1 ，x 2 ，x 3 …x n ) The cluster category is expressed asE=（E 1 ，E 2 ，E 3 …E k ），DTwo samples are randomly selectedx j 、x k The method comprises the steps of carrying out a first treatment on the surface of the r represents the number of attributes;
，
substituting the following formula to calculatex j Andx k euclidean distance of (c):
，
calculating Euclidean distance between different samples, and substituting the Euclidean distance into the following formula to obtainx k Data density of data sample points：
，
Selecting data densityMaximum data sample point is used as initial clustering centerC 1 ；
Step 5.22: sequentially calculating the points of the rest data samplesInitial clustering centerC 1 Selecting the data sample point with the largest distance as the next cluster center, sharingKA cluster center;
step 5.3: determining the number K of clustering centers according to the elbow method, and calculating to obtain the square sum of the error of the evaluation index of the elbow methodSSEDetermining the clustering quantity K according to the square error sum SSE of the evaluation indexes:
，
is C i Sample means in the cluster;
step 5.4: calculating the distance between each data sample point in the data set and the clustering center, dividing the data sample point into the class corresponding to the clustering center with the minimum distance, and recalculating the clustering center of each clustering class:
，
step 5.5: and continuously calculating the distance between each data sample point in the data set and the new cluster center, dividing the data sample point into the class corresponding to the new cluster center with the minimum distance, recalculating the cluster center of each cluster class, repeating the process until the cluster center of each class is not changed, and finishing the clustering to obtain the three-level label of the current two-level label.
The invention also discloses a UPFC-based equipment portrait generation system using the UPFC-based equipment portrait generation method, which comprises a UPFC equipment operation data acquisition preprocessing module, a primary label setting module, a secondary label setting module, a tertiary label setting module and a UPFC equipment portrait generation module.
The UPFC equipment operation data acquisition preprocessing module is used for obtaining the operation data of the UPFC equipment and cleaning and preprocessing the operation data;
the primary label setting module is used for setting basic static information, running dynamic information and performance evaluation information of the UPFC equipment as primary labels;
the secondary label setting module is used for dividing the primary label according to UPFC management and analysis dimensions to obtain equipment information, application information, running state information, running fault information, equipment maintenance information, quality evaluation information and function evaluation information, and the equipment information, the application information, the running state information, the running fault information, the equipment maintenance information, the quality evaluation information and the function evaluation information are used as secondary labels of UPFC equipment;
the third-level tag setting module is used for subdividing the second-level tag to obtain rated parameter information, equipment specification information, operational age information, equipment state information, electrical data information, equipment fault information, overhaul stop time information, performance test information, operation environment information, equipment quality grade information, equipment performance effect information, family quality history information, equipment technical indexes, equipment bearing capacity indexes and scheduling management indexes, and the third-level tag is used as a third-level tag of UPFC equipment;
and the UPFC equipment portrait generation module is used for acquiring the numerical values of all three-level labels to form the equipment portrait of the UPFC equipment.
The invention also discloses a terminal, which comprises a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is operative in accordance with the instructions to perform steps in accordance with the UPFC-based device representation generation method described above.
The invention also discloses a computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of the UPFC-based device portrait generation method described above.
Compared with the prior art, the method has the beneficial effects that the device image of the UPFC is constructed based on the data of each dimension in the UPFC operation process. After preprocessing the collected UPFC data, setting a first-level label, a second-level label and a third-level label of the UPFC, and then acquiring the third-level label of the UPFC, wherein three modes mainly comprise direct acquisition, direct calculation and K-means cluster analysis. And selecting a clustering center and a K value according to K-means clustering analysis, and completing the acquisition of the three-level label. The device image framework of the whole UPFC is built, the multidimensional operation characteristics of the device are clearly and comprehensively displayed, the characteristics and the state of the UPFC are more intuitively known, and the comprehensive analysis of UPFC operation data and the multidirectional management and control of the UPFC device are realized.
Drawings
Fig. 1 is a flowchart of a device portrait creation method based on UPFC in the present invention.
FIG. 2 is a diagram of a UPFC device portrait model architecture according to an embodiment of the present invention.
FIG. 3 is a graph of the square sum of error SSE versus the number of cluster centers K in the present invention.
Fig. 4 is a block diagram of a UPFC-based device representation generation system in accordance with the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
The described embodiments of the invention are only some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art without making any inventive effort, are within the scope of the present invention.
Aiming at the defects of the prior art, the invention provides a device portrait generation method based on UPFC, which is characterized in that after UPFC device data are acquired, corresponding data information is converted into corresponding label information from different dimensions by acquiring all-dimensional data in the UPFC operation process, and finally, the UPFC device portrait is formed according to the label information, so that the operation characteristics of all dimensions of the device are displayed more clearly and comprehensively, the characteristics and the states of UPFC are displayed intuitively, the comprehensive analysis of UPFC operation data and the comprehensive control of UPFC devices are realized, and the reliability and the stability of regional power grid operation are improved.
Referring to fig. 1, the device portrait generating method based on UPFC disclosed by the invention comprises the following steps:
step 1: and obtaining the operation data of the UPFC equipment, and cleaning and preprocessing the operation data.
According to a specific embodiment, in step 1, the obtaining operation data of the UPFC further includes:
step 1.1: basic information of equipment such as equipment specifications, rated parameters, models, quality grades and the like is obtained through an enterprise resource planning system (ERP).
Step 1.2: the operation information such as voltage level and the like, the basic information of equipment such as operation years and the like, the performance evaluation information such as equipment test conditions and the like, and the UPFC fault information such as faults, defects or hidden dangers and the like are obtained through an equipment operation and maintenance management system (PMS).
Step 1.3: UPFC service information, system operation schedule information, etc. are obtained through a schedule management system (OMS).
Step 1.4: UPFC engineering control device is used for obtaining UPFC performance evaluation information such as transformer load rate, transformer conversion rate and the like, and UPFC operation electric data such as current, power and the like.
Step 1.5: and obtaining the environment information where the UPFC operates by installing a sensor.
The operation data cleaning pretreatment specifically comprises the steps of carrying out data cleaning treatment such as duplication removal, invalidation removal, abnormality removal and the like after the operation data is obtained.
Step 2: and setting the basic static information, the running dynamic information and the performance evaluation information of the UPFC equipment as primary labels.
The method comprises the steps of taking the whole UPFC as an analysis unit, taking actual UPFC control as a target, setting a UPFC primary label, covering three aspects of basic static information, running dynamic information and performance evaluation information, and particularly combing the basic information, the running information, maintenance and repair and performance evaluation. Table 1 shows the primary labels of the portrait label system of the UPFC equipment according to the embodiment of the invention.
TABLE 1
| Label name | Label category | Label description |
| Basic information | Basic static information tag | The long-term inherent properties of UPFC remain substantially unchanged or change very slowly |
| Operation information | Running dynamic information tags | The tag needs to be updated in time as time goes by |
| Maintenance and repair equipment | Running dynamic information tags | The tag needs to be updated in time as time goes by |
| Evaluation of Performance | Performance evaluation information tag | Recording information with evaluation function obtained by summarizing and summarizing in UPFC operation process |
Wherein the basic information belongs to a basic static information label, is a long-term inherent attribute of UPFC, and basically keeps unchanged or changes slowly; the operation information and maintenance preparation belong to an operation dynamic information label which needs to be updated in time along with time change; the performance evaluation belongs to a performance evaluation information label, and information with evaluation effect, which is summarized and obtained in the UPFC operation process, is recorded.
Step 3: and dividing the primary label according to UPFC management and analysis dimensions to obtain equipment information, application information, running state information, running fault information, equipment maintenance information, quality evaluation information and function evaluation information, wherein the equipment information, the application information, the running state information, the running fault information, the equipment maintenance information, the quality evaluation information and the function evaluation information are used as secondary labels of UPFC equipment.
And carrying out UPFC management and dimension analysis materialization on the basis of the primary label, and setting a UPFC secondary label to cover various aspects of equipment information, application information, running state, running faults, equipment maintenance, quality evaluation and function evaluation. Table 2 shows the second level labels of the UPFC device portrait label system according to an embodiment of the present invention.
TABLE 2
| Primary label | Two-stage label | Number of labels |
| Basic information | Device information and operation information | 2 |
| Operation information | Daily UPFC running time, UPFC accumulated running time, number of running fault devices, class of running fault devices and running fault processing time | 5 |
| Maintenance and repair equipment | During the standby stop, the standby time, the quantity of the overhaul fault equipment, the type of the overhaul fault equipment and the overhaul processing time | 5 |
| Evaluation of Performance | Overall evaluation, focus, quality evaluation, functional evaluation | 4 |
The basic information comprises two secondary labels, namely equipment information and application information; the operation information comprises five secondary labels of UPFC daily operation time, UPFC accumulated operation time, operation fault equipment quantity, operation fault equipment category and operation fault processing time; the maintenance and repair comprises five secondary labels of maintenance and repair stop time, maintenance and repair time, number of maintenance and repair fault equipment, maintenance and repair fault equipment category and maintenance and repair treatment time; the performance evaluation comprises four secondary labels of overall evaluation, important attention, quality evaluation and functional evaluation.
Step 4: subdividing the secondary label to obtain rated parameter information, equipment specification information, operational age information, equipment state information, electrical data information, equipment fault information, overhaul stop time information, performance test information, operation environment information, equipment quality grade information, equipment performance effect information, family quality history information, equipment technical indexes, equipment bearing capacity indexes and scheduling management indexes, and using the secondary label as a tertiary label of UPFC equipment.
The method is further characterized in that a UPFC three-level label is set on the basis of the two-level label, and the UPFC three-level label comprises multiple aspects of rated parameters, equipment specifications, operational years, equipment states, electrical data, equipment fault information, maintenance and stoppage time, performance tests, operation environments, equipment quality grades, equipment performance effects, family quality histories, equipment technical indexes, equipment bearing capacity indexes and scheduling management indexes.
The equipment information secondary label comprises two tertiary labels of rated parameters and equipment specifications; the operation information secondary labels comprise two tertiary labels of operation years and equipment states; the operation state secondary label comprises a tertiary label of electric data such as current, power and the like; the operation fault secondary label comprises a tertiary label of equipment fault information; the equipment overhaul secondary label comprises two tertiary labels for overhaul stop and performance test; the quality evaluation secondary label comprises three tertiary labels of equipment quality grade, equipment performance effect and family history quality; the function evaluation secondary label comprises three tertiary labels including equipment technical indexes, equipment bearing capacity indexes and scheduling management indexes. The UPFC device representation model of fig. 2 shows specific settings for primary, secondary, and tertiary labels.
Step 5: and excavating data generated by UPFC operation of the three-level label by adopting a cluster analysis algorithm to obtain the numerical value of the three-level label, and forming the equipment portrait of the UPFC equipment.
After three-level labels are defined, the UPFC three-level labels are required to be obtained, and three modes of obtaining the three-level labels mainly include direct obtaining, direct calculating and clustering analysis. The method can directly obtain three-level tag values aiming at rated parameters, equipment specifications, equipment states, equipment fault information and equipment quality grades, can directly calculate to obtain the three-level tag values aiming at operational years and maintenance stoppage, and can mine data generated by UPFC operation aiming at the three-level tags which cannot be obtained by the two methods by adopting a K-means cluster analysis algorithm to obtain the numerical values of the three-level tags.
According to a specific embodiment, the step 5 further includes:
step 5.1: aiming at the secondary labels needing to mine and induce data in different periods, the dimension reduction treatment is carried out before the cluster analysis to obtain a one-dimensional data set D. By setting up the collectedmOf individual periodsnThe individual tag data set isW。Is the nth tag data of the mth period.
，
Defining a datasetWMean value of (1)Standard deviation isSWill beWSubtracting the column data level from each column data of (1)The mean value is de-centered and then normalized. Obtaining a standardized matrixV。
Calculating a matrix using formula (1)VCovariance matrix of (2)I is [1, m ]]ObtainingVMaximum eigenvalue and corresponding normalized eigenvector thereofαSubstituting the data into the formula (2) to calculate a dimensionality-reduced data setD。
The expression of the formula (1) is as follows:
(1)
the expression of the formula (2) is as follows:
(2)
step 5.2: the secondary labels which do not need mining generalization for data of different periods are also defined as D, and are aimed at a data setDAnd carrying out K-means cluster analysis to obtain the numerical value of the three-level label. Taking the number of the second-level tag maintenance fault devices under UPFC first-level tag maintenance and repair as an example, the third-level tag is obtained. K-means cluster analysis relies on the selection of cluster centers and the determination of the number of clusters K, and the selection of the cluster centers can be divided into initial cluster center selection and other cluster center selection. The method specifically comprises the following steps:
step 5.21: selecting initial clustering center, and setting sample data set obtained by collection (processing)D=（x 1 ，x 2 ，x 3 …x n ) The cluster category is expressed asE=（E 1 ，E 2 ，E 3 …E k ），DTwo samples are randomly selectedx j 、x k R represents the number of attributes.
，
Substituting the obtained product into the formula (3) to calculatex j Andx k the expression is:
（3）
calculating Euclidean distance between different samples, and substituting the Euclidean distance into a formula (4) to obtainx k Data density of data sample pointsThe expression is:
（4）
selecting data densityMaximum data sample point is used as initial clustering centerC 1 。
Step 5.22: calculating the rest data sample points to an initial clustering center through a formula (3)C 1 Selecting the data sample point with the largest distance as the next cluster center, sharingKAnd clustering centers.
Step 5.3: determining the number K of clustering centers according to the elbow method, and calculating the square sum of the error of the evaluation index of the elbow method by using a formula (5)SSEAnd determining the clustering quantity K according to the square error sum SSE of the evaluation indexes.
(5)
Is C i Sample means in the cluster.
Comparing the different cluster numbers K with reference to FIG. 3 based on actual plant operation and historical dataSum of squares errorSSEThe value interval of K is [1,8 ]]It can be seen thatKWhen the number of the samples is =3,SSEthe number K of clustering centers of the number of the second-level tag overhaul fault devices is selected to be 3, and three third-level tags under the number of the tag overhaul fault devices can be expressed as fewer overhaul fault devices, normal overhaul fault devices and more overhaul fault devices in combination with actual production.
Step 5.4: and (3) calculating the distance between each data sample point in the data set and the clustering center through a formula, and dividing the data sample points into classes corresponding to the clustering centers with the smallest distance. The cluster center of each cluster category is recalculated by equation (6).
The expression of the formula (6) is as follows:
(6)
step 5.5: and continuously calculating the distance between each data sample point in the data set and the new cluster center, and dividing the data sample points into classes corresponding to the new cluster center with the minimum distance. And (3) recalculating the clustering center of each clustering category through a formula (6), and repeating the process until the clustering center of each category is not changed, and finishing clustering to obtain the tertiary label of the current secondary label.
Compared with the prior art, the method has the beneficial effects that the device image of the UPFC is constructed based on the data of each dimension in the UPFC operation process. After preprocessing the collected UPFC data, setting a first-level label, a second-level label and a third-level label of the UPFC, and then acquiring the third-level label of the UPFC, wherein three modes mainly comprise direct acquisition, direct calculation and K-means cluster analysis. And selecting a clustering center and a K value according to K-means clustering analysis, and completing the acquisition of the three-level label. The device image framework of the whole UPFC is built, the multidimensional operation characteristics of the device are clearly and comprehensively displayed, the characteristics and the state of the UPFC are more intuitively known, and the comprehensive analysis of UPFC operation data and the multidirectional management and control of the UPFC device are realized.
The present invention may be a system, method, and/or computer program product. Referring to fig. 4, the invention also discloses a device portrait generation system based on the UPFC based device portrait generation method, which comprises a UPFC device operation data acquisition preprocessing module 1, a primary label setting module 2, a secondary label setting module 3, a tertiary label setting module 4 and a UPFC device portrait generation module 5.
The UPFC equipment operation data acquisition preprocessing module 1 is used for obtaining the operation data of the UPFC equipment and cleaning and preprocessing the operation data;
the primary label setting module 2 is used for setting basic static information, running dynamic information and performance evaluation information of the UPFC equipment as primary labels;
the secondary label setting module 3 is configured to divide the primary label according to UPFC management and analysis dimensions, to obtain device information, operation status information, operation failure information, device maintenance information, quality evaluation information, and function evaluation information, as a secondary label of the UPFC device;
the third-level tag setting module 4 is configured to subdivide the second-level tag to obtain rated parameter information, equipment specification information, operational life information, equipment state information, electrical data information, equipment failure information, overhaul stop time information, performance test information, operation environment information, equipment quality grade information, equipment performance effect information, family quality history information, equipment technical indexes, equipment bearing capacity indexes and scheduling management indexes, and use the rated parameter information, the equipment specification information, the operational life information, the equipment state information, the electrical data information, the equipment failure information, the overhaul stop time information, the performance test information, the operation environment information, the equipment quality grade information, the equipment performance effect information, the family quality history information, the equipment technical indexes, the equipment bearing capacity indexes and the scheduling management indexes as third-level tags of the UPFC equipment;
the UPFC equipment portrait generation module 5 is used for mining data generated by UPFC operation on the three-level label by adopting a cluster analysis algorithm to obtain the numerical value of the three-level label, and forming the equipment portrait of the UPFC equipment.
Based on the foregoing UPFC-based device representation generation method, one skilled in the art will readily recognize that a computer program product may be derived based on the spirit of the present invention. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for causing a processor to implement aspects of the present disclosure. The invention also comprises a terminal which comprises a processor and a storage medium; the storage medium is used for storing instructions; the processor is operative in accordance with the instructions to perform steps in accordance with the UPFC-based device representation generation method described above.
The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.
The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.
Computer program instructions for performing the operations of the present disclosure can be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, c++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of remote computers, the remote computer may be connected to the user computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (e.g., connected through the internet using an internet service provider). In some embodiments, aspects of the present disclosure are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information of computer readable program instructions, which can execute the computer readable program instructions.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (14)
1. The device portrait generation method based on UPFC is characterized by comprising the following steps:
step 1: obtaining operation data of UPFC equipment, and cleaning and preprocessing the operation data;
step 2: setting basic static information, running dynamic information and performance evaluation information of the UPFC equipment as primary labels;
step 3: dividing the primary label according to UPFC management and analysis dimensions to obtain equipment information, application information, running state information, running fault information, equipment maintenance information, quality evaluation information and function evaluation information, wherein the equipment information, the application information, the running state information, the running fault information, the equipment maintenance information, the quality evaluation information and the function evaluation information are used as secondary labels of UPFC equipment;
step 4: subdividing the secondary label to obtain rated parameter information, equipment specification information, operational age information, equipment state information, electrical data information, equipment fault information, overhaul stop time information, performance test information, running environment information, equipment quality grade information, equipment performance effect information, family quality history information, equipment technical indexes, equipment bearing capacity indexes and scheduling management indexes, and using the secondary label as a tertiary label of UPFC equipment;
step 5: and excavating data generated by UPFC operation of the three-level label by adopting a cluster analysis algorithm to obtain the numerical value of the three-level label, and forming the equipment portrait of the UPFC equipment.
2. The UPFC-based device representation generation method according to claim 1, wherein in step 1, the obtaining operation data of the UPFC device further comprises:
step 1.1: obtaining basic information of equipment, including equipment specifications, rated parameters, models and quality grades, through an enterprise resource planning system (ERP);
step 1.2: obtaining operation information, equipment basic information, performance evaluation information and UPFC fault information through an equipment operation and maintenance management system (PMS);
step 1.3: obtaining UPFC overhaul information and system operation scheduling information through a scheduling management system (OMS);
step 1.4: obtaining transformer load rate and transformer conversion rate, and current and power through a UPFC engineering control device;
step 1.5: and obtaining the environment information where the UPFC operates by installing a sensor.
3. The UPFC-based device representation generation method of claim 2, wherein the substantially static information tag in the primary tag is a long-term inherent attribute of UPFC; the running dynamic information label comprises running information and maintenance, and the label is updated in time along with time change; the performance evaluation information label records information with evaluation function which is summarized and obtained in the UPFC operation process.
4. The UPFC-based device representation generation method of claim 3, wherein a secondary tag of the basic static information tags includes device information and operation information; the secondary labels in the operation information comprise UPFC daily operation time, UPFC accumulated operation time, operation fault equipment quantity, operation fault equipment category and operation fault processing time; the second-level label in the maintenance service comprises a service stop time, a service duration, the number of maintenance fault devices, the types of the maintenance fault devices and a maintenance processing duration; the secondary labels in the performance evaluation information labels comprise overall evaluation, important attention, quality evaluation and functional evaluation.
5. The UPFC-based device representation generation method of claim 4, wherein a tertiary label in the device information secondary label comprises a rated parameter and a device specification; the third-level label in the second-level label of the operation information comprises a service life and a device state; the three-level tag in the two-level tag in the running state comprises electric data such as current, power and the like; the third-level label in the operation fault second-level label comprises equipment fault information; the third-level label in the equipment overhaul second-level label comprises overhaul stop time and performance test; the third-level label in the quality evaluation second-level label comprises equipment quality grade, equipment performance effect and family history quality; the three-level label in the function evaluation two-level label comprises equipment technical indexes, equipment bearing capacity indexes and scheduling management indexes.
6. The UPFC-based device representation generation method of claim 5, wherein the cluster analysis further comprises:
step 5.1: aiming at the secondary labels needing to mine and induce data in different periods, before cluster analysis, performing dimension reduction processing through the following processes to obtain a one-dimensional data set D:
by setting up the collectedmOf individual periodsnThe individual tag data set isW；N-th tag data for the m-th period;
，
defining a datasetWMean value of (1)Standard deviation isSWill beWSubtracting the average value of the column data from each column data of the column data to perform decentration, and then performing standardization; obtaining a standardized matrixV；
Calculating a matrixVCovariance matrix of (2)I is [1, m ]]：
，
Determination ofVMaximum eigenvalue and corresponding normalized eigenvector thereofαSubstituting and calculating to obtain a dimensionality-reduced data setD：
，
Step 5.2: for a secondary label which does not need mining and induction for data of different periods, aiming at a data setDK-means cluster analysis is carried out to obtain a three-level markA value of the label; k-means cluster analysis relies on the selection of cluster centers and the determination of the number K of clusters, wherein the selection of the cluster centers is divided into initial cluster center selection and other cluster center selection; the method specifically comprises the following steps:
step 5.21: selecting an initial clustering center, and setting a collected sample data setD=（x 1 ，x 2 ，x 3 …x n ) The cluster category is expressed asE=（E 1 ，E 2 ，E 3 …E k ），DTwo samples are randomly selectedx j 、x k The method comprises the steps of carrying out a first treatment on the surface of the r represents the number of attributes;
，
substituting the following formula to calculatex j Andx k euclidean distance of (c):
，
calculating Euclidean distance between different samples, and substituting the Euclidean distance into the following formula to obtainx k Data density of data sample points：
，
Selecting data densityMaximum data sample point is used as initial clustering centerC 1 ；
Step 5.22: sequentially calculating the rest data sample points to an initial clustering centerC 1 Selecting the data sample point with the largest distance as the next cluster center, sharingKA cluster center;
step 5.3: determining the number K of clustering centers according to the elbow method, and calculating to obtain the square sum of the error of the evaluation index of the elbow methodSSEDetermining the clustering quantity K according to the square error sum SSE of the evaluation indexes:
，
is C i Sample means in the cluster;
step 5.4: calculating the distance between each data sample point in the data set and the clustering center, dividing the data sample point into the class corresponding to the clustering center with the minimum distance, and recalculating the clustering center of each clustering class:
，
step 5.5: and continuously calculating the distance between each data sample point in the data set and the new cluster center, dividing the data sample point into the class corresponding to the new cluster center with the minimum distance, recalculating the cluster center of each cluster class, repeating the process until the cluster center of each class is not changed, and finishing the clustering to obtain the three-level label of the current two-level label.
7. A UPFC-based device representation generation system using the UPFC-based device representation generation method of any one of claims 1-6, comprising a UPFC device operation data acquisition preprocessing module, a primary label setting module, a secondary label setting module, a tertiary label setting module, and a UPFC device representation generation module, wherein:
the UPFC equipment operation data acquisition preprocessing module is used for obtaining the operation data of the UPFC equipment and cleaning and preprocessing the operation data;
the primary label setting module is used for setting basic static information, running dynamic information and performance evaluation information of the UPFC equipment as primary labels;
the secondary label setting module is used for dividing the primary label according to UPFC management and analysis dimensions to obtain equipment information, application information, running state information, running fault information, equipment maintenance information, quality evaluation information and function evaluation information, and the equipment information, the application information, the running state information, the running fault information, the equipment maintenance information, the quality evaluation information and the function evaluation information are used as secondary labels of UPFC equipment;
the third-level tag setting module is used for subdividing the second-level tag to obtain rated parameter information, equipment specification information, operational age information, equipment state information, electrical data information, equipment fault information, overhaul stop time information, performance test information, operation environment information, equipment quality grade information, equipment performance effect information, family quality history information, equipment technical indexes, equipment bearing capacity indexes and scheduling management indexes, and the third-level tag is used as a third-level tag of UPFC equipment;
and the UPFC equipment portrait generation module is used for mining data generated by UPFC operation on the three-level label by adopting a cluster analysis algorithm to obtain the numerical value of the three-level label, so as to form the equipment portrait of the UPFC equipment.
8. The UPFC-based device representation generation system of claim 7, wherein the UPFC device runs a data acquisition preprocessing module further configured to:
obtaining basic information of equipment, including equipment specifications, rated parameters, models and quality grades, through an enterprise resource planning system (ERP);
obtaining operation information, equipment basic information, performance evaluation information and UPFC fault information through an equipment operation and maintenance management system (PMS);
obtaining UPFC overhaul information and system operation scheduling information through a scheduling management system (OMS);
obtaining transformer load rate and transformer conversion rate, and current and power through a UPFC engineering control device;
and obtaining the environment information where the UPFC operates by installing a sensor.
9. The UPFC-based device representation generation system of claim 8, wherein a substantially static information tag in the primary tag is a long-term inherent attribute of UPFC; the running dynamic information label comprises running information and maintenance, and the label is updated in time along with time change; the performance evaluation information label records information with evaluation function which is summarized and obtained in the UPFC operation process.
10. The UPFC-based device representation generation system of claim 9, wherein a secondary tag of the substantially static information tags comprises device information and operational information; the secondary labels in the operation information comprise UPFC daily operation time, UPFC accumulated operation time, operation fault equipment quantity, operation fault equipment category and operation fault processing time; the second-level label in the maintenance service comprises a service stop time, a service duration, the number of maintenance fault devices, the types of the maintenance fault devices and a maintenance processing duration; the secondary labels in the performance evaluation information labels comprise overall evaluation, important attention, quality evaluation and functional evaluation.
11. The UPFC-based device representation generation system of claim 10, wherein a tertiary label in the device information secondary label comprises a nominal parameter, a device specification; the third-level label in the second-level label of the operation information comprises a service life and a device state; the three-level tag in the two-level tag in the running state comprises electric data such as current, power and the like; the third-level label in the operation fault second-level label comprises equipment fault information; the third-level label in the equipment overhaul second-level label comprises overhaul stop time and performance test; the third-level label in the quality evaluation second-level label comprises equipment quality grade, equipment performance effect and family history quality; the three-level label in the function evaluation two-level label comprises equipment technical indexes, equipment bearing capacity indexes and scheduling management indexes.
12. The UPFC-based device representation generation system of claim 11, wherein the UPFC device representation generation module is further to:
aiming at the secondary labels needing to mine and induce data in different periods, before cluster analysis, performing dimension reduction processing through the following processes to obtain a one-dimensional data set D:
by setting up the collectedmOf individual periodsnThe individual tag data set isW；N-th tag data for the m-th period;
，
defining a datasetWMean value of (1)Standard deviation isSWill beWSubtracting the average value of the column data from each column data of the column data to perform decentration, and then performing standardization; obtaining a standardized matrixV；
Calculating a matrixVCovariance matrix of (2)I is [1, m ]]：
，
Determination ofVMaximum eigenvalue and corresponding normalized eigenvector thereofαSubstituting and calculating to obtain a dimensionality-reduced data setD：
，
For not requiring needlesSecond-level tags for mining and summarizing data of different periods, aiming at data setsDPerforming K-means cluster analysis to obtain the numerical value of the three-level label; k-means cluster analysis relies on the selection of cluster centers and the determination of the number K of clusters, wherein the selection of the cluster centers is divided into initial cluster center selection and other cluster center selection; the method specifically comprises the following steps:
selecting an initial clustering center, and setting a collected sample data setD=（x 1 ，x 2 ，x 3 …x n ) The cluster category is expressed asE=（E 1 ，E 2 ，E 3 …E k ），DTwo samples are randomly selectedx j 、x k The method comprises the steps of carrying out a first treatment on the surface of the r represents the number of attributes;
，
substituting the following formula to calculatex j Andx k euclidean distance of (c):
，
calculating Euclidean distance between different samples, and substituting the Euclidean distance into the following formula to obtainx k Data density of data sample points：
，
Selecting data densityMaximum data sample point is used as initial clustering centerC 1 ；
Sequentially calculating the rest data sample points to an initial clustering centerC 1 Selecting the data sample point with the largest distance as the next cluster center, sharingKA cluster center;
determining the number K of clustering centers according to the elbow method, and calculating to obtain the square sum of the error of the evaluation index of the elbow methodSSE，Determining the clustering quantity K according to the square error sum SSE of the evaluation indexes:
，
is C i Sample means in the cluster;
calculating the distance between each data sample point in the data set and the clustering center, dividing the data sample point into the class corresponding to the clustering center with the minimum distance, and recalculating the clustering center of each clustering class:
，
and continuously calculating the distance between each data sample point in the data set and the new cluster center, dividing the data sample point into the class corresponding to the new cluster center with the minimum distance, recalculating the cluster center of each cluster class, repeating the process until the cluster center of each class is not changed, and finishing the clustering to obtain the three-level label of the current two-level label.
13. A terminal comprising a processor and a storage medium; the method is characterized in that:
the storage medium is used for storing instructions;
the processor being operative in accordance with the instructions to perform the steps of the UPFC-based device representation generating method of any one of claims 1-6.
14. A computer readable storage medium having stored thereon a computer program, wherein the program when executed by a processor implements the steps of the UPFC-based device representation generating method of any one of claims 1-6.
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Inventor after: Chen Zhijun Inventor after: Yu Jiayan Inventor after: Huang Yufeng Inventor after: Tong Ming Inventor after: Zhang Mengjie Inventor after: Wang Chuyang Inventor after: Wang Jun Inventor after: Chen Bin Inventor after: Yu Chao Inventor after: Zhang Ke Inventor after: Yang Xiangyu Inventor after: Zhang Bin Inventor after: Pang Wenchen Inventor after: Zhou Qiang Inventor before: Chen Zhijun Inventor before: Huang Yufeng Inventor before: Tong Ming Inventor before: Zhang Mengjie Inventor before: Wang Chuyang Inventor before: Wang Jun Inventor before: Chen Bin Inventor before: Yu Chao Inventor before: Zhang Ke Inventor before: Yang Xiangyu Inventor before: Zhang Bin Inventor before: Pang Wenchen Inventor before: Zhou Qiang |