WO2023130763A1 - 变压器状态分析方法、系统、设备和存储介质 - Google Patents
变压器状态分析方法、系统、设备和存储介质 Download PDFInfo
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- 230000002159 abnormal effect Effects 0.000 claims description 8
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- G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
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- G—PHYSICS
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- G06F2119/08—Thermal analysis or thermal optimisation
Definitions
- the present application relates to the field of electric power automation, in particular, to a transformer state analysis method, system, equipment and storage medium.
- the transformer has complete monitoring measures for its current, voltage, power, temperature and other power grid operation data.
- Some substations are also equipped with online detection devices and sound monitoring devices for monitoring partial discharge, gas in oil, noise, etc.
- traditional power grid operation data is difficult to fully evaluate the operation status of transformers.
- the current real-time monitoring system can receive transformer overload alarms, gas anomaly alarms, etc., but cannot perform a complete assessment of the transformer status.
- Each exemplary embodiment of the present application provides a transformer state analysis method, system, device, and storage medium, which can be used to solve the problems of data source, mapping, and calculation of transformer equipment, and can realize the mapping of digital twins to physical models, thereby realizing Intelligent operation and maintenance of transformer equipment.
- a transformer state analysis method including:
- the state data of the transformer calculated by the digital twin is obtained for providing control decision prompts.
- the known data is sampled and acquired through an external system, and includes static data, dynamic data, and business data of the transformer, specifically including sampled current value, sampled oil temperature, ambient temperature, and transformer structured data.
- the missing data includes electrical data, temperature field data and electromagnetic field data, specifically including equivalent initial load factor, stratified oil temperature and electromagnetic field distribution data in the transformer.
- the step of calculating the missing data in real time according to the known data includes:
- the electromagnetic field distribution data is calculated through finite element analysis.
- the formula for calculating the equivalent initial load factor is:
- I *(eqv0) represents the equivalent initial load factor
- I *(i) represents the per-unit value of the transformer sampling data obtained by the i+1th sampling when the transformer is known to be in normal operation
- the value range of i is from 0 to n
- n is a set natural number
- ⁇ t 0 represents the preset initial value of the data sampling interval data, when i is greater than or equal to 1
- ⁇ t i represents that the transformer is in normal operation
- t is the data sampling interval between the i-th sampling and the i-1-th sampling
- t represents the preset initial load equivalent time.
- the finite element calculation formula of the layered oil temperature in the transformer is:
- ⁇ is the known thermal conductivity along each coordinate
- x, y, and z are known spatial coordinates
- T is the layered oil temperature in the transformer.
- the finite element calculation formula of the electromagnetic field distribution data is:
- A is the axial vector magnetic potential, that is, the electromagnetic field distribution data, is the known horizontal reluctance, is the known vertical magnetoresistivity, J is the known source current density, and x, y are the known spatial coordinates.
- the step of mapping the known data and the missing data to the three-dimensional model to form the digital twin includes:
- the three-dimensional model is divided into a limited number of non-overlapping units, and in each of the units, multiple nodes are selected as calculation interpolation points for the known data and the missing data.
- the step of performing real-time data synchronization between the digital twin and the transformer through the variable-period data update includes:
- the static data and the business data are pushed by the external system to trigger updates or update periodically, and save the updated data to a relational database;
- the dynamic data is set as a separate update of different periods; and all updates are set as a unified period, and the updated data is saved to a real-time library; and the digital twin is obtained from the relational library and The real-time library reads data to realize data synchronization with the transformer.
- the step of triggering or periodically updating the static data and the business data by the external system includes:
- the data update period of the external system is set as a fixed period, and the static data and the service data are updated in the fixed period.
- the step of acquiring the state data of the transformer calculated by the digital twin includes:
- a final stable runtime of the transformer is determined based on the stable runtime of the plurality of different states.
- the plurality of different states includes electrical states, thermal states and electromagnetic states.
- the formula for calculating the final stable running time of the transformer is:
- t e , t h , t m are the stable operation durations of the electrical state, the thermal state and the electromagnetic state respectively, and the calculation methods of t e , t h , t m are respectively:
- I *(eqv0) , T n , A n are the equivalent initial load coefficient, the layered oil temperature in the transformer and the electromagnetic field distribution data, respectively, Se , Sh h , S m are the preset influence coefficients .
- the step of providing control decision prompts includes:
- the warning level is set according to the final stable running time of the transformer, and corresponding prompts are provided.
- the providing a control decision prompt includes: providing a corresponding processing decision and establishing a priority according to different influencing factors in the calculation of the final stable running time of the transformer.
- the step of setting the warning level and providing the corresponding reminder includes:
- S represents the state value, when S is equal to 0, it means normal, when it is equal to 1, it means abnormal, when it is equal to 2, it means an alarm, t is the final stable operation time of the transformer, and t0 is a preset normal critical value , t 1 is the preset abnormal critical value.
- a transformer state analysis system including: a digital twin, configured to form a three-dimensional model by mapping equipment state data of the transformer, and perform real-time data synchronization with the transformer;
- An equipment status module configured to acquire the equipment status data of the transformer and perform real-time data synchronization with the digital twin
- the intelligent decision-making module is configured to provide control decision prompts according to the equipment state data of the transformer.
- an electronic device including: one or more processors;
- a storage device configured to store one or more programs
- the one or more processors are made to execute the steps in the aforementioned method.
- a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the aforementioned method is implemented.
- the problem of data integrity is solved by integrating the static data, dynamic data and business data of each system, and calculating the missing electrical, temperature field, and electromagnetic field data in real time.
- variable cycle refresh combined with 3D modeling, and using finite element analysis, the mapping of digital twins to physical models can be realized, and the problem of insufficient real-time performance of general analysis methods can be solved.
- Fig. 1 is a flowchart of a transformer state analysis method according to an embodiment of the present application.
- Fig. 2 is a schematic diagram of a transformer state analysis system according to an embodiment of the present application.
- Fig. 3 is a flowchart of digital twin data refresh in an embodiment of the present application.
- Fig. 4 is a flow chart of transformer state prediction according to an embodiment of the present application.
- Fig. 5 is a structural block diagram of a transformer state analysis system according to an embodiment of the present application.
- Fig. 6 is a block diagram of an electronic device according to an embodiment of the present application.
- Example embodiments will now be described more fully with reference to the accompanying drawings.
- Example embodiments may, however, be embodied in many forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.
- the same reference numerals denote the same or similar parts in the drawings, and thus their repeated descriptions will be omitted.
- This application provides a transformer state analysis method, system, equipment and storage medium, using digital twin technology as a means to solve the state evaluation and prediction of transformer equipment, and realize the mapping of digital twin body to physical model, so as to realize the intelligent operation of transformer equipment dimension.
- Fig. 1 shows a flowchart of a transformer state analysis method according to an exemplary embodiment of the present application.
- the known data of the transformer is acquired, and the missing data is calculated in real time based on the known data.
- the known data is acquired through external system sampling, and includes static data, dynamic data and business data.
- the known data and the missing data are mapped to a three-dimensional model to form a digital twin.
- the three-dimensional model can be divided into a finite number of non-overlapping units, and in each unit, a certain number of nodes can be selected as calculation interpolation points for known data and the missing data.
- the 3D model can be divided into equipment model, building model and environment model, and establish a functional relationship with known static data, dynamic data, business data and calculated electrical data, temperature field data, and electromagnetic field data to form a digital twin .
- the real-time data synchronization between the digital twin and the transformer is carried out through variable-period data update.
- the static data and business data can be updated by an external system or updated periodically (for example, daily), and the updated data can be stored in the relational database.
- the dynamic data can be set to be updated separately at different periods according to the data type of the dynamic data, and at the same time set a unified period for all updates, and save the updated data to the real-time database.
- the digital twin can read data from the relational database and the real-time database to achieve data synchronization with the transformer.
- the stable running time of the electrical state, thermal state and electromagnetic state of the transformer can be obtained, and the final running time of the transformer can be obtained according to the stable running time of each state.
- the final running time of the transformer may be the minimum value among the stable running time of the transformer's electrical state, thermal state and electromagnetic state.
- Fig. 2 shows a schematic diagram of a transformer state analysis system according to an exemplary embodiment of the present application.
- the known data of the transformer is obtained, and the missing data is calculated according to the obtained data. Map the acquired known data and calculated missing data to a disassembled 3D model to form a digital twin.
- known data can be sampled and acquired by an external system, including: static data, dynamic data and business data.
- Static data can include: AC side voltage level, DC side voltage level, no-load loss, load loss, operating status, short-circuit impedance, maximum operating current before short-circuit, maximum operating voltage before short-circuit, power factor angle before short-circuit, converter transformer network Side minimum operating voltage, and rated power, etc.
- Dynamic data can include: feeder voltage, side voltage of A set of protection valve, side voltage of B set of protection valve, side voltage of C set of protection valve, partial discharge frequency, peak value of partial discharge discharge signal, capacitive monitoring unbalanced current, capacitive monitoring, etc.
- Business data may include: fault events, fault records, defect records, bushing maintenance test work, converter transformer maintenance test work, bushing inspection records, converter transformer inspection records, bushing tests, tap changer tests, and Rheological off-line oil chromatography test, etc.
- the missing data is obtained by calculating known data, including: electrical data, temperature field data and electromagnetic field data.
- Electrical data may include an equivalent starting load factor, calculated by the following formula:
- I *(eqv0) represents the equivalent initial load factor
- I *(i) represents the per unit value of the transformer sampling data obtained from the i+1th sampling when the transformer is known to be in normal operation, and i is taken as 0 to n, n is a set natural number
- ⁇ t 0 represents the initial value of the preset data sampling interval data, when i is greater than or equal to 1
- ⁇ t i represents the ith sampling and the i-1th sampling when the transformer is in normal operation
- the data sampling interval between samples, t represents the preset initial load equivalent time.
- the temperature field data may include the stratified oil temperature in the transformer, and may be calculated by the following finite element analysis formula combined with a three-dimensional model:
- ⁇ is the known thermal conductivity along each coordinate, is the known heat generation rate per unit volume, x, y, z are known spatial coordinates, and T is the stratified oil temperature in the transformer.
- the electromagnetic field data may include electromagnetic field distribution data, and may be calculated by the following finite element analysis formula combined with a three-dimensional model:
- A is the axial vector magnetic potential, that is, the electromagnetic field distribution data, is the known horizontal reluctance, is the known vertical magnetoresistivity, J is the known source current density, and x, y are the known spatial coordinates.
- the three-dimensional model is divided into a finite number of non-overlapping units, and in each unit, a certain number of nodes are selected as calculation interpolation points for known data and missing data.
- the three-dimensional model may include a device model, a building model and an environment model, wherein the number of nodes is not limited and is only related to computer performance.
- variable period data updates can be performed to realize data synchronization between digital twins and physical objects (ie, transformers).
- the updated data pushed by the external system can be saved in the relational library.
- the update data of the external system can be updated periodically (for example, daily) and saved in the relational library.
- the static data can include factory configuration information of the transformer, etc., and can be obtained through the transformer information system.
- Business data can include transformer daily maintenance data, test data and logs, etc., and can be obtained through the transformer test system.
- different update cycles can be set according to different types of data to be updated to the real-time database separately, and at the same time, all types of data are updated under a unified cycle and saved to the real-time database.
- the dynamic data may include dynamic parameters of the transformer such as oil temperature and the like.
- the digital twin reads data from the relational database and the real-time database to achieve data synchronization with the transformer.
- the electrical state, thermal state and electromagnetic state of the transformer are obtained, and the final stable running time of the transformer is calculated, and control decision prompts are given at the same time.
- the stable operation durations of the electrical state, the thermal state and the electromagnetic state can be calculated respectively by using known data and missing data.
- the stable running time of the electrical state can be calculated by the following formula:
- I *(eqv0) is the equivalent initial load coefficient
- Se is a preset influence coefficient
- the stable operating time of the thermal state can be calculated by the following formula:
- T n is the stratified oil temperature in the transformer
- Sh is the preset influence coefficient
- the stable operating time of the electromagnetic state can be calculated by the following formula:
- a n is the electromagnetic field distribution data
- S m is a preset influence coefficient
- the final stable running time of the transformer is the minimum value of the stable running time of the electrical state, thermal state and electromagnetic state, namely:
- t e , t h , t m are the stable operation time of electrical state, thermal state and electromagnetic state respectively.
- the stable operation time of the electrical state, thermal state and electromagnetic state obtained by the digital twin, and the final stable operation time of the transformer the trend of transformer operation is predicted.
- different levels of prompts are provided.
- different processing decisions are provided, and priorities are set according to the stable duration of the electrical state, thermal state and electromagnetic state.
- Fig. 3 shows a flow chart of refreshing digital twin body data according to an exemplary embodiment of the present application.
- known data including static data, business data and dynamic data are obtained through an external system, and data updates are performed on the known data.
- the data update of the system can be triggered by the real-time push of the external system data update.
- the external system does not support data update subscription, you can set the data update cycle of the external system (such as daily or weekly, etc.), and update the system data in a fixed cycle.
- both the static data and the updated data of the business data can be stored in the relational database.
- the corresponding update cycle can be set to update to the real-time library separately.
- the current, voltage and power data of the transformer need to be updated to the real-time database in real time
- the gas detection data in oil can be set to be updated to the real-time database every hour.
- the digital twin reads the data in the relational database and the real-time database in real time, and calculates the state of the transformer based on this.
- Fig. 4 shows a flow chart of transformer state prediction according to an exemplary embodiment of the present application.
- the digital twin calculates the electrical state, thermal state, and electromagnetic state of the transformer based on known data and missing data, and converts them into the stable running time of the corresponding state.
- the stable operation of the transformer's electrical state, thermal state, and electromagnetic state can be obtained through the calculation formula of the stable operation time of the electrical state, the calculation formula of the stable operation time of the thermal state, and the calculation formula of the stable operation time of the electromagnetic state, respectively. duration.
- the warning level can be set through the status value and corresponding prompts can be provided.
- S represents the state value, equal to 0 means normal, equal to 1 means abnormal, equal to 2 means alarm, t is the final stable running time of the transformer, t 0 is the preset normal critical value, t 1 is the preset abnormal critical value value.
- the factors that affect the final stable running time of the transformer include electrical influence factors, thermal influence factors and electromagnetic influence factors. Provide corresponding processing decisions according to different influencing factors, and set priorities.
- the final stable running time of the transformer calculated by the electrical influence factors is 1 hour, and the preset abnormal critical value is 3 hours.
- the warning level is warning and needs to be dealt with immediately.
- the processing decision is to reduce the power, thereby reducing the transformer load.
- the final stable running time of the transformer calculated by thermal influence factors is 2 hours, and the preset abnormal critical value is 3 hours.
- the warning level is warning and needs to be dealt with immediately.
- the processing decision is to reduce the power and activate the fire prevention plan.
- steps in the flow chart of FIG. 1 are displayed sequentially as indicated by the arrows, these steps are not necessarily executed sequentially in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least some of the steps in Fig. 1 may include multiple sub-steps or multiple stages, these sub-steps or stages are not necessarily executed at the same time, but may be executed at different times, the execution of these sub-steps or stages The order is not necessarily performed sequentially, but may be performed alternately or alternately with at least a part of other steps or sub-steps or stages of other steps.
- Fig. 5 shows a structural block diagram of a transformer state analysis system according to an exemplary embodiment of the present application.
- a transformer state analysis system according to an exemplary embodiment of the present application will be described in detail below with reference to FIG. 5 .
- the transformer state analysis system includes a digital twin 501 , an equipment state module 503 , and an intelligent decision-making module 505 .
- the digital twin 501 is configured to be formed by mapping the equipment status data of the transformer to a three-dimensional model, and to perform real-time data synchronization with the transformer.
- the equipment status module 503 is configured to obtain the equipment status data of the transformer, and perform real-time data synchronization with the digital twin.
- the intelligent decision module 505 is configured to provide control decision prompts based on the equipment status data of the transformer.
- FIG. 6 shows a block diagram of an electronic device according to an exemplary embodiment of the present application.
- electronic device 600 is in the form of a general-purpose computing device.
- Components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different system components (including the storage unit 620 and the processing unit 610), a display unit 640, and the like.
- the storage unit stores program codes, and the program codes can be executed by the processing unit 610, so that the processing unit 610 executes the methods described in this specification according to various exemplary embodiments of the present application.
- the processing unit 610 may execute the method as shown in FIG. 1 .
- the processing unit 610 shown in FIG. 6 can also implement the control logic shown in FIGS. 2 to 4 when executing the computer program.
- the storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 6201 and/or a cache storage unit 6202 , and may further include a read-only storage unit (ROM) 6203 .
- RAM random access storage unit
- ROM read-only storage unit
- Storage unit 620 may also include a program/utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, Implementations of networked environments may be included in each or some combination of these examples.
- Bus 630 may represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local area using any of a variety of bus structures. bus.
- the electronic device 600 can also communicate with one or more external devices 700 (such as keyboards, pointing devices, Bluetooth devices, etc.), and can also communicate with one or more devices that enable the user to interact with the electronic device 600, and/or communicate with Any device (eg, router, modem, etc.) that enables the electronic device 600 to communicate with one or more other computing devices. Such communication may occur through input/output (I/O) interface 650 .
- the electronic device 600 can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN) and/or a public network such as the Internet) through the network adapter 660 .
- the network adapter 660 can communicate with other modules of the electronic device 600 through the bus 630 . It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives And data backup storage system, etc.
- a software product may utilize any combination of one or more readable media.
- the readable medium may be a readable signal medium or a readable storage medium.
- the readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: electrical connection with one or more conductors, portable disk, hard disk, random access memory (RAM), read only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.
- a computer readable storage medium may include a data signal carrying readable program code in baseband or as part of a carrier wave traveling as part of a data signal. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.
- a readable storage medium may also be any readable medium other than a readable storage medium that can send, propagate or transport a program for use by or in conjunction with an instruction execution system, apparatus or device.
- the program code contained on the readable storage medium may be transmitted by any suitable medium, including but not limited to wireless, cable, optical cable, RF, etc., or any suitable combination of the above.
- Program codes for performing the operations of the present application can be written in any combination of one or more programming languages, including object-oriented programming languages—such as Java, C++, etc., as well as conventional procedural programming Language - such as "C" or similar programming language.
- the program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server to execute.
- the remote computing device may be connected to the user computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (for example, using an Internet service provider). business to connect via the Internet).
- LAN local area network
- WAN wide area network
- Internet service provider for example, using an Internet service provider
- the above-mentioned computer-readable medium carries one or more programs, and when the above-mentioned one or more programs are executed by one device, the computer-readable medium can realize the aforementioned functions.
- modules in the above embodiments can be distributed in the device according to the description of the embodiment, and corresponding changes can also be made in one or more devices that are only different from the embodiment.
- the modules in the above embodiments can be combined into one module, and can also be further split into multiple sub-modules.
- the digital twin is mapped to the physical model, which can Solve the problem of data integrity and lack of real-time performance of general analysis methods.
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Abstract
本申请提供一种变压器状态分析方法、系统、设备和存储介质，涉及电力自动化领域，其中，一种变压器状态分析方法，包括：获取变压器的已知数据，并根据已知数据实时计算得出缺失数据（S101）；将已知数据和缺失数据映射至三维模型以形成数字孪生体（S103）；通过可变周期的数据更新对数字孪生体与变压器进行实时数据同步（S105）；获取数字孪生体计算的变压器的状态数据，以用于提供控制决策提示（S107）。
Description
相关申请
本申请要求于2022年1月4日提交中国专利局、申请号为2022100143974、申请名称为“一种变压器状态分析方法、系统、设备和存储介质”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
本申请涉及电力自动化领域，具体而言，涉及一种变压器状态分析方法、系统、设备和存储介质。
变压器作为最重要的变电设备，其电流、电压、功率及温度等电网运行数据均有完备的监控措施。部分变电站还装备了在线检测装置、声音监测装置，用于监测局部放电、油中气体、噪音等。然而，传统的电网运行数据难以对变压器运行状态完整评估。具体地说，目前的实时监控系统可以收到变压器过负荷告警、气体异常告警等，但无法对变压器状态进行完整的评估。
发明内容
本申请各示例性的实施例提供一种变压器状态分析方法、系统、设备和存储介质，可用于解决变压器设备的数据来源、映射、计算的问题，能够实现数字孪生体对物理模型映射，从而实现对变压器设备的智能运维。
根据本申请的一方面，提供一种变压器状态分析方法，包括：
获取变压器的已知数据，并根据所述已知数据实时计算得出缺失数据；
将所述已知数据和所述缺失数据映射至三维模型以形成数字孪生体；
通过可变周期的数据更新对所述数字孪生体与所述变压器进行实时数据同步；以及
获取由所述数字孪生体计算的所述变压器的状态数据，以用于提供控制决策提示。
在一实施例中，所述已知数据通过外部系统来采样并获取，并包括所述变压器的静态数据、动态数据和业务数据，具体包括采样的电流值、采样的油温、环境温度和变压器结构数据。
在一实施例中，所述缺失数据包括电气数据、温度场数据和电磁场数据，具体包括等效起始负荷系数、所述变压器内的分层油温和电磁场 分布数据。
在一实施例中，所述根据所述已知数据实时计算得出所述缺失数据的步骤，包括：
配置所述已知数据；
根据所述采样的电流值计算所述等效起始负荷系数；
根据所述采样的油温、所述环境温度和所述等效起始负荷系数，结合所述三维模型，通过有限元分析计算所述变压器内的所述分层油温；以及
根据所述变压器结构数据，结合所述三维模型，通过有限元分析计算所述电磁场分布数据。
在一实施例中，所述等效起始负荷系数的计算公式为：
所述等效起始负荷系数的计算公式为：
其中，I
*(eqv0)表示所述等效起始负荷系数，I
*(i)表示已知的所述变压器处于正常运行状态时第i+1次采样得出的变压器采样数据的标幺值，i的取值范围为0至n，n为设定的自然数；Δt
0表示数据采样间隔数据的预设的初始值，当i大于或等于1时，Δt
i表示所述变压器处于正常运行状态时第i次采样和第i-1次采样之间的数据采样间隔，t表示预设的初始负荷等效时间。
在一实施例中，所述变压器内的所述分层油温的有限元计算公式为：
其中，λ表示为已知的沿着各坐标的导热系数，
为已知的单位体积的生热率，x、y、z为已知的空间坐标,T为所述变压器内的所述分层油温。
在一实施例中，所述电磁场分布数据的有限元计算公式为：
其中，A为轴向矢量磁位，即所述电磁场分布数据，
为已知的水平磁阻率，
为已知的垂直磁阻率，J为已知的源电流密度，x、y为已知的空间坐标。
在一实施例中，所述将所述已知数据和所述缺失数据映射至所述三维模型以形成所述数字孪生体的步骤，包括：
将所述三维模型划分为有限个互不重叠的单元，在每一个所述单元中，选择多个节点作为所述已知数据和所述缺失数据的计算插值点。
在一实施例中，所述通过所述可变周期的数据更新对所述数字孪生 体与所述变压器进行实时数据同步的步骤，包括：
所述静态数据和所述业务数据由所述外部系统推送触发更新或进行定周期地更新，并保存更新数据至关系库；
根据所述动态数据的数据类型将所述动态数据设置为不同周期的单独更新；和设置为统一周期的全部更新，并保存更新数据至实时库；以及所述数字孪生体从所述关系库和所述实时库读取数据，以实现与所述变压器的数据同步。
在一实施例中，所述所述静态数据和所述业务数据由所述外部系统推送触发更新或进行定周期地更新的步骤，包括：
当所述外部系统支持数据更新订阅时，通过所述外部系统的数据更新的实时推送，触发所述静态数据和所述业务数据的数据更新；以及
当所述外部系统不支持数据更新订阅时，则设定所述外部系统的所述数据更新的周期为固定周期，并以所述固定周期进行所述静态数据和所述业务数据的数据更新。
在一实施例中，所述获取由所述数字孪生体计算的所述变压器的所述状态数据的步骤，包括：
根据所述已知数据和所述缺失数据，获取所述变压器的多个不同状态，以及所述多个不同状态的稳定运行时长；以及
基于所述多个不同状态的所述稳定运行时长确定所述变压器的最终稳定运行时长。
在一实施例中，所述多个不同状态包括电气状态、热力状态和电磁状态。
在一实施例中，所述变压器的所述最终稳定运行时长的计算公式为：
t＝min(t
e,t
h,t
m)，
其中，t
e、t
h、t
m分别为所述电气状态、所述热力状态和所述电磁状态的稳定运行时长，t
e、t
h、t
m的计算方法分别为：
t
e＝I
*(eqv0)*S
e，
t
h＝min(T
1*S
h1,T
2*S
h2,…,T
n*S
hn)，
t
m＝min(A
1*S
m1,A
2*S
m2,…,A
n*S
mn)，
其中，I
*(eqv0)、T
n、A
n分别为等效起始负荷系数、所述变压器内的分层油温和电磁场分布数据，S
e、S
h、S
m分别为预设的影响系数。
在一实施例中，所述提供控制决策提示的步骤，包括：
根据所述变压器的最终稳定运行时长设定预警等级，并提供对应的提示。
在一实施例中，所述提供控制决策提示包括：根据所述变压器的最终稳定运行时长计算时的不同影响因素，提供对应的处理决策，并设立优先级。
在一实施例中，所述设定所述预警等级并提供所述对应的提示的步骤，包括：
根据状态量值设定所述预警等级并提供所述对应的提示；
其中，所述状态量值的计算公式为：
其中，S表示所述状态量值，当S等于0时表示正常，等于1时表示异常，等于2时表示告警，t为所述变压器的最终稳定运行时长，t
0为预设的正常临界值，t
1为预设的异常临界值。
根据本申请的一方面，提供一种变压器状态分析系统，包括：数字孪生体，配置为通过变压器的设备状态数据映射至三维模型形成，与所述变压器进行实时数据同步；
设备状态模块，配置为获取所述变压器的所述设备状态数据，并与所述数字孪生体进行实时数据同步；以及
智能决策模块，配置为根据所述变压器的所述设备状态数据，提供控制决策提示。
根据本申请的一方面，提供一种电子设备，包括：一个或多个处理器；
存储装置，配置为存储一个或多个程序；
其中，当所述一个或多个程序被所述一个或多个处理器执行时，使得所述一个或多个处理器执行如前述的方法中的步骤。
根据本申请的一方面，提供一种计算机可读存储介质，其上存储有计算机程序，所述程序被处理器执行时实现如前述的方法。
发明人发现，影响变压器稳定运行的因素包括电气、温度和磁场状态。这些特征量往往来自于多个系统，其中的部分无法直接计算。当电力系统运行方式改变或者变电站内另外一台变压器故障跳闸时，会造成当前变压器过负荷。此时，使用者迫切需要知道变压器允许运行的具体时间，以便安排处理。但是，传统变电站的自动化系统无法提供变压器允许运行的具体时长，而是仅能提供提示。因此，当变电站的运行人员通过电网运行数据或者在线监测数据发现变压器运行异常时，此时再进行处理往往为时已晚，从而会影响变压器的正常运行寿命。
根据本申请实施例的技术方案，具有如下一种或多种有益效果：
在一实施例中，通过集成各个系统的静态数据、动态数据及业务数据，并实时计算缺失的计算出缺失的电气、温度场、电磁场数据，解决数据完整性问题。
在一实施例中，通过可变周期刷新，结合三维建模，利用有限元分析，能够实现数字孪生体对物理模型映射，解决一般分析方法实时性不足的问题。
在一实施例中，能够综合分析结果，并实现设备状态的预测，协助使用者对设备的智能运维。
应当理解的是，以上的一般描述和后文的细节描述仅是示例性的，并不能限制本申请。
为了更清楚地说明本申请实施例中的技术方案，下面将对实施例描述中所需要使用的附图作简单地介绍。显而易见地，下面描述中的附图仅仅是本申请的一些实施例。
图1是本申请一实施例的一种变压器状态分析方法流程图。
图2是本申请一实施例的一种变压器状态分析系统示意图。
图3是本申请一实施例的数字孪生体数据刷新的流程图。
图4是本申请一实施例的变压器状态预测流程图。
图5是本申请一实施例的变压器状态分析系统的结构框图。
图6是本申请一实施例的电子设备的框图。
现在将参考附图更全面地描述示例实施例。然而，示例实施例能够以多种形式实施，且不应被理解为限于在此阐述的实施例。相反，提供这些实施例使得本申请将全面和完整，并将示例实施例的构思全面地传达给本领域的技术人员。在图中相同的附图标记表示相同或类似的部分，因而将省略对它们的重复描述。
所描述的特征、结构或特性可以以任何合适的方式结合在一个或更多实施例中。在下面的描述中，提供许多具体细节从而给出对本公开的实施例的充分理解。然而，本领域技术人员将意识到，可以实践本公开的技术方案而没有这些特定细节中的一个或更多，或者可以采用其它的方式、组元、材料、装置或操作等。在这些情况下，将不详细示出或描述公知结构、方法、装置、实现、材料或者操作。
附图中所示的流程图仅是示例性说明，不是必须包括所有的内容和操作/步骤，也不是必须按所描述的顺序执行。例如，有的操作/步骤还可以分解，而有的操作/步骤可以合并或部分合并，因此实际执行的顺序 有可能根据实际情况改变。
本申请的说明书和权利要求书及上述附图中的术语“第一”、“第一”等是用于区别不同对象，而不是用于描述特定顺序。此外，术语“包括”和“具有”以及它们任何变形，意图在于覆盖不排他的包含。例如包含了一系列步骤或单元的过程、方法、系统、产品或设备没有限定于已列出的步骤或单元，而是可选地还包括没有列出的步骤或单元，或可选地还包括对于这些过程、方法、产品或设备固有的其他步骤或单元。
本申请提供一种变压器状态分析方法、系统、设备和存储介质，以数字孪生技术作为解决变压器设备状态评估、预测的手段，实现数字孪生体对物理模型的映射，从而实现对变压器设备的智能运维。
下面将参照附图，对根据本申请实施例的一种变压器状态分析方法进行详细说明。
图1示出根据本申请一示例性实施例的一种变压器状态分析方法流程图。
如图1所示，在S101，获取变压器的已知数据，并根据已知数据实时计算得出缺失数据。
在一实施例中，已知数据通过外部系统采样来获取，并且包括静态数据、动态数据和业务数据。
根据静态数据、动态数据和业务数据，计算出缺失数据，包括电气数据、温度场数据和电磁场数据。
在S103，将已知数据和缺失数据映射至三维模型以形成数字孪生体。
将三维模型可划分为有限个互不重叠的单元，在每一个单元中，可选择一定数量的节点作为已知数据和所述缺失数据的计算插值点。
例如，将三维模型可划分为设备模型、建筑模型和环境模型，与已知的静态数据、动态数据、业务数据和计算出的电气数据、温度场数据、电磁场数据建立函数关系，形成数字孪生体。
在S105，通过可变周期的数据更新对数字孪生体与变压器进行实时数据同步。
在一实施例中，静态数据和业务数据可由外部系统推送更新或进行固定周期地(例如，每日)更新，并保存更新数据至关系库。
对于动态数据，可根据动态数据的数据类型将动态数据设置为不同周期地单独更新，并同时设置统一周期全部更新，并保存更新数据至实时库。
数字孪生体可从关系库和实时库读取数据，实现与变压器的数据同步。
在S107，获取数字孪生体计算的变压器的状态数据，以用于提供控制决策提示。
在一实施例中，基于已知数据和缺失数据，可获取变压器的电气状态、热力状态和电磁状态的稳定运行时长，并根据各状态的稳定运行时 长得出变压器的最终运行时长。
其中，变压器的最终运行时长可为变压器的电气状态、热力状态和电磁状态的稳定运行时长中的最小值。
根据变压器的电气状态、热力状态和电磁状态的稳定运行时长以及变压器的最终运行时长，进行智能决策，可对变压器的运转趋势进行判断及预测。
图2示出根据本申请一示例性实施例的一种变压器状态分析系统示意图。
如图2所示，首先，获取变压器的已知数据，根据获取的这些数据，计算出缺失数据。将获取的已知数据和计算出的缺失数据，映射至可拆解的三维模型，形成数字孪生体。
在一实施例中，已知数据可由外部系统采样并获取，包括：静态数据、动态数据及业务数据。
静态数据可包括：交流侧电压等级、直流侧电压等级、空载损耗、负载损耗、运行状态、短路阻抗、短路前最高工作电流、短路前最高操作电压、短路前功率因数角、换流变网侧最小工作电压，以及额定功率等。
动态数据可包括：馈线电压、A套保护阀侧电压、B套保护阀侧电压、C套保护阀侧电压、局部放电频次、局部放电放电信号峰值、容性监测不平衡电流、容性监测等效电容、容性监测介质损耗、首端套管压力、本体浮杆式油位、夹件接地电流、绕组热点计算温度、铁芯接地电流、本体压力式油位、顶层油温、顶层油温2、油色谱C2H2、油色谱C2H4、油色谱C2H6、油色谱CH4、油色谱CO、油色谱CO2、油色谱H2，以及油色谱O2等。
业务数据可包括：故障事件、故障记录、缺陷记录、套管维修测试工作、换流变维修测试工作、套管巡视记录、换流变巡检记录、套管测试、分接开关测试，以及换流变离线油色谱测试等。
在一实施例中，缺失数据通过已知数据计算获得，包括：电气数据、温度场数据和电磁场数据。
电气数据可包括等效起始负荷系数，通过下列公式计算得出：
其中，I
*(eqv0)表示等效起始负荷系数，I
*(i)表示已知的变压器处于正常运行状态时第i+1次采样得出的变压器采样数据的标幺值，i取0至n，n为设定的自然数；Δt
0表示预设的数据采样间隔数据的初始值，当i大于等于1时，Δt
i表示变压器处于正常运行状态时第i次采样和第i-1次采样之间的数据采样间隔，t表示预设的初始负荷等效时间。
在一实施例中，温度场数据可包括变压器内的分层油温，并可通过下列有限元分析公式并结合三维模型计算得出：
其中，λ表示为已知的沿着各坐标的导热系数，
为已知的单位体积的生热率，x、y、z为已知的空间坐标,T即为变压器内的分层油温。
在一实施例中，电磁场数据可包括电磁场分布数据，并可通过下列有限元分析公式并结合三维模型计算得出：
其中，A为轴向矢量磁位即电磁场分布数据，
为已知的水平磁阻率，
为已知的垂直磁阻率，J为已知的源电流密度，x、y为已知的空间坐标。
进一步地，将三维模型划分为有限个互不重叠的单元，在每一个单元中，选择一定数量的节点作为已知数据和缺失数据的计算插值点。
在一实施例中，三维模型可包括设备模型、建筑模型和环境模型，其中，节点数量不设限制，只与计算机性能相关。
其次，根据不同的数据类型，可进行可变周期的数据更新，以实现数字孪生体与物理对象(即，变压器)的数据同步。
在一实施例中，对于静态数据和业务数据，如果外部系统能够提供订阅功能，则可将外部系统推送的更新数据保存至关系库。
如果外部系统无法提供订阅功能，则可将外部系统的更新数据进行固定周期地(例如每日)更新并保存至关系库。
例如，静态数据可包括变压器的出厂配置信息等，并可通过变压器信息系统获取。
业务数据可包括变压器日常维护数据、测试数据及日志等，并可通过变压器测试系统获取。
在一实施例中，对于动态数据，可根据不同类型的数据设置不同的更新周期单独更新至实时库，并同时在统一的周期下进行全部类型数据的更新，并保存至实时库。
例如，动态数据可包括变压器的动态参数如油温等。
数字孪生体从关系库和实时库读取数据，实现与变压器的数据同步。
最后，根据获得的已知数据和计算出的缺失数据，得出变压器的电气状态、热力状态和电磁状态，并计算出变压器的最终稳定运行时长，同时给出控制决策提示。
在一实施例中，可通过已知数据和缺失数据，分别计算出电气状态、热力状态和电磁状态的稳定运行时长。其中，电气状态的稳定运行时长可通过下列公式计算求得：
t
e＝I
*(eqv0)*S
e
其中，I
*(eqv0)为所述等效起始负荷系数，S
e为预设的影响系数。
热力状态的稳定运行时长可通过下列公式计算求得：
t
h＝min(T
1*S
h1,T
2*S
h2,…,T
n*S
hn)
其中，T
n为变压器内的分层油温，S
h为预设的影响系数。
电磁状态的稳定运行时长可通过下列公式计算求得：
t
m＝min(A
1*S
m1,A
2*S
m2,…,A
n*S
mn)
其中，A
n为电磁场分布数据，S
m为预设的影响系数。
变压器的最终稳定运行时长为电气状态、热力状态和电磁状态的稳定运行时长中的最小值，即：
t＝min(t
e,t
h,t
m)
其中，t
e、t
h、t
m分别为电气状态、热力状态和电磁状态的稳定运行时长。
根据由数字孪生体获取的电气状态、热力状态和电磁状态的稳定运行时长，以及变压器的最终稳定运行时长，对变压器运转的趋势进行预测。根据不同的稳定运行时长，提供不同等级方式的提示。根据稳定运行时长计算时的电气、热力和电磁等影响因素，提供不同的处理决策，并根据电气状态、热力状态和电磁状态的稳定时长设定优先级。
图3示出根据本申请一示例性实施例的数字孪生体数据刷新的流程图。
如图3所示，通过外部系统获取包括静态数据、业务数据和动态数据在内的已知数据，并对已知数据进行数据更新。
对于静态数据和业务数据，如外部系统支持数据更新订阅，可通过外部系统数据更新的实时推送，触发系统的数据更新。
如外部系统不支持数据更新订阅，则可设定外部系统数据更新的周期(如每日或每周等)，并以固定周期进行系统的数据更新。
在一实施例中，静态数据和业务数据的更新数据均可保存至关系库。
对于动态数据，可根据数据的类型，设置对应的更新周期以单独更新至实时库。例如，变压器的电流、电压和功率数据，需实时更新至实时库，而油中气体检测数据，则可设置为每小时更新至实时库。
此外，在统一的周期(如每日或每周)进行动态数据中全部类型数据的更新，并保存至实时库。
数字孪生体实时读取关系库和实时库中的数据，并以此为依据计算得出变压器的状态。
图4示出根据本申请一示例性实施例的变压器状态预测流程图。
如图4所示，首先，数字孪生体根据已知数据和缺失数据计算出变压器的电气状态、热力状态和电磁状态，并转换为对应状态的稳定运行时长。
在一实施例中，可分别通过电气状态的稳定运行时长计算公式、热力状态的稳定运行时长计算公式和电磁状态的稳定运行时长计算公式获取变压器的电气状态、热力状态和电磁状态各自的稳定运行时长。
进一步地，取变压器的电气状态、热力状态和电磁状态各自的稳定运行时长中的最小值作为变压器的最终稳定运行时长，并根据变压器的最终稳定运行时长，设定预警等级并提供对应的提示。
在一实施例中，可通过状态量值设定预警等级并提供对应的提示。
状态量值计算公式为：
其中，S表示状态量值，等于0表示正常，等于1表示异常，等于2表示告警，t为变压器的最终稳定运行时长，t
0为预设的正常临界值，t
1为预设的异常临界值。
影响变压器的最终稳定运行时长的因素包括电气影响因素、热力影响因素和电磁影响因素。根据不同的影响因素提供对应的处理决策，并设立优先级。
例如，经过电气影响因素计算得到的变压器的最终稳定运行时长为1小时，且预设的异常临界值为3小时。此时预警级别为告警，需要立刻处理。处理决策为降低功率，从而降低变压器负载。
再例如，经过热力影响因素计算得到的变压器的最终稳定运行时长为2小时，且预设的异常临界值为3小时。此时预警级别为告警，需要立刻处理。处理决策为降低功率，并启动消防预案。
应该理解的是，虽然图1的流程图中的各个步骤按照箭头的指示依次显示，但是这些步骤并不是必然按照箭头指示的顺序依次执行。除非本文中有明确的说明，这些步骤的执行并没有严格的顺序限制，这些步骤可以以其它的顺序执行。而且，图1中的至少一部分步骤可以包括多个子步骤或者多个阶段，这些子步骤或者阶段并不必然是在同一时刻执行完成，而是可以在不同的时刻执行，这些子步骤或者阶段的执行顺序也不必然是依次进行，而是可以与其它步骤或者其它步骤的子步骤或者阶段的至少一部分轮流或者交替地执行。
图5示出根据本申请示例实施例的一种变压器状态分析系统的结构框图。
下面参照图5，对根据本申请示例实施例的一种变压器状态分析系统进行详细说明。
变压器状态分析系统包括数字孪生体501、设备状态模块503，以及智能决策模块505。数字孪生体501配置为通过变压器的设备状态数据映射至三维模型形成，并与变压器进行实时数据同步。设备状态模块503配置为获取所述变压器的设备状态数据，并与数字孪生体进行实时数据同步智能决策模块505配置为根据变压器的设备状态数据，以提供控制决策提示。
图6示出根据本申请一示例性实施例的电子设备的框图。
需要理解的是，图6所示的电子设备600仅仅是一个示例，不应对本申请实施例的功能和使用范围带来任何限制。
如图6所示，电子设备600是以通用计算设备的形式表现。电子设备600的组件可以包括但不限于：至少一个处理单元610、至少一个存储单元620、连接不同系统组件(包括存储单元620和处理单元610)的总线630、显示单元640等。其中，存储单元存储有程序代码，程序代码可以被处理单元610执行，使得处理单元610执行本说明书描述的根据本申请各种示例性实施方式的方法。例如，处理单元610可以执行如图1中所示的方法。图6中所示的处理单元610执行计算机程序时还可实现图2至图4所示的控制逻辑。
存储单元620可以包括易失性存储单元形式的可读介质，例如随机存取存储单元(RAM)6201和/或高速缓存存储单元6202，还可以进一步包括只读存储单元(ROM)6203。
存储单元620还可以包括具有一组(至少一个)程序模块6205的程序/实用工具6204，这样的程序模块6205包括但不限于：操作系统、一个或者多个应用程序、其它程序模块以及程序数据，这些示例中的每一个或某种组合中可能包括网络环境的实现。
总线630可以为表示几类总线结构中的一种或多种，包括存储单元总线或者存储单元控制器、外围总线、图形加速端口、处理单元或者使用多种总线结构中的任意总线结构的局域总线。
电子设备600也可以与一个或多个外部设备700(例如键盘、指向设备、蓝牙设备等)通信，还可与一个或者多个使得用户能与该电子设备600交互的设备通信，和/或与使得该电子设备600能与一个或多个其它计算设备进行通信的任何设备(例如路由器、调制解调器等等)通信。这种通信可以通过输入/输出(I/O)接口650进行。并且，电子设备600还可以通过网络适配器660与一个或者多个网络(例如局域网(LAN)，广域网(WAN)和/或公共网络，例如因特网)通信。网络适配器660可以通过总线630与电子设备600的其它模块通信。应当明白，尽管图中未示出，可以结合电子设备600使用其它硬件和/或软件模块，包括但不限于：微代码、设备驱动器、冗余处理单元、外部磁盘驱动阵列、RAID系统、磁带驱动器以及数据备份存储系统等。
通过以上的实施方式的描述，本领域的技术人员易于理解，这里描 述的示例实施例可以通过软件实现，也可以通过软件结合必要的硬件的方式来实现。根据本申请实施例的技术方案可以以软件产品的形式体现出来，该软件产品可以存储在一个非易失性存储介质(可以是CD-ROM，U盘，移动硬盘等)中或网络上，包括若干指令以使得一台计算设备(可以是个人计算机、服务器、移动终端或者网络设备等)执行根据本申请实施例的方法。
软件产品可以采用一个或多个可读介质的任意组合。可读介质可以是可读信号介质或者可读存储介质。可读存储介质例如可以为但不限于电、磁、光、电磁、红外线、或半导体的系统、装置或器件，或者任意以上的组合。可读存储介质的更具体的例子(非穷举的列表)包括：具有一个或多个导线的电连接、便携式盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、光纤、便携式紧凑盘只读存储器(CD-ROM)、光存储器件、磁存储器件、或者上述的任意合适的组合。
计算机可读存储介质可以包括在基带中或者作为载波一部分传播的数据信号，其中承载了可读程序代码。这种传播的数据信号可以采用多种形式，包括但不限于电磁信号、光信号或上述的任意合适的组合。可读存储介质还可以是可读存储介质以外的任何可读介质，该可读介质可以发送、传播或者传输用于由指令执行系统、装置或者器件使用或者与其结合使用的程序。可读存储介质上包含的程序代码可以用任何适当的介质传输，包括但不限于无线、有线、光缆、RF等等，或者上述的任意合适的组合。
可以以一种或多种程序设计语言的任意组合来编写用于执行本申请操作的程序代码，程序设计语言包括面向对象的程序设计语言—诸如Java、C++等，还包括常规的过程式程序设计语言—诸如“C”语言或类似的程序设计语言。程序代码可以完全地在用户计算设备上执行、部分地在用户设备上执行、作为一个独立的软件包执行、部分在用户计算设备上部分在远程计算设备上执行、或者完全在远程计算设备或服务器上执行。在涉及远程计算设备的情形中，远程计算设备可以通过任意种类的网络，包括局域网(LAN)或广域网(WAN)，连接到用户计算设备，或者，可以连接到外部计算设备(例如利用因特网服务提供商来通过因特网连接)。
上述计算机可读介质承载有一个或者多个程序，当上述一个或者多个程序被一个该设备执行时，使得该计算机可读介质实现前述功能。
本领域技术人员可以理解上述各模块可以按照实施例的描述分布于装置中，也可以进行相应变化唯一不同于本实施例的一个或多个装置中。上述实施例的模块可以合并为一个模块，也可以进一步拆分成多个子模块。
在一实施例中，通过集成各个系统的已知数据并实时计算缺失的计 算出缺失数据，以及可变周期刷新，结合三维建模，利用有限元分析，实现数字孪生体对物理模型映射，可解决数据完整性及一般分析方法实时性不足的问题。
以上对本申请实施例进行了详细介绍，以上实施例的说明仅用于帮助理解本申请的方法及其核心思想。同时，本领域技术人员依据本申请的思想，基于本申请的具体实施方式及应用范围上做出的改变或变形之处，都属于本申请保护的范围。综上所述，本说明书内容不应理解为对本申请的限制。
Claims (19)
- 一种变压器状态分析方法，包括：获取变压器的已知数据，并根据所述已知数据实时计算得出缺失数据；将所述已知数据和所述缺失数据映射至三维模型以形成数字孪生体；通过可变周期的数据更新对所述数字孪生体与所述变压器进行实时数据同步；以及获取由所述数字孪生体计算的所述变压器的状态数据，以用于提供控制决策提示。
- 根据权利要求1所述的方法，其中，所述已知数据通过外部系统来采样并获取，并包括所述变压器的静态数据、动态数据和业务数据，具体包括采样的电流值、采样的油温、环境温度和变压器结构数据。
- 根据权利要求2所述的方法，其中，所述缺失数据包括电气数据、温度场数据和电磁场数据，具体包括等效起始负荷系数、所述变压器内的分层油温和电磁场分布数据。
- 根据权利要求3所述的方法，其中，所述根据所述已知数据实时计算得出所述缺失数据的步骤，包括：配置所述已知数据；根据所述采样的电流值计算所述等效起始负荷系数；根据所述采样的油温、所述环境温度和所述等效起始负荷系数，结合所述三维模型，通过有限元分析计算所述变压器内的所述分层油温；以及根据所述变压器结构数据，结合所述三维模型，通过有限元分析计算所述电磁场分布数据。
- 根据权利要求4所述的方法，其中，所述等效起始负荷系数的计算公式为：
其中，I *(eqv0)表示所述等效起始负荷系数，I *(i)表示已知的所述变压器处于正常运行状态时第i+1次采样得出的变压器采样数据的标幺值，i的取值范围为0至n，n为设定的自然数；Δt 0表示数据采样间隔数据的预设的初始值，当i大于或等于1时，Δt i表示所述变压器处于正常运行 状态时第i次采样和第i-1次采样之间的数据采样间隔，t表示预设的初始负荷等效时间。 - 根据权利要求4所述的方法，其中，所述变压器内的所述分层油温的有限元计算公式为：
其中，λ表示为已知的沿着各坐标的导热系数，为已知的单位体积的生热率，x、y、z为已知的空间坐标,T为所述变压器内的所述分层油温。
- 根据权利要求4所述的方法，其中，所述电磁场分布数据的有限元计算公式为：
其中，A为轴向矢量磁位，即所述电磁场分布数据，为已知的水平磁阻率，
为已知的垂直磁阻率，J为已知的源电流密度，x、y为已知的空间坐标。
- 根据权利要求1所述的方法，其中，所述将所述已知数据和所述缺失数据映射至所述三维模型以形成所述数字孪生体的步骤，包括：将所述三维模型划分为有限个互不重叠的单元，在每一个所述单元中，选择多个节点作为所述已知数据和所述缺失数据的计算插值点。
- 根据权利要求2所述的方法，其中，所述通过所述可变周期的数据更新对所述数字孪生体与所述变压器进行实时数据同步的步骤，包括：所述静态数据和所述业务数据由所述外部系统推送触发更新或进行定周期地更新，并保存更新数据至关系库；根据所述动态数据的数据类型将所述动态数据设置为不同周期的单独更新；和设置为统一周期的全部更新，并保存更新数据至实时库；以及所述数字孪生体从所述关系库和所述实时库读取数据，以实现与所述变压器的数据同步。
- 根据权利要求9所述的方法，其中，所述所述静态数据和所述业务数据由所述外部系统推送触发更新或进行定周期地更新的步骤，包括：当所述外部系统支持数据更新订阅时，通过所述外部系统的数据更新的实时推送，触发所述静态数据和所述业务数据的数据更新；以及当所述外部系统不支持数据更新订阅时，则设定所述外部系统的所 述数据更新的周期为固定周期，并以所述固定周期进行所述静态数据和所述业务数据的数据更新。
- 根据权利要求1所述的方法，其中，所述获取由所述数字孪生体计算的所述变压器的所述状态数据的步骤，包括：根据所述已知数据和所述缺失数据，获取所述变压器的多个不同状态，以及所述多个不同状态的稳定运行时长；以及基于所述多个不同状态的所述稳定运行时长确定所述变压器的最终稳定运行时长。
- 根据权利要求11所述的方法，其中，所述多个不同状态包括电气状态、热力状态和电磁状态。
- 根据权利要求12所述的方法，其中，所述变压器的所述最终稳定运行时长的计算公式为：t＝min(t e,t h,t m)，其中，t e、t h、t m分别为所述电气状态、所述热力状态和所述电磁状态的稳定运行时长，t e、t h、t m的计算方法分别为：t e＝I *(eqv0)*S e，t h＝min(T 1*S h1,T 2*S h2,…,T n*S hn)，t m＝min(A 1*S m1,A 2*S m2,…,A n*S mn)，其中，I *(eqv0)、T n、A n分别为等效起始负荷系数、所述变压器内的分层油温和电磁场分布数据，S e、S h、S m分别为预设的影响系数。
- 根据权利要求1所述的方法，其中，所述提供控制决策提示的步骤，包括：根据所述变压器的最终稳定运行时长设定预警等级，并提供对应的提示。
- 根据权利要求1所述的方法，其中，所述提供控制决策提示包括：根据所述变压器的最终稳定运行时长计算时的不同影响因素，提供对应的处理决策，并设立优先级。
- 根据权利要求14或15所述的方法，其中，所述设定所述预警等级并提供所述对应的提示的步骤，包括：根据状态量值设定所述预警等级并提供所述对应的提示；其中，所述状态量值的计算公式为：
其中，S表示所述状态量值，当S等于0时表示正常，等于1时表示异常，等于2时表示告警，t为所述变压器的最终稳定运行时长，t 0为预设的正常临界值，t 1为预设的异常临界值。 - 一种变压器状态分析系统，包括：数字孪生体，配置为通过变压器的设备状态数据映射至三维模型形成，与所述变压器进行实时数据同步；设备状态模块，配置为获取所述变压器的所述设备状态数据，并与所述数字孪生体进行实时数据同步；以及智能决策模块，配置为根据所述变压器的所述设备状态数据，提供控制决策提示。
- 一种电子设备，包括：一个或多个处理器；存储装置，配置为存储一个或多个程序；其中，当所述一个或多个程序被所述一个或多个处理器执行时，使得所述一个或多个处理器执行如权利要求1-16中任一所述的方法中的步骤。
- 一种计算机可读存储介质，其上存储有计算机程序，其中，所述程序被处理器执行时执行如权利要求1-16中任一所述的方法中的步骤。
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