State evaluation method and device of power transformer, electronic equipment and storage medium

Abstract

The invention discloses a state evaluation method and device for a power transformer, electronic equipment and a storage medium, and the method comprises the steps: obtaining the historical time sequence operation data and real-time operation data of the power transformer of the same supply target in a power supply network, and carrying out the calculation to obtain an operation condition difference coefficient and an operation state index of the power transformer, carrying out correction calculation on the two to obtain a running state correction index of the power transformer; and inputting the operation state correction index into a digital twinborn model for simulation to obtain a plurality of state vector values of the power transformer, and calculating a simulation state score of the power transformer according to the state vector values to judge whether the operation state of the power transformer is normal or not. By implementing the method, the actual operation state of the power transformer can be accurately reflected, the operation condition difference between different power transformers is effectively made up, the accuracy of the digital twin model in power transformer state evaluation is improved, and a reliable basis is provided for evaluation of the operation state of the power transformer.

Description

Technical Field

[0001] The present invention belongs to the technical field of power transformer status assessment, and in particular relates to a power transformer status assessment method, device, electronic equipment and storage medium. Background Art

[0002] In the power system, power transformers are one of the key equipment, and accurate evaluation of their operating status is crucial to ensure the stable operation of the power grid. In order to achieve real-time monitoring and status evaluation of power transformers, digital twin models have become a powerful tool. Digital twin models are a technology that combines physical systems with virtual simulation models, and achieves real-time monitoring and status evaluation by simulating the operation of physical systems in a virtual environment.

[0003] However, when the existing digital twin model performs virtual simulation on power transformers, due to the significant differences in operating conditions of different power transformers during actual operation, the digital twin model is difficult to accurately reflect the actual operating conditions of each transformer during the verification process, resulting in poor accuracy of the simulation results, which in turn reduces the accuracy of the digital twin model in power transformer status assessment. Summary of the invention

[0004] The embodiments of the present invention provide a method, device, electronic device and storage medium for evaluating the state of a power transformer, which fully consider the differences in operating conditions of different power transformers during actual operation, improve the accuracy of the digital twin model in evaluating the state of the power transformer, and thus ensure the safety and stability of the power system.

[0005] An embodiment of the present invention provides a method for evaluating the state of a power transformer, comprising:

[0006] Obtain the historical time series operation data of the power transformers supplying the same target in the power supply network.

[0007] Based on the historical time-series operation data, the operating condition difference coefficient of the power transformer is calculated.

[0008] Calculate the state loss value of the power transformer caused by potential operating influencing factors in real-time operating data.

[0009] The state loss value is input into the preset multi-factor state evaluation model to obtain the operating state index of the power transformer.

[0010] The operating state index of the power transformer and the corresponding operating condition difference coefficient are corrected and calculated to obtain the operating state correction index of the power transformer.

[0011] The operating status correction index is input into the digital twin model for simulation to obtain several state vector values ​​of the power transformer.

[0012] A simulation state score of the power transformer is calculated according to the state vector value of the power transformer, and whether the operating state of the power transformer is normal is judged according to the simulation state score.

[0013] Furthermore, the operation condition difference coefficient of the power transformer is calculated based on the historical time series operation data, including:

[0014] Each characteristic parameter in the historical time series operation data is clustered to obtain several operation characteristic clusters of the power transformer; the characteristic parameters include: voltage, current, power, oil temperature and winding temperature.

[0015] The cluster centers of several operation characteristic clusters of the power transformer are used as several standardized characteristic parameters of the power transformer.

[0016] Each characteristic parameter in the historical time series operation data is standardized to obtain a standardized historical operation data array of the power transformer.

[0017] The vector distance between each element in the standardized historical operation data array of the power transformer and the corresponding standardized characteristic parameter of the power transformer is calculated to obtain the dimension vector deviation index of each characteristic parameter.

[0018] The characteristic parameter of the dimension vector deviation index beyond the preset deviation range is used as the unique characteristic parameter of the power transformer.

[0019] According to the unique characteristic parameters of the power transformer, the operating condition difference coefficient of the power transformer is calculated.

[0020] Further, the potential operation influencing factors are determined by:

[0021] The real-time operation data of the power transformer is obtained, and the fluctuation characteristic parameters in the real-time operation data are screened out by the triple standard deviation method.

[0022] According to the fluctuation characteristic parameters at each moment, the changing trend of the operation fluctuation characteristic parameters is determined.

[0023] The operation fluctuation characteristic parameters with abnormal change trends are regarded as potential operation influencing factors of the power transformer.

[0024] Furthermore, the preset multi-factor status assessment model includes:

[0025]

[0026] Among them, P k is the operating status index of the kth power transformer; C k is the initialization state index of the kth power transformer; β kis the basic state loss coefficient of the kth power transformer; is the state loss coefficient of the i-th potential operating influencing factor on the power transformer in the k-th power transformer; is the state loss value of the power transformer caused by the i-th potential operating influencing factor in the k-th power transformer; ∈ is the error term.

[0027] Furthermore, the operating state index of the power transformer and the corresponding operating condition difference coefficient are corrected and calculated to obtain the operating state correction index of the power transformer, including:

[0028] The operating status correction index of the power transformer is calculated by the following formula:

[0029] P′ k =P k ×H k

[0030] Among them, P′ k is the operating status correction index of the kth power transformer; P k is the operating status index of the kth power transformer; H k is the operating condition difference coefficient of the kth power transformer.

[0031] Further, the simulation state score of the power transformer is calculated according to the state vector value of the power transformer, including:

[0032] A number of state vector values ​​of the power transformer are normalized to obtain a number of normalized comprehensive vector indicators of the power transformer.

[0033] According to several normalized comprehensive vector indicators of the power transformer, influence entropies of several normalized comprehensive vector indicators are calculated.

[0034] Weights are assigned to several normalized comprehensive vector indicators of the power transformer according to the influence entropy, so as to obtain weights corresponding to the several normalized comprehensive vector indicators.

[0035] According to several normalized comprehensive vector indicators of the power transformer and the corresponding weights, the simulation state score of the power transformer is calculated.

[0036] Furthermore, judging whether the operating state of the power transformer is normal or not according to the simulation state score includes:

[0037] It is determined whether the simulation state score of the power transformer is within the ideal operation score range. If so, it is determined that the operation state of the power transformer is normal. If not, it is determined that the operation state of the power transformer is abnormal.

[0038] Based on the above method embodiment, the present invention provides a corresponding device embodiment.

[0039] An embodiment of the present invention provides a state assessment device for a power transformer, comprising: a data acquisition module, a difference assessment module, an operation state index generation module, a state correction module, a simulation module and a state assessment module.

[0040] The data acquisition module is used to obtain the historical time-series operation data of the power transformers with the same supply direction in the power supply network.

[0041] The difference evaluation module is used to calculate the operating condition difference coefficient of the power transformer based on the historical time series operation data.

[0042] The operation status index generation module is used to calculate the state loss value of the power transformer caused by the potential operation influencing factors in the real-time operation data. The state loss value is input into a preset multi-factor state evaluation model to obtain the operation status index of the power transformer.

[0043] The state correction module is used to perform correction calculation on the operating state index of the power transformer and the corresponding operating condition difference coefficient to obtain the operating state correction index of the power transformer.

[0044] The simulation module is used to input the operating state correction index into the digital twin model for simulation to obtain several state vector values ​​of the power transformer.

[0045] The state evaluation module is used to calculate the simulation state score of the power transformer according to the state vector value of the power transformer, and judge whether the operating state of the power transformer is normal according to the simulation state score.

[0046] Based on the above method embodiment, the present invention provides a corresponding electronic device embodiment.

[0047] An embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for evaluating the state of a power transformer described in any one of the above method embodiments can be implemented.

[0048] Based on the above method item embodiments, the present invention provides a corresponding storage medium item embodiment.

[0049] An embodiment of the present invention provides a storage medium having a computer program stored thereon. When the computer program is executed by a processor, the state evaluation of the power transformer described in any one of the above method embodiments can be implemented.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] The embodiment of the present invention provides a state assessment method, device, electronic device and storage medium for a power transformer, which obtains the operating condition difference coefficient and operating state index of the power transformer by calculation, and performs correction calculation on the two to obtain the operating state correction index of the power transformer. The operating state correction index is input into a digital twin model for simulation to obtain multiple state vector values ​​of the power transformer, and the simulation state score of the power transformer is further calculated based on the state vector value to determine whether its operating state is normal. The correction calculation of the operating state index in the present invention can more accurately reflect the actual operating state of the power transformer, effectively make up for the operating condition differences between different power transformers, improve the accuracy of the digital twin model in the state assessment of the power transformer, and provide a reliable basis for the evaluation of the operating state of the power transformer. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a flow chart of a method for evaluating the state of a power transformer provided in one embodiment of the present invention.

[0053] Figure 2 It is a structural schematic diagram of a power transformer status assessment device provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] like Figure 1 As shown, an embodiment of the present invention provides a method for evaluating the state of a power transformer, which at least includes the following steps:

[0056] Step S1, obtaining historical time-series operation data of power transformers of the same supply target in the power supply network.

[0057] Preferably, the historical time series operation data of the power transformer is obtained by the following steps: the historical operation data of the power transformer is obtained, and the historical operation data is sorted according to the timestamp of each historical operation data to obtain the historical time series operation data of the power transformer.

[0058] It can be understood that by sorting the historical operation data according to timestamps, the continuity and time correlation of the historical operation data are ensured, so that when calculating the operating condition difference coefficient of the power transformer, the actual operating conditions of the power transformer in different time periods can be more accurately reflected.

[0059] Step S2: Calculate the operating condition difference coefficient of the power transformer according to the historical time series operating data.

[0060] Specifically, in a preferred embodiment, the calculating of the operating condition difference coefficient of the power transformer according to the historical time series operating data includes:

[0061] Each characteristic parameter in the historical time series operation data is clustered to obtain several operation characteristic clusters of the power transformer; the characteristic parameters include: voltage, current, power, oil temperature and winding temperature.

[0062] The cluster centers of several operation characteristic clusters of the power transformer are used as several standardized characteristic parameters of the power transformer.

[0063] Each characteristic parameter in the historical time series operation data is standardized to obtain a standardized historical operation data array of the power transformer.

[0064] The vector distance between each element in the standardized historical operation data array of the power transformer and the corresponding standardized characteristic parameter of the power transformer is calculated to obtain the dimension vector deviation index of each characteristic parameter.

[0065] The characteristic parameter of the dimension vector deviation index beyond the preset deviation range is used as the unique characteristic parameter of the power transformer.

[0066] According to the unique characteristic parameters of the power transformer, the operating condition difference coefficient of the power transformer is calculated.

[0067] It should be noted that the clustering method is an existing data analysis method, which can be selected for clustering by methods such as K-means clustering, DBSCAN algorithm, hierarchical clustering, fuzzy C-means clustering, Gaussian mixture model, BIRCH, etc.

[0068] Exemplarily, each characteristic parameter in the historical time series operation data is standardized by the following formula:

[0069]

[0070]

[0071] Among them, U′ i is the i-th standardized characteristic parameter; U i is the i-th characteristic parameter; μ is the mean of the characteristic parameter; σ is the standard deviation of the characteristic parameter; n is the total number of characteristic parameters.

[0072] Optionally, the dimension vector deviation index of each feature parameter is calculated by the following formula:

[0073] d i=|U′ i -Z i |

[0074] Among them, U′ i is the i-th standardized characteristic parameter; Z i is the standardized characteristic parameter;

[0075] Exemplarily, the calculation formula of the operating condition difference coefficient of the power transformer is:

[0076]

[0077] Among them, G is the operating condition difference coefficient of the power transformer, H is the unique characteristic parameter set of the power transformer, τ is the preset deviation range, ρ is the difference coefficient, and n is the total number of characteristic parameters.

[0078] It can be understood that by identifying the unique characteristic parameters of the power transformer and calculating the operating condition difference coefficient, the actual operating condition of the power transformer can be more accurately reflected, thereby enhancing the accuracy and reliability of subsequent operating status correction calculations.

[0079] Step S3: Calculate the state loss value of the power transformer caused by the potential operation influencing factors in the real-time operation data.

[0080] Specifically, in a preferred embodiment, the potential operation influencing factors are determined in the following manner:

[0081] The real-time operation data of the power transformer is obtained, and the fluctuation characteristic parameters in the real-time operation data are screened out by the triple standard deviation method.

[0082] According to the fluctuation characteristic parameters at each moment, the changing trend of the operation fluctuation characteristic parameters is determined.

[0083] The operation fluctuation characteristic parameters with abnormal change trends are regarded as potential operation influencing factors of the power transformer.

[0084] Exemplarily, an edge IoT sensor is used as a collector for obtaining real-time operation data, and the real-time operation data is collected according to the collection frequency of the edge IoT sensor.

[0085] Preferably, the specific steps of screening out the fluctuation characteristic parameters in the real-time operation data by the triple standard deviation method are:

[0086] Calculate the mean and standard deviation of each characteristic parameter in the real-time operation data of the power transformer.

[0087] Based on the mean and standard deviation of the feature parameter, calculate the range of three times the standard deviation.

[0088] For each characteristic parameter, check whether there is a characteristic parameter outside the range of three times the standard deviation.

[0089] If the characteristic parameter exceeds the range of three times the standard deviation, the characteristic parameter is recorded as a fluctuating characteristic parameter.

[0090] Exemplarily, the abnormal change trend refers to a change trend that exceeds a preset change trend range.

[0091] It is understandable that the three-times standard deviation method is used to screen the fluctuation characteristic parameters in the real-time operation data, ensuring the accurate identification of the fluctuation characteristic parameters. Furthermore, by analyzing the changing trend of the fluctuation characteristic parameters at each moment, it is possible to accurately determine which characteristic parameters are abnormal, and thus use them as potential operating influencing factors. This step can timely identify the potential influencing factors that affect the operating status of the power transformer, and also provides accurate data support for the subsequent correction calculation of the operating status indicators.

[0092] Step S4: input the state loss value into a preset multi-factor state evaluation model to obtain an operating state index of the power transformer.

[0093] Specifically, in a preferred embodiment, the preset multi-factor status assessment model includes:

[0094]

[0095] Among them, P k is the operating status index of the kth power transformer; C k is the initialization state index of the kth power transformer; β k is the basic state loss coefficient of the kth power transformer; is the state loss coefficient of the i-th potential operating influencing factor on the power transformer in the k-th power transformer; is the state loss value of the power transformer caused by the i-th potential operating influencing factor in the k-th power transformer; ∈ is the error term.

[0096] It should be noted that the manufacturing parameters of the power transformer are used as the initialization state index C of the power transformer. k .

[0097] It can be understood that by inputting the state loss value into the preset multi-factor state assessment model, not only the role of the basic state loss coefficient and various potential operating influences are taken into account, but also the manufacturing parameters of the power transformer are used as the initialization state indicators. It can comprehensively integrate various potential operating influencing factors and generate more accurate power transformer operating state indicators, ensuring the comprehensiveness and accuracy of the state assessment.

[0098] Step S5: performing correction calculation on the operating state index of the power transformer and the corresponding operating condition difference coefficient to obtain the operating state correction index of the power transformer.

[0099] Specifically, in a preferred embodiment, the operating state correction index of the power transformer is calculated by the following formula:

[0100] P′ k =P k ×H k

[0101] Among them, P′ k is the operating status correction index of the kth power transformer; P k is the operating status index of the kth power transformer; H k is the operating condition difference coefficient of the kth power transformer.

[0102] It can be understood that by correcting and calculating the operating status index of the power transformer and the operating condition difference coefficient, the state assessment error caused by the difference in operating conditions between different power transformers can be effectively corrected, ensuring that the assessment result is closer to the actual operating status of the power transformer, making subsequent simulation and state judgment more accurate.

[0103] Step S6: input the operating status correction index into the digital twin model for simulation to obtain several state values ​​of the power transformer.

[0104] Optionally, the digital twin model can be constructed based on the operating characteristic cluster of the power transformer. The digital twin model is a virtual entity constructed by simulating the physical entity power transformer based on digital calculation, and is an unconventional learning model.

[0105] It can be understood that by inputting the operating status correction index into the digital twin model for simulation, multiple state values ​​of the power transformer can be simulated to accurately reflect its actual operation situation.

[0106] Step S7: Calculate the simulation state score of the power transformer according to the state vector value of the power transformer, and determine whether the operating state of the power transformer is normal according to the simulation state score.

[0107] Specifically, in a preferred embodiment, calculating the simulation state score of the power transformer according to the state vector value of the power transformer includes:

[0108] A number of state vector values ​​of the power transformer are normalized to obtain a number of normalized comprehensive vector indicators of the power transformer.

[0109] According to several normalized comprehensive vector indicators of the power transformer, influence entropies of several normalized comprehensive vector indicators are calculated.

[0110] Weights are assigned to several normalized comprehensive vector indicators of the power transformer according to the influence entropy, so as to obtain weights corresponding to the several normalized comprehensive vector indicators.

[0111] According to several normalized comprehensive vector indicators of the power transformer and the corresponding weights, the simulation state score of the power transformer is calculated.

[0112] Specifically, in a preferred embodiment, judging whether the operating state of the power transformer is normal according to the simulation state score includes:

[0113] It is determined whether the simulation state score of the power transformer is within the ideal operation score range. If so, it is determined that the operation state of the power transformer is normal. If not, it is determined that the operation state of the power transformer is abnormal.

[0114] It should be noted that the normalization processing method is an existing method, which can be selected as Min-Max normalization, maximum absolute value normalization, logarithmic scaling normalization, decimal scaling normalization and other methods for normalization processing.

[0115] Preferably, the influence entropy of the normalized comprehensive vector index is calculated by the following formula:

[0116] S(v ki )=-v ki log(v ki )

[0117] Among them, S(v ki ) is the normalized comprehensive vector index v of the kth power transformer ki The influence of entropy; v ki is the i-th normalized comprehensive vector index of the k-th power transformer.

[0118] Exemplarily, the influence entropy of the normalized comprehensive vector index is calculated by the following formula:

[0119]

[0120] Among them, w ki is the normalized comprehensive vector index v of the kth power transformer ki The weight of S(v ki ) is the normalized comprehensive vector index v of the kth power transformer i The influence of entropy; v ki is the i-th normalized comprehensive vector index of the k-th power transformer; v kjis the jth normalized comprehensive vector index of the kth power transformer; L is the total amount of the normalized comprehensive vector index.

[0121] Specifically, the simulation status score of the power transformer is calculated by the following formula:

[0122]

[0123] Among them, F k Score the simulation status of the kth power transformer; w ki is the normalized comprehensive vector index v of the kth power transformer ki The weight of v ki is the i-th normalized comprehensive vector index of the k-th power transformer.

[0124] It can be understood that by normalizing several state vector values ​​of the power transformer, different state vector values ​​can be compared on the same scale. By calculating the influence entropy of the normalized comprehensive vector index, the influence of each index on the overall state of the power transformer can be accurately identified, and the corresponding weights can be assigned, ensuring that the evaluation of the operating state of the power transformer is more objective and comprehensive. By comparing the simulation state score with the ideal operation score range, it is possible to quickly determine whether the operating state of the power transformer is normal, which provides an accurate basis for the maintenance and optimization of the power transformer and significantly improves the operating reliability and safety of the power system.

[0125] Based on the above method embodiment, the present invention provides a corresponding device embodiment.

[0126] like Figure 2 As shown, an embodiment of the present invention provides a state assessment device for a power transformer, including: a data acquisition module 101, a difference assessment module 102, an operation state index generation module 103, a state correction module 104, a simulation module 105 and a state assessment module 106.

[0127] The data acquisition module 101 is used to obtain historical time-series operation data of power transformers of the same supply target in the power supply network.

[0128] The difference evaluation module 102 is used to calculate the operating condition difference coefficient of the power transformer according to the historical time series operation data.

[0129] The operation status index generating module 103 is used to calculate the state loss value of the power transformer caused by the potential operation influencing factors in the real-time operation data, and input the state loss value into a preset multi-factor state evaluation model to obtain the operation status index of the power transformer.

[0130] The state correction module 104 is used to perform correction calculation on the operating state index of the power transformer and the corresponding operating condition difference coefficient to obtain the operating state correction index of the power transformer.

[0131] The simulation module 105 is used to input the operating state correction index into the digital twin model for simulation to obtain several state vector values ​​of the power transformer.

[0132] The state evaluation module 106 is used to calculate the simulation state score of the power transformer according to the state vector value of the power transformer, and determine whether the operating state of the power transformer is normal according to the simulation state score.

[0133] It should be noted that the embodiments of the device described above correspond to the above embodiments of the present invention, and can implement any of the methods described above in the present invention. In addition, the embodiments of the above-mentioned device are merely schematic, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0134] Based on the above method embodiment of the present invention, a corresponding electronic device embodiment is provided.

[0135] An embodiment of the present invention provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the state assessment method of a power transformer described in any one of the present invention is implemented, or, when the processor executes the computer program, the functions of each module in the above-mentioned device embodiments are implemented.

[0136] Exemplarily, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the terminal device.

[0137] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0138] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0139] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the terminal device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0140] Based on the above method item embodiments, the present invention provides a corresponding storage medium item embodiment.

[0141] Another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute any one of the above-mentioned power transformer status assessment methods of the present invention.

[0142] The storage medium is a computer-readable storage medium, and the computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0143] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the described specific features, structures, materials or characteristics may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0144] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for evaluating the condition of a power transformer, characterized in that: include: Obtain historical time-series operation data of power transformers with the same supply target in the power supply network; Calculate the operating condition difference coefficient of the power transformer based on the historical time series operation data; Calculate the state loss value of the power transformer by the potential operating influencing factors in the real-time operating data; Input the state loss value into a preset multi-factor state evaluation model to obtain the operating state index of the power transformer; The operating state index of the power transformer and the corresponding operating condition difference coefficient are corrected and calculated to obtain the operating state correction index of the power transformer; Input the operating state correction index into the digital twin model for simulation to obtain several state vector values ​​of the power transformer; A simulation state score of the power transformer is calculated according to the state vector value of the power transformer, and whether the operating state of the power transformer is normal is judged according to the simulation state score.

2. A method for evaluating the state of a power transformer according to claim 1, characterized in that: The step of calculating the operating condition difference coefficient of the power transformer according to the historical time series operating data includes: Clustering each characteristic parameter in the historical time series operation data to obtain several operation characteristic clusters of the power transformer; the characteristic parameters include: voltage, current, power, oil temperature and winding temperature; The cluster centers of several operation characteristic clusters of the power transformer are used as several standardized characteristic parameters of the power transformer; Performing standardization processing on each characteristic parameter in the historical time series operation data to obtain a standardized historical operation data array of the power transformer; Calculate the vector distance between each element in the standardized historical operation data array of the power transformer and the corresponding standardized characteristic parameter of the power transformer, and obtain the dimension vector deviation index of each characteristic parameter; The characteristic parameter of the dimension vector deviation index beyond the preset deviation range is used as the unique characteristic parameter of the power transformer; According to the unique characteristic parameters of the power transformer, the operating condition difference coefficient of the power transformer is calculated.

3. A method for evaluating the state of a power transformer according to claim 1, characterized in that: The potential operational impact factors are determined by: Obtain the real-time operation data of the power transformer and filter out the fluctuation characteristic parameters in the real-time operation data by the triple standard deviation method; According to the fluctuation characteristic parameters at each moment, determine the change trend of the operation fluctuation characteristic parameters; The operation fluctuation characteristic parameters with abnormal change trends are regarded as potential operation influencing factors of the power transformer.

4. A method for evaluating the state of a power transformer according to claim 1, characterized in that: The preset multi-factor status assessment model includes: Among them, P k is the operating status index of the kth power transformer; C k is the initialization state index of the kth power transformer; β k is the basic state loss coefficient of the kth power transformer; is the state loss coefficient of the i-th potential operating influencing factor on the power transformer in the k-th power transformer; is the state loss value of the power transformer caused by the i-th potential operating influencing factor in the k-th power transformer; ∈ is the error term.

5. A method for evaluating the state of a power transformer according to claim 1, characterized in that: The operation state index of the power transformer and the corresponding operation condition difference coefficient are corrected and calculated to obtain the operation state correction index of the power transformer, including: The operating status correction index of the power transformer is calculated by the following formula: P′ k =P k ×H k Among them, P′ k is the operating status correction index of the kth power transformer; P k is the operating status index of the kth power transformer; H k is the operating condition difference coefficient of the kth power transformer.

6. A method for evaluating the condition of a power transformer according to claim 1, characterized in that: The simulation state score of the power transformer is calculated according to the state vector value of the power transformer, including: Normalizing a plurality of state vector values ​​of the power transformer to obtain a plurality of normalized comprehensive vector indicators of the power transformer; According to several normalized comprehensive vector indicators of the power transformer, influence entropies of several normalized comprehensive vector indicators are calculated; Assigning weights to several normalized comprehensive vector indicators of the power transformer according to the influence entropy, and obtaining weights corresponding to the several normalized comprehensive vector indicators; According to several normalized comprehensive vector indicators of the power transformer and the corresponding weights, the simulation state score of the power transformer is calculated.

7. A method for evaluating the state of a power transformer according to claim 1, characterized in that: The simulation status score is used to determine whether the operating status of the power transformer is normal, including: It is determined whether the simulation state score of the power transformer is within the ideal operation score range. If so, it is determined that the operation state of the power transformer is normal. If not, it is determined that the operation state of the power transformer is abnormal.

8. A device for evaluating the state of a power transformer, characterized in that: include: Data acquisition module, difference assessment module, operation status indicator generation module, status correction module, simulation module and status assessment module; The data acquisition module is used to obtain the historical time-series operation data of the power transformer of the same supply target in the power supply network; The difference evaluation module is used to calculate the operating condition difference coefficient of the power transformer based on the historical time series operation data; The operation status index generation module is used to calculate the state loss value of the power transformer caused by the potential operation influencing factors in the real-time operation data; input the state loss value into a preset multi-factor state evaluation model to obtain the operation status index of the power transformer; The state correction module is used to perform correction calculation on the operating state index of the power transformer and the corresponding operating condition difference coefficient to obtain the operating state correction index of the power transformer; The simulation module is used to input the operating state correction index into the digital twin model for simulation to obtain several state vector values ​​of the power transformer; The state evaluation module is used to calculate the simulation state score of the power transformer according to the state vector value of the power transformer, and judge whether the operating state of the power transformer is normal according to the simulation state score.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for evaluating the state of the power transformer as described in any one of claims 1 to 7 can be implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for evaluating the state of a power transformer as described in any one of claims 1 to 7 can be implemented.

Images