CN115951159A - Running state analysis method and device of dry-type transformer and storage medium - Google Patents
Running state analysis method and device of dry-type transformer and storage medium Download PDFInfo
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Abstract
The method, the device and the storage medium can calculate expected operation temperature range data according to a pre-acquired data association function, and collected environment temperature data and operation power data within a preset time before the current moment, determine an operation state result of the dry-type transformer according to a relation between the collected operation temperature data and the expected operation temperature range data, and determine the operation state result in a reasonable, accurate and simple mode.
Description
Technical Field
The present disclosure relates to the field of dry transformers, and in particular, to a method and an apparatus for analyzing an operating state of a dry transformer, and a storage medium.
Background
The dry type transformer refers to a transformer in which an iron core and a winding are not immersed in insulating oil, and is widely applied to places such as local lighting, high-rise buildings, airports, wharf CNC (computerized numerical control) mechanical equipment and the like. The dry-type transformer is easy to have abnormal faults, and the dry-type transformer cannot normally run when the abnormal faults are serious. In order to avoid the condition that the dry-type transformer cannot normally operate due to abnormal faults, maintenance personnel are generally required to regularly and preventively maintain the dry-type transformer, the maintenance time of the dry-type transformer is difficult to reasonably determine, the labor cost is undoubtedly increased if the maintenance frequency is higher, the frequency of the dry-type transformer which cannot normally operate due to the abnormal faults is increased if the maintenance frequency is lower, guidance is undoubtedly provided for the maintenance of the maintenance personnel if the operation state of the dry-type transformer can be reasonably analyzed, the maintenance time is favorably and reasonably determined, and the normal operation of the dry-type transformer is reliably ensured at lower labor cost.
Disclosure of Invention
The application provides an operation state analysis method and device of a dry-type transformer and a storage medium, which can reasonably analyze the operation state of the dry-type transformer.
In a first aspect, the present application provides a method for analyzing an operation state of a dry type transformer. The method is applied to a server, the server is connected with a plurality of dry-type transformers, and the method comprises the following steps:
acquiring environmental temperature data, operating temperature data and operating power data of each dry-type transformer within a preset time before the current moment;
for each dry-type transformer, substituting environmental temperature data and operating power data within a preset time length before the current time into a pre-acquired data correlation function, and calculating to obtain expected operating temperature range data of the current time, wherein the data correlation function is used for calculating operating temperature data at the tail end of the preset time length according to the environmental temperature data and the operating power data within the preset time length;
judging whether the operation temperature data at the current moment is in the expected operation temperature range data or not;
if yes, returning to a normal result of the running state;
if not, returning the running state abnormal result.
By adopting the technical scheme, expected operation temperature range data can be calculated according to the pre-acquired data correlation function and the acquired environmental temperature data and operation power data within the preset time before the current moment, the operation state result of the dry-type transformer is determined according to the relation between the acquired operation temperature data and the expected operation temperature range data, and the mode of determining the operation state result is reasonable, accurate and simple.
Further, the data association function is a piecewise function;
when the operation power data are rated operation power within the preset time, the expected operation temperature of the dry-type transformer
；
When the operation power data consists of rated operation power and overload operation power within a preset time, the expected operation temperature of the dry-type transformer
=/>
；
When the operation power data consists of rated operation power and starting operation power within a preset time length, the expected operation temperature of the dry-type transformer
=/>
；
The expected operating temperature range data is a data range from the expected operating temperature T minus the confidence error to the expected operating temperature T plus the confidence error;
wherein,
，/>
，/>
，/>
；
Further, the air conditioner is provided with a fan,
、/>
、/>
and &>
Are all based on a pre-divided ambient temperature rangeA determined piecewise function when>
In a determined ambient temperature range, a set of constants can be determined>
、/>
、/>
、/>
。
Further, the dividing method of the environmental temperature range comprises the following steps:
acquiring historical operation big data of the dry-type transformer, wherein the historical operation big data comprises historical operation data carrying a transformer identifier and a timestamp identifier, and the historical operation data comprises environment temperature data, operation temperature data and operation power data;
determining rated operation data in the historical big data based on the timestamp and the rated power data, wherein the rated operation data comprises historical operation data of continuous preset duration, and the operation power data of the historical operation data is rated operation power;
calculating the average ambient temperature of the ambient temperature data and the average operating temperature of the operating temperature data in each rated operating data; grouping the average operating temperatures at equal intervals to obtain a plurality of groups of average operating temperatures;
determining an average ambient temperature range according to the radiation range corresponding to the average ambient temperature in each group of average operating temperatures;
and sorting all the average ambient temperature ranges to obtain the division result of the ambient temperature ranges, wherein the set of all the ambient temperature ranges is a continuous ambient temperature range whole.
Further, a specific method for determining an average ambient temperature range from the radiation range corresponding to the average ambient temperature in each group of average operating temperatures is: and sorting the average ambient temperatures corresponding to the group of average operating temperatures from large to small, removing the average ambient temperatures of the first ten percent and the last ten percent from the sorting result, and determining the data range between the maximum value and the minimum value in the remaining average ambient temperatures as the radiation range.
In a second aspect, the present application provides an operating state analyzing apparatus for a dry type transformer. The device includes:
the data acquisition module is used for acquiring environmental temperature data, operating temperature data and operating power data of each dry-type transformer within a preset time before the current moment;
the data calculation module is used for substituting environmental temperature data and running power data in a preset time length before the current time into a pre-acquired data correlation function for each dry-type transformer to calculate and obtain expected running temperature range data of the current time, and the data correlation function is used for calculating running temperature data at the tail end of the preset time length according to the environmental temperature data and the running power data in the preset time length; and
the result determining module is used for generating an operation state result according to the judgment result of the data calculating module; and if the operation temperature data at the current moment is in the corresponding expected operation temperature range data, the operation state result is normal operation, otherwise, the operation state result is abnormal operation.
In a third aspect, the present application provides a computer readable storage medium having stored therein a computer software program capable of being loaded by a processor and performing any one of the methods as described in the first aspect above.
It should be understood that the statements described in this summary are not intended to limit the scope of the disclosure, or the various features described in this summary. Other features of the present application will become apparent from the following description.
Drawings
The above and other features, advantages and aspects of various embodiments of the present application will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, like or similar reference characters designate like or similar elements, and wherein:
FIG. 1 illustrates an exemplary operating environment in which embodiments of the present application can operate;
fig. 2 is a flowchart illustrating an operation state analysis method of a dry type transformer in an embodiment of the present application;
fig. 3 is a block diagram showing an operation state analysis device of a dry-type transformer in the embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The application provides an operation state analysis method and device of a dry-type transformer and a storage medium, which can calculate expected operation temperature range data of the dry-type transformer at the current moment according to the environmental temperature data and the operation state data of the dry-type transformer at the current time period, can judge whether the operation state of the dry-type transformer is abnormal or not by combining the operation temperature data of the dry-type transformer at the current moment, and are beneficial to timely finding the abnormal operation state of the dry-type transformer.
FIG. 1 illustrates a schematic diagram of an exemplary operating environment 100 in which embodiments of the present application can operate.
Referring to fig. 1, the operation environment 100 includes a server 110 and a plurality of dry-type transformers 120, and each dry-type transformer 120 is configured with an environment temperature detection module, an operation temperature detection module, and an operation power detection module.
The environment temperature detection module is configured to obtain environment temperature data corresponding to the dry-type transformer 120, and the environment temperature detection module may be specifically configured as a temperature sensor, which is disposed outside the housing of the dry-type transformer 120, and detects an obtained temperature reflecting a temperature of an environment where the dry-type transformer 120 is located; the operation temperature detection module is used for detecting operation temperature data of the designated position of the dry-type transformer 120, in the embodiment of the application, the operation temperature detection module is specifically set as a temperature sensor, and is specifically arranged inside the shell of the dry-type transformer 120, and the operation temperature data is influenced by the heat generated by the coil of the dry-type transformer 120 and the temperature of the environment; the operation power detection module is configured to detect operation power data of the dry-type transformer 120, and specifically includes a voltage sensor and a current sensor that are arranged in pair, where the voltage sensor and the current sensor are arranged on an input side of the dry-type transformer 120 and are respectively configured to acquire input voltage data and input current data of the dry-type transformer 120, so as to obtain the operation power data, where the operation power data refers to input power and is equal to a product of the input voltage data and the input current data.
Each dry-type transformer 120 is in communication connection with the server 110, so as to enable the server 110 to obtain the ambient temperature data, the operating temperature data, and the operating power data of each dry-type transformer 120, specifically, a mobile communication module connected to the ambient temperature detection module, the operating temperature detection module, and the operating power detection module may be disposed on the dry-type transformer 120, and the mobile communication module is connected to the server 110 through a mobile communication network, so that the ambient temperature data, the operating temperature data, and the operating power data of the dry-type transformer 120 can be transmitted to the server 110. The ambient temperature data, the operating temperature data, and the operating power data all carry a transformer identification and a timestamp.
Fig. 2 is a flowchart illustrating an operation state analysis method of a dry type transformer in an embodiment of the present application. Method 200 may be performed by server 110 in fig. 1.
Referring to fig. 2, a method 200 includes:
s210: the environmental temperature data, the operation temperature data and the operation power data of each dry-type transformer 120 within a preset time before the current time are obtained.
The server 110 receives the environment temperature data, the operation temperature data and the operation power data sent by the dry-type transformer 120, that is, the process of obtaining the environment temperature data, the operation temperature data and the operation power data is realized. The server 110 identifies the dry-type transformer 120 corresponding to different environmental temperature data, operating temperature data and operating power data through the transformer identifier, and identifies the time corresponding to the different environmental temperature data, operating temperature data and operating power data through the timestamp.
S220: for each dry-type transformer 120, the environmental temperature data and the operating power data within a preset time period before the current time are substituted into the pre-acquired data association function, and the expected operating temperature range data at the current time are calculated.
And the data correlation function is used for calculating the operation temperature data at the end moment of the preset duration according to the environment temperature data and the operation power data in the preset duration.
Before the method of the step, the method also comprises a step of acquiring the data association function. A data correlation function corresponds to a class of dry-type transformers 120, where a particular class or model of dry-type transformers 120 refers to dry-type transformers 120 manufactured according to the same drawing and parameter requirements and having the same specifications. For convenience of description, all the dry-type transformers 120 in the present embodiment belong to one category, and there is only one data correlation function in the method 200 of the present embodiment.
Considering that there are three possible operating states when the dry-type transformer 120 is operating, the first is the operating state during the start-up phase, the second is the rated operating state, and the third is the overload operating state. In practical application, it is considered that the preset time period is not too long in practical selection, and the dry-type transformer 120 basically does not operate in an overload manner within a short time after the start-up stage, so that three possibilities exist for the operation state of the dry-type transformer 120 within the preset time period, the first possibility is that only the rated operation state exists within the preset time period, the second possibility is that only the rated operation state and the overload operation state exist within the preset time period, and the third possibility is that only the operation state of the start-up stage and the rated operation state exist within the preset time period.
The data correlation function includes an expected temperature determination function that is determined as a piecewise function based on the three operating conditions of the dry transformer 120.
Expected operating temperature of the dry-type transformer 120 when the operating power data are rated operating power (first type) for a preset time period
The formula indicates that the expected operating temperature is equal to the nominal operating temperature.
Expected operating temperature of the dry type transformer 120 when the operating power data consists of rated operating power and overload operating power (second type) for a preset time period
=/>
The equation expresses that the expected operating temperature is equal to the nominal operating temperature plus the time integral of several times (the overload divided by the unit overload) the unit overload temperature rise.
Expected operating temperature of the dry-type transformer 120 when the operating power data consists of the rated operating power and the starting operating power for a preset period of time (third type)
=/>
The equation represents the expected operating temperature equal to the nominal operating temperature minus the change temperature per unit time (the average change in temperature per unit time from the start-up temperature data to the nominal temperature data after the start-up phase ends at the nominal operating power) and the time between the current time and the time when the expected operating temperature first stabilizes at the nominal operating temperature after the start-up phase ends.
Wherein,
，/>
，/>
，/>
。
in the above-mentioned formula, the compound has the following structure,
is the average ambient temperature, which represents the average of the ambient temperature data over a preset time period, < >>
Is a nominal operating temperature, which represents the operating temperature of the dry transformer 120 after a predetermined period of operation in the nominal state, and->
Represents the nominal operating power and>
indicates an overload operating power and->
Unit overload value>
Unit time->
Is a unit overload temperature rise which is equal to the dry transformer 120 to compare>
Is out high>
Is/are>
Run->
Temperature of time minus dry transformer 120 to +>
Run->
The prevailing temperature is->
In order to provide starting temperature data, which represents operating temperature data at the end of the starting phase of the dry-type transformer 120, based on the temperature data determined in advance>
For a preparation time period, which represents the time period required for the operating temperature data to change from the start-up temperature data to the setpoint operating temperature after the end of the start-up phase of the dry-type transformer 120 and the operation at the setpoint operating power, is->
The duration from the end of the start-up phase to the current moment.
The method for dividing the environment temperature range specifically comprises the following steps: acquiring historical operation big data of the dry-type transformer 120, wherein the historical operation big data comprises historical operation data carrying a transformer identifier and a timestamp identifier, and the historical operation data comprises environment temperature data, operation temperature data and operation power data; determining rated operation data in the historical big data based on the timestamp and the rated power data, wherein the rated operation data comprises historical operation data of continuous preset duration, and the operation power data of the historical operation data is rated operation power; calculating the average ambient temperature of the ambient temperature data and the average operating temperature of the operating temperature data in each rated operating data; grouping the average operating temperatures at equal intervals to obtain a plurality of groups of average operating temperatures; determining an average ambient temperature range according to the radiation range corresponding to the average ambient temperature in each group of average operating temperatures; and sorting all the average ambient temperature ranges to obtain the division result of the ambient temperature ranges, wherein the set of all the ambient temperature ranges is a continuous ambient temperature range whole.
The specific method for determining an average ambient temperature range according to the radiation range corresponding to the average ambient temperature in each group of average operating temperatures is as follows: the average ambient temperatures corresponding to a group of average operating temperatures are sorted from large to small, the first ten percent (larger) and the last ten percent (smaller) of the average ambient temperatures are removed from the sorting result, and the data range between the maximum value and the minimum value of the remaining average ambient temperatures (i.e., the middle eighty percent) is determined as the radiation range.
The finishing comprises the steps of cutting two adjacent average environment temperature ranges with overlapped parts to enable the boundaries of the two adjacent average environment temperature ranges to be adjacent, and supplementing the adjacent average environment temperature ranges with intervals to enable the boundaries of the two adjacent average environment temperature ranges to be adjacent. In summary, the resulting ambient temperature range is entirely continuous with no break points in between, and the overall range is the data range from the minimum value in all ambient temperature ranges to the maximum value in all ambient temperature ranges.
The data correlation function is also obtained by historical big data training of the dry-type transformer 120, and the specific training process is not disclosed here. Through simulation verification of the applicant, when the dry-type transformer 120 normally operates for a preset time, the environmental temperature data and the operating power data within the preset time are substituted into the data association function, and the error between the obtained expected operating temperature T and the acquired operating temperature data at the end moment of the preset time is always smaller than the confidence error, so that the data association function is considered to be established under the confidence error.
The expected operating temperature range data is the range of data between the expected operating temperature T minus the confidence error to the expected operating temperature plus the confidence error.
Thus, the data association function is clear, and in the method of this step, for each dry-type transformer 120, the server 110 may substitute the ambient temperature data and the operating power data within the preset time period before the current time into the pre-obtained data association function, so as to calculate the expected operating temperature range data at the current time. Regarding the selection of the preset time, the preset time is generally not shorter than the time of the starting stage, nor shorter than the time required for the starting temperature data to change to the rated operation temperature, and certainly, the preset time is not too long, and in the embodiment of the present application, the preset time is specifically selected to be 15min.
Of course, in this embodiment, only one type of operation state analysis method for the dry transformer 120 is presented, if the operation states of multiple types of dry transformers 120 need to be analyzed, multiple data association functions may be trained, and the transformer identifier of the dry transformer 120 may include a type identifier.
In addition, considering the operation loss of the dry-type transformer 120, the operation time of the historical operation big data for training the data correlation function should not differ too much, so as to avoid the data correlation function from generating a large error due to the operation loss of the dry-type transformer 120 itself, for example, the historical operation data of the dry-type transformer 120 in the last month that the same batch is used for training the data correlation function.
S230: it is determined whether the current time operating temperature data of the dry-type transformer 120 is within the corresponding expected operating temperature range data.
After the expected operation temperature range data of the dry-type transformer 120 is obtained through calculation, it is only necessary to judge whether the collected current operation temperature data is within the expected operation temperature range, and the process is simple data size judgment and is not described in detail.
S240: and generating an operation state result according to the judgment result of the step S230.
And if the operation temperature data at the current moment is in the corresponding expected operation temperature range data, the operation state result is normal operation, otherwise, the operation state result is abnormal operation. The operation status result may be presented in any form, for example, sent to a designated mobile terminal or displayed on a display in a text form, and the operation status result needs to carry a transformer identifier so as to indicate the operation status of the dry-type transformer 120.
Whether the dry-type transformer 120 normally operates is analyzed through the environmental temperature data, the operation power data and the operation temperature data in the method 200, so that not only can abnormal faults of the dry-type transformer 120 be found in time, but also the fault finding mode is simple and convenient, a large amount of detection equipment is not needed, the abnormity of the dry-type transformer 120 can be found in time only through an algorithm, guidance can be provided for overhaul opportunities of overhaul personnel, and finally data support can be provided for guaranteeing the normal operation of the dry-type transformer 120 at low cost.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combinations, but those skilled in the art should understand that the present application is not limited by the described order of acts, as some steps may be performed in other orders or simultaneously according to the embodiments of the present application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are exemplary embodiments and that the acts and modules referred to are not necessarily required in this application.
Fig. 3 is a block diagram showing an operation state analysis apparatus for a dry type transformer in the embodiment of the present application. The apparatus 300 may be implemented as the server 110 in fig. 1 or included in the server 110 in fig. 1.
Referring to fig. 3, the apparatus 300 includes:
the data obtaining module 310 is configured to obtain environmental temperature data, operating temperature data, and operating power data of each dry-type transformer 120 within a preset time period before the current time;
the data calculation module 320 is configured to substitute, for each dry-type transformer 120, the ambient temperature data and the operating power data within a preset time period before the current time into a pre-acquired data correlation function, and calculate to obtain expected operating temperature range data at the current time, where the data correlation function is configured to calculate the operating temperature data at the end time of the preset time period according to the ambient temperature data and the operating power data within the preset time period; and
a result determining module 330, configured to generate an operation state result according to the determination result of the data calculating module 320; and if the operation temperature data at the current moment is in the corresponding expected operation temperature range data, the operation state result is normal operation, otherwise, the operation state result is abnormal operation.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the described module may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.
In particular, according to an embodiment of the present application, the process described above with reference to the flowchart fig. 2 may be implemented as a computer software program. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a machine-readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network, and/or installed from a removable medium.
It should be noted that the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In embodiments of the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In embodiments of the present application, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
As another aspect, the present application also provides a computer-readable storage medium, which may be included in an electronic device; or may be separate and not incorporated into the electronic device. The computer readable storage medium stores one or more programs which, when executed by one or more processors, perform a method 200 for analyzing an operational status of a dry-type transformer described herein.
The foregoing description is only exemplary of the preferred embodiments of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the disclosure. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Claims (7)
1. An operation state analysis method for a dry-type transformer, which is applied to a server (110), wherein the server (110) is connected with a plurality of dry-type transformers (120), and comprises the following steps:
acquiring environmental temperature data, operating temperature data and operating power data of each dry-type transformer (120) within a preset time length before the current moment;
for each dry-type transformer (120), substituting the environmental temperature data and the operating power data within a preset time length before the current time into a pre-acquired data correlation function, and calculating to obtain the expected operating temperature range data of the current time, wherein the data correlation function is used for calculating the operating temperature data at the terminal time of the preset time length according to the environmental temperature data and the operating power data within the preset time length;
judging whether the operating temperature data at the current moment is in the expected operating temperature range data;
if yes, returning to a normal result of the running state;
if not, returning the running state abnormal result.
2. The method of claim 1, wherein the data correlation function is a piecewise function;
when the operation power data in the preset time period are rated operation power, the expected operation temperature of the dry-type transformer (120)
；
An expected operating temperature of the dry-type transformer (120) when the operating power data consists of a rated operating power and an overload operating power for a preset time period
=/>
；
An expected operating temperature of the dry-type transformer (120) when the operating power data consists of a rated operating power and a starting operating power within a preset time period
=/>
；
The expected operating temperature range data is a data range from the expected operating temperature T minus the confidence error to the expected operating temperature T plus the confidence error;
wherein,
，/>
，/>
，/>
；
3. The method of claim 2,
、/>
、
and &>
Are each a piecewise function determined based on a pre-divided ambient temperature range when>
In a determined ambient temperature range, a set of constants can be determined>
、/>
、/>
、/>
。
4. The method of claim 3, wherein the dividing of the ambient temperature range comprises:
acquiring historical operation big data of a dry-type transformer (120), wherein the historical operation big data comprises historical operation data carrying a transformer identifier and a timestamp identifier, and the historical operation data comprises environment temperature data, operation temperature data and operation power data;
determining rated operation data in the historical big data based on the timestamp and the rated power data, wherein the rated operation data comprise historical operation data with continuous preset duration, and the operation power data of the historical operation data are rated operation power;
calculating the average ambient temperature of the ambient temperature data and the average operating temperature of the operating temperature data in each rated operating data; grouping the average operating temperatures at equal intervals to obtain a plurality of groups of average operating temperatures;
determining an average ambient temperature range according to the radiation range corresponding to the average ambient temperature in each group of average operating temperatures;
and sorting all the average ambient temperature ranges to obtain the division result of the ambient temperature ranges, wherein the set of all the ambient temperature ranges is a continuous ambient temperature range whole.
5. The method of claim 4, wherein determining an average ambient temperature range from the radiation ranges corresponding to the average ambient temperatures in each set of average operating temperatures comprises: and sorting the average ambient temperatures corresponding to the group of average operating temperatures from large to small, removing the average ambient temperatures of the first ten percent and the last ten percent from the sorting result, and determining the data range between the maximum value and the minimum value in the remaining average ambient temperatures as the radiation range.
6. An operation state analysis device for a dry-type transformer, comprising:
the data acquisition module (310) is used for acquiring environmental temperature data, operation temperature data and operation power data of each dry-type transformer (120) within a preset time length before the current moment;
the data calculation module (320) is used for substituting the environmental temperature data and the running power data in the preset time length before the current time into a pre-acquired data correlation function for each dry-type transformer (120) to calculate and obtain the expected running temperature range data of the current time, and the data correlation function is used for calculating the running temperature data at the tail end time of the preset time length according to the environmental temperature data and the running power data in the preset time length; and
a result determining module (330) for generating an operation state result according to the judgment result of the data calculating module (320); and if the operation temperature data at the current moment is in the corresponding expected operation temperature range data, the operation state result is normal operation, otherwise, the operation state result is abnormal operation.
7. A computer-readable storage medium, in which a computer software program is stored which can be loaded by a processor and which executes the method of any one of claims 1-5.
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