CN116067524A - Real-time temperature monitoring method for internal components of oil immersed transformer - Google Patents
Real-time temperature monitoring method for internal components of oil immersed transformer Download PDFInfo
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Abstract
The invention provides a real-time temperature monitoring method for an internal component of an oil immersed transformer, which belongs to the technical field of transformers and specifically comprises the following steps: and acquiring the real-time temperature of the internal components of the oil immersed transformer based on the temperature sensor, judging whether the real-time temperature is larger than a second threshold value and the running load of the transformer is larger than a first load threshold value when the real-time temperature does not belong to abnormal temperature, if so, constructing a temperature prediction model based on the real-time temperature, the environment temperature and the running load of the internal components of the oil immersed transformer, obtaining the predicted temperature of the oil immersed transformer after the first time threshold value, determining whether to output alarm signals based on the predicted temperature, and if not, acquiring the temperature acquisition frequency of the temperature sensor based on the running load, the environment temperature and the real-time temperature of the oil immersed transformer by adopting a prediction model based on a machine learning algorithm, and acquiring the real-time temperature based on the temperature acquisition frequency, thereby further improving the running reliability of the temperature sensor.
Description
Technical Field
The invention belongs to the technical field of transformers, and particularly relates to a real-time temperature monitoring method for an internal component of an oil immersed transformer.
Background
In order to realize real-time temperature monitoring of an oil immersed transformer, in the method for monitoring hot spot temperature of an oil immersed transformer of patent grant publication No. CN106706164B, the following technical problems are solved by determining a corrected winding index, monitoring the environment temperature and the actual load current of a winding in real time, monitoring the hot spot temperature of the oil immersed transformer after correcting the winding index in real time, and the like:
1. the temperature acquisition frequency of the oil immersed transformer is not considered to change, and the internal temperature acquisition device of the oil immersed transformer is powered by a lithium battery or the like, and under normal conditions, if the load and the external temperature do not change, the internal temperature change of the transformer is slow, so that if the temperature acquisition frequency cannot be determined according to the load and the external temperature change condition of the oil immersed transformer, the endurance stability and the like of the temperature acquisition device are affected.
2. The temperature monitoring result of the oil immersed transformer is not considered to be checked, and because the internal operation environment of the oil immersed transformer is severe and extremely large electromagnetic interference exists, the measurement result and the transmission signal of the temperature sensor are greatly influenced, and if the temperature monitoring result of the temperature sensor cannot be screened and corrected, the accuracy of the final temperature monitoring result cannot be ensured.
Aiming at the technical problems, the invention provides a real-time temperature monitoring method for an internal component of an oil immersed transformer.
Disclosure of Invention
In order to achieve the purpose of the invention, the invention adopts the following technical scheme:
according to one aspect of the invention, a real-time temperature monitoring method for an internal component of an oil immersed transformer is provided.
The real-time temperature monitoring method for the internal components of the oil immersed transformer is characterized by comprising the following steps of:
s11, acquiring real-time temperature of an internal component of the oil immersed transformer based on a temperature sensor, judging whether the real-time temperature belongs to abnormal temperature or not, and if not, entering a step S12;
s12, judging whether the real-time temperature is greater than a first threshold value, if so, outputting an alarm signal, and if not, entering a step S13;
s13, judging whether the real-time temperature is greater than a second threshold value and the operation load of the oil-immersed transformer is greater than a first load threshold value, if yes, entering a step S14, otherwise, acquiring the real-time temperature of an internal component of the oil-immersed transformer based on the operation load of the oil-immersed transformer and the environment temperature of the oil-immersed transformer by adopting a prediction model based on a machine learning algorithm, acquiring the temperature acquisition frequency of the temperature sensor, and returning to the step S11 to acquire the real-time temperature based on the temperature acquisition frequency;
s14, constructing a temperature prediction model based on the real-time temperature of the internal components of the oil-immersed transformer, the ambient temperature of the oil-immersed transformer and the operation load of the oil-immersed transformer, obtaining the predicted temperature of the internal components of the oil-immersed transformer after a first time threshold, and determining whether to output an alarm signal based on the predicted temperature.
By firstly identifying abnormal temperature, the problem of inaccurate temperature measurement caused by external interference of temperature acquisition signals inside the oil immersed transformer or faults of a temperature sensor is avoided, and the accuracy and reliability of temperature monitoring of the transformer are further ensured.
Through based on the operating load of the oil immersed transformer the ambient temperature of the oil immersed transformer, the real-time temperature of the internal components of the oil immersed transformer adopts a prediction model based on a machine learning algorithm to obtain the temperature acquisition frequency of the temperature sensor, thereby realizing the determination of the temperature acquisition frequency of the temperature sensor of the transformer from multiple angles, further reducing the unnecessary data acquisition and transmission of the temperature sensor of the oil immersed transformer, further ensuring the stability and reliability of the operation of the temperature sensor, simultaneously ensuring the reliability of the temperature monitoring of the transformer, and ensuring the operation safety of the transformer.
The construction of the predicted temperature is realized based on various factors, so that the technical problem that the accurate control of the temperature of the transformer cannot be realized due to independent dependence on temperature monitoring data is avoided, the reliability of the temperature control of the transformer is further ensured, the operation safety and stability of the whole transformer are promoted, and the operation life of the transformer is ensured.
In another aspect, embodiments of the present application provide a computer system, including: a communicatively coupled memory and processor, and a computer program stored on the memory and capable of running on the processor, characterized by: the processor runs the computer program to obtain the real-time temperature monitoring method for the internal components of the oil immersed transformer.
In another aspect, the present invention provides a computer storage medium having a computer program stored thereon, which when executed in a computer causes the computer to perform a method for real-time temperature monitoring of an internal component of an oil-immersed transformer as described above.
Additional features and advantages will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.
Fig. 1 is a flow chart of a real-time temperature monitoring method for an internal component of an oil-immersed transformer according to embodiment 1;
fig. 2 is a flowchart of specific steps of making a judgment of an abnormal temperature according to embodiment 1;
FIG. 3 is a flowchart of specific steps of temperature acquisition frequency determination of the temperature sensor according to embodiment 1;
fig. 4 is a flowchart of specific steps in the construction of a predicted temperature of an oil immersed transformer after a first time threshold according to embodiment 1.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can 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 disclosure 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 in the drawings denote the same or similar structures, and thus detailed descriptions thereof will be omitted.
The terms "a," "an," "the," and "said" are used to indicate the presence of one or more elements/components/etc.; the terms "comprising" and "having" are intended to be inclusive and mean that there may be additional elements/components/etc. in addition to the listed elements/components/etc.
In order to realize real-time temperature monitoring of an oil immersed transformer, the prior art often does not consider the change of the temperature acquisition frequency of the oil immersed transformer, because the internal temperature acquisition device of the oil immersed transformer supplies power in a lithium battery or other modes, if the temperature acquisition frequency cannot be determined according to the load and external temperature variation conditions of the oil immersed transformer, the endurance stability of the temperature acquisition device and the like can be influenced, meanwhile, the temperature monitoring result of the oil immersed transformer is not considered for verification, and because the internal operation environment of the oil immersed transformer is severe and has extremely large electromagnetic interference, the measurement result and the transmission signal of the temperature sensor are greatly influenced, and if the temperature monitoring result of the temperature sensor cannot be screened and corrected, the accuracy of the final temperature monitoring result cannot be ensured.
Example 1
To solve the above-mentioned problems, according to one aspect of the present invention, as shown in fig. 1, there is provided a real-time temperature monitoring method for an internal component of an oil-immersed transformer, which is characterized by specifically comprising:
s11, acquiring real-time temperature of an internal component of the oil immersed transformer based on a temperature sensor, judging whether the real-time temperature belongs to abnormal temperature or not, and if not, entering a step S12;
specifically, the temperature sensor adopts a temperature monitoring sensor based on ultrahigh frequency RFID, and the real-time temperature monitored by the temperature sensor is transmitted to a monitoring server through a ceramic antenna, and the monitoring server determines the abnormal temperature, the temperature acquisition frequency and the predicted temperature.
For example, a temperature monitoring sensor based on ultrahigh frequency RFID is affected by an internal magnetic field of a transformer and an oil medium during signal transmission, and a frequency deviation phenomenon occurs in a frequency deviation process of ultrahigh frequency electromagnetic wave transmission, and the monitoring server is responsible for performing filtering processing on a transmission signal of the temperature monitoring sensor first, and correcting the frequency of the transmission signal to overcome the frequency deviation phenomenon thereof, and a specific correction threshold is determined according to experimental data of the frequency deviation of the transmission signal in the same oil medium.
Specifically, as shown in fig. 2, the specific steps for determining the abnormal temperature are as follows:
s21, judging whether the real-time temperature is greater than a third threshold value, if so, determining that the real-time temperature belongs to an abnormal temperature, and if not, entering a step S22;
for example, when the real-time temperature is 200 degrees, and the measurement range of the temperature monitoring sensor is 150 degrees at maximum, and the third threshold is 150 degrees, it is determined that the real-time temperature belongs to the abnormal temperature.
In particular, for example, in addition to being greater than the third threshold, when it is smaller than a certain threshold, that is, smaller than the measurement range of the temperature monitoring sensor, it is determined that the real-time temperature belongs to the abnormal temperature.
S22, judging whether the absolute value of the difference value between the real-time temperature and the temperature of the internal component of the oil-immersed transformer acquired last time by the temperature sensor is larger than a first fluctuation threshold value, if so, determining that the real-time temperature belongs to an abnormal temperature, and if not, entering a step S23;
for example, when the real-time temperature is 200 degrees, the temperature of the oil immersed transformer acquired by the temperature sensor last time is 90 degrees, the absolute value of the difference is 110 degrees, and the temperature of the transformer cannot be changed drastically within a certain time threshold, for example, the first threshold is 5 degrees, it is determined that the real-time temperature belongs to an abnormal temperature.
S23 determines that the real-time temperature does not belong to an abnormal temperature.
For example, in the actual operation process, the abnormal temperature may be determined according to the deviation between the predicted value of the real-time temperature of the transformer and the real-time temperature of the transformer, and the predicted value of the real-time temperature of the transformer may be determined by constructing a prediction model according to the operation load of the transformer, the temperature data acquired last time, and the external environment temperature.
In a specific example, the prediction model is constructed in a manner consistent with the specific steps of constructing the predicted temperature of the oil immersed transformer after the first time threshold.
By firstly identifying abnormal temperature, the problem of inaccurate temperature measurement caused by external interference of temperature acquisition signals inside the oil immersed transformer or faults of a temperature sensor is avoided, and the accuracy and reliability of temperature monitoring of the transformer are further ensured.
S12, judging whether the real-time temperature is greater than a first threshold value, if so, outputting an alarm signal, and if not, entering a step S13;
in a specific example, the first threshold is determined according to a maximum temperature limit of operation of the internal components of the transformer, and if the specific maximum temperature limit is 100 degrees, it is determined whether the real-time temperature is greater than 100 degrees, and if and only if it is not greater than 100 degrees, step S13 is entered.
S13, judging whether the real-time temperature is greater than a second threshold value and the operation load of the oil-immersed transformer is greater than a first load threshold value, if yes, entering a step S14, otherwise, acquiring the real-time temperature of an internal component of the oil-immersed transformer based on the operation load of the oil-immersed transformer and the environment temperature of the oil-immersed transformer by adopting a prediction model based on a machine learning algorithm, acquiring the temperature acquisition frequency of the temperature sensor, and returning to the step S11 to acquire the real-time temperature based on the temperature acquisition frequency;
specifically, the first threshold value and the second threshold value are determined according to rated operation temperatures of internal components of the oil immersed transformer, wherein the larger the rated operation temperature is, the larger the first threshold value and the second threshold value are, and the first threshold value is larger than the second threshold value.
For example, when the real-time temperature is greater than the second threshold value and the operation load is greater than the first load threshold value, it is explained that the real-time temperature of the transformer is not out of the standard, but the operation load is large, and if the operation is continued, a higher temperature may be generated, so that the construction of the predicted temperature is necessary.
Specifically, as shown in fig. 3, the specific steps for determining the temperature acquisition frequency of the temperature sensor are as follows:
s31, judging whether the real-time temperature of the internal components of the oil immersed transformer is greater than a fourth threshold value, if so, taking the basic acquisition frequency as the temperature acquisition frequency of the temperature sensor, and if not, entering step S32;
for example, if the real-time temperature is 90 degrees and the fourth threshold value is 80 degrees, the internal temperature of the transformer is high, so that the basic acquisition frequency needs to be used as the temperature acquisition frequency of the temperature sensor, and the temperature acquisition frequency does not need to be reduced.
S32, judging whether the operation load of the oil immersed transformer is larger than a second load threshold, if so, taking the basic acquisition frequency as the temperature acquisition frequency of the temperature sensor, and if not, entering step S33;
s33, obtaining correction of the temperature acquisition frequency by adopting a prediction model based on a GA-WNN algorithm based on the running load of the oil immersed transformer, the ambient temperature of the oil immersed transformer and the real-time temperature of the internal components of the oil immersed transformer, and obtaining the temperature acquisition frequency of the temperature sensor based on the correction of the temperature acquisition frequency and the basic acquisition frequency.
Specific examples of the construction of the prediction model of the GA-WNN algorithm include the following specific steps:
step 1: determining a network structure of a wavelet neural network, initializing network parameters including telescopic parameters to be optimized, translation parameters and network interlayer connection weights, setting a wavelet neural network learning rate, and determining node numbers of all layers of the network;
step 2: initializing each population, individual parameter values, error range values,
step 3: and (5) classifying the population. If K groups are set, the clustering center exists.
Step 4: selection of individuals of similar population. The similarity value of the same type of individuals is determined according to the acquired fuzzy similarity matrix, and the similarity of two different individuals is determined, wherein the calculation formula of the similarity value is as follows:
r ij ＝1-c(d(x i ,x j ))α
wherein c and α are two positive numbers, r ij The value range of the similarity is 0,1]。
Step 5: and (5) individual competing. Calculating fitness value f of individual i Individuals with higher fitness values, i.e., excellent individual selection, are selected.
For example, when an individual with higher fitness is selected, the cross mutation operation is needed to be performed on the original individual, wherein the mutation type adopts a single-point cross mode, and an initial value is set according to the cross probability, so that the parallel cross behavior of different individuals is realized; if the chromosome meets the standard, a mutation operator or a uniform mutation operator is adopted to execute mutation operation. Suppose the j-th group of genes x of the i-th individual ij And performing mutation operation, wherein a mutation algorithm is as follows:
in the formula, r 2 Is a random number; g is the number of iterations; g_max is the upper iteration count; r is a random number between 0 and 1, x max 、x min The maximum and minimum of genetic variation, respectively.
Step 6: and determining whether the population range meets the requirements. If yes, continuing to execute other operations; otherwise, returning to the step 5.
Step 7: it is determined whether the target error meets the requirements. If yes, continuing to execute; if not, returning to step 5.
Step 8: and (3) realizing a learning process, obtaining optimal parameters, and putting the optimal parameters into WNN again for training.
Step 9: and predicting to obtain the correction quantity of the temperature acquisition frequency.
Specifically, the calculation formula of the temperature acquisition frequency of the temperature sensor is as follows:
wherein P is 1 、P J The correction amount and the basic acquisition frequency of the temperature acquisition frequency are respectively, T is the real-time temperature of the internal components of the oil immersed transformer, and T is 1 Is a time threshold.
Through based on the operating load of the oil immersed transformer the ambient temperature of the oil immersed transformer, the real-time temperature of the internal components of the oil immersed transformer adopts a prediction model based on a machine learning algorithm to obtain the temperature acquisition frequency of the temperature sensor, thereby realizing the determination of the temperature acquisition frequency of the temperature sensor of the transformer from multiple angles, further reducing the unnecessary data acquisition and transmission of the temperature sensor of the oil immersed transformer, further ensuring the stability and reliability of the operation of the temperature sensor, simultaneously ensuring the reliability of the temperature monitoring of the transformer, and ensuring the operation safety of the transformer.
S14, constructing a temperature prediction model based on the real-time temperature of the internal components of the oil-immersed transformer, the ambient temperature of the oil-immersed transformer and the operation load of the oil-immersed transformer, obtaining the predicted temperature of the internal components of the oil-immersed transformer after a first time threshold, and determining whether to output an alarm signal based on the predicted temperature.
The construction of the predicted temperature is realized based on various factors, so that the technical problem that the accurate control of the temperature of the transformer cannot be realized due to independent dependence on temperature monitoring data is avoided, the reliability of the temperature control of the transformer is further ensured, the operation safety and stability of the whole transformer are promoted, and the operation life of the transformer is ensured.
Specifically, as shown in fig. 4, the specific steps of constructing the predicted temperature of the oil immersed transformer after the first time threshold are as follows:
s41, acquiring real-time temperature of an internal component of the oil immersed transformer, wherein the environment temperature and the running load construct an input set;
s42, transmitting the input set to a temperature prediction model based on a GA-WNN algorithm to obtain a prediction result;
for example, the steps for constructing the prediction model based on the GA-WNN algorithm are already analyzed as described above, and will not be described in detail herein.
S43, obtaining the predicted temperature of the internal components of the oil immersed transformer after the first time threshold based on the predicted result.
For example, the first time threshold is generally determined according to the capacity of the transformer, and the value ranges from 10 to 20 minutes, wherein the larger the capacity of the transformer, the larger the first time threshold.
Specifically, when the predicted temperature is greater than a first threshold, an alarm signal is output, and when the predicted temperature is greater than a second threshold and the operating load of the oil immersed transformer is greater than a second load threshold, the alarm signal is output, wherein the second load threshold is greater than the first load threshold, and the alarm signal is specifically determined according to the rated load of the oil immersed transformer.
Example 2
In an embodiment of the present application, a computer system is provided, including: a communicatively coupled memory and processor, and a computer program stored on the memory and capable of running on the processor, characterized by: the processor runs the computer program to obtain the real-time temperature monitoring method for the internal components of the oil immersed transformer.
Specifically, the embodiment also provides a computer system, which comprises a processor, a memory, a network interface and a database which are connected through a system bus; wherein the processor of the computer system is configured to provide computing and control capabilities; the memory of the computer system includes nonvolatile storage medium, internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The computer device network interface is used for communicating with an external terminal through a network connection. The computer program is executed by the processor to implement a real-time temperature monitoring method for an internal component of an oil-immersed transformer as described above.
Example 3
The invention provides a computer storage medium, on which a computer program is stored which, when executed in a computer, causes the computer to carry out a method for real-time temperature monitoring of an internal component of an oil-immersed transformer as described above.
In particular, it will be understood by those skilled in the art that implementing all or part of the above-described methods of the embodiments may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
In the several embodiments provided in this application, it should be understood that the disclosed systems and methods may be implemented in other ways as well. The system embodiments described above are merely illustrative, for example, of the flowcharts and block diagrams in the figures that illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. 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.
In addition, functional modules in the embodiments of the present invention may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored on a computer readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method of the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
With the above-described preferred embodiments according to the present invention as an illustration, the above-described descriptions can be used by persons skilled in the relevant art to make various changes and modifications without departing from the scope of the technical idea of the present invention. The technical scope of the present invention is not limited to the description, but must be determined according to the scope of claims.
Claims (10)
1. The real-time temperature monitoring method for the internal components of the oil immersed transformer is characterized by comprising the following steps of:
s11, acquiring real-time temperature of an internal component of the oil immersed transformer based on a temperature sensor, judging whether the real-time temperature belongs to abnormal temperature or not, and if not, entering a step S12;
s12, judging whether the real-time temperature is greater than a first threshold value, if so, outputting an alarm signal, and if not, entering a step S13;
s13, judging whether the real-time temperature is greater than a second threshold value and the operation load of the oil-immersed transformer is greater than a first load threshold value, if yes, entering a step S14, otherwise, acquiring the real-time temperature of an internal component of the oil-immersed transformer based on the operation load of the oil-immersed transformer and the environment temperature of the oil-immersed transformer by adopting a prediction model based on a machine learning algorithm, acquiring the temperature acquisition frequency of the temperature sensor, and returning to the step S11 to acquire the real-time temperature based on the temperature acquisition frequency;
s14, constructing a temperature prediction model based on the real-time temperature of the internal components of the oil-immersed transformer, the ambient temperature of the oil-immersed transformer and the operation load of the oil-immersed transformer, obtaining the predicted temperature of the internal components of the oil-immersed transformer after a first time threshold, and determining whether to output an alarm signal based on the predicted temperature.
2. The method for monitoring the real-time temperature according to claim 1, wherein the temperature sensor is a temperature monitoring sensor based on ultrahigh frequency RFID, and the real-time temperature monitored by the temperature sensor is transmitted to a monitoring server through a ceramic antenna, and the monitoring server determines the abnormal temperature, the temperature acquisition frequency and the predicted temperature.
3. The method for monitoring the temperature in real time according to claim 1, wherein the specific steps of performing the judgment of the abnormal temperature are:
judging whether the real-time temperature is greater than a third threshold value, if so, determining that the real-time temperature belongs to an abnormal temperature, and if not, entering the next step;
judging whether the absolute value of the difference value between the real-time temperature and the temperature of the internal component of the oil-immersed transformer acquired last time by the temperature sensor is larger than a first fluctuation threshold value, if so, determining that the real-time temperature belongs to an abnormal temperature, and if not, entering the next step;
and determining that the real-time temperature does not belong to the abnormal temperature.
4. The method of real-time temperature monitoring according to claim 1, wherein the first and second thresholds are determined based on a rated operating temperature of an internal component of the oil-filled transformer, wherein the greater the rated operating temperature, the greater the first and second thresholds, and the first threshold is greater than the second threshold.
5. The method for monitoring temperature in real time according to claim 1, wherein the specific steps of determining the temperature acquisition frequency of the temperature sensor are:
judging whether the real-time temperature of the internal components of the oil immersed transformer is greater than a fourth threshold value, if so, taking the basic acquisition frequency as the temperature acquisition frequency of the temperature sensor, and if not, entering the next step;
judging whether the operation load of the oil immersed transformer is larger than a second load threshold, if so, taking the basic acquisition frequency as the temperature acquisition frequency of the temperature sensor, and if not, entering the next step;
based on the running load of the oil immersed transformer, the ambient temperature of the oil immersed transformer and the real-time temperature of the internal components of the oil immersed transformer, a prediction model based on a GA-WNN algorithm is adopted to obtain the correction amount of the temperature acquisition frequency, and the temperature acquisition frequency of the temperature sensor is obtained based on the correction amount of the temperature acquisition frequency and the basic acquisition frequency.
6. The method for monitoring temperature in real time according to claim 5, wherein the calculation formula of the temperature acquisition frequency of the temperature sensor is:
wherein P is 1 、P J The correction amount and the basic acquisition frequency of the temperature acquisition frequency are respectively, T is the real-time temperature of the internal components of the oil immersed transformer, and T is 1 Is a time threshold.
7. The method for monitoring the temperature in real time according to claim 1, wherein the construction of the predicted temperature of the oil immersed transformer after the first time threshold comprises the following specific steps:
acquiring the real-time temperature of an internal component of the oil immersed transformer, wherein the environment temperature and the running load construct an input set;
transmitting the input set to a temperature prediction model based on a GA-WNN algorithm to obtain a prediction result;
and obtaining the predicted temperature of the internal component of the oil immersed transformer after the first time threshold based on the predicted result.
8. The method of real-time temperature monitoring according to claim 1, wherein an alarm signal is output when the predicted temperature is greater than a first threshold value, and when the predicted temperature is greater than a second threshold value and the operating load of the oil-filled transformer is greater than a second load threshold value, wherein the second load threshold value is greater than the first load threshold value, in particular determined based on the rated load of the oil-filled transformer.
9. A computer system, comprising: a communicatively coupled memory and processor, and a computer program stored on the memory and capable of running on the processor, characterized by: the processor, when executing the computer program, performs a real-time temperature monitoring method for an internal component of an oil-immersed transformer according to any one of claims 1-8.
10. A computer storage medium having stored thereon a computer program which, when executed in a computer, causes the computer to perform a real-time temperature monitoring method for an internal component of an oil immersed transformer as claimed in any one of claims 1-8.
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