CN117634924A - Method, device, equipment and medium for predicting dissolved gas in transformer oil - Google Patents
Method, device, equipment and medium for predicting dissolved gas in transformer oil Download PDFInfo
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Abstract
The invention relates to a prediction method, a device, equipment and a medium for dissolved gas in transformer oil, wherein the method comprises the following steps: collecting an original time sequence of any gas content in transformer oil, and performing one-time accumulation generation operation on the original time sequence to generate a first-order accumulation sequence; obtaining a background value in the first-order accumulation sequence, introducing a vector to optimize the background value, and obtaining an optimized sequence; establishing a gray differential equation based on the optimization sequence, and calculating gray predicted gray action quantity to obtain a discrete time response function model of dissolved gas in the transformer oil; and performing one-time subtraction generation on the discrete time response function model, and performing error detection on the discrete time response function model to obtain a prediction model of dissolved gas in the transformer oil. The prediction model of the dissolved gas in the transformer oil provided by the invention can reasonably and accurately predict the content change trend of the dissolved gas in the oil in a period of time in the future of the transformer, thereby improving the fault diagnosis rate of the transformer.
Description
Technical Field
The invention relates to the technical field of power system monitoring, in particular to a method, a device, equipment and a medium for predicting dissolved gas in transformer oil.
Background
The transformer mainly plays roles of boosting and reducing in a power system, is one of core equipment of a power plant, and the operation health state of the transformer is closely related to the safe and stable operation of the power plant. However, the working environment of the transformer is severe, and the equipment needs to be in a high-load running state for a long time, so that various faults of the transformer can be avoided, and the running health state of the transformer is affected.
At present, whether the transformer operates normally or not is mainly judged and analyzed by oil chromatography, and whether and what kind of faults occur or not is judged according to the content of dissolved gas in oil. The state evaluation and fault diagnosis of the transformer judge the current state of the transformer, and the mode can prevent the latent fault from developing into serious fault to a certain extent, but the reserved maintenance adjustment time is very small. In the actual running process of the power grid, the power grid is generally required to be repaired by stopping, so that the normal transmission of electric energy is directly influenced, short-time or long-time range power failure is caused, the power system is fluctuated, and further serious consequences are brought to the economic development of individuals and countries.
Therefore, how to reasonably and accurately predict the content change trend of the dissolved gas in the oil in a period of time in the future of the transformer and judge whether the transformer fails in a period of time in the future based on the content of the dissolved gas in the oil is a problem to be solved urgently.
Disclosure of Invention
In view of the foregoing, it is necessary to provide a method, apparatus, device and medium for predicting the content of dissolved gas in transformer oil, which are used to reasonably and accurately predict the content change trend of the dissolved gas in the oil in a future period of time of the transformer, and to determine whether the transformer fails in a future period of time based on the content of the dissolved gas in the oil.
In order to achieve the above object, in a first aspect, the present invention provides a method for predicting dissolved gas in transformer oil, including:
collecting an original time sequence of any gas content in transformer oil, and performing one-time accumulation generation operation on the original time sequence to generate a first-order accumulation sequence;
obtaining a background value in the first-order accumulated sequence, introducing a vector to optimize the background value to obtain an optimized sequence, wherein the background value is the midpoint of the sequence accumulated value;
establishing a gray differential equation based on the optimization sequence, and calculating gray predicted gray action quantity to obtain a discrete time response function model of dissolved gas in transformer oil;
and performing one-time subtraction generation on the discrete time response function model, and performing error detection on the discrete time response function model to obtain a prediction model of dissolved gas in the transformer oil.
Further, the original time sequence of any gas content in the transformer oil is as follows:
wherein,for sampling time +.>For +.>The gas concentration of the dissolved gas of the transformer oil;
the original time sequence is subjected to one-time accumulation generation operation to generate a first-order accumulation sequence, wherein the first-order accumulation sequence is as follows:
wherein,，/>for +.>When transformer oil dissolves gas, the gas concentration generated by accumulation of the gas is increased.
Further, in introducing vectorsFor background value->Before the optimization, the method further comprises the following steps:
vector introduced by ant colony optimization algorithmAnd performing optimal estimation.
Further, the introducing the vector optimizes the background value, including:
vector employing optimal estimationFor background value->Optimizing to obtainAccurate calculation of background value +.>：。
Further, the step of establishing a gray differential equation based on the optimization sequence and calculating gray predicted gray action amount to obtain a discrete time response function model of dissolved gas in transformer oil comprises the following steps:
the gray differential equation is:wherein->The ash action amount predicted for gray;
performing least square estimation on the gray predicted gray effect amount parameter array:wherein, the method comprises the steps of, wherein,、/>is an intermediate variable +.>Is a matrix->Transposed matrix of>Is a matrix->Inverse matrix of (2), and->，Wherein, matrix->Column number of lines->Gray effect amount predicted for gray>Coefficients of (2);
obtaining a discrete time response function model of dissolved gas in transformer oil based on the gray predicted gray action amount:。
further, the performing error checking on the discrete time response function model includes performing a relative error checking and an absolute error checking on the discrete time response function model:
wherein->Is a relative error checking formula for a discrete time response function model, < >>Is an absolute error checking formula for a discrete time response function model.
Further, the prediction model is:
。
in a second aspect, the present invention also provides a device for predicting dissolved gas in transformer oil, including:
the sequence accumulation module is used for collecting an original time sequence of any gas content in the transformer oil, and performing one-time accumulation on the original time sequence to generate a first-order accumulation sequence;
the background value optimizing module is used for acquiring a background value in the first-order accumulated sequence, introducing a vector to optimize the background value to obtain an optimized sequence, wherein the background value is the midpoint of the sequence accumulated value;
the differential equation building module is used for building a gray differential equation based on the optimization sequence, calculating gray predicted gray action quantity and obtaining a discrete time response function model of dissolved gas in the transformer oil;
and the error checking module is used for performing one-time subtraction on the discrete time response function model, and performing error checking on the discrete time response function model to obtain a prediction model of dissolved gas in the transformer oil.
In a third aspect, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing the steps in the method for predicting dissolved gas in transformer oil described above when executing the computer program.
In a fourth aspect, the present invention also provides a computer storage medium storing a computer program which, when executed by a processor, implements the steps of the method for predicting dissolved gas in transformer oil as described above.
The beneficial effects of adopting the embodiment are as follows:
the method reasonably improves the original gray theory, optimizes the background value by introducing vectors, then establishes a gray differential equation according to an optimization sequence, and finally builds a prediction model of dissolved gas in transformer oil. The prediction accuracy of the content of dissolved gas in the transformer oil at the next moment is improved, so that the fault diagnosis rate of the transformer is improved.
Drawings
FIG. 1 is a schematic flow chart of an embodiment of a method for predicting dissolved gas in transformer oil according to the present invention;
FIG. 2 is a schematic diagram of a prediction result of an unused optimized gray model according to an embodiment of the invention;
fig. 3 is a schematic diagram of a prediction result after optimizing a gray model by using an ant colony algorithm according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of an embodiment of a device for predicting dissolved gas in transformer oil according to the present invention;
fig. 5 is a schematic structural diagram of an embodiment of an electronic device according to the present invention.
Detailed Description
Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form a part hereof, and together with the description serve to explain the principles of the invention, and are not intended to limit the scope of the invention.
In the description of the present invention, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. Furthermore, the meaning of "a plurality of" means two or more, unless specifically defined otherwise. Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
The invention provides a prediction method, a device, equipment and a medium for dissolved gas in transformer oil, aiming at the prediction of the content of the dissolved gas in the transformer oil, as most transformers are arranged outdoors and are inevitably influenced by external uncertain factors in different environments, fluctuation change is generated in data of taken samples, so that a traditional gray model cannot obtain a relatively accurate prediction result when aiming at the actual problem in the actual operation of the transformer, and further whether the transformer fails cannot be judged. Therefore, the original gray theory can be reasonably improved, and the prediction accuracy is improved by providing the prediction method of the dissolved gas in the transformer oil based on the combination of the ant colony algorithm and the gray model, so that the fault diagnosis rate of the transformer is improved.
Specific embodiments are described in detail below:
referring to fig. 1, fig. 1 is a flow chart of an embodiment of a method for predicting dissolved gas in transformer oil according to the present invention, and a specific embodiment of the present invention discloses a method for predicting dissolved gas in transformer oil, which includes:
step S101: collecting an original time sequence of any gas content in transformer oil, and performing one-time accumulation generation operation on the original time sequence to generate a first-order accumulation sequence;
step S102: obtaining a background value in a first-order accumulated sequence, introducing a vector to optimize the background value to obtain an optimized sequence, wherein the background value is the midpoint of the accumulated value of the sequence;
step S103: establishing a gray differential equation based on the optimization sequence, and calculating gray predicted gray action quantity to obtain a discrete time response function model of dissolved gas in the transformer oil;
step S104: and performing one-time subtraction generation on the discrete time response function model, and performing error detection on the discrete time response function model to obtain a prediction model of dissolved gas in the transformer oil.
It will be appreciated that the transformer will produce normal gas during operation, and the rate of production is slow, but if the transformer fails, the rate and amount of production will vary significantly, and based on this characteristic, the transformer operating condition can be monitored by monitoring the dissolved gas in the transformer oil in real time.
The method reasonably improves the original gray theory, optimizes the background value by introducing vectors, then establishes a gray differential equation according to an optimization sequence, and finally builds a prediction model of dissolved gas in transformer oil. The prediction accuracy of the content of dissolved gas in the transformer oil at the next moment is improved, so that the fault diagnosis rate of the transformer is improved.
In one embodiment of the invention, the original time sequence of any gas content in transformer oil is:
wherein,for sampling time +.>For +.>The gas concentration of the dissolved gas of the transformer oil;
the original time sequence is subjected to one-time accumulation generation operation to generate a first-order accumulation sequence, wherein the first-order accumulation sequence is as follows:
wherein,，/>for +.>When transformer oil dissolves gas, the gas concentration generated by accumulation of the gas is increased.
Wherein, the transformer oil chromatographic data contains a plurality of gases, such as hydrogen, methane, ethane, ethylene and acetylene. And selecting the historical gas concentration of any gas in the oil chromatograph as input, and constructing a prediction model of dissolved gas in the transformer oil by taking the predicted concentration of the selected gas type at the next moment as output.
In one embodiment of the invention, introducing vectors to optimize background values includes:
vector employing optimal estimationFor background value->Optimizing to obtain accurate calculation formula +.>：
。
It will be appreciated that because of the background valueTaking the midpoint of the sequence accumulated value, when the gas concentration in the oil chromatograph varies drastically, the model will produce a larger deviation, thus introducing the vector of the optimal estimate +.>For background value->Optimizing to obtain background value->Accurate calculation +.>：/>。
In one embodiment of the invention, in introducing vectorsFor background value->Before the optimization, the method further comprises the following steps:
vector introduced by ant colony optimization algorithmAnd performing optimal estimation.
Firstly, it should be noted that the ant colony optimization algorithm is proposed by inspired by a search mechanism when an ant establishes a shortest path from a nest to food in the foraging process of the ant, and is a novel simulated evolutionary colony optimization algorithm.
In particular, vectorsThe optimal estimation procedure of (1) is as follows:
first, determining ant number, estimating vector according to constraint conditionIs the initial value range of->. Then uniformly dividing the value interval, namely according to the vector +.>Will +.>The smallest possible uniform division into +.>Part, generate->Each coordinate point is the coordinate of the ant position. At the beginning of the optimization, the pheromone concentration at each coordinate point is equal and constant, i.e. +.>A plurality of ants are randomly assigned to the coordinates. Then calculate the position of each ant and the corresponding target value +.>Wherein, the target value is the mean square error of the predicted value and the corresponding actual value, namely: />. In the square with smaller target value, the concentration of the pheromone is stronger, and the update formula of the pheromone is as follows: />Wherein->Is constant (I)>Is->Sub-cyclic pheromone concentration. Finally judging whether the optimal ant reaches the required precision, if so, solving the optimal solution vector +.>And optimizing the background value to finally obtain a more accurate prediction result of the gas concentration data in the oil chromatograph in a future period of time. Referring to table 1, table 1 is a reference table for precision grade according to an embodiment of the present invention.
Table 1 precision grade reference table
In one embodiment of the invention, a gray differential equation is established based on an optimization sequence, and a gray predicted gray action amount is calculated to obtain a discrete time response function model of dissolved gas in transformer oil, comprising:
the gray differential equation is:wherein->The ash action amount predicted for gray;
least square estimation is performed on the gray predicted gray effect parameter sequence:wherein->、/>Is an intermediate variable +.>Is a matrix->Transposed matrix of>Is a matrix->Inverse matrix of (2), and->Wherein, matrix->Column number of lines->Gray effect amount predicted for gray>Coefficient of->；
Obtaining a discrete time response function model of dissolved gas in transformer oil based on gray predicted gray action amount:。
in one embodiment of the invention, error checking the discrete-type time response function model includes performing a relative error check and an absolute error check on the discrete-type time response function model:
wherein->Is a relative error checking formula for a discrete time response function model, < >>Is an absolute error checking formula for a discrete time response function model.
Then, performing one-time subtraction on the discrete time response function model to obtain a prediction model of dissolved gas in the transformer oil:。
it can be understood that the method can output the predicted value of the content of the dissolved gas in the transformer oil by obtaining the predicted model of the dissolved gas in the transformer oil, namely the optimal model of the transformer fault prediction, and can discover early-stage latent faults in advance, and timely take urgent repair measures, thereby greatly reducing the occurrence of accidents and having important significance for safe and stable operation of the power system.
According to the invention, the ant colony algorithm is used for optimizing the gray model, so that the problem of low prediction accuracy of the gray model is solved, and a good premise is provided for subsequent fault diagnosis. Referring to fig. 2 and fig. 3, fig. 2 is a schematic diagram of a prediction result of an unused optimized gray model according to an embodiment of the invention, and fig. 3 is a schematic diagram of a prediction result of an optimized gray model according to an embodiment of the invention. Therefore, the prediction method of the dissolved gas in the transformer oil can improve the prediction precision of the gas concentration data in the oil chromatograph at the next moment, thereby improving the fault diagnosis rate of the transformer.
In order to better implement the method for predicting the dissolved gas in the transformer oil in the embodiment of the present invention, referring to fig. 4 correspondingly, fig. 4 is a schematic structural diagram of an embodiment of a device for predicting the dissolved gas in the transformer oil provided by the present invention, where the embodiment of the present invention provides a device 400 for predicting the dissolved gas in the transformer oil, including:
the sequence accumulation module 401 is configured to collect an original time sequence of any gas content in transformer oil, and perform a primary accumulation generation operation on the original time sequence to generate a first-order accumulation sequence;
the background value optimizing module 402 is configured to obtain a background value in the first-order accumulated sequence, introduce a vector to optimize the background value, and obtain an optimized sequence, where the background value is a midpoint of the accumulated value of the sequence;
the differential equation building module 403 is configured to build a gray differential equation based on the optimization sequence, and calculate a gray action amount of gray prediction, so as to obtain a discrete time response function model of dissolved gas in transformer oil;
the error checking module 404 is configured to perform one-time subtraction on the discrete time response function model, and perform error checking on the discrete time response function model to obtain a prediction model of dissolved gas in transformer oil.
What needs to be explained here is: the apparatus 400 provided in the foregoing embodiments may implement the technical solutions described in the foregoing method embodiments, and the specific implementation principles of the foregoing modules or units may be referred to the corresponding content in the foregoing method embodiments, which is not described herein again.
Based on the prediction method of the dissolved gas in the transformer oil, the embodiment of the invention also correspondingly provides electronic equipment, which comprises: a processor and a memory, and a computer program stored in the memory and executable on the processor; the steps in the method for predicting dissolved gas in transformer oil according to the above embodiments are implemented by the processor when executing the computer program.
A schematic structural diagram of an electronic device 500 suitable for use in implementing embodiments of the present invention is shown in fig. 5. The electronic device in the embodiment of the present invention may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a car-mounted terminal (e.g., car navigation terminal), etc., and a stationary terminal such as a digital TV, a desktop computer, etc. The electronic device shown in fig. 5 is only an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.
An electronic device includes: a memory and a processor, where the processor may be referred to as a processing device 501 hereinafter, the memory may include at least one of a Read Only Memory (ROM) 502, a Random Access Memory (RAM) 503, and a storage device 508 hereinafter, as shown in detail below:
as shown in fig. 5, the electronic device 500 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 501, which may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 502 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 503. In the RAM503, various programs and data required for the operation of the electronic apparatus 500 are also stored. The processing device 501, the ROM502, and the RAM503 are connected to each other via a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
In general, the following devices may be connected to the I/O interface 505: input devices 506 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 507 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 508 including, for example, magnetic tape, hard disk, etc.; and communication means 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 5 shows an electronic device 500 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead.
In particular, according to embodiments of the present invention, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program embodied on a non-transitory computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 509, or from the storage means 508, or from the ROM 502. The above-described functions defined in the method of the embodiment of the present invention are performed when the computer program is executed by the processing means 501.
Based on the above method for predicting the dissolved gas in the transformer oil, the embodiments of the present invention further provide a computer readable storage medium, where one or more programs are stored, and the one or more programs may be executed by one or more processors, so as to implement the steps in the method for predicting the dissolved gas in the transformer oil according to the above embodiments.
Those skilled in the art will appreciate that all or part of the flow of the methods of the embodiments described above may be accomplished by way of a computer program to instruct associated hardware, where the program may be stored on a computer readable storage medium. Wherein the computer readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory, etc.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention.
Claims (10)
1. A method for predicting dissolved gas in transformer oil, comprising:
collecting an original time sequence of any gas content in transformer oil, and performing one-time accumulation generation operation on the original time sequence to generate a first-order accumulation sequence;
obtaining a background value in the first-order accumulated sequence, introducing a vector to optimize the background value to obtain an optimized sequence, wherein the background value is the midpoint of the sequence accumulated value;
establishing a gray differential equation based on the optimization sequence, and calculating gray predicted gray action quantity to obtain a discrete time response function model of dissolved gas in transformer oil;
and performing one-time subtraction generation on the discrete time response function model, and performing error detection on the discrete time response function model to obtain a prediction model of dissolved gas in the transformer oil.
2. The method for predicting dissolved gas in transformer oil according to claim 1, wherein the original time sequence of any gas content in the transformer oil is:
wherein,for sampling time +.>For +.>The gas concentration of the dissolved gas of the transformer oil;
the original time sequence is subjected to one-time accumulation generation operation to generate a first-order accumulation sequence, wherein the first-order accumulation sequence is as follows:
wherein,，/>for +.>When transformer oil dissolves gas, the gas concentration generated by accumulation of the gas is increased.
3. The method for predicting dissolved gas in transformer oil according to claim 1, wherein the vector is introducedFor background value->Before the optimization, the method further comprises the following steps:
vector introduced by ant colony optimization algorithmAnd performing optimal estimation.
4. A method of predicting dissolved gas in transformer oil as claimed in claim 3, wherein said introducing vector optimizes said background value, comprising:
vector employing optimal estimationFor background value->Optimizing to obtain accurate calculation of background value：
。
5. The method for predicting dissolved gas in transformer oil according to claim 4, wherein the establishing a gray differential equation based on the optimization sequence and calculating a gray predicted gray action amount to obtain a discrete time response function model of the dissolved gas in transformer oil comprises:
the gray differential equation is:wherein->The ash action amount predicted for gray;
performing least square estimation on the gray predicted gray effect amount parameter array:wherein->、/>Is an intermediate variable +.>Is a matrix->Transposed matrix of>Is a matrix->And (2) inverse matrix of (2)，/>Which is provided withIn matrix->Column number of lines->Gray effect amount predicted for gray>Coefficients of (2);
obtaining a discrete time response function model of dissolved gas in transformer oil based on the gray predicted gray action amount:。
6. the method of claim 1, wherein said error checking the discrete time response function model comprises performing a relative error check and an absolute error check on the discrete time response function model:
wherein, the method comprises the steps of, wherein,is a relative error checking formula for a discrete time response function model, < >>Is an absolute error checking formula for a discrete time response function model.
7. The method for predicting dissolved gas in transformer oil according to claim 6, wherein the prediction model is:
。
8. a predictive device for dissolved gas in transformer oil, comprising:
the sequence accumulation module is used for collecting an original time sequence of any gas content in the transformer oil, and performing one-time accumulation on the original time sequence to generate a first-order accumulation sequence;
the background value optimizing module is used for acquiring a background value in the first-order accumulated sequence, introducing a vector to optimize the background value to obtain an optimized sequence, wherein the background value is the midpoint of the sequence accumulated value;
the differential equation building module is used for building a gray differential equation based on the optimization sequence, calculating gray predicted gray action quantity and obtaining a discrete time response function model of dissolved gas in the transformer oil;
and the error checking module is used for performing one-time subtraction on the discrete time response function model, and performing error checking on the discrete time response function model to obtain a prediction model of dissolved gas in the transformer oil.
9. An electronic device comprising a memory and a processor, wherein the memory is configured to store a program; the processor, coupled to the memory, is configured to execute the program stored in the memory to implement the steps in the method for predicting dissolved gas in transformer oil according to any one of the preceding claims 1 to 7.
10. A computer readable storage medium storing a computer readable program or instructions which, when executed by a processor, is capable of carrying out the steps of the method of predicting dissolved gas in transformer oil according to any one of claims 1 to 7.
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