CN113204919A - Method and device for predicting state of main transformer conservator based on digital twinning and electronic equipment - Google Patents

Method and device for predicting state of main transformer conservator based on digital twinning and electronic equipment Download PDF

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CN113204919A
CN113204919A CN202110503178.8A CN202110503178A CN113204919A CN 113204919 A CN113204919 A CN 113204919A CN 202110503178 A CN202110503178 A CN 202110503178A CN 113204919 A CN113204919 A CN 113204919A
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oil temperature
trend line
main transformer
prediction
influence factor
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CN113204919B (en
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姚建光
印吉景
戴永东
翁蓓蓓
徐进东
陆子渊
鞠玲
王茂飞
蒋中军
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Taizhou Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
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Taizhou Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
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    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention discloses a method and a device for predicting the state of a main transformer conservator based on digital twinning and electronic equipment, wherein the method comprises the following steps: establishing a digital twin main transformer conservator model; establishing a target variable prediction model, a quantifiable influence factor prediction model and a total influence factor prediction model; operating the digital twin main transformer conservator model and acquiring detection data; inputting the detection data into the target variable prediction model, the quantifiable influence factor prediction model and the total influence factor prediction model respectively to obtain a first oil temperature prediction trend line, a second oil temperature prediction trend line and a third oil temperature prediction trend line respectively; according to the first oil temperature prediction trend line, the second oil temperature prediction trend line and the third oil temperature prediction trend line, state prediction is carried out on a main transformer conservator; the method can effectively predict and analyze the future health state of the main transformer conservator, and provides a solution for reliable operation of equipment.

Description

Method and device for predicting state of main transformer conservator based on digital twinning and electronic equipment
Technical Field
The invention relates to the technical field of operation and maintenance of electrical equipment, in particular to a method and a device for predicting a state of a main transformer conservator based on digital twinning and electronic equipment.
Background
A digital twin is a virtual model created digitally for a physical object to simulate behavior in its real-world environment, reflecting the full lifecycle process of the corresponding physical device. After the digital twin transformer substation is built, the intelligent cloud platform can be used for sensing the running state of the transformer substation in a full-state mode, and real-time monitoring is achieved. The 'twin' transformer substation easily acquires power station data and performs analysis and processing, so that the problems of lack of a unified data platform, lack of data analysis, difficulty in judgment of equipment risks and the like are thoroughly solved, and the global and full-life-cycle management of the transformer substation is realized.
Due to the fact that various types of defects or faults can generate temperature rising phenomena in the operation process of the transformer, such as: the transformer bears the impact time of short-circuit current for too long, the internal pressure is increased, the mechanical damage is caused, and the like; if the short-circuit current is large, relay protection delay action even refuses action, deformation can be serious, even winding damage is caused, if the temperature continuously rises, equipment damage can be caused, abnormal power failure accidents are caused, influence is brought to personal safety and power supply stability, the temperature of a main transformer conservator is one of the most characteristic features, and the trend prediction of the temperature of the conservator is particularly important.
In the traditional electric power operation and maintenance work, the health state of equipment is usually judged by means of daily inspection of the equipment, measurement point data acquisition of an electric power monitoring system, electric overhaul tests and the like, but when equipment faults are detected by means of the methods, the faults are usually developed to a relatively serious degree, great difficulty is brought to maintenance, due to the fact that predictive analysis cannot be carried out on the future health state problems of the equipment, operation and maintenance personnel can only carry out passive maintenance on the equipment, namely, a solution is provided for the generated defects or faults, and the reliability of equipment operation is greatly influenced.
Patent document "a temperature monitoring and early warning system for transformer" (CN11584216B), it is disclosed to set up temperature sensor in the transformer conservator, and arrange temperature sensor in other positions of transformer, through the temperature information that each temperature sensor gathered, carry out temperature monitoring and early warning to the transformer, however, this scheme only carries out the early warning through the control temperature, only when the oil temperature reaches the decomposition critical point soon or is about to reach the insulation failure critical point, can only be perceived, can not predict the future trend of change of the oil temperature of the conservator of main transformer, also can not predict the state of the conservator of main transformer.
Disclosure of Invention
The invention provides a method and a device for predicting the state of a main transformer conservator based on digital twinning and electronic equipment, which can effectively predict and analyze the future health state of the main transformer conservator and provide a solution for the reliable operation of the equipment.
A main transformer conservator state prediction method based on digital twinning comprises the following steps:
establishing a digital twin main transformer conservator model;
establishing a target variable prediction model, a quantifiable influence factor prediction model and a total influence factor prediction model;
operating the digital twin main transformer conservator model and acquiring detection data;
inputting the detection data into the target variable prediction model, the quantifiable influence factor prediction model and the total influence factor prediction model respectively to obtain a first oil temperature prediction trend line, a second oil temperature prediction trend line and a third oil temperature prediction trend line respectively;
and predicting the state of the main transformer conservator according to the first oil temperature prediction trend line, the second oil temperature prediction trend line and the third oil temperature prediction trend line.
Further, establishing the target variable prediction model based on ideal data;
training historical sample data based on the quantifiable influence factor to obtain the quantifiable influence factor prediction model;
and training to obtain the full influence factor prediction model based on the historical sample data of the quantifiable influence factor and the historical sample data of the non-quantifiable influence factor.
Further, the ideal data comprises load current, ambient temperature, ambient humidity, altitude and operation life of the main transformer conservator in an ideal operation state;
the quantifiable influence factors comprise load current, ambient temperature, ambient humidity, altitude and the operation life of main transformer conservator equipment in a non-ideal operation state;
the non-quantifiable influence factors comprise pollution characteristic parameters, vibration characteristic parameters, process characteristic parameters, material characteristic parameters, sand characteristic parameters, salt fog characteristic parameters, illumination intensity and ultrasonic effective values.
Further, according to the first oil temperature prediction trend line, the second oil temperature prediction trend line and the third oil temperature prediction trend line, the state prediction of the main transformer conservator is performed, and the method comprises the following steps:
obtaining the normal oil temperature range of the normal operation of the main transformer oil conservator according to the first oil temperature prediction trend line;
and judging whether the quantifiable influence factor of the main transformer oil conservator needs to be regulated and controlled in the future or not according to the second oil temperature prediction trend line and the normal oil temperature range.
Further, according to the second oil temperature prediction trend line and the normal oil temperature range, whether the quantifiable influence factor of the main transformer conservator needs to be regulated and controlled in the future or not is judged, and the method comprises the following steps:
when the second oil temperature prediction trend line exceeds the normal range of the oil temperature, determining that the future quantitative influence factor of the main transformer conservator needs to be regulated and controlled;
and if the second oil temperature prediction trend line does not exceed the normal range of the oil temperature, determining that the quantifiable influence factor of the main transformer conservator does not need to be regulated and controlled in the future.
Further, according to the first oil temperature prediction trend line, the second oil temperature prediction trend line and the third oil temperature prediction trend line, the state prediction of the main transformer conservator is performed, and the method comprises the following steps:
obtaining the normal oil temperature range of the normal operation of the main transformer oil conservator according to the first oil temperature prediction trend line;
and judging whether the main transformer oil conservator has defects or not according to the third oil temperature prediction trend line and the normal range of the oil temperature.
Further, according to the third oil temperature prediction trend line and the normal oil temperature range, determining whether the main transformer conservator has a defect includes:
when the third oil temperature prediction trend line exceeds the normal range of the oil temperature, determining that the main transformer conservator has defects;
and if the third oil temperature prediction trend line does not exceed the normal range of the oil temperature, determining that the main transformer conservator has no defect.
Further, after determining that the main transformer conservator has a defect, the method further comprises the following steps:
determining the time corresponding to the intersection point of the third oil temperature prediction trend line and the first oil temperature prediction trend line as the time when the fault is about to occur;
determining the time corresponding to the third oil temperature prediction trend line exceeding the first preset oil temperature as the time of entering the serious fault;
and the time corresponding to the third oil temperature prediction trend line exceeding the second preset oil temperature is the time when the main transformer conservator has irreversible faults.
A main transformer conservator state prediction device based on digital twinning comprises:
the twin system establishing module is used for establishing a digital twin main transformer conservator model;
the prediction model establishing module is used for establishing a target variable prediction model, a quantitative influence factor prediction model and a total influence factor prediction model;
the data acquisition module is used for operating the digital twin main transformer conservator model and acquiring detection data;
the oil temperature prediction module is used for respectively inputting the detection data into the target variable prediction model, the quantifiable influence factor prediction model and the total influence factor prediction model to respectively obtain a first oil temperature prediction trend line, a second oil temperature prediction trend line and a third oil temperature prediction trend line;
and the state prediction module is used for predicting the state of the main transformer conservator according to the first oil temperature prediction trend line, the second oil temperature prediction trend line and the third oil temperature prediction trend line.
An electronic device comprises a processor and a storage module, wherein the storage module stores a plurality of instructions, and the processor is used for reading the instructions and executing the method.
A computer-readable storage medium storing a plurality of instructions readable by a processor and performing the method described above.
According to the method, the device and the electronic equipment for predicting the state of the main transformer conservator based on the digital twin, provided by the invention, the oil temperature of the main transformer conservator is predicted according to the established model, the future health state of the main transformer conservator can be effectively predicted and analyzed, whether the quantitative influence factors need to be regulated and controlled and whether defects exist can be effectively judged, particularly for the condition that the defects exist but the faults do not occur, the defects can be timely found, the occurrence of serious faults is avoided, and the reliability of the operation of the main transformer conservator is ensured to the maximum extent.
Drawings
Fig. 1 is a flowchart of an embodiment of a method for predicting a state of a main transformer conservator based on a digital twin according to the present invention.
Fig. 2 is a scene schematic diagram of a situation where quantitative influence factors need to be regulated and controlled and defects exist in the method for predicting the state of the main transformer conservator based on the digital twinning.
Fig. 3 is a scene schematic diagram of the situation that quantitative influence factors need to be regulated and controlled but no defect exists in the main transformer conservator state prediction method based on the digital twinning.
Fig. 4 is a scene schematic diagram of a situation in which a quantitative influence factor does not need to be regulated and controlled but has a defect in the method for predicting the state of the main transformer conservator based on the digital twin provided by the invention.
Fig. 5 is a scene schematic diagram of the main transformer conservator state prediction method based on digital twinning, which is provided by the invention, wherein the quantitative influence factors do not need to be regulated and controlled and no defect exists.
Fig. 6 is a schematic structural diagram of an embodiment of a main transformer conservator state prediction device based on a digital twin according to the present invention.
Detailed Description
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
Referring to fig. 1, the present embodiment provides a method for predicting a state of a main transformer conservator based on a digital twin, including:
s1, establishing a digital twin main transformer conservator model;
s2, establishing a target variable prediction model, a quantifiable influence factor prediction model and a total influence factor prediction model;
s3, operating the digital twin main transformer conservator model and acquiring detection data;
s4, inputting the detection data into the target variable prediction model, the quantifiable influence factor prediction model and the total influence factor prediction model respectively, and obtaining a first oil temperature prediction trend line, a second oil temperature prediction trend line and a third oil temperature prediction trend line respectively;
and S5, predicting the state of the main transformer conservator according to the first oil temperature prediction trend line, the second oil temperature prediction trend line and the third oil temperature prediction trend line.
Specifically, in step S1, the main transformer conservator component is digitally modeled by using techniques such as laser point cloud and oblique photography, so as to obtain a digital twin main transformer conservator model, which has an access capability of supporting various standard communication protocols and can simulate the operation of physical equipment.
Further, in step S2, the target variable prediction model is established based on ideal data, where the ideal data includes relevant operation data generated by the main transformer conservator in an ideal operation state, including but not limited to load current, ambient temperature, ambient humidity, altitude, operation age of the main transformer conservator device in the ideal operation state, and the ideal operation state refers to an operation environment state in which the device is very healthy and is not interfered by any external factor, and the target variable prediction model is an ideal prediction model, and aims to find a fluctuation range of the oil temperature under an ideal external condition, and the fluctuation range can be used as a reference for determining that the health condition of the device is normal.
And training historical sample data based on the quantifiable influence factor to obtain the quantifiable influence factor prediction model. The quantifiable influence factor is a parameter which can accurately give a specific value, the influence factor can directly cause the oil temperature to change, and the quantifiable influence factor comprises but is not limited to load current, ambient temperature, ambient humidity, altitude, the operation life of main transformer conservator equipment and the like in a non-ideal state. The quantifiable influence factor has two layers of meanings, wherein firstly, the data corresponding to the influence factor is not caused by defect reasons, secondly, the data has specific quantification values, and the two must be satisfied at the same time.
The ideal data and the quantifiable influence factor data are similar and refer to laboratory environments, but the difference is that the ideal data are based on the contents of industry standard, equipment production manufacturing guide rules and the like, the normal fluctuation range of the equipment operation data of the equipment under the laboratory conditions is framed, the quantifiable influence factor is an actual data change curve after the equipment discharges external interference, and the operation data may exceed the normal fluctuation range.
The quantitative influence factor prediction model only considers the quantitative influence factor for the prediction of the oil temperature, is established based on a laboratory environment, sets the non-quantitative influence factor to be in an ideal state, and finds out the dynamic mathematical relationship between the oil temperature and the variable influence factor.
The non-quantifiable influence factor is a parameter which does not give a specific value, or a phenomenon or data characteristic parameter caused by a certain defect reason, and includes but is not limited to a pollution characteristic parameter, a vibration characteristic parameter, a process characteristic parameter, a material characteristic parameter, a sand characteristic parameter, a salt spray characteristic parameter, illumination intensity and an ultrasonic effective value.
The non-quantifiable influence factors are factors except the quantifiable influence factors which are classified into the non-quantifiable influence factors, and comprise data factors which are not caused by defects but cannot be quantified or characteristic parameter factors (whether numerical values can be quantified or not) caused by some defect reasons.
The full influence factor comprises a quantifiable influence factor and a non-quantifiable influence factor, and the full influence factor prediction model is obtained based on historical sample data of the quantifiable influence factor and historical sample data of the non-quantifiable influence factor.
The full influence factor prediction model is used for predicting the future change trend of the oil temperature under the actual action of all the current quantifiable influence factors and non-quantifiable influence factors, and the prediction has strong guiding significance on whether the equipment has defect problems or not and the development degree change of the defect problems.
Further, in step S3, the digital twin main transformer conservator model is operated, and detection data is obtained, where the detection data includes: load current, ambient temperature, ambient humidity, altitude and the operation life of the main transformer conservator under the operation state.
Further, in step S4, the detection data is input into the target variable prediction model, a time sequence of the main transformer conservator temperature measurement points in an ideal external environment can be obtained, the temperature measurement points are connected to obtain a first oil temperature prediction trend line, and the first oil temperature prediction trend line is an oil temperature range of the main transformer conservator under a normal condition.
And inputting the detection data into the quantitative influence factor prediction model to obtain a time sequence of main transformer conservator temperature measuring points under the influence of the quantitative influence factor, and connecting the temperature measuring points to obtain a second oil temperature prediction trend line.
And inputting the detection data into the total influence factor prediction model to obtain a time sequence of main transformer conservator temperature measuring points under the influence of quantifiable influence factors and non-quantifiable factors, and connecting the temperature measuring points to obtain a third oil temperature prediction trend line.
Further, in step S5, the predicting a state of the main transformer conservator according to the first oil temperature prediction trend line, the second oil temperature prediction trend line, and the third oil temperature prediction trend line includes:
obtaining the normal oil temperature range of the normal operation of the main transformer oil conservator according to the first oil temperature prediction trend line;
judging whether the quantifiable influence factor of the main transformer conservator needs to be regulated and controlled in the future or not according to the second oil temperature prediction trend line and the normal oil temperature range;
and judging whether the main transformer oil conservator has defects or not according to the third oil temperature prediction trend line and the normal range of the oil temperature.
Wherein, according to the second oil temperature prediction trend line and the normal range of oil temperature, judge whether the main change conservator can quantitative influence factor needs to be regulated and control in the future, include:
when the second oil temperature prediction trend line exceeds the normal range of the oil temperature, determining that the future quantitative influence factor of the main transformer conservator needs to be regulated and controlled;
and if the second oil temperature prediction trend line does not exceed the normal range of the oil temperature, determining that the quantifiable influence factor of the main transformer conservator does not need to be regulated and controlled in the future.
And determining that the quantifiable influence factor needs to be regulated and controlled, and changing the parameter of the quantifiable influence factor by means of regulation and control, so that the main transformer conservator can enter a normal operation state.
According to the third oil temperature prediction trend line and the normal range of the oil temperature, whether the main transformer conservator has defects is judged, and the method comprises the following steps:
when the third oil temperature prediction trend line exceeds the normal range of the oil temperature, determining that the main transformer conservator has defects;
and if the third oil temperature prediction trend line does not exceed the normal range of the oil temperature, determining that the main transformer conservator has no defect.
Specifically, referring to fig. 2 to 5, where K1 is a first oil temperature prediction trend line, K2 is a second oil temperature prediction trend line, K3 is a third oil temperature prediction trend line, and the first oil temperature prediction trend line K1 is an oil temperature range of the main transformer conservator under normal conditions, in fig. 2, the second oil temperature prediction trend line K2 partially exceeds the first oil temperature prediction trend line K1, so that the quantifiable influence factor of the main transformer conservator needs to be adjusted and controlled in the future, and the third oil temperature prediction trend line K3 exceeds the first oil temperature prediction trend line K1, so that the main transformer conservator has a defect. In fig. 3, the second oil temperature prediction trend line K2 partially exceeds the first oil temperature prediction trend line K1, so that the quantifiable influence factor of the main transformer conservator needs to be adjusted and controlled in the future, and the third oil temperature prediction trend line K3 does not exceed the first oil temperature prediction trend line K1, so that the main transformer conservator does not have defects. In fig. 4, the second oil temperature prediction trend line K2 does not exceed the first oil temperature prediction trend line K1, so that the quantifiable influence factor of the main transformer conservator does not need to be adjusted in the future, and the third oil temperature prediction trend line K3 exceeds the first oil temperature prediction trend line K1, so that the main transformer conservator has a defect. In fig. 5, the second oil temperature prediction trend line K2 does not exceed the first oil temperature prediction trend line K1, so that the quantifiable influence factor of the main transformer conservator does not need to be adjusted, and the third oil temperature prediction trend line K3 does not exceed the first oil temperature prediction trend line K1, so that the main transformer conservator does not have defects.
If the main transformer conservator has defects, the faults can be caused in a future period of time, therefore, fault prediction can be carried out through a third oil temperature prediction trend line, and after the main transformer conservator is determined to have defects, the method further comprises the following steps:
determining the time corresponding to the intersection point of the third oil temperature prediction trend line and the first oil temperature prediction trend line as the time when the fault is about to occur;
determining the time corresponding to the third oil temperature prediction trend line exceeding the first preset oil temperature as the time of entering the serious fault;
and determining the moment corresponding to the second preset oil temperature exceeded in the third oil temperature prediction trend line as the moment when the main transformer conservator has the irreversible fault, wherein the power failure fault can be caused at the moment.
Referring to FIGS. 2 and 4, t1The time at which the fault is about to occur, t2Will exceed the first preset oil temperature T at any moment1To enter a critical failure time, t3Will exceed the second preset oil temperature T at any moment2And the moment when the main transformer conservator has irreversible faults.
Further, after determining that the main transformer conservator has a defect, the method further comprises the following steps:
and determining the defect reason according to a pre-established knowledge graph of the main transformer conservator.
Each device defect cause may produce one or more phenomenon characteristics (e.g., sound, image, smell, etc.), although there may be 2 different defect causes with the same phenomenon characteristics or data characteristics. And also by one or more data characteristics (sensor data, test data, secondary relay protection data, etc.). By extracting the defect case information, finding out which phenomenon characteristics and data characteristics correspond to each defect reason, and a judging method, a judging logic, a solution and measures corresponding to the defect reasons, and establishing a structured knowledge base table containing the information, the main transformer conservator knowledge graph can be generated, and the query of a knowledge entity is facilitated.
According to the method provided by the embodiment, the oil temperature of the main transformer conservator is predicted according to the established model, the future health state of the main transformer conservator can be effectively predicted and analyzed, whether quantitative influence factors need to be regulated and controlled and whether defects exist can be effectively judged, particularly, the defects can be timely found under the condition that the defects exist but the faults do not occur, the serious faults are avoided, and the reliability of the operation of the main transformer conservator is ensured to the maximum extent.
Referring to fig. 6, in some embodiments, there is also provided a digital twin-based main transformer conservator state prediction device, including:
the twin system establishing module 201 is used for establishing a digital twin main transformer conservator model;
the prediction model establishing module 202 is used for establishing a target variable prediction model, a quantifiable influence factor prediction model and a total influence factor prediction model;
the data acquisition module 203 is used for operating the digital twin main transformer conservator model and acquiring detection data;
the oil temperature prediction module 204 is configured to input the detection data into the target variable prediction model, the quantifiable impact factor prediction model and the total impact factor prediction model respectively, and obtain a first oil temperature prediction trend line, a second oil temperature prediction trend line and a third oil temperature prediction trend line respectively;
and the state prediction module 205 is configured to perform state prediction on the main transformer conservator according to the first oil temperature prediction trend line, the second oil temperature prediction trend line, and the third oil temperature prediction trend line.
Specifically, the prediction model establishing module 202 establishes the target variable prediction model based on ideal data; training historical sample data based on the quantifiable influence factor to obtain the quantifiable influence factor prediction model; and training to obtain the full influence factor prediction model based on the historical sample data of the quantifiable influence factor and the historical sample data of the non-quantifiable influence factor.
The state prediction module 205 is further configured to obtain a normal oil temperature range of the main transformer conservator in normal operation according to the first oil temperature prediction trend line; judging whether the quantifiable influence factor of the main transformer conservator needs to be regulated and controlled in the future or not according to the second oil temperature prediction trend line and the normal oil temperature range; and judging whether the main transformer oil conservator has defects or not according to the third oil temperature prediction trend line and the normal range of the oil temperature.
The state prediction module 205 is further configured to determine that the quantifiable influence factor of the main transformer conservator needs to be adjusted and controlled in the future when the second oil temperature prediction trend line exceeds the normal oil temperature range; and if the second oil temperature prediction trend line does not exceed the normal range of the oil temperature, determining that the quantifiable influence factor of the main transformer conservator does not need to be regulated and controlled in the future.
The state prediction module 205 is further configured to determine that the main transformer conservator has a defect when the third oil temperature prediction trend line exceeds the normal oil temperature range; and if the third oil temperature prediction trend line does not exceed the normal range of the oil temperature, determining that the main transformer conservator has no defect.
The state prediction module 205 is further configured to determine a time corresponding to an intersection point of the third oil temperature prediction trend line and the first oil temperature prediction trend line as a time when a fault is about to occur; determining the time corresponding to the third oil temperature prediction trend line exceeding the first preset oil temperature as the time of entering the serious fault; and the time corresponding to the third oil temperature prediction trend line exceeding the second preset oil temperature is the time when the main transformer conservator has irreversible faults.
The device provided by the embodiment predicts the oil temperature of the main transformer conservator according to the established model, can effectively predict and analyze the future health state of the main transformer conservator, comprises the steps of determining whether the influence factors need to be regulated and controlled and effectively judging whether defects exist, can timely discover the defects particularly under the condition that the defects exist but the faults do not occur, avoids serious faults, and ensures the reliability of the operation of the main transformer conservator to the maximum extent.
In some embodiments, an electronic device is further provided, which includes a processor and a storage module, the storage module stores a plurality of instructions, and the processor is configured to read the plurality of instructions and execute the method described above.
In some embodiments, a computer-readable storage medium is also provided, wherein the computer storage medium stores a plurality of instructions that can be read by a processor and perform the above-mentioned method.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention. It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (10)

1. A main transformer conservator state prediction method based on digital twinning is characterized by comprising the following steps:
establishing a digital twin main transformer conservator model;
establishing a target variable prediction model, a quantifiable influence factor prediction model and a total influence factor prediction model;
operating the digital twin main transformer conservator model and acquiring detection data;
inputting the detection data into the target variable prediction model, the quantifiable influence factor prediction model and the total influence factor prediction model respectively to obtain a first oil temperature prediction trend line, a second oil temperature prediction trend line and a third oil temperature prediction trend line respectively;
and predicting the state of the main transformer conservator according to the first oil temperature prediction trend line, the second oil temperature prediction trend line and the third oil temperature prediction trend line.
2. The method of claim 1, wherein the target variable prediction model is built based on ideal data;
training historical sample data based on the quantifiable influence factor to obtain the quantifiable influence factor prediction model;
and training to obtain the full influence factor prediction model based on the historical sample data of the quantifiable influence factor and the historical sample data of the non-quantifiable influence factor.
3. The method of claim 2, wherein the ideal data includes load current, ambient temperature, ambient humidity, altitude, and life of a main transformer conservator device in ideal operating conditions;
the quantifiable influence factors comprise load current, ambient temperature, ambient humidity, altitude and the operation life of main transformer conservator equipment in a non-ideal operation state;
the non-quantifiable influence factors comprise pollution characteristic parameters, vibration characteristic parameters, process characteristic parameters, material characteristic parameters, sand characteristic parameters, salt fog characteristic parameters, illumination intensity and ultrasonic effective values.
4. The method of claim 3, wherein predicting a state of a main transformer conservator according to the first, second and third oil temperature prediction trend lines comprises:
obtaining the normal oil temperature range of the normal operation of the main transformer oil conservator according to the first oil temperature prediction trend line;
and judging whether the quantifiable influence factor of the main transformer oil conservator needs to be regulated and controlled in the future or not according to the second oil temperature prediction trend line and the normal oil temperature range.
5. The method of claim 4, wherein determining whether a quantifiable impact factor of the main conservator needs to be adjusted in the future according to the second oil temperature prediction trend line and the normal range of oil temperature comprises:
when the second oil temperature prediction trend line exceeds the normal range of the oil temperature, determining that the future quantitative influence factor of the main transformer conservator needs to be regulated and controlled;
and if the second oil temperature prediction trend line does not exceed the normal range of the oil temperature, determining that the quantifiable influence factor of the main transformer conservator does not need to be regulated and controlled in the future.
6. The method of claim 1, wherein predicting a state of a main transformer conservator according to the first, second and third oil temperature prediction trend lines comprises:
obtaining the normal oil temperature range of the normal operation of the main transformer oil conservator according to the first oil temperature prediction trend line;
and judging whether the main transformer oil conservator has defects or not according to the third oil temperature prediction trend line and the normal range of the oil temperature.
7. The method of claim 6, wherein determining whether the main transformer conservator is defective according to the third predicted trend line for oil temperature and the normal range for oil temperature comprises:
when the third oil temperature prediction trend line exceeds the normal range of the oil temperature, determining that the main transformer conservator has defects;
and if the third oil temperature prediction trend line does not exceed the normal range of the oil temperature, determining that the main transformer conservator has no defect.
8. The method of claim 7, wherein after determining that the main conservator is defective, further comprising:
determining the time corresponding to the intersection point of the third oil temperature prediction trend line and the first oil temperature prediction trend line as the time when the fault is about to occur;
determining the time corresponding to the third oil temperature prediction trend line exceeding the first preset oil temperature as the time of entering the serious fault;
and the time corresponding to the third oil temperature prediction trend line exceeding the second preset oil temperature is the time when the main transformer conservator has irreversible faults.
9. A main transformer conservator state prediction device based on digital twinning is characterized by comprising:
the twin system establishing module is used for establishing a digital twin main transformer conservator model;
the prediction model establishing module is used for establishing a target variable prediction model, a quantitative influence factor prediction model and a total influence factor prediction model;
the data acquisition module is used for operating the digital twin main transformer conservator model and acquiring detection data;
the oil temperature prediction module is used for respectively inputting the detection data into the target variable prediction model, the quantifiable influence factor prediction model and the total influence factor prediction model to respectively obtain a first oil temperature prediction trend line, a second oil temperature prediction trend line and a third oil temperature prediction trend line;
and the state prediction module is used for predicting the state of the main transformer conservator according to the first oil temperature prediction trend line, the second oil temperature prediction trend line and the third oil temperature prediction trend line.
10. An electronic device comprising a processor and a memory module, the memory module storing a plurality of instructions, the processor being configured to read the plurality of instructions and execute the method according to any one of claims 1 to 8.
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