CN112685949A - Transformer health prediction method based on digital twinning - Google Patents
Transformer health prediction method based on digital twinning Download PDFInfo
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- 230000036541 health Effects 0.000 title claims abstract description 68
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- 238000010801 machine learning Methods 0.000 claims abstract description 7
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Abstract
The invention relates to the technical field of health degree prediction of marine equipment, in particular to a method for predicting the health of a marine transformer by using a digital twin technology. By combining a machine learning model, a rapid transformer health prediction model can be established, and can be updated regularly to cope with working conditions and environmental changes, so that the defects in the prior art are obviously overcome. The method solves the problems of low transformer fault prediction and positioning capability, low isolation capability, untimely equipment health management and the like in the original mode, and has important significance for improving the running efficiency of the ship and exerting the efficiency of relevant equipment of the ship.
Description
Technical Field
The invention relates to the technical field of health degree prediction of marine equipment, in particular to a digital twin-based transformer health prediction method.
Background
Along with the continuous service of the novel ship which adopts a brand-new design concept, an advanced informatization technology and a new process in China, the complexity and the technical requirements of equipment of the novel ship are greatly improved, the failure occurrence frequency of the ship equipment is increased, the maintenance requirements of the ship equipment are also greatly improved, and therefore the health prediction technology is one of important technologies on which the ship normally sails. Due to the development of the sensing technology and the technology of the internet of things and the dynamic change of the operation environment of complex equipment, the monitoring data volume of the marine transformer is multiplied, and the characteristics of high speed, changeability and other typical industrial big data are presented. However, current fault Prediction and Health Management (PHM) related systems and key technical research are mainly driven by monitoring data of the transformer in a known ideal operation state, and it is difficult to meet the requirements of precision and adaptability of real-time health degree evaluation and prediction of complex equipment in a dynamic variable operation environment.
Under the condition that sudden faults do not occur, the health state of the transformer is subject to the degradation rule of the health state, the sub-health state and the fault state, and if the transformer is not maintained and repaired in time in the sub-health state to cause the fault shutdown of the transformer, the sum of the whole loss is larger than the cost paid by early health maintenance. Therefore, the method for effectively predicting the health degree of the marine transformer and predicting the health degree of the marine transformer aiming at different working conditions is a problem to be solved in the field.
Disclosure of Invention
The invention aims to solve the problems of inaccurate model, incomplete data, insufficient virtual-real interaction and the like of the applied PHM technology under the dynamic change of the operation environment of a transformer on a ship. Therefore, the invention adopts the following specific technical scheme:
a transformer health prediction method based on digital twinning specifically comprises the following steps:
step one, signal acquisition is carried out on a customized marine transformer entity, data used for representing the running state of the customized marine transformer entity are obtained and stored in a transformer data management platform.
And step two, establishing a model for simulating the health prediction of the transformer for the overall structure of the transformer to be predicted. And determining and verifying parameters influencing the health degree of the transformer in the established model by combining an offline transformer health degree attenuation test, and establishing the universal digital twin body by using the parameters.
And thirdly, modifying the general digital twin body by utilizing the stored historical running state data based on a modification algorithm, and establishing a digital twin body model of the entity transformer.
And step four, analyzing comprehensive information of the operation working conditions and the environmental changes of the transformer from the historical operation data of the transformer, and generating probability distribution of different operation working conditions and environmental conditions based on the comprehensive information.
And fifthly, inputting the generated operating conditions and environments into the digital twin body, and simulating the transformer health degradation path under each operating condition and environment to obtain a simulated transformer health degradation data set.
And step six, establishing a transformer health prediction model based on a machine learning algorithm, taking signals such as temperature, voltage and current of the marine transformer as input, and taking the health prediction model result of the transformer as output.
And seventhly, training the transformer health prediction model by using the simulation transformer health degradation data set, and predicting the health degree of the entity transformer by using the trained health prediction model.
And step eight, acquiring and storing the working condition of the marine transformer, the environmental change on the ship and the habit of operators in real time, and updating the transformer health prediction model regularly.
Further, the data for characterizing the operation state in the first step includes: voltage, current, temperature distribution, frequency, insulation detection, etc. of the transformer.
Furthermore, in the second step, a theoretical model for simulating the health degree of the transformer is established from the aspects of electromagnetics, thermodynamics and the like, and a universal digital twin body is established by applying the mechanisms of alternating magnetic flux induction, transformer structure change and the like.
Further, in the third step, the general digital twin is corrected, and specifically, the data of capacity loss, impedance increase and the like of the physical marine transformer is compared with the simulation result of the general digital twin.
Further, the statistical information in the fourth step is obtained by specifically extracting temperature and frequency requirements from historical data, and the probability distribution is generated based on an auto-encoder algorithm.
Further, the machine learning algorithm in the sixth step adopts a Support Vector Machine (SVM) based algorithm.
The invention has the beneficial effects that: by establishing a digital twin body of the marine transformer, the health degradation track of the transformer under different working conditions is generated, and the inconsistency, the change of the environment and the working conditions of the transformer can be effectively dealt with. The transformer health prediction model can be established by combining a machine learning model, and the regular updating is realized to deal with the working condition and the environmental change, so that the defects in the prior art are obviously overcome. The method solves the problems of low transformer fault prediction and positioning capability, low isolation capability, untimely equipment health management and the like in the original mode, and has important significance for improving the running efficiency of the ship and exerting the efficiency of relevant equipment of the ship.
Drawings
FIG. 1 is a schematic overall view of the process provided by the present invention.
Fig. 2 is a schematic diagram of a construction process of a health prediction model of a transformer.
Detailed Description
For the purpose of enhancing the understanding of the present invention, the present invention will be described in further detail with reference to the accompanying drawings and examples, which are provided for the purpose of illustration only and are not intended to limit the scope of the present invention.
Example (b): a transformer health prediction method is realized based on a digital twinning technology. As shown in fig. 1, the method specifically includes the following steps:
constructing a digital twin body of the marine transformer:
the method comprises the steps that firstly, signal acquisition is carried out on a customized entity marine transformer, data used for representing the running state of the transformer are obtained and stored in a transformer data storage platform; the data for characterizing the operating state thereof, comprising: voltage, current, temperature distribution, frequency, insulation detection, etc. of the marine transformer.
Step two, aiming at the structural material of the transformer to be predicted, a theoretical model for simulating the health degree of the transformer is established from the aspects of electromagnetics, thermodynamics and the like, parameters influencing the health degree of the transformer in the model are determined and verified by combining an offline transformer health degradation test, a universal digital twin body is established based on the parameters, and the mechanism according to which comprises the following steps: alternating magnetic flux induction, structural changes of transformers, etc.;
thirdly, correcting the general digital twin body based on a correction algorithm and by using stored historical running state data, and establishing the digital twin body of the entity transformer by comparing data of capacity loss, impedance increase and the like of the entity transformer with a simulation result of the general digital twin body;
constructing a transformer health prediction model:
and step four, analyzing statistical information of the running state and the environmental change of the transformer from the historical running data of the transformer, for example, extracting current and frequency requirements from the historical data, and generating probability distribution of different running conditions and environmental conditions of the transformer based on the statistical information and a self-encoder algorithm.
Inputting the operating conditions and the environments generated in the fourth step into the digital twin body, and simulating the transformer health degradation paths under all the operating conditions and the environments to obtain a simulated health degradation data set;
and step six, as shown in fig. 2, the construction of the health prediction model of the transformer is completed based on a Support Vector Machine (SVM) machine learning algorithm. Signals such as current, voltage, temperature and the like of the marine transformer are used as input of the support vector machine, and prediction comparison and learning are carried out. If the error between the predicted parameter and the actual parameter is not within the allowable range, performing parameter training and establishing an initial model; and if the current is within the allowable range, finally obtaining a health prediction model of the transformer.
And seventhly, training the transformer health prediction model by using the simulation health degradation data set, and predicting the health degree of the entity transformer by using the trained transformer health prediction model.
And step eight, acquiring and storing the working condition of the marine transformer, the environmental change on the ship and the habit of operators in real time, and updating the transformer health prediction model regularly.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (6)
1. A method for predicting the health of a marine transformer by using a digital twin technology is characterized by comprising two parts of constructing a digital twin body of the transformer and constructing a health prediction model of the transformer, and specifically comprises the following steps:
step one, signal acquisition is carried out on a customized marine transformer entity, data used for representing the running state of the customized marine transformer entity are obtained and stored in a transformer data management platform;
establishing a model for simulating the health prediction of the transformer for the overall structure of the transformer to be predicted, determining and verifying parameters influencing the health degree of the transformer in the established model by combining an offline transformer health degree attenuation test, and establishing a universal digital twin body by using the parameters;
thirdly, modifying the general digital twin body by utilizing the stored historical running state data based on a modification algorithm, and establishing a digital twin body model of the entity transformer;
analyzing comprehensive information of the operation conditions and the environmental changes of the transformer from the historical operation data of the transformer, and generating probability distribution of different operation conditions and environmental conditions based on the comprehensive information;
inputting the generated operating conditions and environments into the digital twin body, and simulating the transformer health degradation paths under all the operating conditions and environments to obtain a simulation transformer health degradation data set;
step six, establishing a transformer health prediction model based on a machine learning algorithm, taking signals such as temperature, voltage and current of the marine transformer as input, and taking the health prediction model result of the transformer as output;
step seven, training the transformer health prediction model by using the simulation transformer health degradation data set, and predicting the health degree of the entity transformer by using the trained health prediction model;
and step eight, acquiring and storing the working condition of the marine transformer, the environmental change on the ship and the habit of operators in real time, and updating the transformer health prediction model regularly.
2. The method for predicting the health of a transformer by using a digital twinning technique as claimed in claim 1, wherein the data used for characterizing the operation state of the transformer in the first step comprises voltage, current, temperature distribution, frequency and insulation detection of the transformer.
3. The method for predicting the health of the transformer by using the digital twinning technology as claimed in claim 2, wherein in the second step, a theoretical model for simulating the health of the transformer is established from the aspects of electromagnetics and mechanics, and a general digital twinning body is established by using alternating magnetic flux induction and a transformer structure change mechanism.
4. The method for predicting the health of the transformer by applying the digital twin technology according to claim 3, wherein the general digital twin is corrected in the third step, and the correction is specifically realized by comparing the capacity loss and the impedance increase data of the physical marine transformer with the simulation result of the general digital twin.
5. The method for predicting the health of a transformer by using a digital twin technology as claimed in claim 4, wherein the statistical information in the fourth step is obtained by extracting temperature and frequency requirements from historical data; generating the probability distribution is based on an auto-encoder algorithm.
6. The method for predicting the health of the transformer by using the digital twin technology as claimed in claim 5, wherein the machine learning algorithm in the sixth step is a support vector machine-based algorithm.
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| CN113204922A (en) * | 2021-05-17 | 2021-08-03 | 上海交通大学 | Transformer running state monitoring and predicting method |
| CN113204919A (en) * | 2021-05-10 | 2021-08-03 | 国网江苏省电力有限公司泰州供电分公司 | Method and device for predicting state of main transformer conservator based on digital twinning and electronic equipment |
| CN113221277A (en) * | 2021-05-14 | 2021-08-06 | 西安交通大学 | Bearing performance degradation evaluation method and system based on digital twin model |
| CN113221352A (en) * | 2021-05-11 | 2021-08-06 | 中国科学院自动化研究所 | Complex equipment health management method and system based on digital twins |
| CN113392593A (en) * | 2021-07-21 | 2021-09-14 | 国网重庆市电力公司电力科学研究院 | Converter transformer temperature field digital twin model construction method |
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| CN113835060A (en) * | 2021-11-23 | 2021-12-24 | 武汉格蓝若智能技术有限公司 | Power transformer online state monitoring method and system based on digital twinning |
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| CN116957361A (en) * | 2023-07-27 | 2023-10-27 | 中国舰船研究设计中心 | Ship task system health state detection method based on virtual-real combination |
| CN116957361B (en) * | 2023-07-27 | 2024-02-06 | 中国舰船研究设计中心 | Ship task system health state detection method based on virtual-real combination |
| CN117217008A (en) * | 2023-09-15 | 2023-12-12 | 国网四川省电力公司电力科学研究院 | Digital twin model construction method of power transmission network transformer |
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