CN112684379A - Transformer fault diagnosis system and method based on digital twinning - Google Patents
Transformer fault diagnosis system and method based on digital twinning Download PDFInfo
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Abstract
The invention belongs to the field of transformer fault diagnosis, in particular to a transformer fault diagnosis system based on digital twins and a method thereof, which consists of a physical system module, a digital twins module and a fault diagnosis module, wherein the method specifically comprises the following steps; using the physical system module to create a three-dimensional model of the transformer and acquire transformer operating state data acquired by a sensor; creating a digital twin model of the transformer using a digital twin module, generating analog data, and calibration of the digital twin model; the invention combines the physical entity of the transformer with the virtual model, corrects the digital twin model according to the state monitoring data obtained by the sensor and the simulation data of the twin model, extracts the characteristic parameters, diagnoses the type of the fault by using a BP neural network algorithm, analyzes the possible reasons of the fault, is beneficial to reducing the maintenance cost and the period of the transformer, improves the fault diagnosis efficiency and ensures that the transformer can safely and reliably run.
Description
Technical Field
The invention belongs to the field of transformer fault diagnosis, and particularly relates to a transformer fault diagnosis system and method based on digital twinning.
Background
The transformer is an important device in power transmission and transformation, power supply and distribution systems, the safe, reliable and stable operation of the transformer is the basis of the safe, reliable and stable operation of a power supply network, and once a fault occurs, the use and the safe operation of electric power can be influenced. And the real-time fault diagnosis is carried out according to the running condition of the transformer, so that the fault can be found and processed in time, and huge economic loss is avoided.
At present, most of transformer fault diagnosis methods combine manual data sampling and online parameter sampling, and maintenance personnel judge the running state of a transformer according to the sampled data. However, because manual judgment is generally only combined with current sampling data, the fault diagnosis efficiency is low, and a fault source and a fault type cannot be found in time.
The rapid development of the digital twin technology provides a new idea for solving the above problems. The digital twin constructs a same entity in a digital world through a digital means to simulate the behavior of the digital twin in a real environment, dynamically presents past and present behaviors or processes, and effectively reflects the operation condition of the system, thereby more truly and comprehensively detecting unpredictable conditions. By means of virtual-real interaction feedback, data fusion analysis, decision iteration optimization and the like, real-time, efficient and intelligent operation or operation services are provided for the physical entity.
Therefore, how to apply the digital twin technology to fault diagnosis of the transformer, map the state of the transformer in the physical space in the digital space, perform interactive feedback between the virtual model and the physical entity, and analyze the fault reason according to the real-time state parameters has become a technical problem which needs to be solved urgently.
Disclosure of Invention
The invention aims to provide a transformer fault diagnosis method based on digital twinning, which is used for monitoring the state of a transformer, alarming when a fault occurs, analyzing the fault and judging the fault reason.
In order to achieve the purpose, the invention adopts the following specific technical scheme:
a transformer fault diagnosis method based on digital twinning comprises the following steps:
step 1: a three-dimensional model of the transformer is created. The structural parameters, the material parameters, the geometric parameters and the physical action relationship of the transformer are obtained through specification and actual measurement and analysis, and the structure and the size of the equipment, particularly the key characteristic parameters, are accurately expressed. Using CAE modeling software, a three-dimensional model of the transformer is created.
Step 2: and acquiring the running state data of the transformer acquired by the sensor. The operating state data includes: the temperature, humidity and noise numerical value information of the environmental state of the transformer reflects the numerical value information of the running state of the transformer and reflects the quality information of the running state of the transformer, and the data are transmitted to the upper computer through the Wi-Fi transmission module;
and step 3: a digital twin model of the transformer is created. Constructing a digital twin model of the transformer on the basis of the steps 1 and 2, completing the establishment of a plurality of dimensions of models such as geometry, physics, behavior and rules, and finally fusing and integrating by connecting models of all layers to form a digital twin virtual model of the transformer, and mainly completing the simulation, optimization, evaluation and real-time monitoring of the working process of the transformer;
and 4, step 4: simulation data is generated. Inputting the running state data of the transformer in the step 2 into the digital twin model in the step 3, and driving the transformer to work in a virtual mode in a computer by the digital twin model to obtain a simulation running state value of the transformer, which evolves along with the change of running time;
and 5: and (4) calibrating the digital twin model. And continuously iterating, synchronously updating and interactively mapping the data between the evolution numerical value and the physical entity obtained in the step 4, updating corresponding parameters in the reference model, and calibrating the digital twin model of the transformer.
Step 6: and diagnosing the fault of the transformer. And selecting input feature vectors, classifying the fault modes, training in a BP neural network, modifying training parameters to continue training if the training requirements are not met, and finally completing fault diagnosis until the neural network meeting the fitting requirements is trained.
Further, the upper computer in the step 2 can interactively display the state and the working condition of the transformer in real time.
Further, the calibration step of the digital twin model in the step 5 is as follows:
adding a virtual sensor in a digital twin virtual development environment to obtain transformer running state data in the virtual environment.
Judging whether the digital twin model is matched with the physical entity of the transformer or not according to the deviation value of the actual running state data and the simulation data of the transformer, if not, calculating the gradient according to the deviation value, adjusting the parameters of the twin model, and repeating the step II until the calibration is finished;
and thirdly, when the digital twin model is matched with the physical entity of the transformer, the calibration of the model is completed.
Further, the input feature vector in step 6 is H2、CH4、C2H6、C2H4And C2H2These 5 dissolved gases are a percentage. The failure modes are: normal, low temperature overheating, medium temperature overheating, high temperature overheating, partial discharge, low energy discharge, high energy discharge.
The invention has the beneficial effects that: the invention combines the physical entity of the transformer with the virtual model, corrects the digital twin model according to the state monitoring data obtained by the sensor and the simulation data of the twin model, extracts the characteristic parameters, diagnoses the type of the fault by using a BP neural network algorithm, analyzes the possible reasons of the fault, is beneficial to reducing the maintenance cost and the period of the transformer, improves the fault diagnosis efficiency and ensures that the transformer can safely and reliably operate.
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Fig. 1 is an overall structural view of the present invention.
Fig. 2 is a flow chart of the present invention.
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 fault diagnosis system based on digital twinning is shown in figure 1 and comprises a physical system module, a digital twinning module and a fault diagnosis module.
As shown in fig. 1, the physical system module is composed of a transformer entity and a sensor, and the module is implemented as follows:
step 1: obtaining key characteristic parameters of the transformer: structural parameters, material parameters, geometric parameters and physical action relations of the transformer are obtained through specification and actual measurement and analysis, and the structure and the size of the equipment, especially key characteristic parameters, are accurately expressed;
step 2: acquiring transformer running state data acquired by a sensor: the running state data comprises temperature, humidity and noise value information of the environmental state of the transformer and the percentage of different gases in the transformer;
and step 3: data processing: and carrying out data processing on the key characteristic parameters of the transformer and the signals acquired by the sensor.
The digital twin module is implemented as follows:
step 1: creating a digital twin model of the transformer: constructing a digital twin model of the transformer, completing the establishment of a plurality of dimensions of models such as geometry, physics, behavior and rules, and finally fusing and integrating by linking models of all layers to form a digital twin virtual model of the transformer, and mainly completing the simulation, optimization, evaluation and real-time monitoring of the working process of the transformer;
step 2: generating simulation data: inputting the running state data of the transformer into a digital twin model, and driving the transformer to work in a computer in a virtual mode by the digital twin model to obtain a simulation running state numerical value of the transformer evolving along with the change of running time;
and step 3: calculating a deviation value: comparing the collected data in the physical system module with the simulation data, and calculating a deviation value of the data;
and 4, step 4: digital twin model calibration: judging whether the digital twin model is matched with the transformer entity or not according to the obtained deviation value, if not, adjusting parameters, calibrating the transformer model, and repeating the step 2, and if so, outputting the digital twin model of the transformer;
the fault diagnosis module is specifically realized as follows:
step 1: extracting a characteristic vector, and converting data in the physical system and the digital twin module into the characteristic vector to form a characteristic vector group;
step 2: and classifying fault modes, namely classifying the transformer fault modes into: normal, low-temperature overheating, medium-temperature overheating, high-temperature overheating, partial discharge, low-energy discharge and high-energy discharge;
and step 3: training a BP neural network:
firstly, inputting a feature vector. Selecting H2、CH4、C2H6、C2H4And C2H2The proportion of the 5 kinds of insulating oil dissolved gas is used as a judgment basis, and the percentage of the 5 kinds of gas is used as an input feature vector of the BP neural network.
And outputting the characteristic vector. And encoding 7 fault types of normal, low-temperature overheating, medium-temperature overheating, high-temperature overheating, partial discharge, low-energy discharge and high-energy discharge to serve as the output of the neural network.
Selecting hidden node number. The neural network of the infinite hidden layer nodes can approximate any continuous function in the mapping interval, but the neural network structure is more complicated due to the increase of the hidden layer nodes, the calculation workload is increased, and the training time is prolonged. Therefore, the number of the hidden layer nodes can be selected to be 13 by comparing the iteration number, the error performance and the mean square error performance optimization function value by selecting the appropriate number of the nodes with reference to the empirical calculation formula of the number of the hidden layer nodes.
And fourthly, training the neural network. And (3) calibrating and verifying the neural network by combining the physical system data of the transformer and the analog data of the digital twin, and modifying the training parameters to continue training if the training requirements are not met until the neural network meeting the fitting requirements is trained.
And 4, step 4: and (5) fault diagnosis. And inputting data acquired in the actual operation process of the transformer into the trained neural network to obtain a fault most similar to the fitting degree of the fault mode, and outputting the current fault type by the neural network to realize fault diagnosis of the transformer.
As shown in fig. 2, a transformer fault diagnosis method based on digital twinning includes the following steps:
step 1: a three-dimensional model of the transformer is created.
Step 2: and acquiring the running state data of the transformer acquired by the sensor.
And step 3: a digital twin model of the transformer is created.
And 4, step 4: simulation data is generated.
And 5: and (4) calibrating the digital twin model.
Step 6: and diagnosing the fault of the transformer.
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 (7)
1. A transformer fault diagnosis system based on digital twinning is characterized by comprising a physical system module, a digital twinning module and a fault diagnosis module, wherein the physical system module is composed of a transformer entity and a sensor.
2. A digital twin-based transformer fault diagnosis method, characterized by using the digital twin-based transformer fault diagnosis system of claim 1, and specifically comprising the following steps;
the method comprises the following steps: using the physical system module to create a three-dimensional model of the transformer and acquire transformer operating state data acquired by a sensor;
step two: creating a digital twin model of the transformer using a digital twin module, generating analog data, and calibration of the digital twin model;
step three: and performing fault diagnosis of the transformer by using a fault diagnosis module.
3. The digital twin-based transformer fault diagnosis method according to claim 2, wherein in the first step, CAE modeling software is used to create a three-dimensional model of the transformer.
4. The digital twin-based transformer fault diagnosis method according to claim 3, wherein the transformer operating state data acquired by the sensor in the first step includes temperature, humidity and noise numerical information of a transformer environment state, numerical information reflecting the transformer operating state and quality information reflecting the transformer operating state, and the data is transmitted to an upper computer through a Wi-Fi transmission module.
5. The digital twin-based transformer fault diagnosis method according to claim 4, wherein the calibration of the digital twin model in the second step comprises:
adding a virtual sensor in a digital twin virtual development environment to obtain transformer running state data in the virtual environment;
judging whether the digital twin model is matched with the physical entity of the transformer or not according to the deviation value of the actual running state data and the simulation data of the transformer, if not, calculating the gradient according to the deviation value, adjusting the parameters of the twin model, and repeating the step II until the calibration is finished;
and thirdly, when the digital twin model is matched with the physical entity of the transformer, the calibration of the model is completed.
6. The transformer fault diagnosis method based on the digital twin as claimed in claim 5, wherein the fault diagnosis of the transformer in the third step is performed by selecting input feature vectors, classifying fault modes, training in a BP neural network, modifying training parameters to continue training if the training requirements are not met, and finally completing fault diagnosis until a neural network meeting the fitting requirements is trained.
7. The digital twin-based transformer fault diagnosis method according to claim 6, wherein the input feature vector is H2、CH4、C2H6、C2H4And C2H2The 5 dissolved gases account for the percentage, and the failure modes comprise normal, low-temperature overheating, medium-temperature overheating, high-temperature overheating, partial discharge, low-energy discharge and high-energy discharge.
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