Rapid calculation method for multiple physical fields of oil-immersed transformer

Abstract

The invention discloses a quick calculation method for multiple physical fields of an oil-immersed transformer, which belongs to the technical field of transformers, and comprises the following steps: constructing a transformer internal multiple physical field simulation analysis model, establishing a transformer three-dimensional equivalent digital model, importing the model into simulation software, calculating heat source density, and performing transformer multiple physical field simulation calculation. And generating a transformer temperature field rapid calculation data set, inputting the data set into an XGBoost rapid calculation model considering the spatial feature relationship to carry out machine learning iteration training, and obtaining a transformer temperature field calculation result through the load coefficient, the heat source density and the environment temperature. According to the rapid calculation method for the multiple physical fields of the oil-immersed transformer, the XGBoost model considering the spatial characteristic relation in the temperature field calculation of the oil-immersed transformer is constructed, the simulation result of the multiple physical fields serves as an input data set to be fused into the XGBoost model, data can be efficiently analyzed, the overfitting risk in the model training process is reduced, and the calculation efficiency is improved. And the reliability of prediction results is ensured.

Description

Technical Field

[0001] The invention relates to the technical field of transformers, and in particular to a fast calculation method for multi-physical fields of oil-immersed transformers. Background Art

[0002] Winding temperature is a key monitoring parameter for evaluating the operating status of oil-immersed transformers, which directly affects the operating efficiency and service life of the transformer. Therefore, in-depth research on the distribution characteristics and evolution of the transformer winding temperature field is of great significance to ensure the safety and stable operation of the transformer. However, as a highly centralized and complex system, the oil-immersed transformer has complex dynamic relationships between multiple physical fields (magnetic, thermal, and fluid). Rapid and accurate calculation of the transformer winding temperature field has become an urgent problem to be solved in the current intersection of electrical engineering and computational science.

[0003] At present, the research methods of transformer temperature calculation mainly include sensor measurement, empirical formula method, thermal circuit model construction and numerical calculation simulation. Among them, although the direct sensor measurement method is accurate, it requires a large number of sensors to be arranged, which is costly and complex to maintain. The empirical formula method is simple to operate, but the accuracy is limited, and the structural characteristics of the transformer are not considered. The thermal circuit model is based on the hot spot analogy principle. It calculates the hot spots of the windings through circuit analysis. Although it can reflect the heat transfer process, the parameter setting is cumbersome. The above methods are difficult to fully obtain the internal temperature distribution of the transformer, so numerical calculation has become the mainstream. Numerical calculation solves the multi-physical field coupling of the transformer through finite element and finite difference methods, and analyzes the temperature changes in detail. However, due to the long calculation cycle of multi-physical field simulation and high computing power requirements, the real-time application of this method is limited. In order to improve the computing efficiency, scholars at home and abroad are exploring temperature prediction methods based on artificial intelligence algorithms. This method does not require an in-depth understanding of the complex structure of the transformer, but captures data features and designs a good model to achieve temperature prediction. Summary of the invention

[0004] The purpose of the present invention is to provide a fast calculation method for the multi-physical field of an oil-immersed transformer, which integrates numerical calculation and machine learning technology to provide a powerful tool for transformer status monitoring and improve the digitalization and intelligence level of the transformer.

[0005] To achieve the above object, the present invention provides a fast calculation method for multi-physical fields of an oil-immersed transformer, comprising the following steps:

[0006] S1. Analyze the coupling relationship between electromagnetic, fluid and temperature physical fields inside the transformer, and clarify the interaction mechanism under relationship mapping;

[0007] S2. Analyze the circulation process of natural oil circulation of transformer, build a multi-physics field simulation analysis model inside the transformer based on the mass, momentum and energy conservation equations, and preset the boundary conditions of transformer oil flow velocity, gravity acceleration and initial temperature;

[0008] S3, establish a three-dimensional equivalent digital model of the transformer and perform mesh division;

[0009] S4, import the three-dimensional equivalent digital model with meshes divided in S3 into the simulation software and set parameters to simulate the actual operating conditions of the transformer. At the same time, the operating losses under different working conditions are calculated by the load equivalent method and converted into heat sources to be applied to the transformer;

[0010] S5. Calculate the ratio of the operating loss to the volume of the transformer structure as the heat source density of the transformer structure;

[0011] S6, substituting the calculated data in S4 and S5 into the three-dimensional equivalent digital model of the transformer in S3, and performing multi-physical field simulation calculation of the transformer according to the control equation in S2;

[0012] S7. According to different working conditions, the load factor, heat source density and ambient temperature are arranged and combined to form different initial input variables, and the multi-physics field simulation calculation of S6 is repeated to generate temperature field simulation results under several working conditions, so as to form a transformer temperature field fast calculation data set;

[0013] S8. Input the fast calculation data set generated by S7 into the XGBoost fast calculation model for iterative machine learning training. The trained model obtains the transformer temperature field calculation results through the load factor, heat source density and ambient temperature.

[0014] Preferably, in S2, the conservation equations of mass, momentum and energy are:

[0015]

[0016] Where ρ represents density, t represents time, v represents fluid velocity vector, p represents fluid pressure, η represents dynamic viscosity, f represents external force, T represents temperature, k represents thermal conductivity, C p represents specific heat capacity, and Q represents heat source.

[0017] Preferably, in S4, the calculation formula of the operating loss is:

[0018]

[0019] Among them, P L and P N They represent load loss and no-load loss respectively, P represents total loss, I and I R Represents actual current and rated current respectively.

[0020] Preferably, in S5, the heat source density calculation formula is:

[0021] S = P / V (5);

[0022] S T =S0[1+β(T-T0)] (6);

[0023] Among them, S represents the heat source density, V represents the volume of the structural part, ST represents the heat source density at temperature T, S0 represents the heat source density at temperature T0, and β represents the temperature coefficient.

[0024] Preferably, in S7, the load factor is the ratio of the actual current to the rated current, i.e., I and I R Ratio.

[0025] Preferably, in S8, the XGBoost fast calculation model calculation process includes the following steps:

[0026] S81. A preset number of regression decision trees are used as the base learners of the XGBoost fast calculation model. The additive model is used for single model training. A new decision tree is added to the model each time the training is performed. The integrated model is as follows:

[0027]

[0028] in, represents the predicted value of the tth decision tree model, f t (x i ) represents the structure of the tth tree;

[0029] S82, Substitute equation (7) into equation (8) and use it as the objective function, continuously modify the model, use the greedy algorithm to generate a tree for node splitting, select the optimal splitting benefit, and obtain the optimal XGBoost model.

[0030]

[0031] Among them, Obj represents the objective function, represents the loss function, Ω(f k ) represents the regularization term;

[0032] S83. Preset a number of position points for the transformer structure on average, and generate a preset number of XGBoost models at the same time. Use the temperature data of the position points as output variables and add them to the input variables of the next model. Finally, merge all the models to form an XGBoost fast calculation model that considers the spatial feature relationship. The output result of the model is the temperature field distribution result of the oil-immersed transformer.

[0033] Preferably, in S81 and S82, the input variables of the first XGBoost model formed are load coefficient, heat source density and ambient temperature, and the output variable is the temperature of the first position point. The input variables of the second model are added to the output variables of the previous model, and so on. The input variables of the preset number of models are load coefficient, heat source density, ambient temperature and temperature data of a preset number of position points minus 1, and the output variable is the temperature of the preset number of position points.

[0034] Therefore, the present invention adopts the above-mentioned fast calculation method for the multi-physical field of oil-immersed transformers. By constructing an oil-immersed transformer model with a multi-physical field coupling mechanism, the multi-physical field simulation calculation results of the oil-immersed transformer under complex working conditions based on the model are used as input data sets and integrated into the XGBoost machine learning prediction model. It can not only efficiently process and analyze large-scale data, but also significantly reduce the overfitting risk in the model training process through its unique gradient boosting mechanism and regularization strategy, thereby ensuring the stability and reliability of the prediction results. In the process of constructing the XGBoost fast calculation model, the spatial feature relationship in the calculation of the temperature field of the oil-immersed transformer is fully considered, so that the model can more accurately reflect the dynamic change process of the internal temperature field of the transformer.

[0035] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a schematic diagram of the circulation process of natural oil circulation in an embodiment of a fast calculation method for multi-physical fields of an oil-immersed transformer of the present invention;

[0037] Figure 2 It is a schematic diagram of an XGBoost algorithm flow chart of an embodiment of a fast calculation method for multi-physical fields of an oil-immersed transformer of the present invention. DETAILED DESCRIPTION

[0038] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0039] Unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0040] Embodiment 1

[0041] The present invention provides a fast calculation method for multi-physical fields of an oil-immersed transformer, comprising the following steps:

[0042] S1. Analyze the coupling relationship between the internal electromagnetic, fluid, temperature and other physical fields of the oil-immersed transformer during long-term service, and clarify the interaction mechanism under the mapping of these relationships.

[0043] S2. Analyze the cycle process of its natural oil circulation, such as Figure 1 As shown, based on the conservation equations of mass, momentum and energy, a multi-physics field simulation analysis model is constructed, and boundary conditions such as transformer oil flow velocity, gravity acceleration and initial temperature are preset.

[0044] The conservation equations for mass, momentum, and energy are:

[0045]

[0046] Where ρ represents density, t represents time, v represents fluid velocity vector, p represents fluid pressure, η represents dynamic viscosity, f represents external force, T represents temperature, k represents thermal conductivity, C p represents specific heat capacity, and Q represents heat source.

[0047] S3. Establish a three-dimensional equivalent digital model of the oil-immersed transformer and perform meshing. Reasonable meshing can better simulate the physical phenomena of the oil-immersed transformer under actual operating conditions, and at the same time improve the accuracy and reliability of the calculation results in subsequent simulation analysis.

[0048] S4, import the three-dimensional equivalent digital model divided into grids in S3 into the simulation software and set parameters to simulate the actual operating conditions of the transformer. At the same time, the operating losses under different working conditions are calculated by the load equivalent method and converted into heat sources to be applied to the transformer. The calculation formula for the operating loss is:

[0049]

[0050] Among them, P L and P N They represent load and no-load losses respectively, P represents total loss, I and I R Represents actual and rated current respectively.

[0051] S5. Calculate the ratio of operating loss to the volume of transformer structural parts and use it as the heat source density of transformer structural parts. The heat source density calculation formula is:

[0052] S = P / V (5);

[0053] S T =S0[1+β(T-T0)] (6);

[0054] Where S represents the heat source density, V represents the volume of the structure, and S T represents the heat source density at temperature T, S0 represents the heat source density at temperature T0, β represents the temperature coefficient. In particular, in the present invention, the winding heat source density is regarded as a function of temperature, and formula (6) is only used for the calculation of the winding heat source density.

[0055] S6. Substitute the calculated data in S4 and S5 into the three-dimensional equivalent digital model of the transformer in S3, and perform multi-physical field simulation calculation of the transformer according to the control equation in S2.

[0056] S7. According to different working conditions, the load factor, heat source density and ambient temperature are arranged and combined to form different initial input variables, and the multi-physics field simulation calculation of S6 is repeated to generate temperature field simulation results under several working conditions, forming a transformer temperature field fast calculation data set. The load factor is the ratio of the actual current to the rated current, that is, I and I R Ratio.

[0057] S8, input the fast calculation data set generated by S7 into the XGBoost fast calculation model that considers the spatial feature relationship, and perform machine learning iterative training. The trained model obtains the transformer temperature field calculation results through the load factor, heat source density and ambient temperature. The calculation process of the XGBoost fast calculation model includes the following steps:

[0058] S81. A preset number of regression decision trees are used as the base learners of the XGBoost fast calculation model. The preset number can be set according to experience. The additive model is used for single model training. Each time a new decision tree is added to the model, the integrated model is as follows:

[0059]

[0060] in, represents the predicted value of the tth decision tree model, f t (x i ) represents the structure of the tth tree.

[0061] S82, Substitute equation (7) into equation (8) and use it as the objective function, continuously modify the model, use the greedy algorithm to generate a tree for node splitting, select the optimal splitting benefit, and obtain the optimal XGBoost model.

[0062]

[0063] Among them, Obj represents the objective function, represents the loss function, Ω(f k ) represents a regular term, which is used to prevent the model from overfitting.

[0064] In S81 and S82, the input variables of the first XGBoost model formed are load coefficient, heat source density and ambient temperature, and the output variable is the temperature of the first position point. The input variables of the second model are added to the output variables of the previous model, and so on. The input variables of the preset number of models are load coefficient, heat source density, ambient temperature and temperature data of the preset number of positions minus 1, and the output variable is the temperature of the preset number of positions.

[0065] S83. Preset a number of position points for the transformer structure on average, and generate a preset number of XGBoost models at the same time. Use the temperature data of the position points as output variables and add them to the input variables of the next model. Finally, merge all the models to form an XGBoost fast calculation model that considers the spatial feature relationship. The output result of the model is the temperature field distribution result of the oil-immersed transformer. The algorithm flow is as follows: Figure 2 .

[0066] Therefore, the present invention adopts the above-mentioned fast calculation method for the multi-physical field of oil-immersed transformers. By constructing an oil-immersed transformer model with a multi-physical field coupling mechanism, the multi-physical field simulation calculation results of the oil-immersed transformer under complex working conditions based on the model are used as input data sets and integrated into the XGBoost machine learning prediction model. It can not only efficiently process and analyze large-scale data, but also significantly reduce the overfitting risk in the model training process through its unique gradient boosting mechanism and regularization strategy, thereby ensuring the stability and reliability of the prediction results. In the process of constructing the XGBoost fast calculation model, the spatial feature relationship in the calculation of the temperature field of the oil-immersed transformer is fully considered, so that the model can more accurately reflect the dynamic change process of the internal temperature field of the transformer.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A fast calculation method for multi-physical fields of oil-immersed transformers, characterized in that: The following steps are involved: S1. Analyze the coupling relationship between electromagnetic, fluid and temperature physical fields inside the transformer, and clarify the interaction mechanism under relationship mapping; S2. Analyze the circulation process of natural oil circulation of transformer, build a multi-physics field simulation analysis model inside the transformer based on the mass, momentum and energy conservation equations, and preset the boundary conditions of transformer oil flow velocity, gravity acceleration and initial temperature; S3, establish a three-dimensional equivalent digital model of the transformer and perform mesh division; S4, import the three-dimensional equivalent digital model with meshes divided in S3 into the simulation software and set parameters to simulate the actual operating conditions of the transformer. At the same time, the operating losses under different working conditions are calculated by the load equivalent method and converted into heat sources to be applied to the transformer; S5. Calculate the ratio of the operating loss to the volume of the transformer structure as the heat source density of the transformer structure; S6, substituting the calculated data in S4 and S5 into the three-dimensional equivalent digital model of the transformer in S3, and performing multi-physical field simulation calculation of the transformer according to the control equation in S2; S7. According to different working conditions, the load factor, heat source density and ambient temperature are arranged and combined to form different initial input variables, and the multi-physics field simulation calculation of S6 is repeated to generate temperature field simulation results under several working conditions, so as to form a transformer temperature field fast calculation data set; S8. Input the fast calculation data set generated by S7 into the XGBoost fast calculation model for iterative machine learning training. The trained model obtains the transformer temperature field calculation results through the load factor, heat source density and ambient temperature.

2. The fast calculation method for multi-physical fields of oil-immersed transformers according to claim 1, characterized in that: In S2, the conservation equations of mass, momentum, and energy are: Where ρ represents density, t represents time, v represents fluid velocity vector, p represents fluid pressure, η represents dynamic viscosity, f represents external force, T represents temperature, k represents thermal conductivity, C p represents specific heat capacity, and Q represents heat source.

3. The fast calculation method for multi-physical fields of oil-immersed transformers according to claim 1, characterized in that: In S4, the calculation formula for operating loss is: Among them, P L and P N They represent load loss and no-load loss respectively, P represents total loss, I and I R Represents actual current and rated current respectively.

4. The fast calculation method for multi-physical fields of oil-immersed transformers according to claim 3 is characterized in that: In S5, the heat source density calculation formula is: S = P / V (5); S T =S0[1+β(T-T0)] (6); Where S represents the heat source density, V represents the volume of the structure, and S T represents the heat source density at temperature T, S0 represents the heat source density at temperature T0, and β represents the temperature coefficient.

5. The fast calculation method for multi-physical fields of oil-immersed transformers according to claim 3 is characterized in that: In S7, the load factor is the ratio of the actual current to the rated current, that is, the ratio of I to IR.

6. The fast calculation method for multi-physical fields of oil-immersed transformers according to claim 1, characterized in that: In S8, the XGBoost fast calculation model calculation process includes the following steps: S81. A preset number of regression decision trees are used as the base learners of the XGBoost fast calculation model. The additive model is used for single model training. A new decision tree is added to the model each time the training is performed. The integrated model is as follows: in, represents the predicted value of the tth decision tree model, f t (x i ) represents the structure of the tth tree; S82, Substitute equation (7) into equation (8) and use it as the objective function, continuously modify the model, use the greedy algorithm to generate a tree for node splitting, select the optimal splitting benefit, and obtain the optimal XGBoost model. Among them, Obj represents the objective function, represents the loss function, Ω(f k ) represents the regularization term; S83. Preset a number of position points for the transformer structure on average, and generate a preset number of XGBoost models at the same time. Use the temperature data of the position points as output variables and add them to the input variables of the next model. Finally, merge all the models to form an XGBoost fast calculation model that considers the spatial feature relationship. The output result of the model is the temperature field distribution result of the oil-immersed transformer.

7. A fast calculation method for multi-physical fields of oil-immersed transformers according to claim 6, characterized in that: In S81 and S82, the input variables of the first XGBoost model formed are load coefficient, heat source density and ambient temperature, and the output variable is the temperature of the first position point. The input variables of the second model are added to the output variables of the previous model, and so on. The input variables of the preset number of models are load coefficient, heat source density, ambient temperature and temperature data of the preset number of positions minus 1, and the output variable is the temperature of the preset number of positions.

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