Efficient electric energy transmission and distribution system based on novel superconducting material

Abstract

The invention belongs to the field of information management systems, and particularly relates to an efficient electric energy transmission and distribution system based on a novel superconducting material. Key problems in the electric energy transmission and distribution process are solved through technologies such as fractal geometry and topological optimization, tensor analysis and multi-physics field coupling. The system can improve the power transmission efficiency, optimize the distribution scheme, enhance the system stability, provide powerful support for the development of a power system, and has important application value.

Description

Technical Field

[0001] The present invention relates to the field of information management systems, and particularly to an efficient electric energy transmission and distribution system based on a novel superconducting material. Background Art

[0002] In the context of the continuous development of the power system, higher requirements are put forward for the efficiency, reliability, and stability of electric energy transmission and distribution. Traditional electric energy transmission and distribution systems face many challenges, such as large losses and frequent failures. The emergence of novel superconducting materials provides a new way to solve these problems. They have characteristics such as zero resistance and high current-carrying capacity, which can significantly reduce the loss of electric energy transmission. However, the application of superconducting materials faces problems such as complex electromagnetic environments and the stability of high-temperature superconducting materials. At the same time, the trend of the intelligentization of the power system also requires the coordinated development of superconducting materials and related technologies to meet the increasing power demand. Summary of the Invention

[0003] The present invention provides an efficient electric energy transmission and distribution system based on a novel superconducting material. The system structure includes: a superconducting cable layout module, a superconducting material performance evaluation module, a fault prediction module, an electromagnetic compatibility design module, and an electric energy distribution optimization module;

[0004] Furthermore, the superconducting cable layout module: Based on the principles of fractal geometry and topological optimization, according to information such as urban geographic information, population distribution, and electricity consumption historical data, designs the arrangement of superconducting cables in underground pipelines and the connection paths between nodes such as substations and distribution rooms to achieve optimal space filling and heat dissipation performance;

[0005] Furthermore, the superconducting material performance evaluation module: Based on tensor analysis and multi-physical field coupling models, accurately evaluates the electromagnetic characteristics, thermal stability, and mechanical stress distribution of superconducting materials under different working conditions;

[0006] Furthermore, the fault prediction module: Constructs a random matrix using random matrix theory and monitors the operating state of superconducting cables in combination with deep learning algorithms to predict the fault type and time; The electromagnetic compatibility design module: Combines the quantum Monte Carlo method with finite element analysis to optimize the electromagnetic structure design of superconducting equipment, reduce electromagnetic interference, and the electromagnetic impact on the external environment;

[0007] Furthermore, the electric energy distribution optimization module: Based on the principles of variational inequality and dynamic programming, formulates an optimal electric energy distribution strategy according to the real-time state of the system and future predicted load to minimize the system operation cost and optimize the power quality;

[0008] Further, the interaction relationships among the modules are as follows: The superconducting cable layout module provides basic layout information for the power distribution optimization module; the superconducting material performance evaluation module provides material performance parameters for the fault prediction module and the electromagnetic compatibility design module.

[0009] Further, the fault prediction module provides fault information for the power distribution optimization module to timely adjust the distribution strategy.

[0010] The electromagnetic compatibility design module ensures the electromagnetic compatibility between the superconducting device and the surrounding environment during operation.

[0011] The present invention also provides an efficient power transmission and distribution system based on a novel superconducting material. The superconducting cable layout module includes a fractal geometry design unit: Based on the fractal geometry principle, the arrangement pattern of the cable in the underground pipeline is determined through an iterative generation method to achieve optimal space filling and heat dissipation performance; a topology optimization unit: Based on the topology optimization algorithm, the connection paths between nodes such as substations and distribution rooms are determined to ensure the minimum power transmission loss; a layout adjustment unit: According to the urban development plan and the load growth prediction, the cable layout structure is dynamically adjusted.

[0012] The present invention also provides an efficient power transmission and distribution system based on a novel superconducting material. The superconducting material performance evaluation module includes an electromagnetic characteristic analysis unit: Based on the tensor analysis method, the electromagnetic characteristics of the superconducting material under different magnetic fields and current densities are accurately evaluated; a thermal stability analysis unit: Through a multi-physics field coupling model, the stability of the superconducting material under the action of the thermal field is analyzed; a mechanical stress analysis unit: Combining the principles of material mechanics, the mechanical stress distribution of the superconducting material under different stress conditions is evaluated; a performance parameter acquisition unit: The electromagnetic characteristics, thermal stability, and mechanical stress distribution and other parameters of the superconducting material are obtained through experimental tests and theoretical analysis.

[0013] The present invention also provides an efficient power transmission and distribution system based on a novel superconducting material. The fault prediction module includes a random matrix generation unit: A random matrix is constructed according to the operating parameters of the superconducting cable; a deep learning algorithm unit: The random matrix is trained, and by identifying statistical characteristics such as the eigenvalue distribution of the matrix, the fault type and time are predicted; a fault information processing unit: The fault prediction results are processed to timely send out warning signals.

[0014] The present invention also provides an efficient electric energy transmission and distribution system based on a novel superconducting material. The electromagnetic compatibility design module includes a quantum Monte Carlo simulation unit: accurately calculating the electromagnetic interaction of microscopic particles in the superconducting system by using the quantum Monte Carlo method; a finite element analysis unit: dividing the superconducting system into a finite number of units and numerically simulating the electromagnetic field of each unit; an electromagnetic structure optimization unit: optimizing the electromagnetic structure design of superconducting devices according to the simulation results; an electromagnetic interference evaluation unit: evaluating the degree of electromagnetic interference of superconducting devices on the surrounding environment during operation.

[0015] The present invention also provides an efficient electric energy transmission and distribution system based on a novel superconducting material. The electric energy distribution optimization module includes a real-time monitoring unit: obtaining the operation status and load information of the system in real time; a strategy formulation unit: formulating an optimal electric energy distribution strategy according to the variational inequality and dynamic programming principles; a dynamic adjustment unit: dynamically adjusting the electric energy distribution scheme according to the real-time status and predicted load; an electric energy distribution control unit: controlling the electric energy distribution process to ensure the fairness and efficiency of electric energy distribution.

[0016] The present invention also provides a superconducting cable layout method based on fractal geometry and topology optimization, including the following steps: data collection: collecting relevant information such as urban geographical information, population distribution, and electricity consumption historical data; fractal geometry design: using fractal geometry software tools to determine the arrangement of cables in underground pipelines based on the self-similarity and iterative generation principle of fractal geometry; topology optimization: applying topology optimization algorithms to determine the connection paths between nodes such as substations and distribution rooms according to the physical characteristics and connection requirements of the cables; layout adjustment: dynamically adjusting the cable layout structure by adjusting the fractal geometry design parameters and topological structure according to urban development plans and load growth predictions.

[0017] The present invention also provides a superconducting material performance evaluation method based on tensor analysis and multi-physical field coupling, including the following steps: model establishment: constructing a multi-physical field coupling model, including electromagnetic field, thermal field, and stress field; parameter acquisition: obtaining the electromagnetic characteristics, thermal stability, and mechanical stress distribution parameters of superconducting materials through experimental tests and theoretical analysis; tensor analysis: analyzing the performance of superconducting materials by using tensor analysis methods; performance evaluation: evaluating the performance of superconducting materials under different working conditions according to the tensor analysis results.

[0018] The present invention also provides a fault prediction method based on random matrix theory and deep learning, including the following steps: data acquisition: acquiring the current, voltage, and temperature parameters of the superconducting cable; random matrix construction: constructing a random matrix according to the acquired data; feature extraction: analyzing the statistical characteristics such as the eigenvalue distribution of the random matrix to extract potential fault features; deep learning training: training a large amount of random matrix data in normal and fault states using deep learning algorithms; fault prediction: predicting the fault type and time through the trained deep learning model.

[0019] The present invention also provides an electromagnetic compatibility design method based on quantum Monte Carlo and finite element analysis, including the following steps: system modeling: dividing the superconducting system into a finite number of units; quantum Monte Carlo calculation: accurately calculating the electromagnetic interaction of microscopic particles in the superconducting system using the quantum Monte Carlo method; finite element analysis: performing numerical simulation of the electromagnetic field for each unit; electromagnetic structure optimization: optimizing the electromagnetic structure design of the superconducting device according to the simulation results.

[0020] Beneficial effects:

[0021] V. Innovative algorithms (I) Multi-scale superconducting material performance prediction algorithm

[0022] In the superconducting material performance evaluation model based on tensor analysis and multi-physics field coupling, the multi-scale superconducting material performance prediction algorithm combines first-principles calculations at the atomic scale with tensor analysis at the macroscopic scale. By calculating the microscopic electronic structure information of the superconducting material through quantum mechanics, and then using this information as the input parameters of the macroscopic tensor model, multi-scale accurate prediction of the performance of the superconducting material under different temperatures, magnetic fields, and current densities is realized, providing strong support for the research and development and application of superconducting materials.

[0023] (II) Adaptive finite element electromagnetic simulation algorithm

[0024] In the electromagnetic compatibility design model based on quantum Monte Carlo and finite element analysis, the adaptive finite element electromagnetic simulation algorithm automatically adjusts the density of the finite element mesh according to the gradient change of the electromagnetic field. Fine meshes are used in regions where the electromagnetic field changes violently (such as the edges and corners of superconducting devices) to improve the simulation accuracy; sparse meshes are used in regions where the electromagnetic field changes gently to reduce the computational amount. This algorithm significantly improves the computational efficiency while ensuring the accuracy of electromagnetic simulation, accelerating the electromagnetic compatibility design process of superconducting devices.

[0025] (III) Intelligent superconducting cable layout algorithm

[0026] In the superconducting cable layout model based on fractal geometry and topology optimization, the intelligent superconducting cable layout algorithm utilizes artificial intelligence technology and big data analysis. According to the geographical information, population distribution, electricity consumption historical data, and future development plans of the city, it predicts the growth trend of power demand in different regions, and then based on the principles of fractal geometry and topology optimization, dynamically designs the layout path and connection method of superconducting cables, enabling the cable layout to adapt to the changes in urban development, improving the efficiency and reliability of power transmission, and reducing construction costs.

[0027] (IV) Reinforcement Learning Power Allocation Algorithm

[0028] In the power allocation optimization model based on variational inequality and dynamic programming, the reinforcement learning power allocation algorithm introduces an agent in reinforcement learning into the power allocation system. The agent interacts with the environment (such as various devices and user loads in the power grid), and learns the optimal power allocation strategy according to the reward signals feedback by the environment (such as reducing operating costs and improving power quality). This algorithm enables the power allocation system to automatically adapt to the complex and changeable operating environment, continuously optimize the allocation scheme, and improve the intelligent level and operating efficiency of the power grid.

[0029] (V) Random Matrix Deep Learning Fault Diagnosis Algorithm

[0030] In the fault prediction model based on random matrix theory and deep learning, the random matrix deep learning fault diagnosis algorithm conducts in-depth learning and analysis on the statistical characteristics such as the eigenvalue distribution of random matrices. By constructing a deep neural network and training a large amount of random matrix data in normal and fault states, the network can automatically identify the characteristic patterns related to faults, accurately predict the fault types and occurrence times in the superconducting power transmission system, and improve the fault warning ability and reliability maintenance level of the system.

[0031] Example 1

[0032] (I) Superconducting Cable Layout Model Based on Fractal Geometry and Topology Optimization

[0033] Due to the characteristics of self-similarity and iterative generation, fractal geometry can be used to design the layout structure of superconducting cables in complex environments, achieving optimal space filling and heat dissipation performance. Topology optimization optimizes the connection topology of the cable network from a macroscopic perspective, determines the best connection method between nodes such as substations and distribution rooms, so as to minimize power transmission losses and improve the reliability of the system. For example, in the urban power grid, fractal geometry is used to design the arrangement of cables in underground pipelines, and topology optimization is combined to determine the connection paths between substations in different regions, improving the power transmission efficiency and the stability of the urban power grid.

[0034] (II) Superconducting Material Performance Evaluation Model Based on Tensor Analysis and Multi-Physical Field Coupling

[0035] The performance of superconducting materials is affected by the interaction of multiple physical fields (such as electromagnetic fields, thermal fields, stress fields, etc.). Tensor analysis can uniformly describe the tensor characteristics and interaction relationships of these physical fields. By establishing a multi-physical field coupling model, based on tensor analysis, the electromagnetic characteristics (such as critical current, AC loss, etc.), thermal stability, and mechanical stress distribution of superconducting materials under different working conditions can be accurately evaluated. For example, when designing a large superconducting magnet for a nuclear fusion device, this model can accurately predict the performance changes of superconducting materials under strong magnetic fields, high currents, and complex thermal environments, providing a basis for the optimized design of superconducting magnets.

[0036] (III) Fault prediction model based on random matrix theory and deep learning

[0037] Use random matrix theory to construct a random matrix that characterizes the operating state of a superconducting power transmission system, and extract the potential fault characteristics of the system by analyzing statistical characteristics such as the eigenvalue distribution of the matrix. Deep learning algorithms (such as convolutional neural networks) then learn and classify these characteristics to predict the possible types and times of faults. For example, by monitoring parameters such as current, voltage, and temperature of a superconducting cable to construct a random matrix, a deep learning model can detect potential faults such as cable insulation aging and local quench of superconducting materials in advance, providing support for preventive maintenance and improving the reliability and safety of the system.

[0038] (IV) Electromagnetic compatibility design model based on quantum Monte Carlo and finite element analysis

[0039] The quantum Monte Carlo method is used to accurately calculate the electromagnetic interactions of microscopic particles in a superconducting system, especially having advantages when dealing with the quantum characteristics of superconducting materials and strongly correlated electron systems. Finite element analysis divides the superconducting system into a finite number of elements and conducts numerical simulations of the electromagnetic field for each element to determine the electromagnetic field distribution and electromagnetic compatibility performance of the system at the macroscopic scale. Combining the two can optimize the electromagnetic structure design of superconducting devices (such as superconducting transformers, superconducting fault current limiters, etc.), reducing electromagnetic interference and the electromagnetic impact on the external environment. For example, when designing a superconducting substation, this model can ensure the electromagnetic compatibility between superconducting devices in the station and with surrounding power equipment, improving the operating stability of the entire substation.

[0040] (V) Power distribution optimization model based on variational inequality and dynamic programming

[0041] Variational inequalities are used to describe the optimal solution problem under different constraint conditions (such as power balance constraints, voltage constraints, superconducting cable capacity constraints, etc.) in a superconducting power transmission and distribution system. Dynamic programming divides the power distribution process into multiple stages. According to the real-time state of the system and the predicted future load, the optimal power distribution strategy is selected at each stage to minimize the system operation cost and optimize the power quality. For example, in a smart grid, considering the intermittency of distributed power sources and the dynamic changes of user loads, this model can dynamically adjust the power transmission path and distribution ratio of superconducting cables to improve the economy and power supply reliability of the grid.

[0042] Example 2

[0043] (1) Superconducting material performance improvement and stability guarantee scheme

[0044] Focus on optimizing the superconducting material performance evaluation model based on tensor analysis and multi-physics field coupling, and the electromagnetic compatibility design model based on quantum Monte Carlo and finite element analysis. In the performance evaluation model, introduce a multi-scale simulation method, combine the first-principles calculation at the atomic scale and tensor analysis at the macroscopic scale to more accurately predict the performance changes of superconducting materials under complex working conditions, and provide a more accurate direction for material improvement. For the electromagnetic compatibility design model, adopt an adaptive mesh refinement technique to automatically adjust the density of the finite element mesh according to the gradient change of the electromagnetic field, improve the accuracy of electromagnetic simulation, reduce electromagnetic interference, and ensure the stability of the superconducting system.

[0045] (2) Power transmission and distribution efficiency optimization scheme

[0046] Focus on the superconducting cable layout model based on fractal geometry and topology optimization, and the power distribution optimization model based on variational inequalities and dynamic programming. In the cable layout model, develop an intelligent layout algorithm to dynamically adjust the layout structure of superconducting cables according to urban development plans and load growth predictions, and improve the cable utilization rate and power transmission efficiency. In the power distribution optimization model, combine a reinforcement learning algorithm to enable the system to autonomously learn and optimize the power distribution strategy according to real-time environmental feedback (such as electricity price fluctuations, changes in renewable energy output, etc.), further reduce the operation cost, and improve the flexibility and efficiency of power distribution.

[0047] Example 3

[0048] (1) Superconducting cable layout

[0049] According to the principles of fractal geometry and topological optimization, the layout of superconducting cables in the urban power grid is designed. First, information such as the geographical information, population distribution, and electricity consumption history data of the city is collected. Then, using the self-similarity and iterative generation characteristics of fractal geometry, the arrangement of the cables in the underground pipelines is determined. Through topological optimization, the connection paths between nodes such as substations and distribution rooms are determined to ensure that the cable layout can achieve optimal space filling and heat dissipation performance, and improve the power transmission efficiency.

[0050] (II) Performance Evaluation of Superconducting Materials

[0051] Using a model based on tensor analysis and multi-physics field coupling, the performance of superconducting materials under different working conditions is evaluated. Through experimental tests and theoretical analysis, parameters such as the electromagnetic characteristics, thermal stability, and mechanical stress distribution of superconducting materials are obtained. For example, when designing a large superconducting magnet, this model is used to simulate and analyze the performance of superconducting materials under strong magnetic fields, high currents, and complex thermal environments, providing a basis for the optimized design of superconducting magnets.

[0052] (III) Fault Prediction

[0053] Based on random matrix theory and deep learning algorithms, the faults of superconducting cables are predicted. By monitoring parameters such as the current, voltage, and temperature of superconducting cables, a random matrix is constructed and analyzed using a deep learning model. When potential fault characteristics are detected, early warning signals are sent in a timely manner to provide support for preventive maintenance and improve the reliability and safety of the system.

[0054] (IV) Electromagnetic Compatibility Design

[0055] A method combining quantum Monte Carlo and finite element analysis is adopted for the electromagnetic compatibility design of superconducting equipment. The superconducting system is divided into a finite number of units. The quantum Monte Carlo method is used to calculate the electromagnetic interactions of microscopic particles, and at the same time, the finite element analysis is used to perform numerical simulations of the electromagnetic fields of each unit. By optimizing the electromagnetic structure design, electromagnetic interference and the electromagnetic impact on the external environment are reduced to ensure the electromagnetic compatibility between superconducting equipment and between superconducting equipment and surrounding power equipment.

[0056] (V) Optimization of Power Distribution

[0057] According to the principles of variational inequality and dynamic programming, the power distribution of superconducting cables is optimized. In the smart grid, considering the intermittency of distributed power sources and the dynamic changes of user loads, an optimal power distribution strategy is formulated. By real-time monitoring the operating status of the system and future predicted loads, the power transmission paths and distribution ratios of superconducting cables are dynamically adjusted to minimize the system operating cost and optimize the power quality.

[0058] Example 4

[0059] Modeling and Solving Process

[0060] (I) Superconducting Cable Layout Model Based on Fractal Geometry and Topological Optimization

[0061] 1. Fractal Geometry Modeling

[0062] 1. Determine the fractal dimension: According to the urban geographical information and cable layout requirements, select an appropriate fractal dimension. For example, in the layout of urban underground pipelines, two-dimensional fractal geometry can be adopted, and the cable arrangement pattern can be determined through an iterative generation method.

[0063] 2. Construct the fractal structure: Using fractal geometry software tools, based on the initial point, perform iterative generation according to the fractal rules. For example, in the fractal structure, each point has self-similarity, and through continuous iterative generation, a complex cable layout is formed.

[0064] 3. Optimize the fractal parameters: According to actual needs, adjust the fractal parameters, such as the fractal ratio, the number of iterations, etc., to achieve optimal space filling and heat dissipation performance.

[0065] 2. Topological Optimization Modeling

[0066] 1. Establish the topological network: Abstract nodes such as substations and distribution rooms as topological nodes, and the cables connecting the nodes are regarded as topological edges. Establish a topological network to ensure that the connection paths between nodes can meet the power transmission requirements.

[0067] 2. Select the topological algorithm: Adopt appropriate topological algorithms, such as the shortest path algorithm, the minimum spanning tree algorithm, etc., to optimize the topological structure. For example, determine the optimal connection path between substations through the shortest path algorithm to reduce power transmission losses.

[0068] 3. Adjust the topological structure: According to the actual situation, adjust the topological structure, such as adding nodes, changing the connection method, etc., to adapt to urban development and changes in power demand.

[0069] (II) Superconducting Material Performance Evaluation Model Based on Tensor Analysis and Multi-Physical Field Coupling

[0070] 1. Tensor Analysis Modeling

[0071] 1. Define tensor elements: According to the physical characteristics of superconducting materials, define tensor elements. For example, in the analysis of electromagnetic characteristics, tensor elements can include current density, magnetic field strength, etc.

[0072] 2. Construct the tensor matrix: Arrange the tensor elements into a tensor matrix according to certain rules. For example, in a multi-physical field coupling model, the tensor matrix can represent the interaction relationship between different physical fields.

[0073] 3. Tensor operations: Perform operations on the tensor matrix, such as tensor multiplication, tensor derivation, etc., to analyze the performance of superconducting materials.

[0074] 2. Multi-physics field coupling modeling

[0075] 1. Establish a physical field model: Establish physical field models for the electromagnetic field, thermal field, stress field, etc. respectively. For example, in the electromagnetic field model, the interaction between the electric field and the magnetic field is described by Maxwell's equations.

[0076] 2. Couple physical fields: Couple different physical field models to establish a multi-physics field coupling model. For example, in the thermal field and electromagnetic field coupling model, analyze the electromagnetic properties of superconducting materials at different temperatures through the interaction of the heat conduction equation and the electromagnetic induction equation.

[0077] 3. Solve the coupled equations: Use numerical methods to solve the coupled equations, such as the finite element method, boundary element method, etc. By solving the coupled equations, obtain the performance parameters of superconducting materials under different working conditions.

[0078] (III) Fault prediction model based on random matrix theory and deep learning

[0079] 1. Random matrix modeling

[0080] 1. Construct a random matrix: According to the operating parameters of the superconducting cable, such as current, voltage, temperature, etc., construct a random matrix. For example, use parameters such as current and voltage as matrix elements to form a random matrix.

[0081] 2. Determine matrix characteristics: Analyze the eigenvalue distribution, determinant and other characteristics of the random matrix. For example, analyze the stability of the random matrix through the eigenvalue distribution.

[0082] 3. Generate a random matrix: Generate a random matrix according to the characteristics of the random matrix. For example, generate a random matrix through a random number generator.

[0083] 2. Deep learning modeling

[0084] 1. Select a deep learning algorithm: Select a suitable deep learning algorithm, such as convolutional neural network (CNN), recurrent neural network (RNN), etc. For example, in the fault prediction model, use the CNN algorithm to extract features and classify the random matrix.

[0085] 2. Train the model: Use a large amount of random matrix data in normal and fault states as training samples to train the deep learning model. For example, through training the model, enable the model to identify fault features.

[0086] 3. Optimize the model: According to the training results, adjust the model parameters to optimize the model performance. For example, by adjusting the weights and biases of the model, improve the prediction accuracy of the model.

[0087] (4) Electromagnetic Compatibility Design Model Based on Quantum Monte Carlo and Finite Element Analysis

[0088] 1. Quantum Monte Carlo Modeling

[0089] 1. Determine the quantum state: According to the quantum characteristics of superconducting materials, determine the quantum state. For example, in superconducting materials, the quantum state can represent the spin state of electrons.

[0090] 2. Simulate the quantum process: Use the quantum Monte Carlo method to simulate quantum processes such as quantum tunneling and quantum entanglement. For example, by simulating the quantum process, analyze the electromagnetic characteristics of superconducting materials.

[0091] 3. Calculate the quantum probability: Calculate the probability distribution of the quantum state, such as the probability density of the quantum state. For example, by calculating the quantum probability, determine the electromagnetic characteristics of superconducting materials in different states.

[0092] 2. Finite Element Analysis Modeling

[0093] 1. Establish a finite element model: Divide the superconducting system into a finite number of elements, and each element has a certain shape and size. For example, in the finite element model, the elements can be triangles, quadrilaterals, etc.

[0094] 2. Determine the boundary conditions: According to the actual situation, determine the boundary conditions. For example, in the superconducting system, the boundary conditions can include the electric field strength, magnetic field strength, etc.

[0095] 3. Solve the finite element equation: Use the finite element method to solve the finite element equation, such as Poisson's equation, Laplace's equation, etc. By solving the finite element equation, obtain the electromagnetic field distribution of the superconducting system.

[0096] (5) Optimal Power Allocation Model Based on Variational Inequality and Dynamic Programming

[0097] 1. Variational Inequality Modeling

[0098] 1. Define the variational inequality: According to the constraint conditions of the power transmission and distribution system, define the variational inequality. For example, under conditions such as power balance constraint and voltage constraint, the variational inequality can be expressed as a function of power allocation.

[0099] 2. Solve the variational inequality: Use numerical methods to solve the variational inequality, such as the Lagrange multiplier method, dual method, etc. By solving the variational inequality, obtain the optimal solution of power allocation.

[0100] 2. Dynamic Programming Modeling

[0101] 1. Determine state variables: Divide the power distribution process into multiple stages and determine the state variables. For example, in each stage, the state variables can include the power distribution amount, load status, etc.

[0102] 2. Establish the state transition equation: Based on the relationship between state variables, establish the state transition equation. For example, in the dynamic programming model, the state transition equation can represent the relationship between the power distribution amount and the load status.

[0103] 3. Solve the dynamic programming equation: Use the dynamic programming method to solve the dynamic programming equation, such as the Bellman equation, optimal control equation, etc. By solving the dynamic programming equation, obtain the optimal strategy for power distribution.

[0104] The present invention proposes an efficient power transmission and distribution system based on a new type of superconducting material. By adopting technologies such as fractal geometry and topological optimization, tensor analysis and multi-physics field coupling, random matrix theory and deep learning, quantum Monte Carlo and finite element analysis, and variational inequality and dynamic programming, the improvement of power transmission efficiency, the optimization of power distribution, and the enhancement of system reliability and stability are achieved. These innovative algorithms provide new ideas and methods for the development of power systems and have important application values.

Claims

1. A highly efficient power transmission and distribution system based on new superconducting materials, characterized in that: The system structure consists of: It includes superconducting cable layout module, superconducting material performance evaluation module, fault prediction module, electromagnetic compatibility design module and power distribution optimization module; The superconducting cable layout module: based on the principles of fractal geometry and topology optimization, and in accordance with information such as urban geographic information, population distribution, and historical electricity consumption data, designs the arrangement of superconducting cables in underground pipelines and the connection paths between nodes such as substations and distribution rooms to achieve optimal space filling and heat dissipation performance; The superconducting material performance evaluation module: based on tensor analysis and multi-physics field coupling model, accurately evaluates the electromagnetic properties, thermal stability and mechanical stress distribution of superconducting materials under different working conditions; The fault prediction module: uses random matrix theory to construct a random matrix, combines deep learning algorithms to monitor the operating status of superconducting cables, and predicts fault types and times; the electromagnetic compatibility design module: combines quantum Monte Carlo method with finite element analysis to optimize the electromagnetic structure design of superconducting equipment and reduce electromagnetic interference and electromagnetic impact on the external environment; The power distribution optimization module: based on variational inequality and dynamic programming principles, formulates the optimal power distribution strategy according to the real-time status of the system and the future predicted load, so as to minimize the system operation cost and optimize the power quality; The interaction relationship between the modules is as follows: the superconducting cable layout module provides basic layout information for the power distribution optimization module; the superconducting material performance evaluation module provides material performance parameters for the fault prediction module and the electromagnetic compatibility design module; The fault prediction module provides fault information to the power distribution optimization module so as to adjust the distribution strategy in time; the electromagnetic compatibility design module ensures the electromagnetic compatibility of the superconducting equipment with the surrounding environment during operation.

2. A highly efficient power transmission and distribution system based on a novel superconducting material as claimed in claim 2, characterized in that: The superconducting cable layout module includes a fractal geometry design unit: based on the fractal geometry principle, the arrangement pattern of the cable in the underground pipeline is determined through iterative generation to achieve optimal space filling and heat dissipation performance; a topology optimization unit: based on the topology optimization algorithm, the connection path between nodes such as substations and distribution rooms is determined to ensure that the power transmission loss is minimized; a layout adjustment unit: according to the urban development plan and load growth forecast, the cable layout structure is dynamically adjusted.

3. A high-efficiency electric energy transmission and distribution system based on a new superconducting material as claimed in claim 2, characterized in that: The superconducting material performance evaluation module includes an electromagnetic characteristic analysis unit: based on the tensor analysis method, the electromagnetic characteristics of the superconducting material under different magnetic fields and current densities are accurately evaluated; a thermal stability analysis unit: through a multi-physical field coupling model, the stability of the superconducting material under the action of the thermal field is analyzed; Mechanical stress analysis unit: Combined with the principles of material mechanics, evaluate the mechanical stress distribution of superconducting materials under different stress conditions; Performance parameter acquisition unit: Obtain parameters such as electromagnetic properties, thermal stability and mechanical stress distribution of superconducting materials through experimental testing and theoretical analysis.

4. A high-efficiency electric energy transmission and distribution system based on a new superconducting material as claimed in claim 2, characterized in that: The fault prediction module includes a random matrix generation unit: constructing a random matrix according to the operating parameters of the superconducting cable; Deep learning algorithm unit: trains random matrices and predicts fault type and time by identifying statistical characteristics such as matrix eigenvalue distribution; fault information processing unit: processes fault prediction results and issues early warning signals in a timely manner.

5. A high-efficiency electric energy transmission and distribution system based on a new superconducting material as claimed in claim 2, characterized in that: The electromagnetic compatibility design module includes a quantum Monte Carlo simulation unit: using the quantum Monte Carlo method to accurately calculate the electromagnetic interaction of microscopic particles in the superconducting system; a finite element analysis unit: dividing the superconducting system into a finite number of units, and performing electromagnetic field numerical simulation on each unit; Electromagnetic structure optimization unit: optimizes the electromagnetic structure design of superconducting equipment based on simulation results; Electromagnetic Interference Assessment Unit: Assess the degree of electromagnetic interference caused by superconducting equipment to the surrounding environment during operation.

6. A highly efficient power transmission and distribution system based on a novel superconducting material as claimed in claim 2, characterized in that: The power distribution optimization module includes a real-time monitoring unit: to obtain the operating status and load information of the system in real time; a strategy formulation unit: to formulate the optimal power distribution strategy based on variational inequalities and dynamic programming principles; Dynamic adjustment unit: dynamically adjust the power distribution plan according to the real-time status and predicted load; Power distribution control unit: controls the power distribution process to ensure the fairness and efficiency of power distribution.

7. A superconducting cable layout method based on fractal geometry and topology optimization, characterized in that: The process includes the following steps: data collection: collecting relevant information such as urban geographic information, population distribution, and electricity consumption history data; fractal geometry design: using fractal geometry software tools to determine the arrangement of cables in underground pipelines based on the self-similarity and iterative generation principles of fractal geometry; topology optimization: using topology optimization algorithms to determine the connection paths between nodes such as substations and distribution rooms based on the physical properties and connection requirements of cables; layout adjustment: dynamically adjusting the cable layout structure by adjusting fractal geometry design parameters and topology structures based on urban development plans and load growth forecasts.

8. A superconducting material performance evaluation method based on tensor analysis and multi-physical field coupling, characterized in that: The model establishment process includes the following steps: constructing a multi-physical field coupling model, including electromagnetic field, thermal field, and stress field; parameter acquisition: obtaining the electromagnetic properties, thermal stability, and mechanical stress distribution parameters of superconducting materials through experimental testing and theoretical analysis; tensor analysis: using tensor analysis methods to analyze the performance of superconducting materials; performance evaluation: evaluating the performance of superconducting materials under different working conditions based on the tensor analysis results.

9. A fault prediction method based on random matrix theory and deep learning, characterized in that: The following steps are included: data collection: collecting the current, voltage and temperature parameters of the superconducting cable; random matrix construction: constructing a random matrix based on the collected data; feature Extraction: Analyze the statistical characteristics of random matrices such as eigenvalue distribution to extract potential fault features; Deep learning training: Use deep learning algorithms to train a large amount of random matrix data in normal and fault states; Fault prediction: Predict fault type and time through trained deep learning models.

10. An electromagnetic compatibility design method based on quantum Monte Carlo and finite element analysis, characterized in that: It includes the following steps: system modeling: dividing the superconducting system into a finite number of units; quantum Monte Carlo calculation: using the quantum Monte Carlo method to accurately calculate the electromagnetic interactions of microscopic particles in the superconducting system; finite element analysis: numerical simulation of the electromagnetic field for each unit; electromagnetic structure optimization: optimizing the electromagnetic structure design of the superconducting device based on the simulation results.