Power equipment operation optimization method and system based on digital twinning

Abstract

The invention relates to the technical field of equipment operation optimization, in particular to an electric power equipment operation optimization method and system based on digital twinning. The method comprises the following steps: acquiring operation data of physical power equipment through a sensor; building a digital twinborn model of the power equipment by utilizing modeling software Simulink based on the operation data; establishing bidirectional data streams from the physical power equipment to the digital twinborn model and from the digital twinborn model to the physical power equipment, and performing data synchronization; performing state estimation on the synchronized digital twin model by using a particle filtering algorithm, and introducing a physical degradation model of equipment aging in the state estimation process; and based on the estimated state, optimizing the operation condition of the equipment through a particle swarm optimization algorithm. According to the design of the invention, the digital twinborn model synchronized with the physical power equipment is constructed, and the particle filter algorithm is utilized to carry out accurate state estimation, so that the operation state of the power equipment can be monitored in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment operation optimization, and in particular to a method and system for optimizing the operation of electric power equipment based on digital twins. Background Art

[0002] Traditional power equipment operation and maintenance usually relies on regular inspections and responsive maintenance after a fault occurs. This method cannot foresee potential faults and may cause sudden equipment downtime, increasing maintenance costs and time, and reducing system reliability. With the increasing complexity and scale of power networks, efficient and fine management of power equipment has become increasingly difficult. Traditional methods often have difficulty providing sufficient data support to make optimal decisions, resulting in inefficient resource utilization. Power equipment will undergo a natural aging process during long-term use, which will affect its performance. If these factors are not considered, it may lead to inaccurate state estimation, which in turn affects the safety and efficiency of the equipment. Power equipment will undergo a natural aging process during long-term use, which will affect its performance. If these factors are not considered, it may lead to inaccurate state estimation, which in turn affects the safety and efficiency of the equipment. Therefore, a method and system for optimizing the operation of power equipment based on digital twins are provided. Summary of the invention

[0003] The purpose of the present invention is to provide a method and system for optimizing the operation of power equipment based on digital twins, so as to solve the problems raised in the above background technology that traditional power equipment lacks real-time monitoring and predictive maintenance, is difficult to achieve refined management, and is affected by equipment aging and performance degradation.

[0004] To achieve the above object, the present invention aims to provide a method for optimizing the operation of electric power equipment based on digital twins, comprising: S1. Collecting operation data of physical power equipment through sensors; S2. Use the modeling software Simulink to build a digital twin model of power equipment based on operating data; S3. Establish a bidirectional data flow from the physical power equipment to the digital twin model and from the digital twin model back to the physical power equipment for data synchronization; S4. Use the particle filter algorithm to perform state estimation on the synchronized digital twin model, and introduce the physical degradation model of equipment aging in the process of state estimation; S5. Based on the estimated state, the operating status of the equipment is optimized by a particle swarm optimization algorithm.

[0005] As a further improvement of the technical solution, in S1, the operation data of the power equipment includes: current ,Voltage , speed .

[0006] As a further improvement of the technical solution, in S2, a digital twin model of the power equipment is constructed based on the operation data using the modeling software Simulink, including the following steps: S2.1. Preprocessing the collected power equipment operation data; S2.2, import the collected current, voltage and speed data through the Simulink input module; S2.3. Build a digital twin model of power equipment in Simulink.

[0007] As a further improvement of the technical solution, in S2.3, the digital twin model of the power equipment is: Electrical dynamic equations: ; Mechanical dynamics equation: ; in, Represents resistance; represents inductance; Represents capacitance; Indicates instantaneous torque; Indicates the speed of the electrical equipment; represents the moment of inertia; represents the damping coefficient; Indicates the moment; Represents the differential symbol.

[0008] As a further improvement of the technical solution, in S3, a bidirectional data flow is established from the physical device to the digital twin model and from the digital twin model back to the physical device to perform data synchronization, including the following steps: S3.1. Transmit the collected operation data to the digital twin platform through the communication protocol: ; in, Indicates time Operational data transferred from physical power equipment to the digital twin model; S3.2. After receiving the operating data from the physical power equipment, parse the communication protocol and convert the operating data into a format acceptable to the Simulink model ; S3.3. Input the parsed data into the digital twin model and update the state variables of the digital twin model.

[0009] As a further improvement of the technical solution, in S4, the state estimation of the synchronized digital twin model is performed using a particle filter algorithm, including the following steps: S4.1. Generate a set of initial particles based on historical power equipment status information: ; in, Indicates The initial state of a particle, The covariance matrix representing the initial state represents the particle index, Indicates the initial state of the power equipment; S4.2. At every moment Use the system's state transition equation to predict the state of each particle; S4.3. Based on real-time observation data received from physical devices and converted into a format acceptable to the model , update the weight of each particle by measuring the model: ; in, Indicates at time The predicted observed value of represents the mapping function from the state vector of the power device to the observed data; represents the observation noise; A state vector representing the power device; S4.4. Update particle weights based on observations : ; in, represents the observation likelihood function; S4.5. At every moment The state estimation of power equipment is calculated by weighted average. In view of the aging of power equipment, the physical degradation model of equipment aging is introduced into the weighted average calculation, and the weighted average calculation is further optimized in view of the insulation degradation of power equipment components.

[0010] As a further improvement of the technical solution, in S4.5, the state estimation of the device is calculated by weighted average as follows: ; in, Indicates the power equipment at the time state estimation; Indicates Particles at time Status; Indicates Particles at time The weight of represents the total number of particles; In view of the aging of power equipment, the physical degradation model of equipment aging is introduced in the weighted average calculation: ; in, It represents the state estimation after introducing the physical degradation model of equipment aging; Indicates the aging coefficient of power equipment; Physical degradation models that represent equipment aging; The weighted average calculation method is further optimized for insulation degradation of power equipment components: ; in, represents a further optimized state estimate; Indicates the insulation degradation coefficient; Indicates Particles at time Insulation degradation function.

[0011] As a further improvement of the technical solution, in S5, based on the estimated state, the operation status of the equipment is optimized by a particle swarm optimization algorithm, including the following steps: S5.1. Determine the size of the particle swarm and the maximum number of iterations ; S5.2. Initialize the position of each particle and speed , and initialize the individual optimal position of each particle and the global optimal position ; S5.3, construct a fitness function in combination with the state estimation of the power equipment, and optimize the fitness function considering the spatial position of the power equipment; S5.4. Update particle velocity and position and calculate the new fitness value of the current particle ; S5.5. Update individual and global optimality: if , then update the individual optimal: ; if , then update the global optimum: ; S5.6. Determine whether the maximum number of iterations has been reached ; S5.7, according to the final global optimal position , adjust the operating parameters of the power equipment to make them close to the optimal state: ; S5.8. Apply the optimized operating parameter settings to actual power equipment.

[0012] As a further improvement of the technical solution, in S5.3, the fitness function is: ; in, Represents the fitness function value for the operating state of the power equipment; Indicates the number of components of the device; Indicates The impedance of each component; Indicates The current of each component; Indicates The height of the component; Indicates The voltage of each component; represents the weight of resistance loss; represents the height-adjusted resistance loss weight; represents the weight of the efficiency factor; Represents the index of the device component; Considering the spatial location of power equipment, the fitness function is optimized: ; in, Indicates the spatial distribution location of the device; represents the spatial objective function; Represents the weight coefficient of spatial distribution.

[0013] On the other hand, the present invention provides an electric power equipment operation optimization system based on digital twins, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any one of the above-mentioned electric power equipment operation optimization methods based on digital twins.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. In the power equipment operation optimization method and system based on digital twin, by building a digital twin model synchronized with the physical power equipment and using the particle filter algorithm for accurate state estimation, the method can monitor the operating status of the power equipment in real time, including electrical performance, thermal performance, and aging of insulation materials. This allows the system to promptly detect potential problems, such as increased risk of failure due to insulation degradation, so that maintenance measures can be taken in advance, reducing the probability of sudden failures and improving the safety and reliability of power equipment operation.

[0015] 2. In the power equipment operation optimization method and system based on digital twin, the particle swarm optimization algorithm is used to adjust the operating parameters of the power equipment. Based on the fitness function that takes into account factors such as equipment aging and spatial position, it ensures that the optimization process not only focuses on the current operating efficiency, but also takes into account the long-term performance degradation. This can not only reduce energy consumption, but also extend the service life of the equipment and improve the overall operational efficiency. In addition, by optimizing the collaborative relationship between different equipment, the comprehensive efficiency of the power system is further improved, and the optimal allocation and utilization of resources is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The figure is a flow chart of the overall method of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Example 1: As a wind turbine located in a remote area is exposed to a harsh environment for a long time, it begins to experience performance degradation, specifically reduced efficiency, unstable output power, and increased failure frequency; these problems lead to increased maintenance costs and reduced energy production. In addition, due to the remote location, regular manual inspections are expensive and time-consuming, so a more effective monitoring and optimization method is needed; See also Figure 1 As shown, this embodiment provides a method for optimizing the operation of power equipment based on digital twins, comprising the following steps: S1. Collecting operation data of physical power equipment through sensors; In this embodiment, the operating data of the power equipment includes: current ,Voltage , speed .

[0019] S2. Use the modeling software Simulink to build a digital twin model of the power equipment based on the operating data, so that it can reflect the operating status of the equipment in real time; In this embodiment, Simulink can achieve real-time data synchronization between physical power equipment and its digital twin, allowing engineers to monitor equipment status in real time and adjust model parameters based on the latest operating data; Simulink provides a rich component library and toolbox that can be used to create highly accurate power equipment models, including electrical, thermodynamic and other dynamic characteristics; combined with advanced technologies such as particle filtering algorithms, Simulink can help perform more accurate state estimation and future trend prediction, which is crucial for preventive maintenance; The digital twin model of power equipment is constructed based on the operating data using the modeling software Simulink, which includes the following steps: S2.1. Preprocess the collected power equipment operation data, including filtering, denoising, etc.; S2.2, import the collected current, voltage and speed data through the input module of Simulink (such as signal source, sensor module, etc.); S2.3. Build a digital twin model of the power equipment in Simulink. Use the components and toolboxes provided by Simulink to build the model. The model should be able to simulate the real behavior of the power equipment, including but not limited to electrical performance, thermal performance and other dynamic characteristics. Furthermore, the digital twin model of power equipment is: Electrical dynamic equations: ; Mechanical dynamics equation: ; in, Represents resistance; represents inductance; It represents capacitance, which is the ability of a capacitor to store charge; Indicates instantaneous torque; Indicates the speed of the electrical equipment; represents the moment of inertia; represents the damping coefficient; Indicates the moment; Represents the differential symbol.

[0020] S3. Establish a bidirectional data flow from the physical power equipment to the digital twin model and from the digital twin model back to the physical power equipment to synchronize data and allow real-time monitoring and control instruction delivery; In this embodiment, by transmitting the operating data of the physical device (such as current, voltage, temperature, etc.) to the digital twin model, it can be ensured that the model always reflects the latest device status; the digital twin model can generate optimal control instructions based on the current status, and send these instructions back to the physical device through the data stream to achieve automatic adjustment and optimization; the bidirectional data stream allows the digital twin model to continuously learn and adapt to changes in actual operating conditions, and improve its simulation accuracy of the dynamic behavior of power equipment; the digital twin model can be used for simulation and analysis to find the best operating parameter settings, and these optimized parameters can be applied to the physical equipment through the data stream to improve efficiency or reduce costs; Establishing a bidirectional data flow from the physical device to the digital twin model and from the digital twin model back to the physical device for data synchronization includes the following steps: S3.1. Transmit the collected operation data to the digital twin platform through the communication protocol (MQTT): ; in, Indicates time Operational data transferred from physical power equipment to the digital twin model; S3.2. After receiving the operating data from the physical power equipment, parse the communication protocol and convert the operating data into a format acceptable to the Simulink model ; S3.3. Input the parsed data into the digital twin model and update the state variables of the digital twin model.

[0021] S4. Use the particle filter algorithm to perform state estimation on the synchronized digital twin model, and introduce the physical degradation model of equipment aging in the process of state estimation; In this embodiment, the particle filter algorithm approximates the probability distribution of the system through a set of random samples (particles), and updates the states and weights of these particles in real time according to new observation data, thereby realizing the estimation of the system state; the particle filter algorithm does not need to assume that the system is linear or the observation noise is Gaussian distributed, so it is very suitable for complex nonlinear systems such as power equipment, and can adapt to various types of probability distributions, not just Gaussian distributions, which makes it more flexible and accurate in dealing with uncertainties in the real world; the particle filter can update the state estimate of the system in real time based on the latest observation data, ensuring that the digital twin model is always synchronized with the physical equipment, and by continuously adjusting the particle weights, the particle filter algorithm can automatically adapt to changes in the system, including factors such as equipment aging and changes in environmental conditions; the particle filter provides a probability density function for state estimation, which not only gives the most likely state, but also provides information about the uncertainty of the estimate, which is helpful for risk assessment and decision making; The particle filter algorithm is used to estimate the state of the synchronized digital twin model, including the following steps: S4.1. Generate a set of initial particles based on historical power equipment status information: ; in, Indicates The initial state of a particle, represents the covariance matrix of the initial state (reflecting the uncertainty of the initial estimate), represents the particle index, Indicates the initial state of the power equipment; S4.2. At every moment The state of each particle is predicted using the system's state transition equation (a mathematical formula that describes how a system evolves from one point in time to the next). The prediction step enables the particle filter to make forward-looking estimates of future states based on the latest control inputs and process models, without relying on immediate observations. The generated set of new particles will serve as the basis for the next correction step, where they adjust their weights based on new observations to more accurately reflect the state of the real system: ,in, represents control input; represents process noise; S4.3. Based on real-time observation data received from physical devices and converted into a format acceptable to the model , update the weight of each particle by measuring the model: ; in, Indicates at time The predicted observed value of The mapping function from the state vector of the power device to the observed data is used to convert the internal state vector of the power device into directly measurable observed data, such as voltage, current, and temperature. represents observation noise, which is usually assumed to be a random variable with zero mean, reflecting the inevitable uncertainty or error in the observation process; A state vector representing the power device; S4.4. Update particle weights based on observations : ; in, represents the observation likelihood function, that is, given the particle state When the observation data The probability density of ,in, represents the covariance matrix of the observation noise; S4.5. At every moment , the state estimation of power equipment is calculated by weighted average. In view of the aging of power equipment, the physical degradation model of equipment aging is introduced into the weighted average calculation, and the weighted average calculation is further optimized for the insulation degradation of power equipment components; Furthermore, the main purpose of calculating the state estimate of power equipment by weighted averaging is to integrate the information of all particles to obtain a more accurate and reliable system state estimate. This method can effectively process multiple possible states (i.e., particles) generated by the particle filter algorithm and integrate them into a single optimal estimate; each particle represents a possible state of the system, and these particles are generated based on different assumptions and random sampling. Through weighted averaging, these scattered possibilities can be brought together to form a more accurate overall estimate; the state estimate of a single particle may be affected by noise or model errors, but through weighted averaging, these outliers can be smoothed out to obtain a more stable result; compared to directly selecting the optimal particle as the final estimate, weighted averaging utilizes the information of all particles, avoids wasting computing resources, and improves the overall efficiency of the algorithm; The state estimate of the device is calculated by weighted average: ; in, Indicates the power equipment at the time state estimation; Indicates Particles at time The state of represents the estimation of the device state by each particle in the particle filter algorithm; Indicates Particles at time The weight of represents the total number of particles; Over time, the performance of electrical equipment will decline due to aging and wear. The aging process will cause changes in the electrical performance, thermal performance, etc. of the equipment, which will directly affect its operating efficiency and safety. By introducing a degradation model, these changes can be taken into account during state estimation, so that the digital twin model can more accurately reflect the current state of the equipment; equipment aging and degradation may cause deviations between some initial observations and actual performance. For example, the degradation of insulating materials will affect the response of current and voltage, which in turn affects the state estimation of the digital twin model. If these degradation factors are not considered, the estimated results may deviate from the actual situation. By introducing aging degradation factors in the weighted average, this deviation can be corrected and the accuracy of the estimate can be improved; In view of the aging of power equipment, the physical degradation model of equipment aging is introduced in the weighted average calculation: ; ; in, It represents the state estimation after introducing the physical degradation model of equipment aging; Indicates the aging coefficient of power equipment; Physical degradation models that represent equipment aging; Indicates time The power output represents the power performance of the device at different time points; Indicates the maximum power output of the device, usually the rated power of the device, used to calculate the relative degree of degradation; The insulating materials in power equipment (such as transformers, cables, switchgear, etc.) will gradually deteriorate as the usage time increases. This degradation will cause the insulation performance of the equipment to decrease, increase the risk of failures (such as electrical breakdown, short circuit, leakage, etc.), and may even cause the equipment to shut down. In order to accurately reflect the true state of the equipment, the impact of insulation degradation must be taken into account in the state estimation; insulation degradation will cause changes in operating parameters such as current and voltage, which will directly affect the operating state of the equipment. If insulation degradation is ignored, it will lead to deviations in the state estimation results, affecting the judgment of the health status of the equipment. By introducing the insulation degradation factor in the weighted average calculation, the state estimation can more truly reflect the current performance of the equipment and increase the accuracy of the prediction; The weighted average calculation method is further optimized for insulation degradation of power equipment components: ; ; in, represents a further optimized state estimate; Indicates the insulation degradation coefficient, which reflects the degree of influence of insulation material degradation on the equipment status estimation. It is a positive number, representing the influence of insulation degradation on the performance of power equipment. Indicates Particles at time The insulation degradation function represents the The degree of influence of individual particles on insulation degradation; The coefficient representing the deterioration of the insulation material is a positive number used to adjust the speed of insulation degradation; Indicates Particles at time The insulation degradation rate indicates the degree of degradation of the insulation material at that moment and is related to factors such as temperature, humidity, and usage load.

[0022] S5. Based on the estimated state, the operating status of the equipment is optimized by a particle swarm optimization algorithm; In this embodiment, the particle swarm optimization (PSO) algorithm is an optimization method based on swarm intelligence, in which multiple particles fly in the solution space and adjust their own trajectories according to the historical best positions of individuals and the group to find the global optimal solution; the PSO algorithm uses swarm intelligence and can effectively search in a larger solution space through the update mechanism of individual and global optimal positions, reducing the risk of falling into the local optimum. For problems with multiple potential optimal solutions, PSO can explore different solution areas and find the global optimum or multiple high-quality solutions; since each particle is guided by its own historical best position and the global best position of the group, the PSO algorithm can usually converge to the vicinity of the optimal solution at a faster speed. As the number of iterations increases, the particle swarm will gradually concentrate on the most promising solution area, further accelerating the convergence speed; Based on the estimated state, the particle swarm optimization algorithm is used to optimize the operating status of the equipment, including the following steps: S5.1. Determine the size of the particle swarm and the maximum number of iterations ; S5.2. Initialize the position of each particle and speed , and initialize the individual optimal position of each particle and the global optimal position ; S5.3, construct a fitness function in combination with the state estimation of the power equipment, and optimize the fitness function considering the spatial position of the power equipment; The main purpose of constructing a fitness function in combination with the state estimation of power equipment is to provide a quantitative standard in the particle swarm optimization (PSO) algorithm to evaluate the quality of the solution represented by each particle. This fitness function is the core of the optimization process, which guides how particles update their positions and velocities to find the optimal solution; by incorporating the latest state estimation results into the fitness function, it can ensure that the optimization process is always based on the most realistic power equipment operation data. This makes the optimization plan closer to the actual situation and improves its practicality and effectiveness; combined with state estimation, the fitness function can more accurately assess potential risks, such as failure probability or safety issues, and help formulate more reasonable maintenance plans and emergency plans; Furthermore, the fitness function is: ; in, Represents the fitness function value for the operating state of the power equipment; Indicates the number of components of the device; Indicates The impedance of each component; Indicates The current of each component; Indicates The height of the component; Indicates The voltage of each component; represents the weight of resistance loss; represents the height-adjusted resistance loss weight; represents the weight of the efficiency factor; Represents the index of the device component; The equipment in the power system is usually distributed, and the operating status of each device is not only affected by its own parameters, but also by the operating status of other devices, physical location and network topology; spatial distribution affects the transmission path and efficiency of electricity. Optimizing spatial factors can help reduce transmission losses and improve system operating efficiency; the transmission efficiency of the power system is affected by the layout between devices, especially in distributed power systems (such as microgrids or regional power grids). Optimizing the location of equipment can help reduce the complexity of the transmission path and improve the transmission efficiency of power flow; long-distance power transmission is not only prone to increased losses, but may also cause system instability due to long lines or unreasonable layout. By optimizing the location of equipment, the impact of long connections on system stability can be reduced. At the same time, reasonable spatial layout can help reduce the risk of large-scale chain failures caused by single-point failures; Considering the spatial location of power equipment, the fitness function is optimized: ; ; in, Indicates the spatial distribution location of the device; Represents the spatial objective function, which is used to optimize the synergy between different devices; Represents the weight coefficient of spatial distribution; Representation and equipment A collection of adjacent devices; Indicates the device With equipment the weight of the connection between them (e.g. the capacity or importance of the power transmission path); Indicates the index of the current device; Indicates the index of the neighbor device; Indicates the device The spatial position of (coordinate vector); Indicates the device The spatial position of (coordinate vector); S5.4. Update particle velocity and position and calculate the new fitness value of the current particle ; S5.5. Update individual and global optimality: if , then update the individual optimal position (each particle has its own historical optimal position , which is the best position that the particle has ever visited. , particles can remember the best solution they encounter and use it as a reference point for subsequent exploration): ; if , then update the global optimal ( is the best solution found by all particles in the entire particle swarm, by updating , particle swarms can share each other’s information and promote the accumulation of collective wisdom, which helps to speed up convergence and increase the probability of finding the global optimal solution): ; S5.6. Determine whether the maximum number of iterations has been reached ; S5.7, according to the final global optimal position , adjust the operating parameters of the power equipment to make them close to the optimal state: ; S5.8. Apply the optimized operating parameter settings to actual power equipment to comprehensively improve the operating conditions of the equipment and achieve higher efficiency, better stability and lower costs.

[0023] Embodiment 2: This embodiment provides an electric power equipment operation optimization system based on digital twins, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement any one of the above-mentioned electric power equipment operation optimization methods based on digital twins.

[0024] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only preferred examples of the present invention, and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A method for optimizing the operation of power equipment based on digital twins, characterized in that: The following steps are involved: S1. Collecting operation data of physical power equipment through sensors; S2. Use the modeling software Simulink to build a digital twin model of power equipment based on operating data; S3. Establish a bidirectional data flow from the physical power equipment to the digital twin model and from the digital twin model back to the physical power equipment for data synchronization; S4. Use the particle filter algorithm to perform state estimation on the synchronized digital twin model, and introduce the physical degradation model of equipment aging in the process of state estimation; S5. Based on the estimated state, the operating status of the equipment is optimized by a particle swarm optimization algorithm.

2. The method for optimizing power equipment operation based on digital twin according to claim 1, characterized in that: In S1, the operating data of the power equipment includes: current ,Voltage , speed .

3. The method for optimizing power equipment operation based on digital twin according to claim 2 is characterized in that: In S2, a digital twin model of the power equipment is constructed based on the operation data using the modeling software Simulink, including the following steps: S2.

1. Preprocessing the collected power equipment operation data; S2.2, import the collected current, voltage and speed data through the Simulink input module; S2.

3. Build a digital twin model of power equipment in Simulink.

4. The method for optimizing power equipment operation based on digital twin according to claim 3 is characterized in that: In S2.3, the digital twin model of the power equipment is: Electrical dynamic equations: ； Mechanical dynamics equation: ； in, Represents resistance; represents inductance; Represents capacitance; Indicates instantaneous torque; Indicates the speed of the electrical equipment; represents the moment of inertia; represents the damping coefficient; Indicates the moment; Represents the differential symbol.

5. The method for optimizing power equipment operation based on digital twin according to claim 4 is characterized in that: In S3, a bidirectional data flow is established from the physical device to the digital twin model and from the digital twin model back to the physical device to perform data synchronization, including the following steps: S3.

1. Transmit the collected operation data to the digital twin platform through the communication protocol: ； in, Indicates time Operational data transferred from physical power equipment to the digital twin model; S3.

2. After receiving the operating data from the physical power equipment, parse the communication protocol and convert the operating data into a format acceptable to the Simulink model ; S3.

3. Input the parsed data into the digital twin model and update the state variables of the digital twin model.

6. The method for optimizing power equipment operation based on digital twin according to claim 5 is characterized in that: In S4, the state of the synchronized digital twin model is estimated using a particle filter algorithm, including the following steps: S4.

1. Generate a set of initial particles based on historical power equipment status information: ； in, Indicates The initial state of a particle, The covariance matrix representing the initial state represents the particle index, Indicates the initial state of the power equipment; S4.

2. At every moment Use the system's state transition equation to predict the state of each particle; S4.

3. Based on real-time observation data received from physical devices and converted into a format acceptable to the model , update the weight of each particle by measuring the model: ； in, Indicates at time The predicted observed value of represents the mapping function from the state vector of the power device to the observed data; represents the observation noise; A state vector representing the power device; S4.

4. Update particle weights based on observations : ； in, represents the observation likelihood function; S4.

5. At every moment The state estimation of power equipment is calculated by weighted average. In view of the aging of power equipment, the physical degradation model of equipment aging is introduced into the weighted average calculation, and the weighted average calculation is further optimized in view of the insulation degradation of power equipment components.

7. The method for optimizing power equipment operation based on digital twin according to claim 6 is characterized in that: In S4.5, the state estimate of the device is calculated by weighted average as follows: ； in, Indicates the power equipment at the time state estimation; Indicates Particles at time Status; Indicates Particles at time The weight of represents the total number of particles; In view of the aging of power equipment, the physical degradation model of equipment aging is introduced in the weighted average calculation: ； in, It represents the state estimation after introducing the physical degradation model of equipment aging; Indicates the aging coefficient of power equipment; Physical degradation models that represent equipment aging; The weighted average calculation method is further optimized for insulation degradation of power equipment components: ； in, represents a further optimized state estimate; Indicates the insulation degradation coefficient; Indicates Particles at time Insulation degradation function.

8. The method for optimizing power equipment operation based on digital twin according to claim 7 is characterized in that: In S5, based on the estimated state, the operating condition of the device is optimized by a particle swarm optimization algorithm, including the following steps: S5.

1. Determine the size of the particle swarm and the maximum number of iterations ; S5.

2. Initialize the position of each particle and speed , and initialize the individual optimal position of each particle and the global optimal position ; S5.3, construct a fitness function in combination with the state estimation of the power equipment, and optimize the fitness function considering the spatial position of the power equipment; S5.

4. Update particle velocity and position and calculate the new fitness value of the current particle ; S5.

5. Update individual and global optimality: if , then update the individual optimal: ； if , then update the global optimum: ； S5.

6. Determine whether the maximum number of iterations has been reached ; S5.7, according to the final global optimal position , adjust the operating parameters of the power equipment to make them close to the optimal state: ; S5.

8. Apply the optimized operating parameter settings to actual power equipment.

9. The method for optimizing power equipment operation based on digital twin according to claim 8, characterized in that: In S5.3, the fitness function is: ； in, Represents the fitness function value for the operating state of the power equipment; Indicates the number of components of the device; Indicates The impedance of each component; Indicates The current of each component; Indicates The height of the component; Indicates The voltage of each component; represents the weight of resistance loss; represents the height-adjusted resistance loss weight; represents the weight of the efficiency factor; Represents the index of the device component; Considering the spatial location of power equipment, the fitness function is optimized: ； in, Indicates the spatial distribution location of the device; represents the spatial objective function; Represents the weight coefficient of spatial distribution.

10. A power equipment operation optimization system based on digital twins, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: The processor executes a computer program to implement the digital twin-based power equipment operation optimization method as described in any one of claims 1 to 9.

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