Power equipment operation control method and system based on digital twinborn technology

Abstract

The invention provides a power equipment operation control method and system based on a digital twinning technology. The method comprises the steps of obtaining physical space operation parameters, power system operation data and equipment inspection image data of physical power equipment, generating a digital twinborn model based on the physical space operation parameters, the power system operation data and the equipment inspection image data, and obtaining a digital twinborn model according to equipment state evolution data of the digital twinborn model. The method comprises the steps of generating an insulation state parameter and a thermal state parameter, generating a control instruction according to the insulation state parameter and the thermal state parameter, transmitting the control instruction to physical power equipment, collecting feedback data generated after the physical power equipment executes the control instruction, and correcting a digital twin model by using the feedback data. Therefore, the operation state of the physical power equipment can be subsequently controlled. According to the technical scheme provided by the invention, the problems of control lag and deviation caused by lack of future state deduction in a traditional scheme are effectively avoided.

Description

Technical Field

[0001] This application relates to the field of power equipment operation control technology, and in particular to a power equipment operation control method and system based on digital twin technology. Background Technology

[0002] With the continuous development of smart grids, the operation and control of power systems place higher demands on the reliability and predictability of key power equipment. This requires a technical means that can accurately simulate and proactively regulate the future state of physical equipment while it is in operation, so as to achieve a shift from passive response to proactive intervention.

[0003] Current solutions mainly involve deploying sensor networks to collect multi-dimensional operational data of power equipment in real time, and using data analysis models to assess the health status of the equipment and provide early warnings of faults, thereby generating control recommendations.

[0004] However, the existing solution mainly relies on the analysis of historical and current data, and lacks the ability to dynamically simulate the evolution of equipment status under complex operating conditions in the future. Its control decisions are difficult to accurately reflect the real impact of gradual changes such as equipment insulation aging and heat accumulation, resulting in insufficient predictability and accuracy of control commands. Summary of the Invention

[0005] This application provides a power equipment operation control method and system based on digital twin technology to solve the problem that the existing technology lacks the ability to dynamically simulate the state evolution of equipment under complex future operating conditions, resulting in insufficient predictability and lack of accuracy in control decisions.

[0006] In a first aspect, this application provides a power equipment operation control method based on digital twin technology, including: Acquire physical spatial operating parameters of physical power equipment, power system operating data, and equipment inspection image data; Based on the physical space operation parameters, the power system operation data, and the equipment inspection image data, a digital twin model corresponding to the physical power equipment is generated; Based on the device state evolution data of the digital twin model, the insulation state parameters and thermal state parameters of the physical power equipment under preset operating conditions are generated. Based on the insulation state parameters and the thermal state parameters, control commands are generated for adjusting the operating parameters of the physical power equipment, and the control commands are transmitted to the physical power equipment. The feedback data generated after the physical power equipment executes the control command is collected, and the feedback data is used to correct the digital twin model for subsequent control of the operating status of the physical power equipment.

[0007] Optionally, physical spatial operating parameters of physical power equipment, power system operating data, and equipment inspection image data can be acquired, including: Physical space operating parameters are obtained from sensors attached to physical electrical equipment; Obtain power system operation data from the power monitoring system connected to the physical power equipment; Equipment inspection image data is obtained from the device that inspects the physical power equipment.

[0008] Optionally, based on the physical space operating parameters, the power system operating data, and the equipment inspection image data, a digital twin model corresponding to the physical power equipment is generated, including: Based on the equipment inspection image data, a three-dimensional structural framework of the physical power equipment in digital space is constructed; Within the three-dimensional structural framework, target component regions in the physical electrical equipment that are associated with insulation and heat dissipation capabilities are marked. Extract the dynamic operating parameters associated with the target component region from the physical space operating parameters; Within the three-dimensional structural framework, the correlation between the dynamic operating parameters and the target component region is established; Based on the power system operation data, a set of operational constraints are configured for the three-dimensional structural framework in the digital space; The three-dimensional structural framework containing the aforementioned relationships and operational constraints is determined as a digital twin model corresponding to the physical power equipment.

[0009] Optionally, based on the equipment state evolution data of the digital twin model, insulation state parameters and thermal state parameters of the physical power equipment under preset operating conditions are generated, including: A set of preset future operating conditions are input into the digital twin model, and the digital twin model is driven based on the future operating conditions to generate equipment state evolution data; From the device state evolution data, extract the first change data reflecting the change in the electric field strength inside the physical power equipment, and the second change data reflecting the change in the temperature inside the physical power equipment. Based on the first change data, the first trend of the change of the insulation performance of the physical power equipment over time under the preset working conditions is deduced. Based on the second change data, a second trend of temperature change over time inside the physical power equipment under preset operating conditions is deduced. Based on the first trend of change, a first value characterizing the insulation performance of the physical power equipment is calculated and used as an insulation state parameter. Based on the second trend of change, a second value characterizing the thermal stability of the physical power equipment is calculated and used as a thermal state parameter.

[0010] Optionally, based on the insulation state parameters and the thermal state parameters, a control command for adjusting the operating parameters of the physical electrical equipment is generated, and the control command is transmitted to the physical electrical equipment, including: The insulation status parameter is compared with a preset insulation safety threshold. When the insulation status parameter is lower than the insulation safety threshold, the first adjustment amount of the operating voltage of the physical power equipment is calculated based on the difference between the insulation status parameter and the insulation safety threshold. Based on the first adjustment amount, a first control command is generated; The thermal state parameter is compared with a preset temperature safety threshold. When the thermal state parameter is higher than the temperature safety threshold, a second adjustment amount of the cooling fan speed of the physical power equipment is calculated based on the difference between the thermal state parameter and the temperature safety threshold. Based on the second adjustment amount, a second control command is generated; The first control command and the second control command are encapsulated into a control command, and the control command is transmitted to the local controller of the physical power equipment through a control link.

[0011] Optionally, based on the first adjustment amount, a first control command is generated, including: Based on the sign of the first adjustment amount, the direction of adjustment for the operating voltage of the physical power equipment is determined; The adjustment range of the operating voltage of the physical power equipment is determined based on the absolute value of the first adjustment amount. Based on the adjustment direction and the adjustment magnitude, a specific numerical instruction is generated to instruct the local controller to adjust the output voltage. The specific numerical instructions are encapsulated into a first control instruction that can be recognized and executed by the local controller.

[0012] Optionally, feedback data generated after the physical power equipment executes the control command is collected, and the digital twin model is corrected using the feedback data for subsequent control of the operating status of the physical power equipment, including: After the physical electrical equipment adjusts its operation according to the control command, the actual insulation data and actual thermal data of the physical electrical equipment are collected. Obtain predicted insulation data and predicted thermal data at the same time under the same operating conditions from the digital twin model; The actual insulation data is compared with the predicted insulation data to obtain the insulation data difference; The actual thermal data is compared with the predicted thermal data to obtain the thermal data difference; The insulation data differences and thermal data differences are input into the digital twin model. The parameters used to deduce changes in insulation state and temperature are adjusted. The digital twin model after parameter adjustment is saved for subsequent generation of the physical power equipment state evolution data.

[0013] Secondly, this application provides a power equipment operation control system based on digital twin technology, comprising: The acquisition module is used to acquire physical spatial operating parameters of physical power equipment, power system operating data, and equipment inspection image data; The first generation module is used to generate a digital twin model corresponding to the physical power equipment based on the physical space operation parameters, the power system operation data and the equipment inspection image data. The second generation module is used to generate the insulation state parameters and thermal state parameters of the physical power equipment under preset operating conditions based on the equipment state evolution data of the digital twin model. The third generation module is used to generate control instructions for adjusting the operating parameters of the physical power equipment based on the insulation state parameters and the thermal state parameters, and to transmit the control instructions to the physical power equipment. The data acquisition module is used to acquire feedback data generated after the physical power equipment executes the control command, and to use the feedback data to correct the digital twin model for subsequent control of the operating status of the physical power equipment.

[0014] Thirdly, this application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a power equipment operation control method based on digital twin technology as described in the first aspect above.

[0015] Fourthly, this application provides a computer storage medium storing a computer program, which, when executed by a computer, implements a power equipment operation control method based on digital twin technology as described in the first aspect.

[0016] This application constructs a complete closed loop from physical perception to virtual simulation and then to active control. The method generates a high-fidelity digital twin model by fusing multi-source heterogeneous data, and uses this model to dynamically simulate the future insulation and thermal state of the equipment. Based on the simulation results, it can generate predictive control commands to achieve advanced and precise regulation of the operating state of power equipment, effectively avoiding the control lag and deviation problems caused by the lack of future state simulation in traditional solutions.

[0017] Furthermore, by establishing a continuous self-optimizing learning mechanism, this method compares the actual operating data of the device after executing control commands with the predicted data of the digital twin model in real time, and feeds the differences back into the model to adjust key inference parameters. This enables the digital twin model to continuously approximate the real operating characteristics of the physical device, significantly improving the accuracy of subsequent state predictions, and thus ensuring the long-term reliability and adaptability of the closed-loop control system.

[0018] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A flowchart of a power equipment operation control method based on digital twin technology provided in this application is shown; Figure 2 This application provides a schematic diagram of the structure of a power equipment operation control system based on digital twin technology. Figure 3 A schematic diagram of the structure of a computing device provided in this application is shown. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present application, the technical solution of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0022] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0023] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] Figure 1 This application provides a flowchart of a power equipment operation control method based on digital twin technology, such as... Figure 1 As shown, the method includes: Step 101: Obtain the physical space operation parameters of the physical power equipment, the power system operation data, and the equipment inspection image data.

[0025] Optionally, step 101 may specifically include the following steps: Step 1011: Obtain physical space operating parameters from sensors attached to the physical electrical equipment; Step 1012: Obtain power system operation data from the power monitoring system connected to the physical power equipment; Step 1013: Obtain equipment inspection image data from the device for inspecting the physical power equipment.

[0026] In the above steps, physical space operating parameters refer to quantitative data that characterizes the real-time operating status of the equipment itself, which are directly measured from sensors attached to the physical power equipment body and are used to reflect the immediate electrical and physical status of the equipment. Power system operation data refers to data obtained from the power monitoring system connected to the physical power equipment, which describes the operating status of the power grid environment in which the equipment is located, and is used to define the power grid-side operating conditions and boundary constraints of the equipment. Equipment inspection image data refers to the data obtained by taking pictures or scanning physical electrical equipment through dedicated inspection devices deployed on site. The data records the appearance and status characteristics of the equipment in the form of images and is used to provide visual information such as the physical morphology and temperature distribution of the equipment surface.

[0027] In this embodiment, firstly, various sensors attached to the physical power equipment continuously collect signals reflecting the status of key parts of the physical power equipment. The collected signals are converted by the data processing unit installed locally on the physical power equipment to form physical space operation parameters in a standard format. Secondly, the data acquisition module deployed in the station accesses the database of the power monitoring system at regular intervals through a preset communication interface, extracts the corresponding power grid operation information according to the identification of the physical power equipment, and forms power system operation data. At the same time, the data acquisition module sends control commands to the automated inspection device deployed in the station. The automated inspection device moves to a designated position and controls the onboard imaging component to take pictures of the target physical power equipment. The acquired images are then transmitted back to the data acquisition module to form equipment inspection image data.

[0028] For example, taking a main transformer A in a substation as an example, firstly, real-time winding temperature and leakage current are collected from its own sensors as physical space operating parameters; secondly, the real-time voltage of its connected bus B is queried from the station's monitoring system as power system operating data; at the same time, a track-mounted inspection robot C is controlled to move to the observation point to take infrared pictures of equipment A and generate an infrared thermal image as equipment inspection image data; finally, these three types of data acquired at the same time are aggregated for subsequent use.

[0029] This step constructs a multi-dimensional complementary basic dataset by synchronously collecting sensor data from the device itself, power grid system operating data, and automated inspection image data. This provides a comprehensive and accurate initial input for the subsequent establishment of a high-fidelity digital twin model, ensuring that the model can realistically map the comprehensive state of the physical device from multiple electrical, physical, and visual perspectives.

[0030] Step 102: Based on the physical space operation parameters, the power system operation data, and the equipment inspection image data, generate a digital twin model corresponding to the physical power equipment.

[0031] Optionally, step 102 may specifically include the following steps: Step 1021: Based on the equipment inspection image data, construct a three-dimensional structural framework of the physical power equipment in digital space; Step 1022: In the three-dimensional structural framework, mark the target component areas in the physical electrical equipment that are associated with insulation and heat dissipation capabilities; Step 1023: Extract the dynamic operating parameters associated with the target component region from the physical space operating parameters; Step 1024: In the three-dimensional structural framework, establish the association between the dynamic operating parameters and the target component region; Step 1025: Based on the power system operation data, configure a set of operation constraints for the three-dimensional structural framework in the digital space; Step 1026: The three-dimensional structural framework containing the association and the operating constraints is determined as a digital twin model corresponding to the physical power equipment.

[0032] In the above steps, the target component region is a digital representation of key components related to insulation or heat dissipation capabilities, marked out from the three-dimensional structural framework, used for focused state simulation; Dynamic operating parameters are time-varying data extracted from physical space operating parameters that are directly related to the behavior of the target component area and are used to drive the digital model; The association relationship is a mapping rule established within the three-dimensional structural framework, which binds dynamic operating parameters to the corresponding target component region to ensure that physical data can accurately drive changes in the model state.

[0033] In this embodiment, firstly, a three-dimensional structural framework of the physical power equipment in digital space is generated through three-dimensional reconstruction using equipment inspection image data. Secondly, core components affecting insulation and heat dissipation are identified and marked in the three-dimensional structural framework based on prior knowledge, forming target component regions. Then, monitoring data corresponding to each target component region is matched and filtered from the physical space operating parameters as dynamic operating parameters. Next, a data interface is created for each target component region in the three-dimensional structural framework to access the corresponding dynamic operating parameters and establish a correlation. Simultaneously, an external boundary is set for the three-dimensional structural framework in digital space based on power system operating data, serving as operating constraints. Finally, the three-dimensional structural framework with embedded correlations and configured operating constraints is determined as a digital twin model.

[0034] For example, following the implementation case of the previous step, for the main transformer A device, firstly, a three-dimensional structural framework of the device is generated using infrared thermal images and visible light images; secondly, the winding, high-voltage bushing, and radiator areas are marked as target component areas in the framework; then, the winding temperature and bushing leakage current are extracted from the real-time data of the device A as corresponding dynamic operating parameters; then, the winding temperature data is bound to the winding digital area and the leakage current data is bound to the bushing digital area within the three-dimensional framework to establish a correlation; at the same time, the B bus voltage data obtained from SCADA is converted into operating constraints for the model voltage input; finally, this three-dimensional model containing data binding relationships and external constraints is determined as the digital twin model of the device A.

[0035] This step involves deeply integrating multi-source data with a three-dimensional geometric model to construct a high-fidelity, interactive digital twin model. This model not only reproduces the physical equipment structure but also accurately maps real-time data to key parts and applies real-world environmental constraints, thereby providing a precise virtual experimental environment for simulating the state evolution of the equipment under real-world operating conditions and implementing advanced control.

[0036] Step 103: Based on the equipment state evolution data of the digital twin model, generate the insulation state parameters and thermal state parameters of the physical power equipment under preset operating conditions.

[0037] Optionally, step 103 may specifically include the following steps: Step 1031: Input a set of preset future operating conditions into the digital twin model, and drive the digital twin model based on the future operating conditions to generate equipment state evolution data; Step 1032: Extract from the device state evolution data a first change data reflecting the change in the electric field strength inside the physical power equipment and a second change data reflecting the change in the temperature inside the physical power equipment. Step 1033: Based on the first change data, deduce the first trend of change of the insulation performance of the physical power equipment over time under the preset operating conditions; Step 1034: Based on the second change data, deduce the second change trend of the internal temperature of the physical power equipment over time under the preset operating conditions; Step 1035: Based on the first trend of change, calculate a first value characterizing the insulation performance of the physical power equipment, as an insulation state parameter. Step 1036: Based on the second trend of change, calculate a second value characterizing the thermal stability of the physical power equipment as a thermal state parameter.

[0038] In the above steps, the equipment state evolution data is a time-series dataset that is output by the digital twin model through internal calculation and simulation after receiving the preset future operating conditions. It reflects how the various states of the physical power equipment gradually change over a period of time and is used to characterize the dynamic evolution process of the equipment. The first trend is a pattern derived from the analysis of equipment status evolution data, describing the change of the insulation performance of physical electrical equipment over time, and is used to quantify the decay or fluctuation of insulation capacity. The second trend is derived from the analysis of equipment state evolution data, describing the law of temperature change over time inside physical electrical equipment, and is used to quantify the stability and rate of change of thermal state. The insulation status parameter is a comprehensive evaluation value calculated based on the first trend of change, which is used to intuitively characterize the insulation performance level of physical electrical equipment at the end of the preset operating condition; The thermal state parameter is a comprehensive evaluation value calculated based on the second trend of change, which is used to intuitively characterize the thermal stability level of physical electrical equipment at the end of the preset operating conditions.

[0039] In this embodiment, firstly, a set of preset future operating conditions are input into a digital twin model, driving the model to perform calculations and simulations based on the internal relationships and constraints, thereby outputting equipment state evolution data showing the continuous changes in the states of various components of the physical power equipment over a future period. Secondly, from the equipment state evolution data, first change data reflecting the evolution of electric field intensity in key areas within the physical power equipment over time, and second change data reflecting the evolution of temperature in key areas within the physical power equipment over time, are extracted through data filtering. Next, the first change data is analyzed to deduce the first trend of insulation performance over a preset complete future operating condition period. Simultaneously, the second change data is analyzed to deduce the second trend of temperature within the physical power equipment over the same period. Then, based on the first trend, a first value representing the insulation performance status is calculated using a specific comprehensive evaluation method, serving as an insulation state parameter. Finally, based on the second trend, a second value representing the thermal stability status is calculated using a similar comprehensive evaluation method, serving as a thermal state parameter.

[0040] For example, following the implementation case of the previous step, firstly, a set of preset future operating conditions are input into the model. These conditions include the B bus voltage fluctuating with a specific waveform over the next 24 hours and the periodic change in ambient temperature. The model is then driven to perform simulations to obtain equipment state evolution data on how the winding electric field and temperature change over the next 24 hours. Secondly, the change sequence of electric field intensity at the winding is extracted from this evolution data as the first change data, and the change sequence of winding temperature is extracted as the second change data. Next, the first change data is analyzed to derive a first trend where the fluctuation of electric field intensity leads to an accelerated aging of the insulation material. Simultaneously, the second change data is analyzed to derive a second trend where the winding temperature remains persistently high after the peak load period. Then, a comprehensive evaluation value of the insulation performance is calculated based on the first change trend as an insulation state parameter. Finally, a comprehensive evaluation value of thermal stability is calculated based on the second change trend as a thermal state parameter.

[0041] This step involves driving a digital twin model to simulate under pre-set harsh future operating conditions, extracting and analyzing the evolution patterns of key state data, and ultimately condensing two core quantitative parameters characterizing insulation and thermal states. This transforms the future, complex state evolution process of the equipment into an intuitive and assessable basis for decision-making, enabling quantitative prediction and forward-looking perception of potential risks to power equipment.

[0042] Step 104: Based on the insulation state parameters and the thermal state parameters, generate control instructions for adjusting the operating parameters of the physical power equipment, and transmit the control instructions to the physical power equipment.

[0043] Optionally, step 104 may specifically include the following steps: Step 1041: Compare the insulation status parameter with a preset insulation safety threshold. When the insulation status parameter is lower than the insulation safety threshold, calculate the first adjustment amount of the operating voltage of the physical power equipment based on the difference between the insulation status parameter and the insulation safety threshold. Step 1042: Generate a first control command based on the first adjustment amount; Step 1042 may specifically include the following steps: Based on the numerical sign of the first adjustment amount, the adjustment direction of the operating voltage of the physical power equipment is determined; based on the absolute value of the first adjustment amount, the adjustment range of the operating voltage of the physical power equipment is determined; based on the adjustment direction and the adjustment range, a specific numerical instruction for instructing the local controller to adjust the output voltage is generated; the specific numerical instruction is encapsulated into a first control instruction that the local controller can recognize and execute.

[0044] Step 1043: Compare the thermal state parameter with a preset temperature safety threshold. When the thermal state parameter is higher than the temperature safety threshold, calculate the second adjustment amount of the cooling fan speed of the physical power equipment based on the difference between the thermal state parameter and the temperature safety threshold. Step 1044: Generate a second control command based on the second adjustment amount; Step 1045: Encapsulate the first control instruction and the second control instruction into a control instruction, and transmit the control instruction to the local controller of the physical power equipment through the control link.

[0045] In the above steps, the first adjustment amount is a quantitative value calculated based on the degree to which the insulation status parameter is lower than the insulation safety threshold, used to indicate the specific amount by which the operating voltage of the physical electrical equipment needs to be reduced. The first control command is a command data packet generated based on the first adjustment amount, containing the specific magnitude and execution method of reducing the operating voltage, and is used to directly drive the voltage regulation unit of the physical power equipment. The second adjustment amount is a quantitative value calculated based on the degree to which the thermal state parameters exceed the temperature safety threshold. It is used to indicate the specific amount by which the speed of the cooling fan of the physical electrical equipment needs to be increased. The second control command is a command data packet generated based on the second adjustment amount, containing the specific magnitude and execution method of increasing the cooling fan speed, and is used to directly drive the fan control unit of the physical electrical equipment.

[0046] In this embodiment, firstly, the value of the insulation status parameter is compared with a preset insulation safety threshold. When the value of the insulation status parameter is less than the preset insulation safety threshold, the difference between the insulation safety threshold and the insulation status parameter is calculated. Based on a preset proportional coefficient, the difference is converted into a first adjustment amount that the operating voltage of the physical power equipment needs to be reduced. Secondly, the first adjustment amount is encapsulated into a data format conforming to the communication protocol of the local controller of the physical power equipment to form a first control command. Simultaneously, the value of the thermal status parameter is compared with a preset temperature safety threshold. When the value of the thermal status parameter is greater than the preset temperature safety threshold, the difference between the thermal status parameter and the temperature safety threshold is calculated. Based on another preset proportional coefficient, the difference is converted into a second adjustment amount that the cooling fan speed of the physical power equipment needs to be increased. Then, the second adjustment amount is encapsulated into a data format conforming to the communication protocol of the local controller of the physical power equipment to form a second control command. Finally, the first control command and the second control command are combined and sent to the local controller of the physical power equipment through the control link.

[0047] For example, following the implementation case of the previous step, for the main transformer A equipment, firstly, the calculated insulation state parameters are compared with the preset insulation safety threshold. It is found that the insulation state parameters are lower than the threshold. The difference is calculated, and according to the mapping relationship, the first adjustment amount that the operating voltage needs to be reduced is 5kV. Secondly, this first adjustment amount of 5kV is encapsulated into a first control instruction containing the command "reduce the output voltage by 5kV". At the same time, the calculated thermal state parameters are compared with the preset temperature safety threshold. It is found that the thermal state parameters are higher than the threshold. The difference is calculated, and according to the mapping relationship, the second adjustment amount that the cooling fan speed needs to be increased is 200 revolutions per minute. Then, this second adjustment amount is encapsulated into a second control instruction containing the command "increase the fan speed by 200 revolutions per minute". Finally, the first control instruction and the second control instruction are sent together to the on-load tap changer controller and fan motor controller of equipment A through the station control network.

[0048] This step automatically calculates precise adjustments to equipment operating parameters by comparing the quantified insulation and thermal state prediction results with preset safety thresholds, and generates standardized control commands that can directly drive the underlying actuators. This achieves a seamless connection from state prediction to control action generation, thereby transforming the prediction of future equipment risks into preventative and precise real-time control operations, effectively improving the proactive defense capabilities and safety margins of power equipment operation.

[0049] Step 105: Collect feedback data generated after the physical power equipment executes the control command, and use the feedback data to correct the digital twin model for subsequent control of the operating status of the physical power equipment.

[0050] Optionally, step 105 may specifically include the following steps: Step 1051: After the physical power equipment adjusts its operation according to the control command, collect the actual insulation data and actual thermal data of the physical power equipment; Step 1052: Obtain the predicted insulation data and predicted thermal data at the same time under the same operating conditions from the digital twin model; Step 1053: Compare the actual insulation data with the predicted insulation data to obtain the insulation data difference; Step 1054: Compare the actual thermal data with the predicted thermal data to obtain the thermal data difference; Step 1055: Input the insulation data difference and the thermal data difference into the digital twin model, adjust the parameters used to deduce the insulation state change and temperature change, and save the digital twin model after parameter adjustment for subsequent generation of the physical power equipment state evolution data.

[0051] In the above steps, the insulation data difference is the value obtained by subtracting the actual insulation data collected from the physical electrical equipment from the predicted insulation data obtained from the digital twin model at the same time and under the same operating conditions. It is used to quantify the deviation of the digital model in the insulation state prediction. Thermal data discrepancy is a value obtained by subtracting the actual thermal data collected from physical electrical equipment from the predicted thermal data obtained from the digital twin model at the same time and under the same operating conditions. It is used to quantify the deviation of the digital model in thermal state prediction.

[0052] In this embodiment, firstly, after the physical power equipment completes the adjustment of operating parameters and operates stably for a period of time according to the received control instructions, real-time measurement values ​​reflecting the current insulation and temperature conditions of the physical power equipment are collected again by sensors installed on the physical power equipment, and these are used as actual insulation data and actual thermal data, respectively. Secondly, based on the actual operating conditions of the physical power equipment, the simulated calculation values ​​corresponding to the current moment under these operating conditions are queried and extracted from the digital twin model, and these are used as predicted insulation data and predicted thermal data, respectively. Then, the actual insulation data is subtracted from the predicted insulation data to calculate... The insulation data difference is obtained, and the actual thermal data is subtracted from the predicted thermal data to calculate the thermal data difference. Then, the calculated insulation data difference and thermal data difference are used as inputs and fed back to the parameter adjustment module of the digital twin model. The parameter adjustment module fine-tunes the internal parameters used to calculate the insulation state evolution and the internal parameters used to calculate the thermal state evolution in the digital twin model according to the sign and magnitude of the insulation data difference and the thermal data difference, and according to the preset update rules. Finally, the digital twin model version after this fine-tuning is saved, and the updated digital twin model is used for the generation of a new round of equipment state evolution data.

[0053] For example, following the implementation case of the previous step, after device A executes the control commands of "reducing the output voltage by 5kV" and "increasing the fan speed by 200 rpm" and runs for one hour, firstly, the current dielectric loss factor and winding hot spot temperature are collected from the winding insulation monitoring sensor and temperature sensor of device A as actual insulation data and actual thermal data; secondly, the current actual operating voltage, load, fan speed, and ambient temperature of device A are input into the digital twin model of device A, driving the model to simulate and calculate and output the predicted values ​​of dielectric loss factor and hot spot temperature corresponding to the current moment as predicted insulation data and predicted thermal data; next, the difference between the actual dielectric loss factor and the predicted value is calculated to obtain the insulation data difference, and the difference between the actual hot spot temperature and the predicted value is calculated to obtain the thermal data difference; then, these two difference values ​​are input into the model to slightly correct the parameters in the model that describe the aging rate of the insulation material and the heat dissipation efficiency; finally, the corrected digital twin model of device A is saved for prediction in the next control cycle.

[0054] This step compares the actual measurement data after the physical world executes control with the pre-predicted data of the digital twin model to obtain the prediction deviation. This deviation is then used to dynamically correct the core parameters of the model, thus establishing a closed-loop learning mechanism of "execution-monitoring-comparison-correction". This enables the digital twin model to continuously optimize itself and constantly approach the real dynamic characteristics of the physical device, significantly improving the long-term prediction accuracy and reliability of the model, and laying an adaptive model foundation for continuous and accurate operation control.

[0055] Figure 2 This application provides a schematic diagram of the structure of a power equipment operation control system based on digital twin technology, such as... Figure 2 As shown, the system includes: The acquisition module 21 is used to acquire physical space operation parameters of physical power equipment, power system operation data, and equipment inspection image data; The first generation module 22 is used to generate a digital twin model corresponding to the physical power equipment based on the physical space operation parameters, the power system operation data and the equipment inspection image data; The second generation module 23 is used to generate the insulation state parameters and thermal state parameters of the physical power equipment under preset operating conditions based on the equipment state evolution data of the digital twin model. The third generation module 24 is used to generate control instructions for adjusting the operating parameters of the physical power equipment based on the insulation state parameters and the thermal state parameters, and to transmit the control instructions to the physical power equipment. The acquisition module 25 is used to acquire feedback data generated after the physical power equipment executes the control command, and to use the feedback data to correct the digital twin model for subsequent control of the operating status of the physical power equipment.

[0056] Figure 2 The aforementioned power equipment operation control system based on digital twin technology can execute... Figure 1 The implementation principle and technical effects of the power equipment operation control method based on digital twin technology described in the illustrated embodiment will not be repeated here. The specific methods by which each module and unit of the power equipment operation control system based on digital twin technology in the above embodiments perform operations have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0057] In one possible design, Figure 2 The power equipment operation control system based on digital twin technology in the illustrated embodiment can be implemented as a computing device, such as... Figure 3As shown, the computing device may include a storage component 31 and a processing component 32; The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0058] The processing component 32 is used for the above Figure 1 The embodiment describes a power equipment operation control method based on digital twin technology.

[0059] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0060] Storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0061] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0062] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0063] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0064] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0065] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The embodiment shown is a power equipment operation control method based on digital twin technology.

[0066] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0067] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0068] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0069] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for power equipment operation control based on digital twin technology, characterized in that, include: Acquire physical spatial operating parameters of physical power equipment, power system operating data, and equipment inspection image data; Based on the physical space operation parameters, the power system operation data, and the equipment inspection image data, a digital twin model corresponding to the physical power equipment is generated; Based on the device state evolution data of the digital twin model, the insulation state parameters and thermal state parameters of the physical power equipment under preset operating conditions are generated. Based on the insulation state parameters and the thermal state parameters, control commands are generated for adjusting the operating parameters of the physical power equipment, and the control commands are transmitted to the physical power equipment. The feedback data generated after the physical power equipment executes the control command is collected, and the feedback data is used to correct the digital twin model for subsequent control of the operating status of the physical power equipment.

2. The method according to claim 1, characterized in that, Acquire physical spatial operating parameters of physical power equipment, power system operating data, and equipment inspection image data, including: Physical space operating parameters are obtained from sensors attached to physical electrical equipment; Obtain power system operation data from the power monitoring system connected to the physical power equipment; Equipment inspection image data is obtained from the device that inspects the physical power equipment.

3. The method according to claim 1, characterized in that, Based on the physical space operating parameters, the power system operating data, and the equipment inspection image data, a digital twin model corresponding to the physical power equipment is generated, including: Based on the equipment inspection image data, a three-dimensional structural framework of the physical power equipment in digital space is constructed; Within the three-dimensional structural framework, target component regions in the physical electrical equipment that are associated with insulation and heat dissipation capabilities are marked. Extract the dynamic operating parameters associated with the target component region from the physical space operating parameters; Within the three-dimensional structural framework, the correlation between the dynamic operating parameters and the target component region is established; Based on the power system operation data, a set of operational constraints are configured for the three-dimensional structural framework in the digital space; The three-dimensional structural framework containing the aforementioned relationships and operational constraints is determined as a digital twin model corresponding to the physical power equipment.

4. The method according to claim 1, characterized in that, Based on the equipment state evolution data of the digital twin model, the insulation state parameters and thermal state parameters of the physical power equipment under preset operating conditions are generated, including: A set of preset future operating conditions are input into the digital twin model, and the digital twin model is driven based on the future operating conditions to generate equipment state evolution data; From the device state evolution data, extract the first change data reflecting the change in the electric field strength inside the physical power equipment, and the second change data reflecting the change in the temperature inside the physical power equipment. Based on the first change data, the first trend of the change of the insulation performance of the physical power equipment over time under the preset working conditions is deduced. Based on the second change data, a second trend of temperature change over time inside the physical power equipment under preset operating conditions is deduced. Based on the first trend of change, a first value characterizing the insulation performance of the physical power equipment is calculated and used as an insulation state parameter. Based on the second trend of change, a second value characterizing the thermal stability of the physical power equipment is calculated and used as a thermal state parameter.

5. The method according to claim 1, characterized in that, Based on the insulation state parameters and the thermal state parameters, control commands are generated for adjusting the operating parameters of the physical electrical equipment, and the control commands are transmitted to the physical electrical equipment, including: The insulation status parameter is compared with a preset insulation safety threshold. When the insulation status parameter is lower than the insulation safety threshold, the first adjustment amount of the operating voltage of the physical power equipment is calculated based on the difference between the insulation status parameter and the insulation safety threshold. Based on the first adjustment amount, a first control command is generated; The thermal state parameter is compared with a preset temperature safety threshold. When the thermal state parameter is higher than the temperature safety threshold, a second adjustment amount of the cooling fan speed of the physical power equipment is calculated based on the difference between the thermal state parameter and the temperature safety threshold. Based on the second adjustment amount, a second control command is generated; The first control command and the second control command are encapsulated into a control command, and the control command is transmitted to the local controller of the physical power equipment through a control link.

6. The method according to claim 5, characterized in that, Based on the first adjustment amount, a first control command is generated, including: Based on the sign of the first adjustment amount, the direction of adjustment for the operating voltage of the physical power equipment is determined; The adjustment range of the operating voltage of the physical power equipment is determined based on the absolute value of the first adjustment amount. Based on the adjustment direction and the adjustment magnitude, a specific numerical instruction is generated to instruct the local controller to adjust the output voltage. The specific numerical instructions are encapsulated into a first control instruction that can be recognized and executed by the local controller.

7. The method according to claim 1, characterized in that, Collecting feedback data generated after the physical power equipment executes the control command, and using the feedback data to correct the digital twin model for subsequent control of the physical power equipment's operating status, including: After the physical electrical equipment adjusts its operation according to the control command, the actual insulation data and actual thermal data of the physical electrical equipment are collected. Obtain predicted insulation data and predicted thermal data at the same time under the same operating conditions from the digital twin model; The actual insulation data is compared with the predicted insulation data to obtain the insulation data difference; The actual thermal data is compared with the predicted thermal data to obtain the thermal data difference; The insulation data differences and thermal data differences are input into the digital twin model. The parameters used to deduce changes in insulation state and temperature are adjusted. The digital twin model after parameter adjustment is saved for subsequent generation of the physical power equipment state evolution data.

8. A power equipment operation control system based on digital twin technology, characterized in that, include: The acquisition module is used to acquire physical spatial operating parameters of physical power equipment, power system operating data, and equipment inspection image data; The first generation module is used to generate a digital twin model corresponding to the physical power equipment based on the physical space operation parameters, the power system operation data and the equipment inspection image data. The second generation module is used to generate the insulation state parameters and thermal state parameters of the physical power equipment under preset operating conditions based on the equipment state evolution data of the digital twin model. The third generation module is used to generate control instructions for adjusting the operating parameters of the physical power equipment based on the insulation state parameters and the thermal state parameters, and to transmit the control instructions to the physical power equipment. The data acquisition module is used to acquire feedback data generated after the physical power equipment executes the control command, and to use the feedback data to correct the digital twin model for subsequent control of the operating status of the physical power equipment.

9. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a power equipment operation control method based on digital twin technology as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The device contains a computer program that, when executed by a computer, implements a power equipment operation control method based on digital twin technology as described in any one of claims 1 to 7.

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