Dynamic planning system for power distribution network based on digital twinning

Abstract

The invention relates to the technical field of power grid planning, in particular to a power distribution network dynamic planning system based on digital twinning, which comprises an acquisition module, a simulation module, a first judgment module, a determination module, a second judgment module, an adjustment module, a correction module and an output module. According to the invention, the current and voltage of the equipment and the post-earthquake position offset, inclination angle and insulation resistance value are collected in real time, and the simulation analysis of the digital twin model is combined, so that fault equipment and abnormal equipment can be accurately identified; an acquisition period and a coincidence rate threshold value are dynamically adjusted, monitoring efficiency and accuracy are optimized, a power distribution planning report is generated at the same time, and a scientific basis is provided for optimized operation of the power distribution network; especially in emergency situations such as after an earthquake, quick response and power supply recovery can be realized, the influence of disasters on a power grid is reduced, and the problems of inaccurate monitoring and inflexible planning caused by complex influence of the earthquake and excessive dependence on static parameters are effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid planning, and in particular to a distribution network dynamic planning system based on digital twins. Background Art

[0002] With the widespread integration of distributed energy resources and new loads, the complexity and uncertainty of distribution networks have increased significantly. Traditional static planning methods are no longer able to meet their dynamic and adaptable requirements. Natural disasters such as earthquakes are particularly devastating to distribution networks, potentially causing widespread power outages and equipment damage, severely impacting power supply reliability and resilience. Therefore, there is an urgent need for a dynamic planning system that can optimize resource allocation before extreme events such as earthquakes occur, maintain power supply to critical loads during disasters, and rapidly restore power after disasters. Such a system must possess real-time monitoring, dynamic adjustment, and intelligent decision-making capabilities to enhance the resilience of distribution networks and ensure efficient and reliable power supply in complex and changing operating environments.

[0003] Patent document with publication number CN104517239A discloses a distribution network planning analysis and auxiliary decision-making system, which includes: a data input and output module, which stores the data of the distribution network planning scheme and the results of topology analysis into a data file; a power supply reliability index generation module, which sets the fault type according to the equipment attributes in the data file to calculate the distribution network reliability index, and transmits the reliability index to the data file; a power supply quality index generation module, which calculates the distribution network power supply quality index according to the equipment attributes in the data file, and transmits the power supply quality index to the data file; an anticipated accident analysis module, which calls the data of the power supply reliability index generation module and the power supply quality index generation module, analyzes various situations in which accidents may occur, and transmits the analysis results to the feedback module, which transmits the analysis results to the self-test module through a signal, and the self-test module controls the power supply reliability index generation module and the power supply quality index generation module to recalculate the distribution network reliability index and the power supply quality index; a recording module, which records the accident data of each occurrence into a data file and marks it.

[0004] It can be seen that the distribution network planning analysis and auxiliary decision-making system has the following problems: it mainly relies on static parameters for distribution network planning and analysis, and lacks the ability to monitor and respond to dynamic changes in real time; in the process of data collection and analysis, there is a lack of monitoring of the real-time status of the equipment, and it is impossible to dynamically adjust the collection cycle and analysis strategy. When faced with a complex and changeable power grid operating environment, it is difficult to detect and handle abnormal situations in a timely manner; when analyzing the reliability of the distribution network, the impact of natural disasters such as earthquakes on the equipment is not fully considered; the feedback mechanism is relatively simple, and there is a lack of in-depth mining and dynamic adjustment of the analysis results; there is a lag in the update, and the latest status of the equipment cannot be reflected in real time. When the equipment status changes, the planning scheme cannot be adjusted in time. Summary of the Invention

[0005] To this end, the present invention provides a distribution network dynamic planning system based on digital twins, which is used to overcome the problems of inaccurate monitoring and inflexible planning in the existing technology due to the complex influence of earthquakes and over-reliance on static parameters through digital twin technology, real-time monitoring and dynamic adjustment mechanism.

[0006] To achieve the above objectives, the present invention provides a distribution network dynamic planning system based on digital twins, comprising:

[0007] The acquisition module is used to collect the current and voltage of each monitored device in the distribution network in real time, and collect the position offset, tilt angle and insulation resistance value of each monitored device after the earthquake according to the preset acquisition cycle;

[0008] a simulation module connected to the acquisition module and configured to input the voltage and the current into a preset digital twin model to simulate and obtain a plurality of faulty devices;

[0009] a first determination module, connected to the acquisition module, configured to determine a plurality of first temporary devices according to the current and the voltage;

[0010] a determination module, connected to the acquisition module and the first determination module respectively, for determining a number of second temporary devices according to the position offset and the tilt angle of each of the first temporary devices;

[0011] a second determination module, connected to the acquisition module and the determination module respectively, for determining a number of abnormal devices according to the insulation resistance value of each of the second temporary devices;

[0012] an adjustment module, connected to the simulation module and the second determination module respectively, for adjusting the preset acquisition period according to the coincidence rate of the faulty device and the abnormal device and a preset coincidence rate threshold to form an adjusted acquisition period;

[0013] a correction module connected to the adjustment module, configured to correct the preset overlap rate threshold based on the adjustment acquisition period to form a corrected overlap rate threshold;

[0014] An output module is connected to the second determination module respectively, and is used to generate a power allocation planning report according to the abnormal equipment re-determined based on the adjusted collection cycle or the corrected coincidence rate threshold.

[0015] Furthermore, the first determination module includes:

[0016] a current fluctuation calculation unit, configured to calculate a standard deviation of the current within a preset first determination time period to form a current fluctuation value;

[0017] a voltage fluctuation calculation unit, configured to calculate a standard deviation of the voltage within the preset first determination time period to form a voltage fluctuation value;

[0018] A first determination unit is connected to the current fluctuation calculation unit and the voltage fluctuation calculation unit, respectively, and is used to determine that the device to be monitored is the first temporary device when the current fluctuation value is greater than a preset current fluctuation threshold, or when the voltage fluctuation value is greater than a preset voltage fluctuation threshold, to form a plurality of first temporary devices.

[0019] Furthermore, the determining module includes:

[0020] An offset curve drawing unit is used to draw a curve showing the change of the position offset within a predetermined time period to form an offset curve;

[0021] an inclination curve drawing unit, configured to draw a curve showing a change in the inclination angle within the predetermined time period to form an inclination curve;

[0022] A first determining unit is connected to the offset curve drawing unit and the tilt curve drawing unit respectively, and is used to determine a plurality of second temporary devices according to the offset curve and the tilt curve.

[0023] Furthermore, the first determining unit includes:

[0024] a consistency calculation subunit, configured to calculate the cosine similarity between the offset curve and the tilt curve to form a change consistency;

[0025] The first determining subunit is connected to the consistency calculating subunit and is used to determine the first temporary device as the second temporary device to form a plurality of second temporary devices when the change consistency is greater than a preset consistency threshold.

[0026] Furthermore, the second determination module includes:

[0027] a resistance value comparison unit, configured to compare the insulation resistance value with a preset resistance threshold value to form a resistance comparison result;

[0028] The second determination unit is connected to the resistance value comparison unit and is configured to determine a number of abnormal devices according to the insulation resistance value when the resistance comparison result shows that the insulation resistance is less than the preset resistance threshold.

[0029] Furthermore, the second determining unit includes:

[0030] a resistance change curve drawing subunit, configured to draw a change curve of the insulation resistance value within a preset second determination time period to form a resistance change curve;

[0031] a resistance curve slope calculation subunit, configured to calculate the secant slope of the resistance change curve to form a resistance curve slope;

[0032] The second determination subunit is connected to the resistance curve slope calculation subunit and is used to determine that the second temporary device is the abnormal device when the resistance curve slope is negative and the absolute value of the resistance curve slope is greater than a preset slope absolute value threshold, thereby forming a plurality of abnormal devices.

[0033] Furthermore, the adjustment module includes:

[0034] A reasonable determination unit, configured to determine whether the calculated coincidence rate is reasonable based on the number of the faulty devices and the abnormal devices, and form a reasonable determination result;

[0035] An adjustment unit is connected to the rationality determination unit and is used to adjust the preset acquisition period based on the rationality determination result, the overlap rate of the faulty device and the abnormal device, and a preset overlap rate threshold to form an adjusted acquisition period.

[0036] Furthermore, the rationality determination unit includes:

[0037] a difference absolute value calculation subunit, configured to calculate an absolute value of a difference between the number of the faulty devices and the number of the abnormal devices to form a difference absolute value;

[0038] The reasonable judgment subunit is connected to the difference absolute value calculation subunit, and is used to determine that the number of devices is comparable when the difference absolute value is less than a preset difference absolute value threshold, and to determine that the calculated overlap rate is reasonable, thereby forming the reasonable judgment result.

[0039] Furthermore, the adjustment unit includes:

[0040] A coincidence rate calculation subunit, used to calculate the coincidence rate of each faulty device and each abnormal device to obtain the coincidence rate;

[0041] a type determination subunit, connected to the overlap rate calculation subunit, for determining that the overlap type is a low overlap type when the overlap rate is less than the preset overlap rate threshold;

[0042] An adjustment subunit is connected to the type determination subunit and is used to calculate the relative deviation between the overlap rate and the preset overlap rate threshold based on the low overlap type to form an overlap deviation, and reduce the preset acquisition period according to the overlap deviation and the preset adjustment coefficient to form the adjusted acquisition period.

[0043] Furthermore, the correction module includes:

[0044] a difference deviation calculation unit, configured to calculate a relative deviation between the difference absolute value determined based on the adjusted acquisition period and the preset difference absolute value threshold to form a difference deviation;

[0045] The correction unit is connected to the difference deviation calculation unit and is used to correct the preset overlap rate threshold according to the difference deviation and a preset correction coefficient when the difference deviation is greater than the preset difference deviation threshold to form a corrected overlap rate threshold.

[0046] Compared with the existing technology, the beneficial effect of the present invention is that, by real-time acquisition of the current, voltage, and position offset, tilt angle, and insulation resistance value of the equipment after the earthquake, combined with the simulation analysis of the digital twin model, it is possible to accurately identify faulty equipment and abnormal equipment. The logical correlation of multi-dimensional data analysis and hierarchical judgment ensures the accuracy and timeliness of fault and abnormality identification. The system dynamically adjusts the acquisition cycle and the overlap rate threshold to optimize monitoring efficiency and accuracy; finally, based on the abnormal equipment determined by the adjusted acquisition cycle and the corrected overlap rate threshold, a power allocation planning report is generated, providing a scientific basis for the optimized operation of the distribution network. It improves the reliability, safety, and economy of the distribution network in complex operating environments, especially in emergency situations such as after an earthquake, it can quickly respond and restore power supply, reduce the impact of disasters on the power grid, and ensure the continuity and stability of power supply, effectively solving the problems of inaccurate monitoring and inflexible planning caused by the complex impact of earthquakes and over-reliance on static parameters.

[0047] Furthermore, by monitoring and calculating the standard deviation of current and voltage in real time, combined with threshold determination, devices experiencing abnormal current or voltage fluctuations can be quickly and accurately identified and marked as first-order temporary devices. This not only effectively distinguishes between normal and abnormal operating states, avoiding misjudgment of device status due to short-term fluctuations, but also ensures the timely detection of potential problems, improving monitoring sensitivity and accuracy. It also provides timely and reliable data support for subsequent fault diagnosis and exception handling, effectively improving the distribution network's operational monitoring efficiency and fault response speed, and enhancing the reliability and stability of the entire system.

[0048] Furthermore, by plotting offset and tilt curves and identifying the second temporary device based on these curves, the changing trends of the offset and tilt curves can reveal whether the device has experienced abnormal displacement or tilt due to external factors such as earthquakes or its own faults. By analyzing these two curves, devices with position offsets and tilt angles outside the normal range can be accurately screened and identified as second temporary devices. The system can intuitively reflect the dynamic changes of the device within a preset time period. Not only does it fully utilize the mechanical characteristic data of the device, combined with electrical parameters, it can more comprehensively assess the device status, improve the accuracy of faulty and abnormal device identification, and enhance the precision of device status monitoring. It can also promptly capture potential abnormal trends, providing strong support for subsequent fault diagnosis and maintenance. At the same time, it reduces the possibility of misjudgment, improves the reliability and stability of the system, and effectively ensures the safe operation of the distribution network. Especially after an earthquake, it can quickly identify damaged equipment and accelerate the restoration of power.

[0049] Furthermore, by calculating the cosine similarity of the offset curve and the tilt curve to form a change consistency, and using this to determine the second temporary device, the system can accurately identify the consistency of device status changes. When the change consistency is higher than the preset threshold, it indicates that the device's position offset and tilt angle changes are highly consistent in direction, which usually means that the device may have been affected by similar factors (such as earthquakes), thereby increasing the possibility of device abnormalities. Not only can it effectively screen out devices that exhibit abnormal mechanical properties, further improving the accuracy of identifying faulty and abnormal devices, and improving the accuracy and reliability of equipment status monitoring, it can also effectively reduce misjudgments and avoid unnecessary alarms due to fluctuations in a single indicator. At the same time, it can more comprehensively reflect the actual operating status of the equipment, provide a more scientific basis for fault diagnosis and abnormality handling, thereby improving the operating efficiency and safety of the distribution network. Especially under complex working conditions, it can quickly lock potential faulty equipment and ensure the stability and reliability of power supply.

[0050] Furthermore, by comparing insulation resistance values with preset resistance thresholds to initially identify abnormal devices, the system can quickly identify devices with degraded insulation performance. Insulation resistance is a key indicator of device insulation performance. When the insulation resistance value falls below the preset threshold, it indicates a potential insulation failure or anomaly. The system can quickly and accurately identify devices with degraded insulation performance, thereby promptly detecting potential safety hazards. This not only improves monitoring efficiency and accuracy, but also effectively avoids failures caused by insulation aging or damage, enhancing the reliability and safety of the distribution network. Furthermore, based on clear threshold standards, the system can reduce false positives and ensure that only truly abnormal devices are flagged, thereby optimizing the allocation of maintenance resources and reducing operation and maintenance costs.

[0051] Furthermore, by plotting the change curve of the insulation resistance value and calculating its secant slope, the system can accurately identify the downward trend of the insulation resistance. When the slope of the resistance change curve is negative and its absolute value is greater than the preset threshold, it indicates that the insulation resistance value of the device has dropped significantly in a short period of time. This is usually a clear sign of deterioration in the insulation performance of the equipment, indicating that the insulation performance of the equipment is rapidly deteriorating, and there may be insulation aging, moisture, damage or other potential faults. When the slope meets the preset conditions, the device is promptly determined to be an abnormal device. Not only does it improve the detection sensitivity of insulation faults, but it can also quickly issue an alarm when the insulation resistance drops significantly, so that maintenance measures can be taken in advance to avoid the expansion of equipment failures. It effectively improves the operational reliability of the distribution network, reduces the risk of power outages and maintenance costs, and can quickly identify potential faulty equipment, especially in complex working conditions, to ensure the stability and safety of power supply.

[0052] Furthermore, by analyzing the number of faulty and abnormal devices and comparing the difference between the two, the system ensures comparability between the two numbers, thus avoiding misjudgments caused by large discrepancies. The system dynamically adjusts the data collection cycle based on the calculated coincidence rate and a preset threshold, ensuring more frequent data collection in low coincidence conditions and improving the accuracy of monitoring abnormal devices. This not only improves the flexibility and adaptability of monitoring, but also allows for timely adjustment of monitoring strategies when device status changes, ensuring efficient operation of the system under different operating conditions. This also reduces unnecessary data collection and processing, lowers system resource consumption, improves overall operational efficiency, and enhances the reliability and stability of the distribution network.

[0053] Furthermore, by calculating the absolute difference between the number of faulty and abnormal devices and comparing it with a preset threshold, if the absolute difference is less than the preset threshold, it indicates that the difference in the number of faulty and abnormal devices is within a reasonable range and comparable, thus ensuring the rationality of the coincidence rate calculation. This not only ensures the scientific and accurate calculation of the coincidence rate, but also improves the accuracy and reliability of the monitoring system, avoids misjudgments caused by large differences in the number of devices, and more accurately assesses the coincidence of faulty and abnormal devices. It also provides a scientific basis for dynamically adjusting monitoring strategies, further optimizing the system's operating efficiency and enhancing the fault diagnosis capabilities and operational stability of the distribution network.

[0054] Furthermore, by calculating the overlap rate between faulty and abnormal devices and dynamically adjusting the collection cycle based on the comparison of the overlap rate with a preset threshold, the system can flexibly adapt to the actual operating status of the devices. A high overlap rate indicates a relatively accurate model, and power dispatch reports can be generated directly based on the faulty devices generated by the model, without the need for more precise determinations. A low overlap rate indicates a poor match between the faulty devices generated by the model and the abnormal devices determined, indicating an inaccurate model. More frequent monitoring may be required to detect potential issues. The collection cycle can be reduced and the monitoring frequency increased based on the preset adjustment factor. This not only improves the system's monitoring accuracy and reliability, but also optimizes resource utilization, avoids unnecessary data collection and processing, and thus enhances the system's overall operational efficiency.

[0055] Furthermore, by calculating the relative deviation between the absolute value of the difference and the preset absolute value threshold of the difference, when the difference deviation is less than or equal to the preset difference deviation threshold, it means that more accurate identification has been achieved by adjusting the collection period, and a power dispatch report can be generated based on the abnormal equipment determined by the adjusted collection period; and when the difference deviation exceeds the preset difference deviation threshold, it indicates that the problem still exists after adjusting the collection period, eliminating the influence of the collection period on the identification of abnormal equipment, and dynamically correcting the preset coincidence rate threshold using the difference deviation combined with the preset correction coefficient to form a corrected coincidence rate threshold; the system parameters can be dynamically adjusted according to the actual monitoring data to ensure that under different operating conditions, the system's judgment of faulty equipment and abnormal equipment always maintains high accuracy and reliability; not only improves the system's adaptability, but also optimizes the monitoring strategy, reduces the possibility of misjudgment and missed judgment, thereby enhancing the fault diagnosis capability and operational stability of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 Schematic diagram of the distribution network dynamic planning system based on digital twins in this embodiment;

[0057] Figure 2 This is a decision logic diagram for the first decision module in this embodiment to determine the first temporary device;

[0058] Figure 3 A decision logic diagram for determining the second temporary device by the first determination unit of this embodiment;

[0059] Figure 4 This is a determination logic diagram for determining abnormal devices by the second determination unit of this embodiment. DETAILED DESCRIPTION

[0060] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0061] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0062] See also Figure 1 As shown, it is a schematic diagram of the distribution network dynamic planning system based on digital twins in this embodiment;

[0063] This embodiment provides a distribution network dynamic planning system based on digital twins, including:

[0064] The acquisition module is used to collect the current and voltage of each monitored device in the distribution network in real time, and collect the position offset, tilt angle and insulation resistance value of each monitored device after the earthquake according to the preset acquisition cycle;

[0065] a simulation module connected to the acquisition module and configured to input the voltage and the current into a preset digital twin model to simulate and obtain a plurality of faulty devices;

[0066] a first determination module, connected to the acquisition module, configured to determine a plurality of first temporary devices according to the current and the voltage;

[0067] a determination module, connected to the acquisition module and the first determination module respectively, for determining a number of second temporary devices according to the position offset and the tilt angle of each of the first temporary devices;

[0068] a second determination module, connected to the acquisition module and the determination module respectively, for determining a number of abnormal devices according to the insulation resistance value of each of the second temporary devices;

[0069] an adjustment module, connected to the simulation module and the second determination module respectively, for adjusting the preset acquisition period according to the coincidence rate of the faulty device and the abnormal device and a preset coincidence rate threshold to form an adjusted acquisition period;

[0070] a correction module connected to the adjustment module, configured to correct the preset overlap rate threshold based on the adjustment acquisition period to form a corrected overlap rate threshold;

[0071] An output module is connected to the second determination module respectively, and is used to generate a power allocation planning report according to the abnormal equipment re-determined based on the adjusted collection cycle or the corrected coincidence rate threshold.

[0072] Post-earthquake monitoring refers to the vibration and displacement that distribution network equipment experiences after a natural disaster such as an earthquake. The "various equipment to be monitored" that require key monitoring are various types of electrical equipment and facilities in the distribution network (such as transformers, switches, lines, and meters). These equipment may experience positional shifts, structural tilt, and changes in insulation performance in situations such as earthquakes. These changes can affect normal operation of the equipment and may even cause failures.

[0073] The acquisition module integrates multiple sensors and data acquisition units to enable real-time monitoring of the status of distribution network equipment. Specifically, current and voltage data are collected by current transformers (CTs) and voltage transformers (PTs) installed on the equipment. These analog signals are converted into digital signals by analog-to-digital converters (ADCs) and then transmitted to the central monitoring system via a fieldbus or wireless network. After an earthquake, the module automatically activates the built-in GPS sensor according to a preset acquisition cycle to collect tiny offset information on the equipment's position. The accelerometer and gyroscope are used to measure the equipment's tilt angle in real time. At the same time, the insulation resistance value of the equipment is obtained through an online insulation monitor. All collected data undergoes preliminary filtering and correction before being stored in a database, providing real-time and accurate data support for subsequent digital twin model simulations, fault diagnosis, and power allocation decisions.

[0074] In post-earthquake monitoring, position offset, tilt angle, and insulation resistance complement each other: significant equipment position offset and abnormal tilt angle often indicate structural damage. Structural damage can further affect the insulation performance of the equipment, reducing insulation resistance and increasing the risk of leakage or short circuits. By simultaneously collecting these three types of data, we can comprehensively assess the health of equipment from the perspectives of mechanical stability and electrical safety, providing early warning of faults and effectively improving the overall safety and emergency response capabilities of the distribution network.

[0075] Faulty devices are those simulated through the digital twin model that are unable to operate normally due to electrical faults (such as short circuits, overloads, and insulation breakdowns). These devices typically exhibit significant electrical performance issues, impacting their functionality. Abnormal devices are those identified after an earthquake through a hierarchical assessment of multiple data dimensions, including current, voltage, position offset, tilt angle, and insulation resistance. These devices may not have completely failed, but they may have potential issues, such as position offset, excessive tilt angle, and decreased insulation resistance, which may have been caused by the earthquake. The identification of faulty devices relies more on the simulation and analysis of electrical parameters by the digital twin model. It has no environmental requirements and can be identified using a pre-set model under any conditions. This identification can be used after an earthquake, regardless of the impact of the earthquake on detection accuracy. However, the identification of abnormal devices requires a combination of multiple sensor data, taking into account the earthquake's impact on distribution network equipment, and is determined through hierarchical assessment and comprehensive analysis. This is more aligned with the requirements for distribution network equipment planning during post-earthquake reconstruction.

[0076] After receiving the latest data from the adjustment and correction modules, the output module first aggregates the real-time data of all abnormal devices, including their position offset, tilt angle, insulation resistance, current, and voltage. Based on these parameters, such as insulation resistance, position offset, and tilt angle, and combined with pre-set thresholds, the module classifies the fault level of each device. Using a pre-set assessment model, the system assesses the risk level of each abnormal device, taking into account its location, the status of adjacent devices, and its potential impact on the overall power supply security of the system. Next, the system uses a pre-set power dispatch algorithm (based on an optimization model or intelligent dispatch algorithm) to perform a multi-dimensional analysis of each abnormal device, including its location, the status of adjacent devices, load conditions, and their potential impact on the overall power supply security of the system. Based on the analysis results, the system simulates various emergency response plans, such as fault isolation, load transfer, and backup power supply scheduling. Finally, the output module integrates the analysis results and dispatch recommendations to generate a comprehensive power dispatch planning report that includes device status, fault causes, risk assessment, emergency response measures, and load reconfiguration planning. This report provides a scientific basis for the power grid dispatch center to rapidly locate the fault, isolate the faulted section, and ensure stable power grid operation and power supply security.

[0077] For example, after an earthquake, the system screened out the following abnormal equipment: Equipment A: The position offset was large, and the insulation resistance value decreased, so it was judged as a high-risk device; Equipment B: The tilt angle exceeded the standard, but the insulation resistance value was normal, so it was judged as a medium-risk device; Equipment C: The current fluctuation was large, and the insulation resistance value decreased slightly, so it was judged as a low-risk device.

[0078] The response measures obtained from the integrated analysis results of the output module are as follows: (1) Equipment A: Isolation measures: Disconnect equipment A from the power grid through remote control to prevent the fault from spreading; Load transfer: Transfer the load of equipment A to the adjacent equipment D to ensure that the power supply is not affected; Backup power activation: Start the backup power supply to provide temporary power supply to the affected area. (2) Equipment B: Load adjustment: Appropriately reduce the load of equipment B to avoid equipment overload due to excessive tilt angle. (3) Equipment C: Continuous monitoring: Continuously monitor the current fluctuation of equipment C to observe whether it returns to normal; Preventive maintenance: Schedule regular maintenance and check the insulation resistance value of equipment C to prevent it from further decreasing. Finally, a comprehensive power allocation planning report is generated that includes equipment status, fault causes, risk assessment, emergency response measures and load reconstruction planning.

[0079] The preset acquisition period is the time interval between two consecutive acquisition operations in a data acquisition system. It depends on signal characteristics, system dynamics, resource utilization, and application scenarios, and is typically set between 30 seconds and 5 minutes. In this embodiment, it is set to 2 minutes, which not only meets the needs of real-time monitoring but also effectively utilizes system resources. In emergency situations, it can quickly respond to changes in device status, improving system reliability and efficiency.

[0080] The preset digital twin model is a virtual model built based on the physical characteristics, operating data, and failure modes of power system equipment. It is used to simulate the operating status of equipment under different working conditions, predict faulty equipment, and optimize power allocation. The model combines physical models and data-driven methods to reflect equipment status in real time and support fault diagnosis. In this embodiment, the preset digital twin model is a multi-physics field coupling model that integrates physical modeling and data-driven methods. It is used to accurately reproduce the operating status and failure behavior of key equipment in the distribution network in a virtual environment. Its specific composition and functions include:

[0081] 1. Physical modeling layer

[0082] Structural and material parameters: This section establishes a basic physical model based on the structure, construction materials, and electromagnetic properties of distribution network equipment (such as transformers, switches, and lines). This section uses mathematical formulas and simulation tools (such as finite element analysis) to describe the electrical and mechanical behavior of the equipment under normal and abnormal operating conditions.

[0083] Coupling of equipment status and environment: Considering the displacement, tilt, and insulation attenuation of equipment under earthquakes or other external forces, quantitative simulation of equipment status changes is achieved by comparing the preset static initial state with real-time monitoring data.

[0084] 2. Data-driven correction layer

[0085] Historical data fusion: The historical data accumulated by the equipment during long-term operation (such as equipment failure records and daily operating parameters) is introduced into the model to correct the parameters in the physical model and ensure that the model output is more in line with the actual situation.

[0086] Real-time data feedback: The model is dynamically updated using real-time data collected by sensors (current, voltage, GPS displacement, tilt angle, insulation resistance, etc.). Through this feedback mechanism, the model can instantly reflect the actual operating status of the equipment and predict potential failures.

[0087] 3. Fault simulation and prediction layer

[0088] Fault mode integration: For possible failure modes of equipment, such as short circuit, poor contact, insulation degradation, and structural deformation, corresponding fault states and response mechanisms are preset in the model.

[0089] Simulation: Combining physical modeling and data feedback, this technology simulates the equipment's operating trajectory and fault evolution under different operating conditions. By comparing this with real-time data, the model can identify and issue early warnings for faulty equipment.

[0090] The preset overlap rate threshold is a standard value used to determine the overlap between faulty and abnormal devices during system operation. It depends on system reliability requirements, device fault characteristics, monitoring accuracy, and application scenario requirements, and is typically set between 50% and 90%. In this embodiment, it is set to 70%, which effectively reduces false positives while maintaining the system's sensitivity to faulty devices. This allows for timely detection of overlap between faulty and abnormal devices, allowing for rapid adjustment of monitoring strategies and improving overall system performance and reliability.

[0091] By monitoring the current and voltage of each device in the distribution network in real time and obtaining the post-earthquake device position offset, tilt angle, and insulation resistance values according to a preset collection cycle, the system then inputs the collected voltage and current data into a digital twin model, identifying potential faulty devices through simulation analysis. Simultaneously, a preliminary selection of temporary devices is made based on the current and voltage data. The position offset and tilt angle of these temporary devices are then combined to more accurately identify the second temporary devices. Finally, the faulty devices are identified based on the insulation resistance values of these devices. Based on the coincidence rate of faulty and faulty devices and its comparison with a preset coincidence rate threshold, the collection cycle is dynamically adjusted to optimize monitoring efficiency. Based on the adjusted collection cycle, the coincidence rate threshold is modified to further optimize system performance. Finally, based on the adjusted collection cycle or the modified coincidence rate threshold, the faulty devices are reassessed and a power dispatch planning report is generated, providing decision support for the optimized operation of the distribution network.

[0092] By collecting real-time data on device current, voltage, and post-earthquake position offset, tilt angle, and insulation resistance, combined with simulation analysis using a digital twin model, the system can accurately identify faulty and abnormal devices. The logical correlation between multi-dimensional data analysis and hierarchical judgment ensures accurate and timely fault and anomaly identification. The system dynamically adjusts the collection cycle and overlap rate threshold to optimize monitoring efficiency and accuracy. Finally, based on the adjusted collection cycle and corrected overlap rate threshold, it generates a power dispatch planning report for abnormal devices, providing a scientific basis for optimized distribution network operation. This improves the reliability, safety, and economic efficiency of the distribution network in complex operating environments. In particular, in emergency situations such as after an earthquake, the system can rapidly respond and restore power, reducing the impact of disasters on the grid and ensuring the continuity and stability of power supply. This effectively addresses the issues of inaccurate monitoring and inflexible planning caused by the complex impact of earthquakes and over-reliance on static parameters.

[0093] Please continue reading Figure 2 As shown, it is a decision logic diagram of the first decision module in this embodiment for deciding the first temporary device;

[0094] The first determination module includes:

[0095] a current fluctuation calculation unit, configured to calculate a standard deviation of the current within a preset first determination time period to form a current fluctuation value;

[0096] a voltage fluctuation calculation unit, configured to calculate a standard deviation of the voltage within the preset first determination time period to form a voltage fluctuation value;

[0097] A first determination unit is connected to the current fluctuation calculation unit and the voltage fluctuation calculation unit, respectively, and is used to determine that the device to be monitored is the first temporary device when the current fluctuation value is greater than a preset current fluctuation threshold, or when the voltage fluctuation value is greater than a preset voltage fluctuation threshold, to form a plurality of first temporary devices.

[0098] The preset first determination time is the length of the time window used to statistically analyze current and voltage data to determine whether abnormal fluctuations are occurring in the device. This time window depends on signal characteristics, system dynamics, noise levels, and the application scenario, and is typically set between 10 and 30 seconds. In this embodiment, it is set to 15 seconds, which allows for both timely detection of abnormal fluctuations and noise smoothing, improving the accuracy and reliability of the determination and adapting to dynamic changes in the distribution network.

[0099] The preset current fluctuation threshold is a standard value set during current monitoring to determine whether abnormal current fluctuations occur. It depends on the device's rated current, circuit capacity, power supply capabilities, and actual application scenario, and is typically set between 1.2 and 1.5 times the rated current. In this embodiment, it is set to 1.3 times the rated current to effectively identify abnormal fluctuations while avoiding false alarms, ensuring reliable system operation.

[0100] The preset voltage fluctuation threshold refers to a standard value set when monitoring voltage, which is used to determine whether the voltage fluctuates abnormally. It depends on the rated voltage of the equipment, the grid standard, the equipment tolerance and the actual application scenario. It is usually set between ±5% and ±10% of the rated voltage. In this embodiment, it is set to ±7% of the rated voltage. It can effectively identify abnormal fluctuations and avoid false alarms, ensuring the reliable operation of the system.

[0101] The current fluctuation value and voltage fluctuation value are obtained by calculating the standard deviation of the current and voltage within a preset first determination time period. The current fluctuation value and voltage fluctuation value are then compared with preset current fluctuation thresholds and voltage fluctuation thresholds. If the current fluctuation value exceeds the preset current fluctuation threshold, or the voltage fluctuation value exceeds the preset voltage fluctuation threshold, the device to be monitored is determined to be a first temporary device, and a plurality of first temporary devices are obtained.

[0102] By monitoring and calculating the standard deviation of current and voltage in real time, combined with threshold judgment, it is possible to quickly and accurately identify devices experiencing abnormal current or voltage fluctuations and mark them as first-order temporary devices. This not only effectively distinguishes between normal operation and abnormal conditions, avoiding misjudgment of device status due to short-term fluctuations, but also ensures the timely detection of potential problems, improving monitoring sensitivity and accuracy. It also provides timely and reliable data support for subsequent fault diagnosis and exception handling, effectively improving the distribution network's operational monitoring efficiency and fault response speed, and enhancing the reliability and stability of the entire system.

[0103] Specifically, the determination module includes:

[0104] An offset curve drawing unit is used to draw a curve showing the change of the position offset within a predetermined time period to form an offset curve;

[0105] an inclination curve drawing unit, configured to draw a curve showing a change in the inclination angle within the predetermined time period to form an inclination curve;

[0106] A first determining unit is connected to the offset curve drawing unit and the tilt curve drawing unit respectively, and is used to determine a plurality of second temporary devices according to the offset curve and the tilt curve.

[0107] The preset duration is the length of the time window used to analyze changes in position offset and tilt angle when monitoring device status. It is typically set between 10 and 60 seconds, depending on the device's dynamic characteristics, monitoring accuracy requirements, data processing capabilities, and application scenarios. In this embodiment, it is set to 30 seconds, which effectively smooths short-term fluctuations while avoiding excessive system load, ensuring monitoring accuracy and efficient system operation.

[0108] By continuously monitoring the position offset and tilt angle of the device over a predetermined period of time, and drawing corresponding change curves, namely the offset curve and the tilt curve, the second temporary device is determined based on the change trends and characteristics of the two curves.

[0109] By plotting offset and tilt curves and identifying secondary temporary equipment based on these curves, the changing trends of the offset and tilt curves can reveal whether the equipment has experienced abnormal displacement or tilt due to external factors such as earthquakes or internal faults. By analyzing these two curves, devices with position offsets and tilt angles outside the normal range can be accurately screened and designated as secondary temporary equipment. The system can intuitively reflect the dynamic changes of the equipment within a preset time period. This system not only fully utilizes the mechanical characteristic data of the equipment and combines it with electrical parameters to more comprehensively assess the equipment status, improve the accuracy of faulty and abnormal equipment identification, and enhance the precision of equipment status monitoring, but also promptly captures potential abnormal trends, providing strong support for subsequent fault diagnosis and maintenance. At the same time, it reduces the possibility of misjudgment, improves the reliability and stability of the system, and effectively ensures the safe operation of the distribution network. Especially after an earthquake, it can quickly identify damaged equipment and accelerate the restoration of power.

[0110] Please continue reading Figure 3 As shown, it is a decision logic diagram of the first determination unit determining the second temporary device in this embodiment;

[0111] The first determining unit includes:

[0112] a consistency calculation subunit, configured to calculate the cosine similarity between the offset curve and the tilt curve to form a change consistency;

[0113] The first determining subunit is connected to the consistency calculating subunit and is used to determine the first temporary device as the second temporary device to form a plurality of second temporary devices when the change consistency is greater than a preset consistency threshold.

[0114] The preset consistency threshold is a standard value used to determine the similarity between two curves. It is typically set between 0.8 and 0.95, depending on device characteristics, monitoring accuracy requirements, and application scenario requirements. In this example, it is set to 0.9, which can effectively identify abnormal devices while reducing false positives, ensuring efficient system operation.

[0115] The cosine similarity between the offset curve and the tilt curve is calculated to obtain the change consistency. Then, when the change consistency exceeds a preset consistency threshold, the first temporary device is determined to be the second temporary device, thereby screening out several second temporary devices.

[0116] By calculating the cosine similarity of the offset and tilt curves to form a change consistency, and using this to determine the second temporary device, the system can accurately identify the consistency of device status changes. When the change consistency exceeds the preset threshold, it indicates that the device's position offset and tilt angle changes are highly consistent in direction. This generally means that the device may have been affected by similar factors (such as earthquakes), thereby increasing the possibility of device anomalies. Not only can it effectively screen out devices that exhibit abnormal mechanical characteristics, further improving the accuracy of faulty and abnormal device identification, and enhancing the accuracy and reliability of device status monitoring, it can also effectively reduce misjudgments and avoid unnecessary alarms caused by fluctuations in a single indicator. At the same time, it can more comprehensively reflect the actual operating status of the equipment, providing a more scientific basis for fault diagnosis and anomaly handling, thereby improving the operating efficiency and safety of the distribution network. Especially in complex operating conditions, it can quickly identify potential faulty devices and ensure the stability and reliability of power supply.

[0117] Specifically, the second determination module includes:

[0118] a resistance value comparison unit, configured to compare the insulation resistance value with a preset resistance threshold value to form a resistance comparison result;

[0119] The second determination unit is connected to the resistance value comparison unit and is configured to determine a number of abnormal devices according to the insulation resistance value when the resistance comparison result shows that the insulation resistance is less than the preset resistance threshold.

[0120] The preset resistance threshold is a key parameter for monitoring the insulation status of equipment. It depends on the equipment's rated voltage, current, safety standards, tolerance, and actual operating environment, and is typically set between 1MΩ and 10MΩ. In this embodiment, it is set to 4MΩ, which effectively identifies abnormal changes in insulation resistance while avoiding misjudgments, ensuring safe operation of the equipment.

[0121] The collected insulation resistance value is compared with the preset resistance threshold to obtain a resistance comparison result. If the comparison result shows that the insulation resistance value is less than the preset resistance threshold, the device will be further identified as abnormal based on the insulation resistance value.

[0122] By comparing insulation resistance values against preset resistance thresholds, the system can initially identify abnormal devices and quickly identify devices with degraded insulation performance. Insulation resistance is a key indicator of device insulation performance. When the insulation resistance value falls below the preset threshold, it indicates a potential insulation failure or anomaly. The system can quickly and accurately identify devices with degraded insulation performance, thereby promptly identifying potential safety hazards. This not only improves monitoring efficiency and accuracy, but also effectively prevents failures caused by insulation aging or damage, enhancing the reliability and safety of the distribution network. Furthermore, based on clear threshold standards, the system can reduce false positives and ensure that only truly abnormal devices are flagged, thereby optimizing the allocation of maintenance resources and reducing operation and maintenance costs.

[0123] Please continue reading Figure 4 As shown, it is a determination logic diagram of the second determination unit of this embodiment for determining abnormal equipment;

[0124] The second determining unit includes:

[0125] a resistance change curve drawing subunit, configured to draw a change curve of the insulation resistance value within a preset second determination time period to form a resistance change curve;

[0126] a resistance curve slope calculation subunit, configured to calculate the secant slope of the resistance change curve to form a resistance curve slope;

[0127] The second determination subunit is connected to the resistance curve slope calculation subunit and is used to determine that the second temporary device is the abnormal device when the resistance curve slope is negative and the absolute value of the resistance curve slope is greater than a preset slope absolute value threshold, thereby forming a plurality of abnormal devices.

[0128] The preset second judgment time duration refers to the length of the time window used to plot the resistance change curve when monitoring insulation resistance changes. This time duration depends on the dynamic characteristics of the device's insulation resistance changes, the required monitoring accuracy, and data processing capabilities, and is typically set between 10 and 60 seconds. In this embodiment, it is set to 30 seconds, which effectively smooths short-term fluctuations while ensuring monitoring accuracy, avoiding misjudgments caused by transient abnormal data. At the same time, it does not place an excessive burden on the system's data processing capabilities, ensuring efficient system operation.

[0129] The preset absolute slope threshold is a standard value used to determine whether the slope of the resistance curve is abnormal when analyzing the curve. It depends on the slope range of the insulation resistance change during normal operation of the device, the device's insulation performance, and the safety requirements of the application scenario, and is typically set between 0.1 and 1.0. In this embodiment, it is set to 0.5, which effectively reduces false positives while maintaining system sensitivity. It can promptly detect significant changes in insulation resistance, thereby quickly identifying potential device failures and improving system reliability and safety.

[0130] A resistance change curve is generated by plotting the change in insulation resistance over a preset second determination time period. The secant slope of the resistance change curve is then calculated. If the slope of the resistance curve is negative and its absolute value is greater than a preset slope absolute value threshold, the system determines the second temporary device as an abnormal device, thereby screening out several abnormal devices.

[0131] By plotting the insulation resistance curve and calculating its secant slope, the system can accurately identify declining insulation resistance trends. When the slope of the resistance curve is negative and its absolute value is greater than a preset threshold, it indicates a significant drop in insulation resistance over a short period of time. This is often a clear sign of rapidly deteriorating insulation performance, suggesting a possible aging, moisture, damage, or other potential faults. When the slope meets the preset criteria, the device is promptly identified as abnormal. This not only improves the sensitivity of insulation fault detection but also quickly issues an alarm when a significant drop in insulation resistance occurs, allowing for proactive maintenance measures to prevent escalation of equipment failures. This effectively improves the operational reliability of the distribution network, reduces the risk of power outages and repair costs, and, especially in complex operating conditions, quickly identifies potentially faulty devices, ensuring the stability and security of the power supply.

[0132] Specifically, the adjustment module includes:

[0133] A reasonable determination unit, configured to determine whether the calculated coincidence rate is reasonable based on the number of the faulty devices and the abnormal devices, and form a reasonable determination result;

[0134] An adjustment unit is connected to the rationality determination unit and is used to adjust the preset acquisition period based on the rationality determination result, the overlap rate of the faulty device and the abnormal device, and a preset overlap rate threshold to form an adjusted acquisition period.

[0135] The overlap rate can evaluate the accuracy and reliability of the fault detection system. The overlap rate can be obtained by calculating the ratio of the number of overlaps between faulty devices and abnormal devices to the number of abnormal devices. However, if the difference in the number of faulty devices and abnormal devices is too large, there will be no comparability, and the calculation of the overlap rate is unreasonable.

[0136] The system uses the number of faulty and abnormal devices to determine whether the calculated overlap rate is reasonable. If it is, the system will further adjust the preset collection period based on the overlap rate of faulty and abnormal devices and the preset overlap rate threshold, thus forming a new adjusted collection period.

[0137] By analyzing the number of faulty and abnormal devices and comparing the difference between the two, the system ensures comparability between the two numbers, thus avoiding misjudgments caused by large discrepancies. Dynamically adjusting the data collection cycle based on the calculated coincidence rate and a preset threshold ensures more frequent data collection in low coincidence conditions, improving the accuracy of monitoring abnormal devices. This not only enhances monitoring flexibility and adaptability, but also allows for timely adjustment of monitoring strategies when device status changes, ensuring efficient system operation under varying operating conditions. Furthermore, it reduces unnecessary data collection and processing, lowers system resource consumption, improves overall operational efficiency, and enhances the reliability and stability of the distribution network.

[0138] Specifically, the rational determination unit includes:

[0139] a difference absolute value calculation subunit, configured to calculate an absolute value of a difference between the number of the faulty devices and the number of the abnormal devices to form a difference absolute value;

[0140] The reasonable judgment subunit is connected to the difference absolute value calculation subunit, and is used to determine that the number of devices is comparable when the difference absolute value is less than a preset difference absolute value threshold, and to determine that the calculated overlap rate is reasonable, thereby forming the reasonable judgment result.

[0141] The preset absolute difference threshold is used to determine whether the absolute difference between the number of faulty and abnormal devices is within a reasonable range. This threshold depends on the system's reliability requirements, device fault characteristics, monitoring accuracy, and application scenario requirements. It is typically set between 1 and 10. In this embodiment, it is set to 5. This ensures the system's sensitivity to detecting the number of faulty and abnormal devices while effectively reducing false positives. This allows for timely capture of changes in the number of devices, allowing for rapid adjustments to monitoring strategies and improving overall system performance and reliability.

[0142] By calculating the absolute difference between the number of faulty devices and the number of abnormal devices, if the absolute difference is less than the preset absolute difference threshold, the system will consider the two numbers of devices to be comparable and the calculated overlap rate of faulty and abnormal devices to be reasonable, thus judging it as reasonable.

[0143] By calculating the absolute difference between the number of faulty and abnormal devices and comparing it with a preset threshold, if the absolute difference is less than the preset threshold, it indicates that the difference in the number of faulty and abnormal devices is within a reasonable range and comparable, thus ensuring the rationality of the coincidence rate calculation. This not only ensures the scientific and accurate calculation of the coincidence rate, but also improves the accuracy and reliability of the monitoring system, avoids misjudgments caused by large differences in the number of devices, and enables more accurate assessment of the coincidence of faulty and abnormal devices. It also provides a scientific basis for dynamically adjusting monitoring strategies, further optimizing the system's operating efficiency and enhancing the fault diagnosis capabilities and operational stability of the distribution network.

[0144] Specifically, the adjustment unit includes:

[0145] A coincidence rate calculation subunit, used to calculate the coincidence rate of each faulty device and each abnormal device to obtain the coincidence rate;

[0146] a type determination subunit, connected to the overlap rate calculation subunit, for determining that the overlap type is a low overlap type when the overlap rate is less than the preset overlap rate threshold;

[0147] An adjustment subunit is connected to the type determination subunit and is used to calculate the relative deviation between the overlap rate and the preset overlap rate threshold based on the low overlap type to form an overlap deviation, and reduce the preset acquisition period according to the overlap deviation and the preset adjustment coefficient to form the adjusted acquisition period.

[0148] The preset adjustment factor is a numerical factor used to adjust the preset collection period to make it more accurate or meet certain standards. It depends on the system's reliability requirements, monitoring accuracy, and application scenario requirements, and is typically set between 0.5 and 1.5. In this embodiment, it is set to 0.8, which not only ensures system sensitivity but also reduces false positives, improving system reliability and operational efficiency.

[0149] By calculating the overlap rate between the faulty device and the abnormal device; then, comparing the calculated overlap rate with the preset overlap rate threshold, if the overlap rate is less than the preset overlap rate threshold, the overlap type is determined to be a low overlap type; based on the low overlap type, the relative deviation between the overlap rate and the preset overlap rate threshold is calculated to form the overlap deviation; according to the overlap deviation and the preset adjustment coefficient, the preset collection period is adjusted to form a new adjusted collection period.

[0150] Low overlap refers to situations where the overlap rate between faulty and abnormal devices falls below a preset threshold. This indicates a poor match between the faulty devices identified by the model and the abnormal devices identified, implying deficiencies in the accuracy and reliability of the fault detection system. A low overlap situation suggests that the current acquisition cycle may not be able to effectively capture dynamic changes in device status. Shortening the acquisition cycle to obtain device operating data more promptly can more quickly reflect changes in device status, helping to improve the accuracy and timeliness of fault detection. The system can better adapt to fluctuations in device operating status, thereby increasing fault detection sensitivity.

[0151] By calculating the overlap rate of faulty and abnormal devices and dynamically adjusting the collection cycle based on the comparison of the overlap rate with a preset threshold, the system can flexibly adapt to the actual operating status of the equipment. A high overlap rate indicates a relatively accurate model, and power dispatch reports can be generated directly based on the faulty devices generated by the model, without the need for more precise determinations. A low overlap rate indicates a poor match between the faulty devices generated by the model and the abnormal devices determined, indicating an inaccurate model. More frequent monitoring may be required to detect potential issues. The collection cycle can be reduced and the monitoring frequency increased based on the preset adjustment factor. This not only improves the system's monitoring accuracy and reliability, but also optimizes resource utilization, avoids unnecessary data collection and processing, and thus enhances the system's overall operational efficiency.

[0152] Specifically, the correction module includes:

[0153] a difference deviation calculation unit, configured to calculate a relative deviation between the difference absolute value determined based on the adjusted acquisition period and the preset difference absolute value threshold to form a difference deviation;

[0154] The correction unit is connected to the difference deviation calculation unit and is used to correct the preset overlap rate threshold according to the difference deviation and a preset correction coefficient when the difference deviation is greater than the preset difference deviation threshold to form a corrected overlap rate threshold.

[0155] The preset difference deviation threshold is a standard value used to determine whether the difference deviation exceeds the normal range. It depends on the system's reliability requirements, monitoring accuracy, and the application scenario's required fault detection sensitivity, and is typically set between 10% and 30%. In this embodiment, it is set to 15%, which effectively reduces false positives while maintaining the system's sensitivity to detecting the number of faulty and abnormal devices, ensuring system reliability and stability.

[0156] The preset correction factor is a numerical factor used to modify the preset coincidence rate threshold. It depends on the system's reliability requirements, monitoring accuracy, and the application scenario's sensitivity requirements for fault detection, and is typically set between 0.5 and 1.5. In this embodiment, it is set to 0.8, which effectively reduces false positives while maintaining the system's sensitivity to detecting the number of faulty and abnormal devices, ensuring system reliability and stability.

[0157] The difference deviation is obtained by calculating the relative deviation between the absolute value of the difference determined based on the adjustment of the acquisition period and the preset absolute value threshold of the difference; when the difference deviation is greater than the preset difference deviation threshold, the preset overlap rate threshold is corrected according to the difference deviation and the preset correction coefficient to form a corrected overlap rate threshold.

[0158] By calculating the relative deviation between the absolute value of the difference and the preset absolute value threshold of the difference, when the difference deviation is less than or equal to the preset difference deviation threshold, it means that more accurate identification has been achieved by adjusting the collection period, and a power allocation report can be generated based on the abnormal equipment determined by the adjusted collection period; when the difference deviation exceeds the preset difference deviation threshold, it indicates that the problem still exists after adjusting the collection period, eliminating the influence of the collection period on the identification of abnormal equipment, and dynamically correcting the preset coincidence rate threshold using the difference deviation combined with the preset correction coefficient to form a corrected coincidence rate threshold; the system parameters can be dynamically adjusted according to the actual monitoring data to ensure that under different operating conditions, the system's judgment of faulty equipment and abnormal equipment always maintains high accuracy and reliability; it not only improves the system's adaptability, but also optimizes the monitoring strategy, reduces the possibility of misjudgment and missed judgment, thereby enhancing the fault diagnosis capability and operational stability of the distribution network.

[0159] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

Claims

1. A distribution network dynamic planning system based on digital twins, characterized by: include: The acquisition module is used to collect the current and voltage of each monitored device in the distribution network in real time, and collect the position offset, tilt angle and insulation resistance value of each monitored device after the earthquake according to the preset acquisition cycle; a simulation module connected to the acquisition module and configured to input the voltage and the current into a preset digital twin model to simulate and obtain a plurality of faulty devices; a first determination module, connected to the acquisition module, configured to determine a plurality of first temporary devices according to the current and the voltage; a determination module, connected to the acquisition module and the first determination module respectively, for determining a number of second temporary devices according to the position offset and the tilt angle of each of the first temporary devices; a second determination module, connected to the acquisition module and the determination module respectively, for determining a number of abnormal devices according to the insulation resistance value of each of the second temporary devices; an adjustment module, connected to the simulation module and the second determination module respectively, for adjusting the preset acquisition period according to the coincidence rate of the faulty device and the abnormal device and a preset coincidence rate threshold to form an adjusted acquisition period; a correction module connected to the adjustment module, configured to correct the preset overlap rate threshold based on the adjustment acquisition period to form a corrected overlap rate threshold; An output module is connected to the second determination module respectively, and is used to generate a power allocation planning report according to the abnormal equipment re-determined based on the adjusted collection cycle or the corrected coincidence rate threshold.

2. The distribution network dynamic planning system based on digital twin according to claim 1 is characterized in that: The first determination module includes: a current fluctuation calculation unit, configured to calculate a standard deviation of the current within a preset first determination time period to form a current fluctuation value; a voltage fluctuation calculation unit, configured to calculate a standard deviation of the voltage within the preset first determination time period to form a voltage fluctuation value; A first determination unit is connected to the current fluctuation calculation unit and the voltage fluctuation calculation unit, respectively, and is used to determine that the device to be monitored is the first temporary device when the current fluctuation value is greater than a preset current fluctuation threshold, or when the voltage fluctuation value is greater than a preset voltage fluctuation threshold, to form a plurality of first temporary devices.

3. The distribution network dynamic planning system based on digital twin according to claim 2 is characterized in that: The determination module includes: An offset curve drawing unit is used to draw a curve showing the change of the position offset within a predetermined time period to form an offset curve; an inclination curve drawing unit, configured to draw a curve showing a change in the inclination angle within the predetermined time period to form an inclination curve; A first determining unit is connected to the offset curve drawing unit and the tilt curve drawing unit respectively, and is used to determine a plurality of second temporary devices according to the offset curve and the tilt curve.

4. The distribution network dynamic planning system based on digital twin according to claim 3 is characterized in that: The first determining unit includes: a consistency calculation subunit, configured to calculate the cosine similarity between the offset curve and the tilt curve to form a change consistency; The first determining subunit is connected to the consistency calculating subunit and is used to determine the first temporary device as the second temporary device to form a plurality of second temporary devices when the change consistency is greater than a preset consistency threshold.

5. The distribution network dynamic planning system based on digital twin according to claim 4 is characterized in that: The second determination module includes: a resistance value comparison unit, configured to compare the insulation resistance value with a preset resistance threshold value to form a resistance comparison result; The second determination unit is connected to the resistance value comparison unit and is configured to determine a number of abnormal devices according to the insulation resistance value when the resistance comparison result shows that the insulation resistance is less than the preset resistance threshold.

6. The distribution network dynamic planning system based on digital twin according to claim 5 is characterized in that: The second determining unit includes: a resistance change curve drawing subunit, configured to draw a change curve of the insulation resistance value within a preset second determination time period to form a resistance change curve; a resistance curve slope calculation subunit, configured to calculate the secant slope of the resistance change curve to form a resistance curve slope; The second determination subunit is connected to the resistance curve slope calculation subunit and is used to determine that the second temporary device is the abnormal device when the resistance curve slope is negative and the absolute value of the resistance curve slope is greater than a preset slope absolute value threshold, thereby forming a plurality of abnormal devices.

7. The distribution network dynamic planning system based on digital twin according to claim 6, characterized in that: The adjustment module includes: A reasonable determination unit, configured to determine whether the calculated coincidence rate is reasonable based on the number of the faulty devices and the abnormal devices, and form a reasonable determination result; An adjustment unit is connected to the rationality determination unit and is used to adjust the preset acquisition period based on the rationality determination result, the overlap rate of the faulty device and the abnormal device, and a preset overlap rate threshold to form an adjusted acquisition period.

8. The distribution network dynamic planning system based on digital twin according to claim 7, characterized in that: The rational determination unit includes: a difference absolute value calculation subunit, configured to calculate an absolute value of a difference between the number of the faulty devices and the number of the abnormal devices to form a difference absolute value; The reasonable judgment subunit is connected to the difference absolute value calculation subunit, and is used to determine that the number of devices is comparable when the difference absolute value is less than a preset difference absolute value threshold, and to determine that the calculated overlap rate is reasonable, thereby forming the reasonable judgment result.

9. The distribution network dynamic planning system based on digital twin according to claim 8, characterized in that: The adjustment unit includes: A coincidence rate calculation subunit, used to calculate the coincidence rate of each faulty device and each abnormal device to obtain the coincidence rate; a type determination subunit, connected to the overlap rate calculation subunit, for determining that the overlap type is a low overlap type when the overlap rate is less than the preset overlap rate threshold; An adjustment subunit is connected to the type determination subunit and is used to calculate the relative deviation between the overlap rate and the preset overlap rate threshold based on the low overlap type to form an overlap deviation, and reduce the preset acquisition period according to the overlap deviation and the preset adjustment coefficient to form the adjusted acquisition period.

10. The distribution network dynamic planning system based on digital twin according to claim 9, characterized in that: The correction module includes: a difference deviation calculation unit, configured to calculate a relative deviation between the difference absolute value determined based on the adjusted acquisition period and the preset difference absolute value threshold to form a difference deviation; The correction unit is connected to the difference deviation calculation unit and is used to correct the preset overlap rate threshold according to the difference deviation and a preset correction coefficient when the difference deviation is greater than the preset difference deviation threshold to form a corrected overlap rate threshold.

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