A power distribution network dynamic planning system based on digital twinning

Abstract

The application 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 determination module, a determination module, a second determination module, an adjustment module, a correction module and an output module. The current, voltage, position offset, tilt angle and insulation resistance value of the equipment are collected in real time, the simulation analysis of the digital twinning model is combined, the fault equipment and the abnormal equipment can be accurately identified, the acquisition cycle and the coincidence rate threshold are dynamically adjusted, the monitoring efficiency and the accuracy are optimized, the power allocation planning report is generated, a scientific basis is provided for the optimized operation of the power distribution network, especially in emergency situations such as after an earthquake, power supply can be quickly responded and restored, the influence of disasters on the power grid is reduced, and the problems of inaccurate monitoring and inflexible planning caused by the complex influence of the earthquake and excessive dependence on static parameters are effectively solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid planning, and particularly relates to a power distribution network dynamic planning system based on digital twinning. BACKGROUND

[0002] With the wide access of distributed energy and new type of load, the complexity and uncertainty of power distribution network have significantly increased, and the traditional static planning method has been difficult to meet the dynamic and adaptability requirements. The damage of natural disasters such as earthquakes to the power distribution network is particularly serious, which may lead to large-scale power outages and equipment damage, and seriously affect the power supply reliability and recovery capability. Therefore, a dynamic planning system is urgently needed, which can optimize resource allocation before the occurrence of extreme events such as earthquakes, maintain key load power supply during the disaster, and quickly restore power supply after the disaster. Such a system needs to have real-time monitoring, dynamic adjustment and intelligent decision-making capabilities to improve the resilience of the power distribution network and ensure efficient and reliable power supply in complex and variable operating environments.

[0003] The patent document with publication number CN104517239A discloses a power distribution network planning analysis and auxiliary decision-making system, which includes: a data input and output module that stores the data of the power distribution network planning scheme and the results of the topology analysis into a data file; a power supply reliability index generation module that calculates the power distribution network reliability index according to the fault type set by the device attribute in the data file, and transmits the reliability index to the data file; a power supply quality index generation module that calculates the power distribution network power supply quality index according to the device attribute in the data file, and transmits the power supply quality index to the data file; a contingency analysis module that calls the data of the power supply reliability index generation module and the power supply quality index generation module, analyzes various situations that may occur, and transmits the analysis results to the feedback module, the feedback module transmits the analysis results to the self-checking module through a signal, and the self-checking module controls the power supply reliability index generation module and the power supply quality index generation module to calculate the power distribution network reliability index and the power supply quality index again; a recording module that records the accident data that occurs each time to the data file and makes a mark.

[0004] It can be seen that the power distribution network planning analysis and auxiliary decision-making system has the following problems: it mainly relies on static parameters for power distribution network planning and analysis, lacks real-time monitoring and response capability for dynamic changes; in the data collection and analysis process, the real-time state of the equipment is not monitored, the collection period and analysis strategy cannot be dynamically adjusted, and it is difficult to discover and handle abnormal situations in time when facing complex and variable power grid operating environments; when analyzing the reliability of the power distribution network, the influence of natural disasters such as earthquakes on the equipment is not fully considered; the feedback mechanism is relatively simple, lacks depth mining and dynamic adjustment of the analysis results; the update is lagging, cannot reflect the latest state of the equipment in real time, and cannot adjust the planning scheme in time when the state of the equipment changes. Summary of the Invention

[0005] To address this, the present invention provides a power distribution network dynamic planning system based on digital twins, which overcomes the problems of inaccurate monitoring and inflexible planning caused by the complex effects of earthquakes and over-reliance on static parameters in the prior art through digital twin technology, real-time monitoring, and dynamic adjustment mechanisms.

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

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

[0008] The simulation module, which is connected to the acquisition module, is used to input the voltage and the current into a preset digital twin model to simulate several faulty devices;

[0009] A first determination module, which is connected to the acquisition module, is used to determine a plurality of first temporary devices based on the current and the voltage;

[0010] A determination module, which is connected to the acquisition module and the first determination module respectively, is used to determine a plurality of second temporary devices based on the position offset and tilt angle of each of the first temporary devices;

[0011] The second determination module is connected to the acquisition module and the determination module respectively, and is used to determine a number of abnormal devices based on the insulation resistance value of each of the second temporary devices.

[0012] An adjustment module, which is connected to the simulation module and the second determination module respectively, is used to adjust the preset acquisition cycle according to the overlap rate of the faulty device and the abnormal device and the preset overlap rate threshold, thereby forming an adjusted acquisition cycle;

[0013] A correction module, which is connected to the adjustment module, is used to correct the preset overlap rate threshold based on the adjusted acquisition cycle to form a corrected overlap rate threshold.

[0014] The output module is connected to the second determination module to generate a power dispatching planning report based on the abnormal equipment re-determined based on the adjusted acquisition cycle or the corrected overlap rate threshold.

[0015] Furthermore, the first determination module includes:

[0016] The current fluctuation calculation unit is used to calculate the standard deviation of the current within a preset first judgment period to form the current fluctuation value;

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

[0018] a first determination unit connected with the current fluctuation calculation unit and the voltage fluctuation calculation unit, configured to determine the to-be-monitored device as 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, and form a plurality of first temporary devices.

[0019] Further, the determination module comprises:

[0020] an offset curve plotting unit configured to plot a change curve of the position offset within a preset determination duration, and form an offset curve;

[0021] a tilt curve plotting unit configured to plot a change curve of the tilt angle within the preset determination duration, and form a tilt curve;

[0022] a first determination unit connected with the offset curve plotting unit and the tilt curve plotting unit, configured to determine a plurality of second temporary devices according to the offset curve and the tilt curve.

[0023] Further, the first determination unit comprises:

[0024] a consistency degree calculation sub-unit configured to calculate a cosine similarity of the offset curve and the tilt curve, and form a change consistency degree;

[0025] a first determination sub-unit connected with the consistency degree calculation sub-unit, configured to determine the first temporary device as the second temporary device when the change consistency degree is greater than a preset consistency degree threshold, and form a plurality of second temporary devices.

[0026] Further, the second determination module comprises:

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

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

[0029] Further, the second determination unit comprises:

[0030] a resistance change curve plotting sub-unit configured to plot a change curve of the insulation resistance value within a preset second determination duration, and form a resistance change curve;

[0031] The resistance curve slope calculation subunit is configured to calculate the slope of the resistance change curve, thereby forming a resistance curve slope.

[0032] The second determination subunit is connected with the resistance curve slope calculation subunit and is configured 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 absolute value threshold of the slope, thereby forming a plurality of abnormal devices.

[0033] Further, the adjustment module comprises:

[0034] The reasonable determination unit is configured to determine whether the coincidence rate is reasonable according to the number of the fault devices and the number of the abnormal devices, thereby forming a determination reasonable result.

[0035] The adjustment unit is connected with the reasonable determination unit and is configured to adjust the preset collection period according to the coincidence rate of the fault devices and the abnormal devices and a preset coincidence rate threshold based on the determination reasonable result, thereby forming an adjusted collection period.

[0036] Further, the reasonable determination unit comprises:

[0037] The difference absolute value calculation subunit is configured to calculate the absolute value of the difference between the number of the fault devices and the number of the abnormal devices, thereby forming a difference absolute value.

[0038] The reasonable determination subunit is connected with the difference absolute value calculation subunit and is configured to determine that the device numbers are comparable and determine that the calculation coincidence rate is reasonable when the difference absolute value is less than a preset difference absolute value threshold, thereby forming the determination reasonable result.

[0039] Further, the adjustment unit comprises:

[0040] The coincidence rate calculation subunit is configured to calculate the device coincidence rate of each of the fault devices and each of the abnormal devices, thereby obtaining a coincidence rate.

[0041] The type determination subunit is connected with the coincidence rate calculation subunit and is configured to determine that the coincidence type is a low coincidence type when the coincidence rate is less than the preset coincidence rate threshold.

[0042] The adjustment subunit is connected with the type determination subunit and is configured to calculate the relative deviation of the coincidence rate and the preset coincidence rate threshold based on the low coincidence type, thereby forming a coincidence deviation, and to decrease the preset collection period according to the coincidence deviation and a preset adjustment coefficient, thereby forming the adjusted collection period.

[0043] Further, the correction module comprises:

[0044] The difference deviation calculation unit is configured to calculate a relative deviation of the difference absolute value determined based on the adjusted acquisition period and the preset difference absolute value threshold, thereby forming a difference deviation.

[0045] The correction unit is connected to the difference deviation calculation unit and is configured to correct a preset coincidence rate threshold according to the difference deviation and a preset correction coefficient when the difference deviation is greater than a preset difference deviation threshold, thereby forming a corrected coincidence rate threshold.

[0046] Compared with the prior art, the present application has the beneficial effects that by collecting the current, voltage, and post-shock position offset, tilt angle, and insulation resistance value of the equipment in real time, combined with the simulation analysis of the digital twin model, the faulty equipment and abnormal equipment can be accurately identified, the logical correlation of multi-dimensional data analysis and hierarchical determination ensures the accuracy and timeliness of fault and anomaly identification. The system dynamically adjusts the acquisition period and the coincidence rate threshold, optimizes the monitoring efficiency and accuracy; finally, the abnormal equipment determined based on the adjusted acquisition period and the corrected coincidence rate threshold generates a power allocation planning report, providing a scientific basis for the optimized operation of the distribution network. The reliability, safety, and economy of the distribution network in complex operating environments are improved, especially in emergency situations such as after an earthquake, the power supply can be quickly restored, reducing the impact of disasters on the power grid, ensuring the continuity and stability of power supply, effectively solving the problems of inaccurate monitoring and inflexible planning caused by the complex effects of earthquakes and excessive reliance on static parameters.

[0047] Further, by monitoring and calculating the standard deviation of the current and voltage in real time and combining threshold judgment, the equipment with abnormal current or voltage fluctuation can be quickly and accurately identified and marked as the first temporary equipment. Not only can it effectively distinguish between normal operating state and abnormal state, avoiding misjudgment of equipment state due to short-term fluctuation, but also can ensure timely discovery of potential problems, improve monitoring sensitivity and accuracy, and provide timely and reliable data support for subsequent fault diagnosis and abnormal processing, effectively improving the operation monitoring efficiency and fault response speed of the distribution network, enhancing the reliability and stability of the entire system.

[0048] Further, by plotting the offset curve and the tilt curve and determining the second temporary equipment according to these curves, the trend of the offset curve and the tilt curve can reveal whether the equipment has abnormally displaced or tilted due to external factors such as earthquakes or its own failure. By analyzing these two curves, the equipment whose position offset and tilt angle are beyond the normal range can be accurately screened out as the second temporary equipment. The system can intuitively reflect the dynamic changes of the equipment within the preset determination time. Not only does it make full use of the mechanical characteristic data of the equipment combined with electrical parameters to more comprehensively evaluate the equipment state and improve the accuracy of fault and abnormal equipment identification, but also improves the accuracy and reliability of equipment state monitoring. It can also timely capture potential abnormal trends to provide 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, effectively safeguards the safe operation of the power distribution network, and especially after an earthquake, it can quickly identify damaged equipment to speed up the recovery of power supply.

[0049] Further, by calculating the cosine similarity of the offset curve and the tilt curve to form the change consistency degree, and determining the second temporary equipment based on it, the system can accurately identify the consistency of the equipment state change. When the change consistency degree is higher than the preset threshold, it means that the position offset and tilt angle change of the equipment have a high consistency in direction, which usually means that the equipment may have been affected by similar factors (such as external factors such as earthquakes), thereby increasing the possibility of equipment abnormalities. Not only can it effectively screen out equipment that shows abnormalities in mechanical characteristics, but also further improves the accuracy of fault and abnormal equipment identification, improves the accuracy and reliability of equipment state monitoring, and effectively reduces misjudgment to avoid unnecessary alarms due to fluctuations in a single indicator. At the same time, it can more comprehensively reflect the actual operating conditions of the equipment to provide a more scientific basis for fault diagnosis and abnormal handling, thereby improving the operating efficiency and safety of the power distribution network. Especially in complex working conditions, it can quickly lock potential fault equipment to ensure the stability and reliability of power supply.

[0050] Further, by comparing the insulation resistance value with the preset resistance threshold to preliminarily determine the abnormal equipment, the equipment with decreased insulation performance can be quickly identified. The insulation resistance value is an important indicator of the insulation performance of the equipment. When the insulation resistance value is lower than the preset threshold, it means that the insulation performance of the equipment may have a fault or abnormality. The system can quickly and accurately identify equipment with decreased insulation performance, thereby timely discovering potential safety hazards; not only does it improve the efficiency and accuracy of monitoring, but also effectively avoids faults caused by insulation aging or damage, enhancing the reliability and safety of the power distribution network. At the same time, based on clear threshold standards, the system can reduce misjudgment to ensure that only truly abnormal equipment is marked, thereby optimizing the allocation of maintenance resources and reducing operation and maintenance costs.

[0051] Further, by plotting the change curve of the insulation resistance value and calculating the slope of the secant, 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 decreases significantly in a short time, which is usually a clear sign of deterioration of the insulation performance of the device, indicating that the insulation performance of the device is rapidly deteriorating, and there may be insulation aging, dampness, damage or other potential faults. When the slope meets the preset condition, the device is determined to be an abnormal device in time. Not only does it improve the detection sensitivity of insulation faults, but it also quickly issues an alarm when the insulation resistance decreases significantly, so that maintenance measures can be taken in advance to prevent the expansion of device failure. It effectively improves the operation reliability of the power distribution network, reduces the risk of power failure and maintenance cost, especially in complex working conditions, it can quickly lock the potential fault device, and ensure the stability and safety of power supply.

[0052] Further, by analyzing the number of fault devices and abnormal devices and comparing the difference between the number of fault devices and abnormal devices, the comparability of the number of the two is ensured, so as to avoid misjudgment caused by too large number difference, and the collection period is dynamically adjusted according to the calculation result of the coincidence rate and the preset threshold, so as to ensure that data can be collected more frequently in the case of low coincidence rate, and the monitoring accuracy of abnormal devices is improved. Not only does it improve the flexibility and adaptability of monitoring, but it also adjusts the monitoring strategy in time when the device state changes, so as to ensure that the system can operate efficiently under different operating conditions. At the same time, unnecessary data collection and processing are reduced, system resource consumption is reduced, overall operation efficiency is improved, and the reliability and stability of the power distribution network are enhanced.

[0053] Further, by calculating the absolute value of the difference between the number of fault devices and abnormal devices, and comparing it with the preset threshold, when the absolute value of the difference is less than the preset threshold, it means that the number difference between the fault devices and the abnormal devices is within a reasonable range and is comparable, so as to ensure the rationality of the coincidence rate calculation. Not only does it ensure the scientificity and accuracy of the coincidence rate calculation, improve the accuracy and reliability of the monitoring system, avoid misjudgment caused by too large number difference, and more accurately evaluate the coincidence of fault devices and abnormal devices, but it also provides a scientific basis for dynamically adjusting the monitoring strategy, further optimizes the operation efficiency of the system, and enhances the fault diagnosis capability and operation stability of the power distribution network.

[0054] Further, by calculating the coincidence rate of the fault equipment and the abnormal equipment, and dynamically adjusting the collection period according to the comparison result of the coincidence rate and the preset threshold, the system can flexibly adapt to the actual running state of the equipment. When the coincidence rate is high, it indicates that the model is relatively accurate, and the power allocation report can be generated directly according to the fault equipment generated by the model, without the need for more accurate determination; when the coincidence rate is low, it indicates that the matching degree of the fault equipment generated by the model and the abnormal equipment obtained by determination is not high, the model is not accurate, and more frequent monitoring may be needed to capture potential problems, and the collection period is reduced according to the preset adjustment coefficient, and the monitoring frequency is increased. Not only improves the monitoring accuracy and reliability of the system, but also optimizes the resource utilization, avoids unnecessary data collection and processing, and thus improves the overall operation efficiency of the system.

[0055] Further, by calculating the relative deviation of the difference absolute value and the preset difference absolute value threshold, when the difference deviation is less than or equal to the preset difference deviation threshold, it indicates that more accurate identification is achieved by adjusting the collection period, and the power allocation report can be generated according to the abnormal equipment determined by adjusting the collection period; and when the difference deviation exceeds the preset difference deviation threshold, it indicates that there is still a problem after adjusting the collection period, which excludes the influence of the collection period on identifying the abnormal equipment, and the preset coincidence rate threshold is dynamically corrected by using the difference deviation combined with the preset correction coefficient to form a corrected coincidence rate threshold; can dynamically adjust the system parameters according to the actual monitoring data, ensure that the system always maintains high accuracy and reliability in determining the fault equipment and the abnormal equipment under different operating conditions; not only improves the adaptive ability of the system, but also optimizes the monitoring strategy, reduces the possibility of misjudgment and omission, thereby enhancing the fault diagnosis capability and operation stability of the distribution network. BRIEF DESCRIPTION OF DRAWINGS

[0056] Figure 1 a schematic diagram of the distribution network dynamic planning system based on digital twinning of the present embodiment;

[0057] Figure 2 a determination logic diagram for determining the first temporary equipment by the first determination module of the present embodiment;

[0058] Figure 3 a determination logic diagram for determining the second temporary equipment by the first determination unit of the present embodiment;

[0059] Figure 4 a determination logic diagram for determining the abnormal equipment by the second determination unit of the present embodiment. DETAILED DESCRIPTION

[0060] In order to make the purpose and advantages of the present application more clear and explicit, the present application will be further described below in combination with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present application, and do not limit the present application.

[0061] The preferred embodiments of the present application will be described below with reference to the accompanying drawings. Those skilled in the art will appreciate that the embodiments are only used to explain the technical principles of the present application, and are not used to limit the protection scope of the present application.

[0062] Please refer to Figure 1 The figure is a schematic diagram of the power distribution network dynamic planning system based on digital twinning of the present embodiment.

[0063] The present embodiment provides a power distribution network dynamic planning system based on digital twinning, comprising:

[0064] The acquisition module is used to acquire the current and voltage of each to-be-monitored device in the power distribution network in real time, and to acquire the position offset, tilt angle and insulation resistance value of each to-be-monitored device after the earthquake according to a preset acquisition period.

[0065] The simulation module is connected with the acquisition module, and is used to input the voltage and the current into a preset digital twinning model to simulate a plurality of fault devices.

[0066] The first determination module is connected with the acquisition module, and is used to determine a plurality of first temporary devices according to the current and the voltage.

[0067] The determination module is connected with the acquisition module and the first determination module respectively, and is used to determine a plurality of second temporary devices according to the position offset and the tilt angle of each first temporary device.

[0068] The second determination module is connected with the acquisition module and the determination module respectively, and is used to determine a plurality of abnormal devices according to the insulation resistance value of each second temporary device.

[0069] The adjustment module is connected with the simulation module and the second determination module respectively, and is used to adjust the preset acquisition period according to the coincidence rate of the fault devices and the abnormal devices and a preset coincidence rate threshold, to form an adjusted acquisition period.

[0070] The correction module is connected with the adjustment module, and is used to correct the preset coincidence rate threshold based on the adjusted acquisition period, to form a corrected coincidence rate threshold.

[0071] The output module is connected with the second determination module respectively, and is used to generate a power allocation planning report according to the abnormal devices re-determined based on the adjusted acquisition period or the corrected coincidence rate threshold.

[0072] Post-earthquake refers to the impact of vibration and displacement on power distribution network equipment after natural disasters such as earthquakes. The "devices to be monitored" that need to be monitored are different types of electrical equipment and facilities in the power distribution network, such as transformers, switches, lines, and meters. These devices may experience position displacement, structural tilt, and insulation performance changes under conditions such as earthquakes, which can affect the normal operation of the equipment and even cause failures.

[0073] The acquisition module realizes real-time monitoring of the state of power distribution network equipment by integrating various sensors and data acquisition units. Specifically, current and voltage data are collected by current transformers (CT) and voltage transformers (PT) installed on the equipment. These analog signals are converted into digital signals by an analog-to-digital converter (ADC) and transmitted to the central monitoring system through a field bus or wireless network. After an earthquake, the module will automatically activate the built-in GPS sensor to collect information on the small displacement of the equipment's position according to the pre-set acquisition period. The accelerometer and gyroscope are used to measure the tilt angle of the equipment in real time. At the same time, the insulation resistance value of the equipment is obtained through the online insulation monitor. All collected data is filtered and corrected before being stored in the database, providing real-time and accurate data support for subsequent digital twin model simulation, fault diagnosis, and power allocation decisions.

[0074] In post-earthquake monitoring, the three factors of position displacement, tilt angle, and insulation resistance value complement each other: when the equipment experiences significant position displacement and tilt angle abnormalities, it often indicates physical structure damage, which can further affect the insulation performance of the equipment, reduce the insulation resistance value, and increase the risk of electrical leakage or short circuit. By simultaneously collecting these three types of data, the health status of the equipment can be comprehensively evaluated from the perspectives of mechanical stability and electrical safety, enabling early fault warning and effectively improving the overall safety and emergency response capabilities of the power distribution network.

[0075] Faulty equipment refers to equipment that cannot operate normally due to electrical faults (such as short circuits, overloads, insulation breakdown, etc.) simulated by digital twin models. These devices usually have obvious electrical performance problems that affect their normal functions. Abnormal equipment refers to problematic equipment identified through comprehensive analysis of multi-dimensional data such as current, voltage, position displacement, tilt angle, and insulation resistance after an earthquake. These devices may not have completely failed, but have potential problems such as position displacement, excessive tilt angle, and decreased insulation resistance, which may be caused by the earthquake. The identification of faulty equipment relies more on the simulation and analysis of electrical parameters by digital twin models, which do not require an environment and can be identified under any conditions using pre-set models after an earthquake, without considering the impact of earthquakes on detection accuracy. The identification of abnormal equipment requires the combination of multiple sensor data, reference to the impact of earthquakes on distribution network equipment, and determination through hierarchical determination and comprehensive analysis, which is more in line with the requirements of post-earthquake reconstruction of distribution network equipment planning.

[0076] After receiving the latest data from the adjustment module and the correction module, the output module first aggregates the real-time data of all abnormal equipment, including the position displacement, tilt angle, insulation resistance, current, and voltage of the equipment. Based on the insulation resistance, position displacement, and tilt angle of the equipment, and in combination with pre-set thresholds, the equipment is classified according to fault levels. Using a pre-set evaluation model, the geographical location of the equipment, the status of adjacent equipment, and the potential impact on the overall power supply safety are considered to evaluate the risk level of each abnormal equipment. Next, the system uses a pre-set power dispatching algorithm (based on an optimization model or intelligent dispatching algorithm) to perform multi-dimensional analysis on various abnormal equipment, including the geographical location of the equipment, the status of adjacent equipment, the load situation, and the potential impact on the overall power supply safety. Based on the analysis results, multiple emergency solutions are simulated, such as fault isolation, load transfer, and standby power dispatching. Finally, the output module integrates the analysis results and dispatching recommendations to generate a comprehensive power allocation planning report containing equipment status, fault causes, risk assessment, emergency response measures, and load reconstruction planning, providing scientific basis for the power grid dispatching center to achieve rapid fault location, timely isolation of fault sections, ensure stable operation of the overall power grid, and ensure power supply safety.

[0077] For example: After an earthquake, the system selects the following abnormal equipment: Device A: large position displacement, decreased insulation resistance, determined as high-risk equipment; Device B: excessive tilt angle, but normal insulation resistance, determined as medium-risk equipment; Device C: large current fluctuation, slightly decreased insulation resistance, determined as low-risk equipment.

[0078] The output module integrates the analysis results to obtain the countermeasures: (1) Device A: isolation measures: disconnect Device A from the power grid through remote control to prevent fault propagation; load transfer: transfer the load of Device A to the adjacent Device D to ensure uninterrupted power supply; standby power supply activation: activate the standby power supply to provide temporary power supply for the affected area. (2) Device B: load adjustment: appropriately reduce the load of Device B to avoid overloading due to excessive inclination angle. (3) Device C: continuous monitoring: continuously monitor the current fluctuations of Device C to observe whether it returns to normal; preventive maintenance: schedule regular maintenance to check the insulation resistance value of Device C to prevent further decline. Finally, a comprehensive power allocation planning report is generated, including device status, fault cause, risk assessment, emergency response measures, and load reconstruction planning

[0079] The preset acquisition cycle refers to the time interval between two consecutive acquisition operations in the data acquisition system, which depends on signal characteristics, system dynamics, resource utilization, and application scenarios, and is usually set between 30 seconds and 5 minutes. In this embodiment, it is set to 2 minutes, which can meet the needs of real-time monitoring, reasonably utilize system resources, and quickly respond to changes in device status in emergency situations, improving system reliability and efficiency.

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

[0081] 1. Physical modeling layer

[0082] Structure and material parameters: Establish a basic physical model based on the structure, material composition, and electromagnetic characteristics of equipment in the distribution network, such as transformers, switches, and lines. This part describes the electrical and mechanical behavior of the equipment under normal and abnormal operating conditions through mathematical formulas and simulation tools such as finite element analysis.

[0083] Device state and environmental coupling: Consider the displacement, inclination, and insulation degradation of the equipment under the action of earthquakes or other external forces, and realize quantitative simulation of the change in the state of the equipment by comparing the preset static initial state with real-time monitoring data.

[0084] 2. Data-driven correction layer

[0085] Historical data fusion: Introduce historical data accumulated by the device during long-term operation (such as device failure records, daily operation parameters) into the model, correct the parameters in the physical model, and ensure that the model output is more consistent with the actual situation.

[0086] Real-time data feedback: Use real-time data collected by sensors (current, voltage, GPS displacement, tilt angle, insulation resistance, etc.) to dynamically update the model. Through this feedback mechanism, the model can immediately reflect the actual operating state of the device and predict potential failures.

[0087] 3. Fault simulation and prediction layer

[0088] Fault mode integration: For short-circuit, poor contact, insulation degradation, structural deformation and other fault modes that may occur in the device, preset corresponding fault states and response mechanisms in the model.

[0089] Simulation: Combine physical modeling and data feedback to simulate the running track and fault evolution process of the device under different working conditions. By comparing with real-time data, the model can identify and warn the faulty device.

[0090] The preset coincidence rate threshold refers to a preset standard value used to judge the coincidence degree of the faulty device and the abnormal device in system operation, which depends on system reliability requirements, device fault characteristics, monitoring accuracy and application scenario needs, and is usually set between 50% and 90%. In this embodiment, it is set to 70%, which can ensure the detection sensitivity of the system to the faulty device while effectively reducing false positives, so as to timely capture the coincidence of the faulty device and the abnormal device, thereby quickly adjusting the monitoring strategy and improving the overall performance and reliability of the system.

[0091] By monitoring the current and voltage of each device in the power distribution network in real time, and acquiring the position offset, tilt angle and insulation resistance value of the post-earthquake device according to the preset collection period; then, input the collected voltage and current data into the digital twin model, and identify the potential faulty device through simulation analysis. At the same time, according to the current and voltage data, a number of first temporary devices are preliminarily screened out; further combined with the position offset and tilt angle of these temporary devices, more accurate second temporary devices are determined; according to the insulation resistance value of these devices, the abnormal device is finally determined. According to the coincidence rate of the faulty device and the abnormal device and its comparison with the preset coincidence rate threshold, the collection period is dynamically adjusted to optimize the monitoring efficiency; based on the adjusted collection period, the coincidence rate threshold is corrected to further optimize the system performance. Finally, according to the adjusted collection period or the corrected coincidence rate threshold, the abnormal device is re-evaluated, and a power allocation planning report is generated to provide decision support for the optimized operation of the power distribution network.

[0092] By collecting the current, voltage, and post-shock position offset, tilt angle, and insulation resistance value of the equipment in real time, combined with the simulation analysis of the digital twin model, the faulty equipment and abnormal equipment can be accurately identified. The logical correlation of multi-dimensional data analysis and hierarchical determination ensures the accuracy and timeliness of fault and anomaly identification. The system dynamically adjusts the collection period and coincidence rate threshold to optimize the monitoring efficiency and accuracy. Finally, based on the adjusted collection period and the corrected coincidence rate threshold, an abnormal equipment power allocation planning report is generated, providing a scientific basis for the optimal operation of the distribution network. The reliability, safety, and economy of the distribution network in complex operating environments are improved, especially in emergency situations such as after an earthquake, the system can quickly respond and restore power supply, reducing the impact of disasters on the power grid, ensuring the continuity and stability of power supply, and effectively solving the problem of inaccurate monitoring and inflexible planning due to the complex effects of earthquakes and excessive reliance on static parameters.

[0093] Please continue to read Figure 2 The first determination module determines the determination logic diagram of the first temporary equipment, as shown in the figure;

[0094] The first determination module includes:

[0095] The current fluctuation calculation unit is used to calculate the standard deviation of the current within the preset first determination duration to form a current fluctuation value.

[0096] The voltage fluctuation calculation unit is used to calculate the standard deviation of the voltage within the preset first determination duration to form a voltage fluctuation value.

[0097] The first determination unit is connected with the current fluctuation calculation unit and the voltage fluctuation calculation unit, respectively, to determine that the equipment to be monitored is the first temporary equipment when the current fluctuation value is greater than the preset current fluctuation threshold, or when the voltage fluctuation value is greater than the preset voltage fluctuation threshold, forming a number of first temporary equipment.

[0098] The preset first determination duration is the time window length for statistical analysis of current and voltage data to determine whether the equipment has abnormal fluctuations, which depends on signal characteristics, system dynamics, noise level, and application scenarios, and is usually set between 10 seconds and 30 seconds. In this embodiment, it is set to 15 seconds, which can capture abnormal fluctuations in time and smooth noise, improve the accuracy and reliability of determination, and adapt to the dynamic changes of the distribution network.

[0099] The preset current fluctuation threshold refers to a standard value set when monitoring the current, used to determine whether the current has abnormal fluctuation, and depends on the rated current of the device, the circuit capacity, the power supply capability and the actual application scenario, and is usually set between 1.2 to 1.5 times of the rated current. In this embodiment, it is set to 1.3 times of the rated current, which can effectively identify abnormal fluctuations and avoid false alarms, ensuring the reliable operation of the system.

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

[0101] The standard deviations of the current and voltage in the preset first determination duration are calculated to obtain the current fluctuation value and the voltage fluctuation value; then compared with the preset current fluctuation threshold and voltage fluctuation threshold. 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 as the first temporary device, and a plurality of first temporary devices are obtained.

[0102] By real-time monitoring and calculating the standard deviations of the current and voltage, and combining with the threshold judgment, the device with abnormal current or voltage fluctuation can be quickly and accurately identified and marked as the first temporary device. Not only can it effectively distinguish between normal operation state and abnormal state, avoiding misjudgment of device state due to short-term fluctuation, but also can ensure timely discovery of potential problems, improve the sensitivity and accuracy of monitoring, provide timely and reliable data support for subsequent fault diagnosis and abnormal processing, effectively improve the operation monitoring efficiency and fault response speed of the power distribution network, and enhance the reliability and stability of the entire system.

[0103] Specifically, the determination module comprises:

[0104] The offset curve drawing unit is configured to draw a change curve of the position offset amount in the preset determination duration to form an offset curve.

[0105] The inclination curve drawing unit is configured to draw a change curve of the inclination angle in the preset determination duration to form an inclination curve.

[0106] The first determination unit is respectively connected with the offset curve drawing unit and the inclination curve drawing unit, and is configured to determine a plurality of second temporary devices according to the offset curve and the inclination curve.

[0107] The preset determination duration refers to a time window length for analyzing the position offset and the inclination angle change when monitoring the equipment state, and is usually set between 10 seconds and 60 seconds according to the dynamic characteristics of the equipment, the monitoring accuracy requirement, the data processing capacity and the application scenario. In the embodiment, the preset determination duration is set to 30 seconds, which can effectively smooth short-term fluctuations and avoid excessive system burden, and ensure the accuracy of monitoring and efficient operation of the system.

[0108] By continuously monitoring the position offset and the inclination angle of the equipment within the preset determination duration, the corresponding change curves, i.e., the offset curve and the inclination curve, are drawn. Subsequently, based on the change trend and the characteristics of the two curves, the second temporary equipment is determined.

[0109] By drawing the offset curve and the inclination curve and determining the second temporary equipment according to the curves, the change trend of the offset curve and the inclination curve can reveal whether the equipment has abnormal displacement or inclination due to external factors such as earthquakes or its own failure. By analyzing the two curves, those equipment whose position offset and inclination angle are out of the normal range can be accurately screened and determined as the second temporary equipment. The system can intuitively reflect the dynamic change of the equipment within the preset determination duration. Not only the mechanical characteristic data of the equipment are fully utilized, but also the electrical parameters are combined to more comprehensively evaluate the equipment state, improve the accuracy of fault and abnormal equipment identification, and improve the accuracy of equipment state monitoring. In addition, potential abnormal trends can be captured in time to provide strong support for subsequent fault diagnosis and maintenance. At the same time, the possibility of misjudgment is reduced, the reliability and stability of the system are improved, and the safe operation of the power distribution network is effectively ensured. Especially after an earthquake, damaged equipment can be quickly identified to speed up the recovery of power supply.

[0110] Please continue to refer to Figure 3 Fig. 2 is a determination logic diagram of the first determination unit for determining the second temporary equipment, as shown in the figure, which is the determination logic diagram of the first determination unit for determining the second temporary equipment in the embodiment;

[0111] The first determination unit comprises:

[0112] A consistency degree calculation subunit is configured to calculate the cosine similarity of the offset curve and the inclination curve to form a change consistency degree.

[0113] A first determination subunit is connected with the consistency degree calculation subunit and configured to determine the first temporary equipment as the second temporary equipment when the change consistency degree is greater than a preset consistency degree threshold, and form a plurality of second temporary equipment.

[0114] The preset consistency threshold is a standard value for judging the similarity degree of two curves, and is usually set between 0.8 and 0.95 according to the device characteristics, monitoring accuracy requirements and application scene requirements. In this embodiment, it is set to 0.9, which can effectively identify abnormal devices and reduce misjudgment, ensuring efficient operation of the system.

[0115] The change consistency is obtained by calculating the cosine similarity between the offset curve and the tilt curve. Then, when the change consistency exceeds the preset consistency threshold, the first temporary device is determined as the second temporary device, thereby screening out a plurality of second temporary devices.

[0116] The change consistency is formed by calculating the cosine similarity between the offset curve and the tilt curve, and the second temporary device is determined based on the change consistency. The system can accurately identify the consistency of the device state change. When the change consistency is higher than the preset threshold, it means that the position offset and the tilt angle change of the device have a high consistency in direction, which usually means that the device may be affected by similar factors (such as external factors such as earthquakes), thereby increasing the possibility of device abnormality. Not only can it effectively screen out devices that show abnormalities in mechanical characteristics, but also can further improve the accuracy of fault and abnormal device identification, improve the accuracy and reliability of device state monitoring, and effectively reduce misjudgment to avoid unnecessary alarms due to fluctuations in a single indicator. At the same time, it can more comprehensively reflect the actual operating conditions of the device, provide a more scientific basis for fault diagnosis and abnormal processing, thereby improving the operating efficiency and safety of the power distribution network, especially in complex working conditions, it can quickly lock the potential fault device, and ensure the stability and reliability of power supply.

[0117] Specifically, the second determination module comprises:

[0118] The resistance value comparison unit is configured to compare the insulation resistance value with a preset resistance threshold to form a resistance comparison result.

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

[0120] The preset resistance threshold is a key parameter for monitoring the insulation state of the device, and is usually set between 1MΩ and 10MΩ according to the rated voltage, current, safety standard, tolerance and actual operating environment of the device. In this embodiment, it is set to 4MΩ, which can effectively identify abnormal changes in insulation resistance and avoid misjudgment, ensuring safe operation of the device.

[0121] The insulation resistance value is compared with the preset resistance threshold value to obtain a resistance comparison result. If the comparison result shows that the insulation resistance value is less than the preset resistance threshold value, then the abnormal equipment is further determined according to the insulation resistance value.

[0122] The insulation resistance value is compared with the preset resistance threshold value to obtain a resistance comparison result. If the comparison result shows that the insulation resistance value is less than the preset resistance threshold value, then the abnormal equipment is further determined according to the insulation resistance value.

[0123] Please continue to refer to Figure 4 The second determination unit determines the abnormal equipment according to the insulation resistance value and the preset resistance threshold value.

[0124] The second determination unit includes:

[0125] The resistance change curve drawing subunit is configured to draw a change curve of the insulation resistance value within a preset second determination duration, thereby forming a resistance change curve.

[0126] The resistance curve slope calculation subunit is configured to calculate a slope of the resistance change curve, thereby forming a resistance curve slope.

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

[0128] The preset second determination duration refers to a time window length for drawing the resistance change curve when monitoring the change of the insulation resistance value. The time window length depends on the dynamic characteristics of the insulation resistance change of the equipment, the monitoring accuracy requirement, and the data processing capability, and is usually set between 10 seconds and 60 seconds. In this embodiment, the preset second determination duration is set to 30 seconds, which can effectively smooth short-term fluctuations while ensuring monitoring accuracy, thereby avoiding misjudgment caused by temporary abnormal data. At the same time, the preset second determination duration does not cause excessive burden on the data processing capability of the system, thereby ensuring efficient operation of the system.

[0129] The preset slope absolute value threshold refers to a standard value for judging whether the slope of the curve is abnormal when analyzing the resistance change curve. The standard value depends on the slope range of the insulation resistance change when the equipment is in normal operation, the insulation performance of the equipment, and the safety requirements of the application scenario. The preset slope absolute value threshold is usually set between 0.1 and 1.0. In the embodiment, the preset slope absolute value threshold is set to 0.5. The preset slope absolute value threshold can effectively reduce false positives while ensuring the sensitivity of the system, and can timely capture significant changes in the insulation resistance, thereby quickly identifying potential faults of the equipment and improving the reliability and safety of the system.

[0130] By drawing the change curve of the insulation resistance value within the preset second determination duration, a resistance change curve is formed. Subsequently, the slope of the resistance change curve is calculated. If the resistance curve slope is negative and its absolute value is greater than the preset slope absolute value threshold, the system will determine that the second temporary equipment is an abnormal equipment, thereby screening out several abnormal equipments.

[0131] By drawing the change curve of the insulation resistance value and calculating the slope of the change curve, 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 equipment has decreased significantly in a short period of time, which is usually a clear sign of deterioration of the insulation performance of the equipment, indicating that the insulation performance of the equipment is deteriorating rapidly, and there may be insulation aging, moisture, damage or other potential faults. When the slope meets the preset conditions, the equipment is determined to be an abnormal equipment in a timely manner. Not only does it improve the detection sensitivity of insulation faults, but it also quickly issues an alarm when the insulation resistance decreases significantly, thereby taking maintenance measures in advance to prevent the expansion of equipment failure. This effectively improves the operation reliability of the power distribution network, reduces the risk of power outages and maintenance costs, and especially in complex working conditions, it can quickly lock the potential fault equipment to ensure the stability and safety of power supply.

[0132] Specifically, the adjustment module includes:

[0133] The reasonable determination unit is configured to determine whether the coincidence rate is reasonable according to the number of the fault equipment and the abnormal equipment, and form a determination reasonable result.

[0134] The adjustment unit is connected with the reasonable determination unit, and is configured to adjust the preset collection period according to the coincidence rate of the fault equipment and the abnormal equipment and a preset coincidence rate threshold based on the determination reasonable result, and form an adjusted collection period.

[0135] The coincidence rate can evaluate the accuracy and reliability of the fault detection system. The coincidence rate can be obtained by calculating the ratio of the number of coincidences of the fault equipment and the abnormal equipment to the number of abnormal equipment. However, if the number difference between the fault equipment and the abnormal equipment is too large, there is no comparability, and the coincidence rate is not reasonable.

[0136] The coincidence rate is determined to be reasonable by the number of fault devices and abnormal devices. If it is determined to be reasonable, the system will further adjust the preset collection period according to the coincidence rate of the fault device and the abnormal device and the preset coincidence rate threshold, thereby forming a new adjusted collection period.

[0137] By analyzing the number of fault devices and abnormal devices and comparing the difference between the number of fault devices and abnormal devices, it is ensured that the number of the two is comparable, thereby avoiding misjudgment caused by too large number difference. According to the calculation result of the coincidence rate and the preset threshold, the collection period is dynamically adjusted to ensure that data can be collected more frequently in the case of low coincidence rate, thereby improving the monitoring accuracy of abnormal devices. Not only the flexibility and adaptability of monitoring are improved, but also the monitoring strategy can be adjusted in time when the device state changes, thereby ensuring that the system can efficiently operate under different operating conditions. At the same time, unnecessary data collection and processing are reduced, system resource consumption is reduced, overall operation efficiency is improved, and the reliability and stability of the power distribution network are enhanced.

[0138] Specifically, the reasonable determination unit comprises:

[0139] The difference absolute value calculation subunit is configured to calculate the absolute value of the difference between the number of fault devices and the number of abnormal devices, thereby forming a difference absolute value.

[0140] The reasonable determination subunit is connected with the difference absolute value calculation subunit and is configured to determine that the number of devices is comparable when the difference absolute value is less than a preset difference absolute value threshold, thereby determining that the calculation coincidence rate is reasonable and forming the determination reasonable result.

[0141] The preset difference absolute value threshold is used to determine whether the absolute value of the difference between the number of fault devices and the number of abnormal devices is within a reasonable range, which depends on the reliability requirement of the system, the device fault characteristics, the monitoring accuracy and the application scene demand. It is usually set between 1 and 10, and is set to 5 in this embodiment. It can effectively reduce misjudgment while ensuring the sensitivity of the system in detecting the number of fault devices and abnormal devices. It can timely capture the change of the number of devices, thereby quickly adjusting the monitoring strategy and improving the overall performance and reliability of the system.

[0142] By calculating the absolute value of the difference between the number of fault devices and the number of abnormal devices, the system will consider that the number of the two is comparable and that the coincidence rate of the fault device and the abnormal device is reasonable if the absolute value of the difference is less than the preset difference absolute value threshold.

[0143] By calculating the absolute value of the difference between the number of fault devices and the number of abnormal devices, and comparing it with the preset threshold, when the absolute value of the difference is less than the preset threshold, it means that the difference in the number of fault devices and abnormal devices is within a reasonable range and is comparable, thereby ensuring the rationality of the coincidence rate calculation. Not only ensures the scientificity and accuracy of the coincidence rate calculation, improves the accuracy and reliability of the monitoring system, avoids misjudgment caused by too large difference in the number of devices, can more accurately evaluate the coincidence of fault devices and abnormal devices, but also provides a scientific basis for dynamically adjusting the monitoring strategy, further optimizes the operation efficiency of the system, enhances the fault diagnosis capability and operation stability of the power distribution network.

[0144] Specifically, the adjustment unit comprises:

[0145] The coincidence rate calculation sub-unit is configured to calculate the device coincidence rate of each fault device and each abnormal device to obtain the coincidence rate.

[0146] The type determination sub-unit is connected with the coincidence rate calculation sub-unit and configured to determine the coincidence type as a low coincidence type when the coincidence rate is less than the preset coincidence rate threshold.

[0147] The adjustment sub-unit is connected with the type determination sub-unit and configured to calculate the relative deviation between the coincidence rate and the preset coincidence rate threshold based on the low coincidence type to form a coincidence deviation, and reduce the preset collection period according to the coincidence deviation and a preset adjustment coefficient to form the adjusted collection period.

[0148] The preset adjustment coefficient is a numerical factor for adjusting the preset collection period, which makes it more accurate or meets certain standards, depending on the reliability requirements, monitoring accuracy and application scenario requirements of the system, and is usually set between 0.5 and 1.5. In this embodiment, it is set to 0.8, which not only ensures the sensitivity of the system, but also reduces misjudgment and improves the reliability and operation efficiency of the system.

[0149] By calculating the coincidence rate between the fault devices and the abnormal devices, then comparing the calculated coincidence rate with the preset coincidence rate threshold, if the coincidence rate is less than the preset coincidence rate threshold, it is determined that the coincidence type is a low coincidence type, based on the low coincidence type, the relative deviation between the coincidence rate and the preset coincidence rate threshold is calculated to form a coincidence deviation, and the preset collection period is adjusted according to the coincidence deviation and a preset adjustment coefficient to form a new adjusted collection period.

[0150] The low coincidence type refers to a case where the coincidence rate of the fault equipment and the abnormal equipment is lower than the preset threshold. In this case, it is indicated that the matching degree between the fault equipment identified by the model and the abnormal equipment determined is low, which means that the accuracy and reliability of the fault detection system are insufficient. When the low coincidence type occurs, it is indicated that the current collection period may not effectively capture the dynamic changes of the equipment state, and the collection period can be shortened to obtain the equipment operation data more timely, so as to more quickly reflect the changes of the equipment state, which helps to improve the accuracy and timeliness of fault detection, and the system can better adapt to the fluctuations of the equipment operation state, thereby improving the sensitivity of fault detection.

[0151] By calculating the coincidence rate of the fault equipment and the abnormal equipment, and dynamically adjusting the collection period according to the comparison result of the coincidence rate and the preset threshold, the system can flexibly adapt to the actual operation state of the equipment. When the coincidence rate is high, it is indicated that the model is relatively accurate, and the power allocation report can be generated directly according to the fault equipment generated by the model, without the need for more accurate determination. When the coincidence rate is low, it is indicated that the matching degree between the fault equipment generated by the model and the abnormal equipment determined is not high, and the model is not accurate, and more frequent monitoring may be needed to capture potential problems. According to the preset adjustment coefficient, the collection period is reduced, and the monitoring frequency is increased. Not only the monitoring accuracy and reliability of the system are improved, but also the resource utilization is optimized, unnecessary data collection and processing are avoided, and the overall operation efficiency of the system is improved.

[0152] Specifically, the correction module comprises:

[0153] The difference deviation calculation unit is configured to calculate the relative deviation of the difference absolute value determined based on the adjusted collection period and the preset difference absolute value threshold, to form a difference deviation.

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

[0155] The preset difference deviation threshold is a standard value for judging whether the difference deviation exceeds the normal range, and depends on the reliability requirement of the system, the monitoring accuracy, and the sensitivity requirement of the fault detection in the application scenario. It is usually set between 10% and 30%. In this embodiment, it is set to 15%, which can effectively reduce the misjudgment while ensuring the detection sensitivity of the system to the number of fault equipment and abnormal equipment, and ensure the reliability and stability of the system.

[0156] The preset correction coefficient is a numerical factor for correcting the preset coincidence rate threshold, and is set between 0.5 and 1.5 according to the reliability requirement of the system, the monitoring accuracy, and the sensitivity requirement of the application scene for fault detection. In the embodiment, the preset correction coefficient is set to 0.8, which can effectively reduce the misjudgment while ensuring the detection sensitivity of the system to the number of fault devices and abnormal devices, and ensure the reliability and stability of the system.

[0157] The difference deviation is obtained by calculating the relative deviation of the difference absolute value determined based on the adjusted collection period and the preset difference absolute value threshold. When the difference deviation is greater than the preset difference deviation threshold, the preset coincidence rate threshold is corrected according to the difference deviation and the preset correction coefficient to form a corrected coincidence rate threshold.

[0158] By calculating the relative deviation of the difference absolute value and the preset difference absolute value threshold, when the difference deviation is less than or equal to the preset difference deviation threshold, it indicates that more accurate identification is achieved by adjusting the collection period, and the power allocation report can be generated according to the abnormal device determined based on the adjusted collection period. When the difference deviation exceeds the preset difference deviation threshold, it indicates that there is still a problem after adjusting the collection period, which excludes the influence of the collection period on identifying abnormal devices. The preset coincidence rate threshold is dynamically corrected by 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 the system always maintains high accuracy and reliability in identifying fault devices and abnormal devices under different operating conditions. Not only does this improve the adaptive ability of the system, but it also optimizes the monitoring strategy, reduces the possibility of misjudgment and missed judgment, and thus enhances the fault diagnosis capability and operating stability of the power distribution network.

[0159] The technical solutions of the present application have been described in combination with the preferred embodiments shown in the accompanying drawings, but those skilled in the art will readily understand that the protection scope of the present application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to the related technical features without departing from the principles of the present application, and the technical solutions after these changes or replacements will all fall within the protection scope of the present application.

Claims

1. A digital-twin-based dynamic planning system for power distribution networks, characterized by, The method comprises the following steps: The acquisition module is used to acquire the current and voltage of each device to be monitored in the power distribution network in real time, and to acquire the position offset, tilt angle and insulation resistance value of each device to be monitored after the earthquake according to a preset acquisition period; The simulation module is connected with the acquisition module and is used to input the voltage and current into a preset digital twin model to simulate a plurality of faulty devices; The first determination module is connected with the acquisition module and is used to determine a plurality of first temporary devices according to the current and voltage; The determination module is connected with the acquisition module and the first determination module, and is used to determine a plurality of second temporary devices according to the position offset and tilt angle of each first temporary device; The second determination module is connected with the acquisition module and the determination module, and is used to determine a plurality of abnormal devices according to the insulation resistance value of each second temporary device; The adjustment module is connected with the simulation module and the second determination module, and is used to adjust the preset acquisition period according to the coincidence rate of the faulty devices and the abnormal devices and a preset coincidence rate threshold, forming an adjusted acquisition period; The correction module is connected with the adjustment module, and is used to correct the preset coincidence rate threshold based on the adjusted acquisition period, forming a corrected coincidence rate threshold; The output module is connected with the second determination module, and is used to generate a power allocation plan report according to the abnormal devices determined based on the adjusted acquisition period or the corrected coincidence rate threshold; The first determination module comprises: The current fluctuation calculation unit is used to calculate the standard deviation of the current within a preset first determination time period, forming a current fluctuation value; The voltage fluctuation calculation unit is used to calculate the standard deviation of the voltage within the preset first determination time period, forming a voltage fluctuation value; The first determination unit is connected with the current fluctuation calculation unit and the voltage fluctuation calculation unit, 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, forming a plurality of first temporary devices; The determination module comprises: The offset curve drawing unit is used to draw a change curve of the position offset within a preset determination time period, forming an offset curve; The tilt curve drawing unit is used to draw a change curve of the tilt angle within the preset determination time period, forming a tilt curve; The first determination unit is connected with the offset curve drawing unit and the tilt curve drawing unit, and is used to determine a plurality of second temporary devices according to the offset curve and the tilt curve; The first determination unit comprises: The consistency degree calculation sub-unit is used to calculate the cosine similarity of the offset curve and the tilt curve, forming a change consistency degree; The first determination sub-unit is connected with the consistency degree calculation sub-unit, and is used to determine that the first temporary device is the second temporary device when the change consistency degree is greater than a preset consistency degree threshold, forming a plurality of second temporary devices; The second determination module comprises: The resistance value comparison unit is used to compare the insulation resistance value with a preset resistance threshold, forming a resistance comparison result; A second determination unit connected with the resistance value comparison unit is configured to determine a number of abnormal devices according to the insulation resistance value when the resistance comparison result is that the insulation resistance is less than the preset resistance threshold value; The second determination unit comprises: A resistance change curve drawing subunit configured to draw a change curve of the insulation resistance value within a preset second determination duration, forming a resistance change curve; A resistance curve slope calculation subunit configured to calculate a slope of the resistance change curve, forming a resistance curve slope; A second determination subunit connected with the resistance curve slope calculation subunit is configured 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 absolute value threshold value of the slope, forming the number of abnormal devices.

2. The digital-twin-based dynamic power distribution network planning system of claim 1, wherein, The adjustment module comprises: A reasonable determination unit configured to determine whether the coincidence rate is reasonable according to the number of the fault devices and the number of the abnormal devices, forming a determination reasonable result; An adjustment unit connected with the reasonable determination unit is configured to adjust the preset collection period according to the coincidence rate of the fault devices and the abnormal devices and a preset coincidence rate threshold value based on the determination reasonable result, forming an adjusted collection period.

3. The digital-twin-based dynamic power distribution network planning system of claim 2, wherein, The reasonable determination unit comprises: A difference absolute value calculation subunit configured to calculate an absolute value of a difference between the number of the fault devices and the number of the abnormal devices, forming a difference absolute value; A reasonable determination subunit connected with the difference absolute value calculation subunit is configured to determine that the number of devices is comparable and determine that the calculation of the coincidence rate is reasonable when the difference absolute value is less than a preset difference absolute value threshold value, forming the determination reasonable result.

4. The digital-twin-based dynamic power distribution network planning system of claim 3, wherein, The adjustment unit comprises: A coincidence rate calculation subunit configured to calculate a device coincidence rate of each of the fault devices and each of the abnormal devices, obtaining a coincidence rate; A type determination subunit connected with the coincidence rate calculation subunit is configured to determine that the coincidence type is a low coincidence type when the coincidence rate is less than the preset coincidence rate threshold value; An adjustment subunit connected with the type determination subunit is configured to calculate a relative deviation between the coincidence rate and the preset coincidence rate threshold value based on the low coincidence type, forming a coincidence deviation, and decrease the preset collection period according to the coincidence deviation and a preset adjustment coefficient, forming the adjusted collection period.

5. The digital-twin-based dynamic power distribution network planning system of claim 4, wherein, The correction module comprises: A difference deviation calculation unit configured to calculate a relative deviation between the difference absolute value determined based on the adjusted collection period and the preset difference absolute value threshold value, forming a difference deviation; A correction unit connected with the difference deviation calculation unit is configured to correct the preset coincidence rate threshold value according to the difference deviation and a preset correction coefficient when the difference deviation is greater than a preset difference deviation threshold value, forming a corrected coincidence rate threshold value.

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