Dynamic adjustment method and system for reclosing locking based on voltage mutual inductance

Abstract

The present invention discloses a dynamic adjustment method and system for reclosing locking based on voltage mutual induction, which relates to the technical field of dynamic adjustment of reclosing locking, and includes the following steps: collecting the voltage waveform of the power system, and if the power supply of the system is not restored, predicting the power fault; obtaining the cable equipment data and grid load data when a fault occurs in the power system; constructing an abnormal evaluation model of the power system based on machine learning according to the cable equipment data and grid load data; obtaining the abnormal evaluation coefficient of the power system in the node area where the voltage waveform is higher than the stable voltage, and locating the fault in the node area based on the preset power system safety and stability coefficient; judging whether to perform a locking operation, if a locking operation is performed, collecting the voltage waveform and the abnormal evaluation coefficient of the power system after maintenance to judge whether the fault is eliminated, and closing the locking of the reclosing. The present invention solves the problems of too long locking time and inability to accurately identify the fault type through dynamic adjustment of reclosing locking.

Description

Technical Field

[0001] The present invention relates to the technical field of reclosing lockout dynamic adjustment, and more specifically, to a method and system for reclosing lockout dynamic adjustment based on voltage mutual inductance. Background Art

[0002] Reclosing is a device in the power system used to restore the power-off state of a line caused by a short circuit or other faults. Its working principle is that when a fault occurs, the circuit breaker first disconnects the circuit, but after a short period of time, the reclosing will automatically re-close the circuit to attempt to restore power supply. The main purpose of reclosing is to deal with short-term and transient faults and reduce the power interruption time caused by short-term faults. Lockout is a safety protection mechanism for reclosing under certain fault conditions. When the reclosing attempts to restore power supply and the fault still exists, the reclosing will enter the lockout state to prevent the equipment from being damaged due to repeated closing or causing other safety problems. Lockout usually requires manual operation or remote operation for reset.

[0003] For example, a high-voltage circuit breaker and its reclosing lockout logic function device disclosed in the invention patent announcement with the publication number of CN117080000A includes the following steps: when the switchgear performs a closing operation, timing and counting are carried out. If the preset number of set operations is reached within the set time, reclosing lockout is required, so that the main closing circuit is disconnected and the closing operation cannot be performed. Wait until the internal temperature of the resistor drops and then restart to solve the problem of overheating of the closing resistor sheet caused by frequent closing operations of the circuit breaker and protect the circuit breaker; at the same time, compared with the existing reclosing lockout logic function circuit, the redundancy of components is reduced and the reliability of the circuit breaker reclosing is improved.

[0004] For example, a voltage transformer secondary voltage intelligent protection device disclosed in the invention patent announcement with the publication number of CN107681633B includes the following steps: this device is arranged after the voltage transformer to perform intelligent overcurrent protection on the secondary voltage generated by the voltage transformer and can automatically reclose after the fault disappears. The present invention combines a tripping device, a fault discrimination device and a CPU control device to perform intelligent protection on the circuit. When the circuit is operating normally, the sampling device continuously collects the real-time current value in the circuit and transmits the collected real-time current value to the CPU control device to control the operation of the tripping device through the CPU control device. When the real-time current value is too large, the CPU control device controls the tripping device to trip to protect the voltage transformer. After the tripping device trips, the CPU control device collects the voltage of the fault discrimination device to analyze what kind of fault occurs at the secondary voltage output end. After waiting for the fault to disappear, the CPU control device controls the tripping device to re-close and restore the secondary voltage output of the voltage transformer.

[0005] In the above disclosed technical solutions, at least the following technical problems exist:

[0006] The enabling of the traditional reclosing locking state is generally based on a set time or a specified number of reclosing operations. If the power fails to return to normal within this time or number of operations, the reclosing enters the locking state. However, these methods can lead to problems such as overly long locking times and the inability to accurately identify the type of fault, affecting the recovery efficiency and flexibility of the power system.

[0007] In view of the above problems, the present invention proposes a solution. Summary of the Invention

[0008] To overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a method and system for dynamically adjusting the reclosing lock based on voltage mutual induction. By dynamically adjusting the reclosing lock, problems such as overly long locking times and the inability to accurately identify the type of fault are solved.

[0009] To achieve the above object, the present invention provides the following technical solutions:

[0010] The method for dynamically adjusting the reclosing lock based on voltage mutual induction includes the following steps: After the reclosing is started once, the voltage waveform of the power system is collected. If the power supply of the system has not been restored, a power fault prediction is performed; cable equipment data and grid load data when a fault occurs in the power system are obtained; based on machine learning, a power system anomaly evaluation model is constructed according to the cable equipment data and the grid load data, and a power system anomaly evaluation coefficient is generated; according to the collected voltage waveform of the power system, the node area where the voltage waveform is higher than the stable voltage is obtained, and the power system anomaly evaluation coefficient of the node area where the voltage waveform is higher than the stable voltage is used to perform fault location on the node area based on a preset power system safety and stability coefficient; according to the fault location of the node, it is determined whether to perform a locking operation on the reclosing of the power system. If a locking operation is performed, it is judged whether the fault has been eliminated by collecting the voltage waveform after maintenance and the power system anomaly evaluation coefficient, and the locking operation of the reclosing is automatically turned off.

[0011] In a preferred embodiment, the method for obtaining the cable equipment data is as follows: The basic cable insulation data of the cable equipment data is obtained, and the basic cable insulation data includes the length of the cable, the resistivity of the insulating material, and the cross-sectional area of the cable; the insulation resistance is calculated by measuring the leakage current and leakage voltage of the cable, and the insulation loss factor is obtained based on the medium loss angle tangent detection method according to the insulation resistance; according to the insulation loss factor and the basic cable insulation data, the cable insulation performance coefficient is calculated based on the cable insulation performance formula; the environmental data, historical operating frequency, and historical aging rate are obtained, and the aging rate of the cable is calculated based on the life cycle analysis method; according to the cable insulation performance coefficient and the aging rate of the cable, the cable equipment data is calculated based on a preset cable equipment calculation formula.

[0012] In a preferred embodiment, the method for obtaining the power grid load data is as follows: Obtain the load data of each node and calculate the current load data and power load data based on the load flow calculation method; obtain the power grid frequency data of each node, and obtain the power grid frequency deviation value based on the preset power grid frequency stability value; calculate the standard deviation of the power grid frequency deviation value to obtain the power grid frequency fluctuation coefficient; calculate the power grid load data based on the current load data, power load data, and power grid frequency load coefficient according to the preset power grid load calculation formula.

[0013] In a preferred embodiment, the steps for constructing a power system anomaly evaluation model based on cable equipment data and power grid load data through machine learning to generate a power system anomaly evaluation coefficient are as follows: Obtain historical cable equipment data and historical power grid load data, and perform preprocessing and normalization operations on the historical cable equipment data and historical power grid load data; divide the processed historical cable equipment data and historical power grid load data into a training set and a test set; use the training set to train the model based on machine learning algorithms and automatically adjust the weight ratio of the historical cable equipment data and historical power grid load data. By capturing the abnormal relationship between the historical cable equipment data, historical power grid load data, and the operation of the power system, construct a power system anomaly evaluation model, and verify and evaluate the model through the test set to obtain a trained model; input the newly obtained cable equipment data and power grid load data into the trained power system anomaly evaluation model to generate a power system anomaly evaluation coefficient.

[0014] In a preferred embodiment, the steps for fault location of the node area with a voltage waveform higher than the stable voltage based on the preset power system safety and stability coefficient for obtaining the power system anomaly evaluation coefficient of the node area are as follows: Perform data analysis on the voltage waveform of the collected power system based on the voltage under stable operation of the power system, and mark the node area where the voltage is higher than the stable voltage; obtain the power system anomaly evaluation coefficient of the marked node area, and perform fault location on the node area based on the preset power system safety and stability coefficient; if the power system anomaly evaluation coefficient is greater than the power system safety and stability coefficient, then this node area is a power system fault area; if the power system anomaly evaluation coefficient is less than or equal to the power system safety and stability coefficient, then this node area is an instantaneous fault area.

[0015] In a preferred embodiment, it is determined whether to block the reclosing of the power system according to the fault location of the node. If the blocking operation is performed, the voltage waveform after maintenance and the power system anomaly evaluation coefficient are collected to determine whether the fault has been eliminated, and the blocking operation of the reclosing is automatically turned off. The specific steps are as follows: If the fault location in the node area is an instantaneous fault area, there is no need to perform the blocking operation of the reclosing, and the reclosing is allowed to automatically reclose after a period of time to restore the power supply of the power system; if the fault location in the node area is a power system fault area, the blocking operation of the reclosing needs to be performed; after the blocking operation of the reclosing is performed, relevant maintenance personnel are notified to conduct inspections and repairs on the power system; the voltage waveform after maintenance and the power system anomaly evaluation coefficient are collected; if the voltage waveform is within the stable voltage and the power system anomaly evaluation coefficient is lower than the power system safety and stability coefficient, the fault is eliminated, and the system automatically turns off the blocking operation of the reclosing.

[0016] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0017] 1. By dynamically adjusting the blocking of the reclosing, the stability of the power system can be effectively improved, unnecessary power outage time can be reduced, and at the same time, misoperations and equipment damage can be avoided, and the self-healing ability and reliability of the system can be enhanced.

[0018] 2. By constructing a model through machine learning, patterns and rules can be automatically identified from the data, the prediction accuracy can be improved, the decision-making process can be optimized, and efficient and intelligent solutions can be provided for complex problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic structural diagram of a reclosing blocking dynamic adjustment method based on a voltage mutual inductor provided in an embodiment of the present application.

[0020] Figure 2 It is a schematic structural diagram of a reclosing blocking dynamic adjustment system based on a voltage mutual inductor provided in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0022] Embodiment 1, Figure 1 It is a schematic structural diagram of a reclosing blocking dynamic adjustment method based on a voltage mutual inductor provided in an embodiment of the present application, including the following steps:

[0023] S1. After the reclosing is started once, collect the voltage waveform of the power system. If the power supply of the system is not restored, perform power fault prediction.

[0024] In this example, voltage transformers are installed at each node of the power system, and the voltage waveform of the power system is collected through the voltage transformers. When a fault occurs in the power system, the circuit breaker will trip, disconnecting the circuit of the power system and stopping the power supply. Then the reclosing will perform the first reclosing operation on the circuit breaker to restore the power supply to the power system. If the power supply of the power system is not restored, it is necessary to predict the fault of the power system and locate the fault of the power system. Fault location can quickly identify the location where the fault occurs and reduce the time for maintenance personnel to find the fault point. This is crucial for restoring power supply, especially in the power system. Reducing the power outage time can minimize the impact on industry, commerce, and residents' lives. Quickly locating the fault helps improve the system's recovery speed and reduce the economic losses caused by the fault. Through accurate fault location, the power company can, when a fault occurs, only isolate the fault area instead of shutting down the entire power network or a large part of it. This can minimize the large-scale power outage caused by the fault, ensure the normal power supply in other areas, and improve the continuity and stability of the power supply.

[0025] S2. Obtain the cable equipment data and grid load data when a fault occurs in the power system.

[0026] In this example, obtain the cable equipment data and grid load data when a fault occurs in the power system. The cable equipment data refers to the relevant data of the cable equipment when a fault occurs in the power system, which is crucial for fault diagnosis, protection operations, and restoring normal operation. The cable equipment data usually refers to various monitoring data of the cable itself during the operation of the power system, including the insulation performance, aging rate, fault location information, etc. of the cable. When a fault occurs, the cable equipment data can help engineers analyze the cause of the fault, determine the fault location, evaluate the status of the cable, and take corresponding repair measures.

[0027] Obtaining the cable equipment data has the following advantages for analyzing power system anomalies and assessments:

[0028] Real-time monitoring and fault prediction: Obtaining the data of the cable equipment can achieve real-time monitoring of the power system. By real-time monitoring of key parameters such as the temperature, current, vibration, and insulation resistance of the cable, potential fault signs can be detected in a timely manner before potential anomalies occur in the system. The analysis of this real-time data helps predict potential faults that may occur in the cable equipment in advance, avoid sudden outages or damages, and reduce the power outage risk.

[0029] Early Fault Detection and Location: Cables in power systems are usually buried underground or difficult to directly observe. Traditional detection methods may require outages for inspection or periodic maintenance. By obtaining real-time health data of cables and combining artificial intelligence or machine learning technologies, early fault detection can be achieved. By monitoring current, voltage fluctuations, temperature anomalies, etc., the area with problems in the cable can be quickly located, and then more precise repair or replacement measures can be taken.

[0030] Data-driven Intelligent Diagnosis: Cable equipment data can support intelligent diagnosis based on big data and artificial intelligence. Through long-term accumulated historical data and combined with machine learning models, trend prediction and fault mode analysis of cable equipment can be carried out. This data-based diagnosis can not only detect potential problems of the equipment, but also analyze the equipment aging trend, predict the remaining service life of the equipment, and thus optimize the maintenance plan of the power system.

[0031] Optimizing Maintenance and Resource Allocation: Through comprehensive analysis of cable equipment data, it can help power companies better manage equipment and allocate resources. For example, by using data to predict the likelihood of equipment failure, the maintenance and replacement plans can be reasonably arranged to avoid frequent occurrence of equipment failures. Through accurate fault assessment, unnecessary maintenance work can also be reduced, saving operation and maintenance costs and improving resource utilization efficiency.

[0032] Improving the Stability of the Power System: The stability and security of the power system are closely related to the operating status of equipment. As an important transmission line, the status of the cable has a huge impact on the stability of the system. Through data acquisition and analysis, cable faults or anomalies can be identified and eliminated earlier, avoiding power system instability or collapse caused by cable faults and ensuring the stable operation of the power system.

[0033] Helping to Develop a Reasonable Backup Power Strategy: By analyzing the status data of cable equipment, power companies can accurately assess which parts have potential risks, and then develop more reasonable backup power and emergency response strategies. For example, when the health status of cable equipment is poor, alternative lines or backup equipment can be arranged in advance to ensure that the power system is not affected.

[0034] Enhancing the Intelligence Level of the Power System: With the development of smart grid technology, more and more power systems are beginning to achieve intelligent management by means of data acquisition and analysis. The acquisition and analysis of cable equipment data can further enhance the intelligence level of the power grid, enabling power companies to manage power facilities in a more efficient and accurate way, reducing manual intervention, and enhancing the self-repairing and self-optimizing capabilities of the system.

[0035] Improving Equipment Life and Efficiency: By continuously monitoring the operating status of cable equipment, abnormal operating conditions such as overload and overheating can be detected in a timely manner, and corresponding measures can be taken to avoid premature aging or damage of the equipment, thereby extending the service life of the cable. In addition, the monitoring data can also help optimize the power transmission efficiency and reduce energy consumption.

[0036] The cable equipment data is obtained specifically as follows:

[0037] Obtain the cable insulation basic data of the cable equipment, where the cable insulation basic data includes the length of the cable, the resistivity of the insulating material, and the cross-sectional area of the cable;

[0038] Calculate the insulation resistance by measuring the leakage current and leakage voltage of the cable, and obtain the insulation loss factor based on the dielectric loss tangent detection method according to the insulation resistance;

[0039] Calculate the cable insulation performance coefficient based on the insulation loss factor in combination with the cable insulation basic data according to the cable insulation performance formula;

[0040] Obtain the environmental data, historical operating frequency, and historical aging rate, and calculate the aging rate of the cable based on the life cycle analysis method;

[0041] Calculate the cable equipment data based on the cable insulation performance coefficient in combination with the aging rate of the cable according to the preset cable equipment calculation formula.

[0042] The specific calculation formula of the cable insulation performance coefficient is as follows:

[0043]

[0044] The specific calculation formula of the aging rate of the cable is as follows:

[0045]

[0046] The specific calculation formula of the cable equipment data is as follows:

[0047]

[0048] In the formula, is the cable equipment data, is the aging rate of the cable, is the cable insulation performance coefficient, is the cable utilization rate, is the resistivity of the insulating material, is the length of the cable, is the cross-sectional area of the cable, is the insulation loss factor, is the insulation resistance, is the cable insulation utilization rate, is the historical aging rate of the cable, is the temperature at which the cable is located, is the humidity at which the cable is located, is the historical operating frequency.

[0049] It should be noted that the aging rate of the cable is usually used to evaluate the performance degradation of the cable during long-term use. The aging rate of the cable can also be predicted through life cycle analysis. This method usually comprehensively considers multiple factors such as material aging, usage environment, and operating frequency for long-term life prediction and aging assessment. The insulation performance of cable equipment is one of the key factors to ensure its safe and reliable operation. Obtaining these data plays an important role in obtaining cable equipment data.

[0050] Grid load data refers to the power demand and consumption of various parts of the power system. These load data are crucial for the safe and stable operation of the power system. Especially during a fault, they can help analyze and evaluate the system's response and provide a basis for restoring power supply.

[0051] Obtaining grid load data has the following advantages for analyzing power system anomalies and assessments:

[0052] Real-time monitoring and early warning: By obtaining grid load data in real time, the operating status of the grid can be dynamically monitored, and the load conditions of each node can be understood in real time. This data can help system operators discover overloaded, unbalanced loads, or other abnormal conditions. The load data can help predict possible system overloads or other anomalies and provide early warnings, so that effective measures can be taken to reduce the occurrence of faults.

[0053] Load fluctuation analysis: Analyzing load fluctuations, the grid load is usually affected by various factors such as weather, season, and time period. By obtaining long-term load data, these fluctuation patterns can be analyzed to help determine whether there are anomalies or potential faults. If the load data mutates, it may be due to a sudden increase in certain loads or a fault in the grid (such as a line fault, equipment failure, etc.). The load data can quickly help identify these sudden changes for timely response.

[0054] Optimizing load distribution: Load balancing, by analyzing the load data of each region and each line, the power system can optimize load distribution, avoid overloading or inefficient operation of certain lines, thereby reducing power losses and improving power supply efficiency. Identifying potential bottlenecks, some regions or lines in the grid may become bottlenecks due to excessive load. Through load data analysis, these potential bottlenecks can be discovered and improved or upgraded in advance.

[0055] Fault Diagnosis and Location: Detect abnormal loads. When the load data changes unexpectedly, it may indicate a fault in a certain part of the power system. By monitoring the changes in the load, it can help engineers quickly locate the fault point. Combining with Other Data Sources: Combining load data with other monitoring data (such as voltage, current, frequency, etc.) can provide more accurate fault diagnosis and help improve the speed and accuracy of fault location.

[0056] Improve Grid Dispatching Efficiency: Assist in dispatching decisions. Load data helps grid dispatchers keep track of power demand and the load conditions of each node in real time. By comprehensively analyzing the load data, dispatchers can make more appropriate dispatching decisions to ensure the stable and efficient power supply. Load Forecasting and Planning: Based on the historical trends of load data and combined with external factors such as weather and holidays, the power system can predict future loads and prepare sufficient reserve capacity in advance to avoid power shortages or over-supplies.

[0057] Support Grid Investment and Planning: Support for Planning Decisions: Grid load data is crucial for the long-term planning of the grid. By analyzing historical load data, it can help planners understand the load demands in different regions and time periods, and optimize the grid expansion and upgrade plans. Equipment Upgrade and Replacement: Load data can also help evaluate the load capacity of existing equipment and guide the decision-making on equipment renewal and replacement, thereby improving the overall reliability and efficiency of the grid.

[0058] Support the Stability Assessment of the System: Stability Analysis: Through load data, stability analysis of the power system can be carried out to evaluate the operating stability of the grid under different load conditions and predict possible instability risks (such as voltage collapse, frequency fluctuations, etc.). Optimize System Configuration: Grid load data can also help evaluate the system's response capabilities under high loads or extreme loads, and then guide the configuration and investment decisions of grid facilities.

[0059] The grid load data is obtained specifically as follows:

[0060] Obtain the load data of each node and get the current load data and power load data based on the load flow calculation method;

[0061] Obtain the grid frequency data of each node, and get the grid frequency deviation value based on the preset grid frequency stability value for the grid frequency data;

[0062] Perform the standard deviation calculation method on the grid frequency deviation value to obtain the grid frequency fluctuation coefficient;

[0063] Calculate the grid load data based on the current load data and power load data in combination with the grid frequency load coefficient according to the preset grid load calculation formula.

[0064] The grid load data has the following specific calculation method:

[0065]

[0066] In the formula, is the grid load data of the i-th node, where i = 1, 2, 3,..., R, R is an integer, N is the number of nodes, is the grid frequency data of the i-th node, is the preset grid frequency stability value, is the current load data of the i-th node, is the power load data of the i-th node.

[0067] It should be noted that the frequency change of the grid reflects the balance between power supply and demand. If the load demand suddenly increases or decreases, it will cause the grid frequency to fluctuate. When the current load data and power load data become larger and larger, it will cause the grid load data to become larger and larger, increasing the failure probability of the power system. Therefore, obtaining the grid frequency data, current load data, and power load data is of great importance for calculating the grid load data.

[0068] S3. Based on the cable equipment data and grid load data, construct a power system anomaly evaluation model using machine learning to generate a power system anomaly evaluation coefficient. The specific steps are as follows:

[0069] Obtain historical cable equipment data and historical grid load data, and perform preprocessing and normalization operations on the historical cable equipment data and historical grid load data;

[0070] Divide the processed historical cable equipment data and historical grid load data into a training set and a test set;

[0071] Use the training set to train the model based on machine learning algorithms and automatically adjust the proportion weights of the historical cable equipment data and historical grid load data. By capturing the abnormal relationship between the historical cable equipment data, historical grid load data, and the operation of the power system, construct a power system anomaly evaluation model, and verify and evaluate the model through the test set to obtain a trained model;

[0072] Input the newly obtained cable equipment data and grid load data into the trained power system anomaly evaluation model to generate a power system anomaly evaluation coefficient.

[0073] The power system anomaly evaluation model has the following specific calculation method:

[0074]

[0075] In the formula, is the abnormal evaluation coefficient of the power system, is the cable equipment data, is the power grid load data, is the weight coefficient of the cable equipment data, is the weight coefficient of the power grid load data.

[0076] It should be noted that data preprocessing, normalization and other operations are carried out to ensure its quality and consistency. When the cable equipment data and the power grid load data are getting larger and larger, the abnormal evaluation coefficient of the power system is also getting larger and larger.

[0077] S4. According to the voltage waveform of the power system collected, obtain the node area where the voltage waveform is higher than the stable voltage, and obtain the abnormal evaluation coefficient of the power system in the node area where the voltage waveform is higher than the stable voltage. Based on the preset power system safety and stability coefficient, conduct fault location for the node area. The specific steps are as follows:

[0078] Conduct data analysis on the voltage waveform of the power system collected based on the voltage under stable operation of the power system, and mark the node area where the voltage is higher than the stable voltage;

[0079] Obtain the abnormal evaluation coefficient of the power system in the marked node area, and conduct fault location for the node area based on the preset power system safety and stability coefficient;

[0080] If the abnormal evaluation coefficient of the power system is greater than the power system safety and stability coefficient, then this node area is the fault area of the power system, indicating that the problem causing the power short - circuit is on the power system equipment at this time. For example, the insulation performance of the cable fails or the cable ages, or the high load of the power grid affects the equipment and causes power outage;

[0081] If the abnormal evaluation coefficient of the power system is less than or equal to the power system safety and stability coefficient, then this node area is the transient fault area, and it is not the problem inside the power system that causes the circuit breaker to trip. The occurrence time of the transient fault is usually very short, lasting from a few milliseconds to a few seconds. After the fault, the system or equipment will quickly return to the normal state.

[0082] S5. According to the fault location of the node, judge whether to perform a blocking operation on the reclosing of the power system. If a blocking operation is performed, judge whether the fault is eliminated by collecting the voltage waveform after maintenance and the abnormal evaluation coefficient of the power system, and automatically close the blocking operation of the reclosing.

[0083] If the fault location of the node area is the transient fault area, there is no need to perform the blocking operation on the reclosing, and let the reclosing automatically reclose after a period of time to restore the power supply of the power system;

[0084] If the fault location in the node area is the fault area of the power system, the reclosing operation needs to be blocked, and the reclosing should not be allowed to perform the reclosing operation to prevent the reclosing from not being blocked, resulting in frequent automatic reclosing of the faulty line. If the fault is not effectively cleared, the equipment will frequently attempt to close, which may cause serious damage to the distribution line or electrical equipment (such as circuit breakers, transformers, cables, etc.), and even pose safety hazards such as fires;

[0085] After the reclosing operation is blocked, notify the relevant maintenance personnel to conduct inspections and repairs on the power system;

[0086] Collect the voltage waveform after maintenance and the power system anomaly evaluation coefficient;

[0087] If the voltage waveform is within the stable voltage and the power system anomaly evaluation coefficient is lower than the power system safety and stability coefficient, the fault is eliminated, and the system automatically turns off the blocking operation of the reclosing.

[0088] Embodiment 2, Figure 2 This is a schematic structural diagram of a reclosing blocking dynamic adjustment system based on voltage mutual induction provided by an embodiment of the present application, including a fault prediction initial module, a data acquisition module, a model construction module, a fault location module, and a blocking operation module, and there are connections between the modules:

[0089] The fault prediction initial module is used to collect the voltage waveform of the power system when the reclosing starts once. If the power supply of the system is not restored, power fault prediction is performed;

[0090] The data acquisition module is used to obtain cable equipment data and grid load data when a fault occurs in the power system;

[0091] The model construction module is used to construct a power system anomaly evaluation model based on cable equipment data and grid load data using machine learning and generate a power system anomaly evaluation coefficient;

[0092] The fault location module is used to obtain the node area where the voltage waveform is higher than the stable voltage according to the collected voltage waveform of the power system, obtain the power system anomaly evaluation coefficient of the node area where the voltage waveform is higher than the stable voltage, and judge the fault location of the node based on the preset power system safety and stability coefficient;

[0093] The blocking operation module is used to judge whether to block the reclosing of the power system according to the fault location of the node. If the blocking operation is performed, it is judged whether the fault is eliminated by collecting the voltage waveform after maintenance and the power system anomaly evaluation coefficient, and the blocking operation of the reclosing is automatically turned off.

[0094] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0095] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0096] Those of ordinary skill in the art can realize that the modules and algorithm steps of each example described in combination with the embodiments disclosed in this article can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0097] In addition, the functional modules in each embodiment of this application can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0098] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0099] Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A dynamic adjustment method for reclosing lockout based on voltage mutual inductance, characterized in that: The steps include: When the reclosing switch is activated once, the voltage waveform of the power system is collected. If the power supply of the system is not restored, the power failure prediction is carried out; Obtain cable equipment data and grid load data when a power system failure occurs; According to the cable equipment data and grid load data, a power system abnormality assessment model is constructed based on machine learning to generate a power system abnormality assessment coefficient; According to the collected voltage waveform of the power system, a node area where the voltage waveform is higher than the stable voltage is obtained, and a power system abnormality assessment coefficient of the node area where the voltage waveform is higher than the stable voltage is obtained; and fault location of the node area is performed based on a preset power system safety and stability coefficient; According to the fault location of the node, the reclosing operation of the power system is judged whether to perform the locking operation. If the locking operation is performed, the voltage waveform after maintenance and the power system abnormality assessment coefficient are collected to determine whether the fault has been eliminated, and the locking operation of the reclosing operation is automatically turned off.

2. The method for dynamic adjustment of reclosing lockout based on voltage mutual inductance according to claim 1 is characterized in that: The specific method for obtaining the cable equipment data is as follows: Acquire basic cable insulation data of the cable equipment data, wherein the basic cable insulation data includes the length of the cable, the resistivity of the insulation material, and the cross-sectional area of ​​the cable; The insulation resistance is calculated by measuring the leakage current and leakage voltage of the cable, and the insulation loss factor is obtained based on the insulation resistance and the dielectric loss tangent detection method; The cable insulation performance coefficient is calculated based on the cable insulation performance formula according to the insulation loss factor combined with the basic data of cable insulation; Obtain environmental data, historical operating frequency and historical aging rate to calculate the aging rate of the cable based on the life cycle analysis method; The cable equipment data is calculated based on the preset cable equipment calculation formula according to the cable insulation performance coefficient and the cable aging rate.

3. The method for dynamic adjustment of reclosing lockout based on voltage mutual inductance according to claim 1, characterized in that: The specific method for obtaining the power grid load data is as follows: Obtaining load data of each node and obtaining current load data and power load data based on a load flow calculation method; Obtaining grid frequency data of each node, and obtaining a grid frequency deviation value based on the grid frequency data and a preset grid frequency stability value; The grid frequency fluctuation coefficient is obtained by using the standard deviation calculation method to calculate the grid frequency deviation value; The grid load data is calculated based on the current load data and the power load data in combination with the grid frequency load factor based on a preset grid load calculation formula.

4. The method for dynamic adjustment of reclosing lockout based on voltage mutual inductance according to claim 1, characterized in that: The specific steps of constructing a power system abnormality assessment model based on machine learning according to the cable equipment data and the power grid load data and generating a power system abnormality assessment coefficient are as follows: Acquire historical cable equipment data and historical power grid load data, and perform preprocessing and normalization operations on the historical cable equipment data and historical power grid load data; Dividing the processed historical cable equipment data and historical power grid load data into a training set and a test set; The model is trained based on the machine learning algorithm using the training set and the weights of the historical cable equipment data and the historical power grid load data are automatically adjusted. By capturing the abnormal relationship between the historical cable equipment data and the historical power grid load data and the power system operation, a power system abnormality assessment model is constructed. The model is then verified and evaluated using the test set to obtain a trained model. The newly acquired cable equipment data and grid load data are input into the trained power system anomaly assessment model to generate the power system anomaly assessment coefficient.

5. The method for dynamic adjustment of reclosing lockout based on voltage mutual inductance according to claim 1, characterized in that: The power system abnormality assessment coefficient of the node area where the voltage waveform is higher than the stable voltage is obtained, and the fault location of the node area is performed based on the preset power system safety and stability coefficient. The specific steps are as follows: The collected voltage waveform of the power system is analyzed based on the voltage under stable operation of the power system, and the node areas where the voltage is higher than the stable voltage are marked; Obtain the power system abnormality assessment coefficient of the marked node area, and locate the fault in the node area based on the preset power system safety and stability coefficient; If the power system abnormality assessment coefficient is greater than the power system safety and stability coefficient, the node area is the power system fault area; If the power system abnormality assessment coefficient is less than or equal to the power system safety and stability coefficient, the node area is an instantaneous fault area.

6. The method for dynamic adjustment of reclosing lockout based on voltage mutual inductance according to claim 1, characterized in that: The method of determining whether to perform a locking operation on the reclosing switch of the power system according to the fault location of the node, and if a locking operation is performed, determining whether the fault is eliminated by collecting the voltage waveform after maintenance and the abnormal evaluation coefficient of the power system, and automatically closing the locking operation of the reclosing switch, the specific steps are as follows: If the fault location of the node area is an instantaneous fault area, there is no need to lock the reclosing switch, and the reclosing switch will automatically reclose after a period of time to restore the power supply of the power system; If the fault location of the node area is the power system fault area, the reclosing operation needs to be performed; After the reclosing lock operation is performed, notify the relevant maintenance personnel to check and repair the power system; Collect voltage waveforms after maintenance and power system abnormality assessment coefficients; If the voltage waveform is within the stable voltage and the power system abnormality assessment coefficient is lower than the power system safety stability coefficient, the fault is eliminated and the system automatically turns off the locking operation of the reclosing switch.

7. The method for dynamic adjustment of reclosing lockout based on voltage mutual inductance according to claim 2, characterized in that: The specific calculation formula of the cable insulation performance coefficient is as follows: The specific calculation formula of the aging rate of the cable is as follows: The specific calculation formula for the cable equipment data is as follows: In the formula, For cable equipment data, is the aging rate of the cable, is the cable insulation performance coefficient, is the cable utilization rate, is the resistivity of the insulating material, is the length of the cable, is the cross-sectional area of ​​the cable, is the insulation loss factor, is the insulation resistance, is the cable insulation utilization rate, is the historical aging rate of the cable, is the temperature of the cable, is the humidity of the cable, is the historical operating frequency.

8. The method for dynamic adjustment of reclosing lockout based on voltage mutual inductance according to claim 3, characterized in that: The specific calculation method of the power grid load data is as follows: In the formula, is the grid load data of the i-th node, where i=1,2,3,...,R, R is an integer, N is the number of nodes, is the grid frequency data of the i-th node, is the preset grid frequency stability value, is the current load data of the i-th node, is the power load data of the i-th node.

9. The method for dynamic adjustment of reclosing lockout based on voltage mutual inductance according to claim 4, characterized in that: The specific calculation method of the power system abnormality assessment model is as follows: In the formula, is the power system abnormality assessment coefficient, For cable equipment data, is the grid load data, is the cable equipment data weight coefficient, is the weight coefficient of power grid load data.

10. A system using the voltage mutual inductance-based reclosing lockout dynamic adjustment method according to any one of claims 1 to 9, characterized in that: It includes the fault prediction initial module, data acquisition module, model building module, fault location module, and locking control module. There are connections between the modules: The initial fault prediction module is used to collect the voltage waveform of the power system after the reclosing switch is started once, and to predict the power fault if the system has not restored the power supply; A data acquisition module is used to acquire cable equipment data and grid load data when a power system failure occurs; A model building module is used to build a power system abnormality assessment model based on machine learning according to cable equipment data and power grid load data, and generate a power system abnormality assessment coefficient; A fault location module is used to obtain a node area where the voltage waveform is higher than the stable voltage according to the collected voltage waveform of the power system, obtain a power system abnormality assessment coefficient of the node area where the voltage waveform is higher than the stable voltage, and determine the fault location of the node based on a preset power system safety and stability coefficient; The interlocking control module is used to determine whether to perform an interlocking operation on the power system's reclosing switch according to the fault location of the node. If an interlocking operation is performed, it is used to determine whether the fault has been eliminated by collecting the voltage waveform after maintenance and the power system abnormality assessment coefficient, and automatically close the interlocking operation of the reclosing switch.

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