Methods, devices, equipment, storage media, and program products for error prevention verification based on distribution network self-healing control.

Abstract

This application relates to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for error prevention verification based on distribution network self-healing control. The method includes: acquiring at least a first set of operating parameters and a second set of operating parameters for a target power grid line; wherein the first set of operating parameters is collected based on a first time period; the second set of operating parameters is collected based on a second time period; the first time period is earlier than the second time period; determining a first power grid fault type based on the first set of operating parameters; determining a first target prevention strategy from target prevention strategies based on the first power grid fault type; determining a second power grid fault type based on the second set of operating parameters; and executing the first target prevention strategy if the second power grid fault type is the same as the first power grid fault type. This method can improve fault detection accuracy and increase the self-healing rate of the distribution network.

Description

Technical Field

[0001] This application relates to the field of distribution network self-healing control technology, and in particular to a method, device, computer equipment, computer-readable storage medium and computer program product for preventing errors based on distribution network self-healing control. Background Technology

[0002] With the development of smart distribution networks, self-healing control technology for distribution networks has emerged. Without the need to send relevant technicians for maintenance, the distribution network system can automatically restore the problematic state to a pre-set normal state through some means. This saves human resources and also reduces the dangers for staff during maintenance.

[0003] In traditional technologies, when a fault is detected in a power grid line, the detection results may be inaccurate due to signal loss and other reasons, which will affect the results of subsequent self-healing strategies. If the fault is not dealt with in a timely manner, it may also cause the fault range to expand, making it difficult for the distribution network to self-heal. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, device, computer equipment, computer-readable storage medium, and computer program product based on distribution network self-healing control to address the above-mentioned technical problems, which can improve the accuracy of fault detection and thus improve the self-healing rate of the distribution network.

[0005] Firstly, this application provides a method for error prevention verification based on self-healing control of a distribution network, the method comprising:

[0006] Acquire at least a first set of operating parameters and a second set of operating parameters for the target power grid line; wherein the first set of operating parameters is collected based on a first time period; the second set of operating parameters is collected based on a second time period; and the first time period is earlier than the second time period.

[0007] Based on the first set of operating parameters, the first power grid fault type is determined;

[0008] The first target prevention strategy is determined from the target prevention strategy based on the first power grid fault type;

[0009] Based on the second set of operating parameters, the second power grid fault type is determined;

[0010] If the second power grid fault type is the same as the first power grid fault type, then the first target prevention strategy is executed.

[0011] In one embodiment, the method further includes:

[0012] If the second power grid fault type is different from the first power grid fault type, then the third set of operating parameters of the target power grid line is obtained. The third set of operating parameters is collected based on a third time period, which is later than the second time period.

[0013] Based on the third set of operating parameters, the third power grid fault type is determined;

[0014] If the third power grid fault type is the same as the second power grid fault type, then a second target prevention strategy is determined based on at least one of the third power grid fault type and the second power grid fault type, and the second target prevention strategy is executed.

[0015] If the third power grid fault type is different from the second power grid fault type, an alarm message is output.

[0016] In one embodiment, the first set of operating parameters of the target power grid line carries a type identifier; prior to determining the first target prevention strategy from the target prevention strategy based on the first fault type, the method further includes:

[0017] Match at least one set of historical operating parameters of the target circuit line from the historical database based on the type identifier;

[0018] The first set of operating parameters is compared with the historical operating parameters, and a reference prevention strategy is determined based on the comparison results and the reference prevention strategy is executed.

[0019] Based on the effectiveness of the reference prevention strategy, a target prevention strategy is determined.

[0020] In one embodiment, the historical operating parameters include at least two sets; comparing the first set of operating parameters with the historical operating parameters includes:

[0021] The first operating parameter curve is determined based on the first set of operating parameters and the corresponding time sequence; the corresponding historical operating parameter curve is determined based on each of the historical operating parameters and the corresponding time sequence.

[0022] Obtain the overall similarity between each of the historical operating parameter curves and the first operating parameter curve;

[0023] Each overall similarity score greater than the overall similarity threshold is taken as the target overall similarity score, and the target historical running parameter curve corresponding to the largest target overall similarity score is selected from the target overall similarity scores.

[0024] Compare the first operating parameter curve with the target historical operating parameter curve.

[0025] In one embodiment, the comparison result includes at least one point in the target historical operating parameter curve where the local similarity is greater than a local similarity threshold; determining a reference prevention strategy based on the comparison result includes:

[0026] Select the point with the highest local similarity from the points whose local similarity is greater than the local similarity threshold as the target point;

[0027] The historical running curve of the target within the target time period after the target point is used as the predicted curve of the first running parameter curve.

[0028] Obtain the prevention strategy corresponding to the power grid fault type of the predicted curve, and use it as a reference prevention strategy.

[0029] In one embodiment, determining the target prevention strategy based on the execution effect of the reference prevention strategy includes:

[0030] Obtain the self-healing rate of the power grid lines at the target time point after implementing the reference prevention strategy;

[0031] Delete the reference prevention strategies whose self-healing rate is less than the self-healing rate threshold, and determine the target prevention strategy.

[0032] Secondly, this application provides a fault-prevention verification device based on distribution network self-healing control, the device comprising:

[0033] The first acquisition module is used to acquire at least a first set of operating parameters and a second set of operating parameters of the target power grid line; wherein, the first set of operating parameters is acquired based on a first time period; the second set of operating parameters is acquired based on a second time period; and the first time period is earlier than the second time period.

[0034] The first fault type determination module is used to determine the first power grid fault type based on the first set of operating parameters;

[0035] The first strategy determination module is used to determine a first target prevention strategy from the target prevention strategy based on the first power grid fault type;

[0036] The second fault type determination module is used to determine the second power grid fault type based on the second set of operating parameters;

[0037] An execution module is configured to execute the first target prevention strategy if the second power grid fault type is the same as the first power grid fault type.

[0038] Thirdly, this application provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0039] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0040] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method.

[0041] The aforementioned method, device, computer equipment, computer-readable storage medium, and computer program product for error prevention verification based on distribution network self-healing control first acquires a first set of operating data collected over a first time period of the target power grid line in the smart distribution network, determines the first power grid fault type based on the first set of operating data, and determines a prevention strategy based on the first power grid fault type; secondly, it acquires a second set of operating data collected over a second time period of the target power grid line, determines the second power grid fault type based on the second set of operating data, and finally determines whether the second power grid fault type is the same as the first power grid fault type. If the second power grid fault type is the same as the first power grid fault type, it indicates that the first power grid fault type diagnosed based on the first set of operating parameters is correct, thereby preventing the problem of inaccurate fault diagnosis due to signal loss, improving fault detection accuracy, and executing the first target prevention strategy determined based on the first power grid fault type, thereby improving the self-healing rate of the distribution network. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is an application environment diagram of a method for preventing errors based on self-healing control of a distribution network in one embodiment;

[0044] Figure 2 This is a flowchart illustrating a method for preventing errors in a distribution network self-healing control system, as shown in one embodiment.

[0045] Figure 3 This is a flowchart illustrating the anti-misoperation verification method based on distribution network self-healing control in another embodiment; Figure 4 This is a schematic diagram illustrating the process of generating a target prevention strategy in one embodiment;

[0046] Figure 5 This is a flowchart illustrating the comparison of the first set of operating parameters and historical operating parameters in one embodiment;

[0047] Figure 6 This is a schematic diagram illustrating the process of generating a reference prevention strategy in one embodiment;

[0048] Figure 7 This is a schematic diagram illustrating the process of generating a target prevention strategy in one embodiment;

[0049] Figure 8 This is a schematic diagram of a fault prevention verification system for self-healing control of a distribution network in another embodiment;

[0050] Figure 9 This is a structural block diagram of a fault prevention verification device based on distribution network self-healing control in one embodiment;

[0051] Figure 10 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0053] The error prevention verification method based on distribution network self-healing control provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, the smart distribution network 102 communicates with the server 104 via a network. A data storage system can store the data that the server 104 needs to process. The data storage system can be integrated onto the server 104 or placed on the cloud or other network servers. The system acquires at least a first set of operating parameters and a second set of operating parameters for the target power grid line; wherein the first set of operating parameters is collected based on a first time period; the second set of operating parameters is collected based on a second time period; the first time period is earlier than the second time period; based on the first set of operating parameters, a first power grid fault type is determined; based on the first power grid fault type, a first target prevention strategy is determined from the target prevention strategy; based on the second set of operating parameters, a second power grid fault type is determined; if the second power grid fault type is the same as the first power grid fault type, the first target prevention strategy is executed. The smart distribution network 102 may include several power grid lines. The server 104 can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0054] In one exemplary embodiment, such as Figure 2 As shown, a method for preventing errors in the self-healing control of a distribution network is provided, and this method is applied to... Figure 1 Taking the server in the example, the explanation includes the following steps S202 to S210. Wherein:

[0055] Step S202: Obtain at least a first set of operating parameters and a second set of operating parameters for the target power grid line; wherein, the first set of operating parameters is collected based on a first time period; the second set of operating parameters is collected based on a second time period; the first time period is earlier than the second time period.

[0056] The operating parameters include information such as load, frequency, voltage, and current. The time period can be a target number of seconds, such as within 5 seconds or within 10 seconds. The first time period is earlier than the second time period. The first and second time periods can be adjacent time intervals. Taking a 10-second time period as an example, the first time period is 8:00:00-8:00:10; the second time period is 8:00:10-8:00:20. Alternatively, the first and second time periods can be separated by a target time interval, such as the first time period being 8:00:00-8:00:10, the second time period being 8:00:30-8:00:40, and the first and second time periods being 20 seconds apart.

[0057] Optionally, the server can acquire the first set of operating parameters of the target power grid line in the first time period, such as 8:00:00-8:00:10, and the second set of operating parameters in the second time period, such as 8:00:10-8:00:20, through the data acquisition module, and transmit the acquired first set of operating parameters and the second set of operating parameters to the data processing module and the fault diagnosis module.

[0058] Step S204: Determine the first power grid fault type based on the first set of operating parameters.

[0059] Among them, the types of power grid faults can include open circuit (coded as E01), short circuit (coded as E02), ground fault (coded as E03), and overload (coded as E04).

[0060] Optionally, the server uses a fault diagnosis module to determine whether the first set of received operating parameters has a fault. If a fault occurs, such as overload coded as E04, the server outputs fault information related to "overload", including information such as the fault type "overload" and the location where "overload" was sent.

[0061] Step S206: Determine the first target prevention strategy from the target prevention strategies based on the first power grid fault type.

[0062] The target prevention strategies are stored in the self-healing strategy library. This library stores pre-designed prevention strategies. For example, if the fault type is a disconnection, a corresponding prevention strategy for resolving the disconnection is stored; if the fault type is a short circuit, a corresponding prevention strategy for resolving the short circuit is stored; if the fault type is a ground fault, a corresponding prevention strategy for resolving the ground fault is stored; and if the fault type is an overload, a corresponding prevention strategy for reducing the load is stored.

[0063] Optionally, the server selects a first target prevention strategy that matches the fault type from the target prevention strategies stored in the self-healing strategy library through the self-healing strategy acquisition module. For example, if the fault type is overload, the server selects a "load reduction prevention strategy" that matches the "overload" fault type from the target prevention strategies stored in the self-healing strategy library through the self-healing strategy acquisition module as the first target prevention strategy.

[0064] Step S208: Determine the second power grid fault type based on the second set of operating parameters.

[0065] Optionally, after matching the first target prevention strategy, the server calls the fault diagnosis module to diagnose whether the received second set of operating parameters has a fault, and determines that the second power grid fault type determined according to the second set of operating parameters is one of open circuit, short circuit, grounding, or overload.

[0066] Step S210: If the second power grid fault type is the same as the first power grid fault type, then execute the first target prevention strategy.

[0067] Optionally, the server compares the second power grid fault type with the first power grid fault type using a fault prevention verification module. If the second power grid fault type is identical to the first power grid fault type, the first fault type diagnosis is considered correct, and the self-healing strategy execution module is activated to execute the matching first target prevention strategy.

[0068] Optionally, if the fault type of the second power grid is different from that of the first power grid, the fault type diagnosis of the first power grid is considered incorrect, and the server can output an alarm message. The server can also call the fault diagnosis module to perform fault diagnosis again.

[0069] In the aforementioned error prevention verification method based on distribution network self-healing control, the method first acquires the first set of operating data collected in the first time period of the target power grid line in the smart distribution network, determines the first power grid fault type based on the first set of operating data, and determines the prevention strategy based on the first power grid fault type. Secondly, it acquires the second set of operating data collected in the second time period of the target power grid line, determines the second power grid fault type based on the second set of operating data, and finally determines whether the second power grid fault type is the same as the first power grid fault type. If the second power grid fault type is the same as the first power grid fault type, it indicates that the first power grid fault type diagnosed based on the first set of operating parameters is correct, thereby preventing the problem of inaccurate fault diagnosis due to signal loss, improving fault detection accuracy, and executing the first target prevention strategy determined based on the first power grid fault type, thereby improving the distribution network self-healing rate.

[0070] In one exemplary embodiment, such as Figure 3 As shown, the error prevention verification method based on distribution network self-healing control further includes steps S302 to S308. Wherein:

[0071] Step S302: If the second power grid fault type is different from the first power grid fault type, then obtain the third set of operating parameters of the target power grid line.

[0072] The third set of operating parameters is collected based on a third time period, which is later than the second time period. For example, the first time period is 8:00:00-8:00:10; the second time period is 8:00:10-8:00:20; and the third time period is 8:00:20-8:00:30.

[0073] Optionally, if the fault type of the second power grid is different from that of the first power grid (e.g., the fault type of the first power grid is overload, and the fault type of the second power grid is line breakage), the server obtains the third set of operating parameters of the target power grid line in a third time period, such as 8:00:20-8:00:30, through the data acquisition module, and transmits the third set of operating parameters to the fault diagnosis module.

[0074] Step S304: Determine the third power grid fault type based on the third set of operating parameters.

[0075] Optionally, the server uses a fault diagnosis module to determine the type of fault in the received third set of operating parameters. If the third power grid fault is a line break...

[0076] Step S306: If the third power grid fault type is the same as the second power grid fault type, then determine the second target prevention strategy based on at least one of the third power grid fault type and the second power grid fault type, and execute the second target prevention strategy.

[0077] Optionally, if both the third power grid fault type and the second power grid fault type are disconnection, the server uses the self-healing strategy acquisition module to match the second target prevention strategy for the third power grid fault type from the self-healing strategy library, and the server starts the self-healing strategy execution module to execute the matched second target prevention strategy.

[0078] Optionally, the server can also match a second target prevention strategy for a second type of power grid fault from the self-healing strategy library.

[0079] Step S308: If the fault type of the third power grid is different from that of the second power grid, then an alarm message is output.

[0080] Optional. If the second power grid fault type is a disconnection and the third power grid fault type is a ground fault, and the fault types of the three power grids are different from those of the second power grid, then the server outputs an alarm message.

[0081] In this embodiment, the strategy for matching the second power grid fault type is re-verified by using the operating parameters of the third time period. By using the operating parameters of the target power grid circuit obtained through three time periods, the possibility of signal loss can be reduced as much as possible, which can improve the accuracy of fault type judgment and thus match an accurate prevention strategy. By executing the prevention strategy, the self-healing rate of the distribution network can be improved.

[0082] In one exemplary embodiment, such as Figure 4 As shown, the generation process of the target prevention strategy includes steps S402 to S406, where the first set of operating parameters of the target power grid line carries a type identifier; before determining the first target prevention strategy from the target prevention strategy according to the first fault type, the process further includes steps S402 to S406. Wherein:

[0083] Step S402: Match at least one set of historical operating parameters of the target circuit line from the historical database according to the type identifier.

[0084] The historical database is used to store historical data information of the power grid, including historical operating parameters and historical fault information of each power grid line. Historical operating parameters include historical load, historical frequency, historical voltage and current, etc., and historical fault information includes information such as open circuit, short circuit, grounding, and overload.

[0085] The type identifier is a unique representation of the first set of operating parameters of the target power grid line.

[0086] Optionally, the server obtains the first set of operating parameters and the type identifier carried by the first set of operating parameters, such as A, through the data acquisition module. The data acquisition module sends the type identifier and the first set of operating parameters to the data processing module. The server receives the first set of operating parameters and the corresponding type identifier through the data processing module. The server then matches at least one set of historical operating parameters of the target circuit line from the historical database based on the type identifier. This could be two sets, three sets, or other matching sets of historical operating parameters. The specific number is not limited.

[0087] Step S404: Compare the first set of operating parameters with the historical operating parameters, determine the reference prevention strategy based on the comparison results, and execute the reference prevention strategy.

[0088] Optionally, the server sends the first set of operating parameters and at least one set of historical operating parameters to the similarity matching module through the data processing module. The server compares the first set of operating parameters with the historical operating parameters through the similarity matching module, generates a comparison result, such as obtaining a fault type code based on the historical operating parameters, and outputs it to the prevention strategy planning module. The prevention strategy planning module receives the fault type code and detects the first set of operating parameters. If no abnormality occurs during the operation of the first set of operating parameters, continuous monitoring stops after the target time period. If any operating parameter exceeds the set range during the operation of the first set of operating parameters, the pre-designed reference prevention strategy is immediately retrieved from the prevention strategy storage module and processed according to the set reference prevention strategy.

[0089] Optionally, the server can perform continuous monitoring of the power grid lines in advance before the historical fault warning point, and immediately activate a reference prevention strategy to prevent faults from occurring when an abnormal signal is detected. For example, if line 1 experiences an overload at historical operating parameter 9, continuous monitoring of the first operating parameter will be activated 10-15 minutes in advance. If the current exceeds the set range, a reference prevention strategy to reduce the load will be immediately retrieved from the prevention strategy storage module to avoid subsequent overload faults.

[0090] Step S406: Determine the target prevention strategy based on the implementation effect of the reference prevention strategy.

[0091] Optionally, the server evaluates the effectiveness of the reference prevention strategy through the prevention strategy effectiveness evaluation module. If the self-healing rate of the power grid line is above the self-healing rate threshold after implementing the reference prevention strategy, the reference prevention strategy is retained. If the self-healing rate of the power grid line is below the self-healing rate threshold, the reference prevention strategy is deleted through the prevention strategy update module. The server can also receive instructions through the prevention strategy update module to replace the reference defense strategy below the self-healing rate threshold with a new reference defense strategy. The prevention strategy effectiveness evaluation module is used to statistically analyze the self-healing rate m of the power grid line after q (q=n) hours of using the prevention strategy and evaluate the effectiveness of the prevention strategy. A self-healing rate m > 80% is considered an effective prevention strategy; otherwise, it is considered an ineffective prevention strategy. The prevention strategy update module is used to delete ineffective prevention strategies from the prevention strategy storage module, or administrators can input new prevention strategies to replace ineffective prevention strategies.

[0092] In this embodiment, the first set of operating parameters and historical operating parameters are compared, and a reference prevention strategy is determined based on the comparison results. The target prevention strategy stored in the self-healing strategy library is determined based on the execution effect of the reference prevention strategy. That is, all the prevention strategies stored in the self-healing strategy library are effective prevention strategies, thereby improving the self-healing rate of the power grid.

[0093] Following the above embodiments, as Figure 5 As shown, the historical operating parameters include at least two sets; comparing the first set of operating parameters with the historical operating parameters includes steps S502 to S508. Wherein:

[0094] Step S502: Determine the first operating parameter curve based on the first set of operating parameters and the corresponding time sequence; determine the corresponding historical operating parameter curve based on each historical operating parameter and the corresponding time sequence.

[0095] Optionally, the server uses the data processing module to determine the first operating parameter curve A by receiving the first set of operating parameters and the corresponding time sequence; the server retrieves the plotted historical operating parameter curves from the historical database, wherein the historical operating parameter curves are plotted based on each historical operating parameter and the corresponding time sequence.

[0096] Step S504: Obtain the overall similarity between each historical operating parameter curve and the first operating parameter curve.

[0097] Among them, the overall similarity is the similarity between the entire historical operating parameter curve and the first operating parameter curve.

[0098] Optionally, the server calculates the overall similarity between each historical operating parameter curve and the first operating parameter curve using a similarity matching module. If there are three sets of historical operating parameters, there are three corresponding historical operating parameter curves A'A''A'''. The overall similarity between historical operating parameter curves A'A''A''' and A is calculated separately, such as X%, Y%, Z%.

[0099] Step S506: Obtain each overall similarity greater than the overall similarity threshold as the target overall similarity, and select the target historical running parameter curve corresponding to the largest target overall similarity from the target overall similarities.

[0100] The overall similarity threshold can be α, which is assumed to be 80%.

[0101] Optionally, the server calculates the overall similarity between the historical operating parameter curve A' and A using a similarity matching module, such as the relationship between X%, Y%, Z% and the overall similarity threshold α. If X%, Y%, and Z% are all greater than α, then the target historical operating parameter curve corresponding to the largest overall similarity of X%, Y%, and Z% is selected. For example, if X is the largest, X corresponds to the historical operating parameter curve A'.

[0102] Step S508: Compare the first operating parameter curve with the target historical operating parameter curve.

[0103] Optionally, the server compares the first operating parameter curve A with the target historical operating parameter curve A' using a similarity matching module.

[0104] In this embodiment, the historical operating parameter curve that is closest to the first operating parameter curve is selected by using an overall similarity threshold. The fault types and strategies that appear on the historical operating parameter curves are used as subsequent references, thereby improving the self-healing rate of the power grid when executing strategies.

[0105] Continuing with the aforementioned embodiments, as follows: Figure 6 As shown, the comparison results include at least one point in the target's historical operating parameter curve where the local similarity is greater than the local similarity threshold; based on the comparison results, a reference prevention strategy is determined, including steps S602 to S606. Wherein:

[0106] Step S602: Select the point with the highest local similarity from the points with local similarity greater than the local similarity threshold as the target point.

[0107] Optionally, the server compares the first running parameter curve A with the target historical running curve and historical running parameter A' using a similarity matching unit, and matches points or segments (positions) with a local similarity greater than 80%; if multiple points are matched, the point with the highest local similarity is selected as the target point of the target historical running curve.

[0108] Step S604: Use the historical running curve of the target within the target time period after the target point as the predicted curve of the first running parameter curve.

[0109] Optionally, the server uses the curve within n hours after the target point as the predicted curve of the first running parameter curve.

[0110] Step S606: Obtain the prevention strategy corresponding to the power grid fault type of the predicted curve as a reference prevention strategy.

[0111] Optionally, the server uses the prevention strategy corresponding to the power grid fault type sent on the obtained prediction curve as a reference prevention strategy. For example, if there are three different faults on the prediction curve, such as fault E01 at time T1 (corresponding to prevention strategy 1), fault E02 at time T2 (corresponding to prevention strategy 2), and fault E03 at time T3 (corresponding to prevention strategy 3), then prevention strategy 1, prevention strategy 2, and prevention strategy 3 are all reference prevention strategies.

[0112] Optionally, the server will receive the first fault type code and, based on the time T1 when the fault warning point appears on the prediction curve, start the continuous monitoring module 10-15 minutes in advance to continuously monitor the power grid line until T2 (T2=T1+10-15min); if no abnormality occurs in the middle, the continuous monitoring will stop after T2; if the operating parameters exceed the set range in the middle, the pre-designed prevention strategy will be retrieved from the prevention strategy storage module immediately and the problem will be handled according to the set prevention strategy.

[0113] Continuous monitoring of the power grid lines can be performed in advance before historical fault warning points, and preventive strategies can be activated immediately upon the occurrence of abnormal signals to prevent faults. For example, if line 1 shows an overload at point 9 on the historical curve, continuous monitoring can be activated 10-15 minutes in advance. If the current exceeds the set range, the load reduction prevention strategy will be retrieved immediately from the preventive strategy storage module to avoid subsequent overload faults.

[0114] In this embodiment, a reference prevention strategy is determined based on the target's historical operating parameter curve by using a local similarity threshold. This provides a reference for prevention strategies to address potential problems in the first operating parameter curve in the future. By implementing the reference prevention strategy, the self-healing rate of the power grid can be improved.

[0115] Continuing with the aforementioned embodiments, as follows: Figure 7 As shown, based on the implementation effect of the reference prevention strategy, the target prevention strategy is determined, including steps S702 to S704. Wherein:

[0116] Step S702: Obtain the self-healing rate of the power grid line at the target time point after implementing the reference prevention strategy.

[0117] Optionally, the prediction curve contains three different faults. For example, the fault at time T1 is E01, corresponding to reference prevention strategy 1; the fault at time T2 is E02, corresponding to reference prevention strategy 2; and the fault at time T3 is E03, corresponding to reference prevention strategy 3.

[0118] The server obtains the self-healing rate of the power grid line at the target time point q (q=n) after executing each reference prevention strategy. For example, the self-healing rate at q hours after executing reference prevention strategy 1 is 75%; the self-healing rate at q hours after executing reference prevention strategy 2 is 85%; and the self-healing rate at q hours after executing reference prevention strategy 3 is 90%.

[0119] Step S704: Delete reference prevention strategies with self-healing rates less than the self-healing rate threshold, and determine the target prevention strategy.

[0120] The self-healing rate threshold is denoted as m, which can optionally be 80%.

[0121] Optionally, the server deletes reference prevention strategies with self-healing rates less than the self-healing rate threshold through the prevention strategy update module, deletes reference prevention strategies with self-healing rates less than the self-healing rate threshold, deletes reference prevention strategy 1, and determines the target prevention strategy by reference prevention strategy 2 and reference prevention strategy 3.

[0122] Optionally, the server receives instructions through the prevention policy update module to replace the original reference prevention policy 1 with the new reference prevention policy 1, and the target prevention policy is determined by reference prevention policy 1, reference prevention policy 2 and reference prevention policy 3.

[0123] In this embodiment, the self-healing rate of the power grid line at the target time point after the implementation of the reference prevention strategy is compared with the self-healing rate threshold. The retained reference prevention strategies are all those that can resolve the power grid fault type, thereby improving the self-healing rate of the power grid when these reference prevention strategies are implemented.

[0124] In one exemplary embodiment, such as Figure 8 As shown. The historical database stores historical power grid data, including historical operating parameters and fault information for each power grid line. Historical operating parameters include load, frequency, voltage, and current; historical fault information includes open circuits, short circuits, grounding faults, and overloads. Historical operating parameter curves are plotted, and the locations of occurring faults are mapped onto these curves. Fault alert points are set and coded according to fault type (open circuit E01, short circuit E02, grounding fault E03, overload E04). For example, a fault alert point is highlighted in yellow on the curve and displays fault code E01. The historical operating parameter curves include multiple curves for historical load, historical frequency, historical voltage, historical current, and time.

[0125] The data acquisition module is used to collect the operating parameters of each power grid line in real time, collect the first operating parameters within the first time period, and transmit them to the data processing module and the fault diagnosis module.

[0126] The data processing module is used to receive data at a set time and plot it as a first operating parameter curve based on the first operating parameters and the corresponding time, and then input it into the similarity matching module; wherein, the first operating parameter curve includes multiple curves of load, frequency, voltage, current and time; specifically, the set time means that it can be set to receive data within the most recent 1 hour or data within the most recent 24 hours as needed;

[0127] The similarity matching module obtains the overall similarity between each historical operating parameter curve and the first operating parameter curve; it obtains each overall similarity greater than the overall similarity threshold as the target overall similarity, and selects the target historical operating parameter curve corresponding to the largest target overall similarity from the target overall similarity; it compares the first operating parameter curve and the target historical operating parameter curve, where the overall similarity is >80%; it selects the curve within the next n hours that matches the target historical operating curve as the prediction curve; it determines whether there is a fault warning point on the prediction curve. If not, it means that there is no fault in the next n hours; if there is, it obtains the fault type code and outputs it to the prevention strategy planning module; it compares the first operating parameter curve with the target historical operating parameter curve and matches the positions with a local similarity >80%; if multiple positions are matched, it selects the one with the largest local similarity.

[0128] The prevention strategy planning module receives the fault type code and, based on the time T1 when the fault warning point appears on the prediction curve, starts the continuous monitoring module 10-15 minutes in advance to continuously monitor the power grid line until T2 (T2 = T1 + 10-15 minutes). If no abnormality occurs during this period, continuous monitoring stops after T2. If any operating parameter exceeds the set range, the module immediately retrieves the pre-designed prevention strategy from the prevention strategy storage module and handles the situation according to the set strategy. Continuous monitoring of the power grid line can be performed in advance before historical fault warning points, and the prevention strategy can be immediately activated upon the appearance of an abnormal signal to prevent fault occurrence. For example, if line 1 experiences an overload at point 9 on the historical curve, continuous monitoring is started 10-15 minutes in advance. If the current exceeds the set range, the module immediately retrieves the load reduction prevention strategy from the prevention strategy storage module to avoid subsequent overload faults.

[0129] The prevention strategy effectiveness evaluation module evaluates the execution effect of reference prevention strategies. If the self-healing rate of the power grid is above the self-healing rate threshold after implementing the reference prevention strategy, the reference prevention strategy is retained. If the self-healing rate of the power grid is below the self-healing rate threshold, the reference prevention strategy is deleted through the prevention strategy update module. The server can also receive instructions through the prevention strategy update module to replace the reference defense strategy below the self-healing rate threshold with a new reference defense strategy. The prevention strategy effectiveness evaluation module is used to calculate the self-healing rate m of the power grid after q (q=n) hours of using the prevention strategy and evaluate the effectiveness of the prevention strategy. A self-healing rate m > 80% is considered an effective prevention strategy; otherwise, it is considered an ineffective prevention strategy. The prevention strategy update module is used to delete ineffective prevention strategies from the prevention strategy storage module, or administrators can input new prevention strategies to replace ineffective ones.

[0130] The self-healing strategy library is used to store pre-designed reference prevention strategies and target prevention strategies. The prevention strategy storage module can retrieve them from the self-healing strategy library.

[0131] The fault diagnosis module is used to receive the operating parameters of each power grid line in real time, determine whether a fault has occurred, and output fault information. The fault information includes fault type, fault location, and other information. For example, it receives the first operating parameters of the first time period and performs fault diagnosis; it receives the second operating parameters of the second time period and performs fault diagnosis; it receives the third operating parameters of the third time period and performs fault diagnosis.

[0132] The self-healing strategy acquisition module is used to match pre-designed target prevention strategies from the self-healing strategy library based on fault information.

[0133] After matching the target prevention strategy, the anti-misoperation verification module calls the fault diagnosis module to perform fault diagnosis again and compares the fault type of the second power grid with the fault type of the first power grid to determine whether the two fault types are exactly the same.

[0134] If they are the same, the fault information is considered to be correct, and the first target prevention strategy execution module is activated to execute the matching self-healing strategy.

[0135] If there are differences, the fault type is considered to be incorrect. The fault diagnosis module is called to diagnose the third operating parameter and compare the third power grid fault type with the second power grid fault type to determine whether the fault types are completely the same.

[0136] If they are the same, it is assumed that the fault type of the third power grid is correct with that of the fault type of the second power grid. The target prevention strategy acquisition module matches the pre-designed target prevention strategy from the self-healing strategy library according to the fault type of the third power grid; the self-healing strategy execution module is started to execute the matched target prevention strategy.

[0137] If there is a discrepancy, it is considered that the fault type of the third power grid is incorrect compared to the fault type of the second power grid, and an alarm is triggered on the management side.

[0138] After the self-healing strategy execution module executes the matching target prevention strategy, the continuous monitoring module is activated to continuously monitor the power grid line for 15-30 minutes.

[0139] If no abnormality is detected, continuous monitoring will be stopped upon detection.

[0140] If any operating parameter exceeds the threshold range, it indicates a fault in the fault diagnosis module. In this case, the self-healing strategy will be stopped immediately, and an alarm will be triggered on the management side.

[0141] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0142] Based on the same inventive concept, this application also provides an anti-misoperation verification device for implementing the above-mentioned anti-misoperation verification method based on distribution network self-healing control. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the anti-misoperation verification device based on distribution network self-healing control provided below can be found in the limitations of the anti-misoperation verification method based on distribution network self-healing control described above, and will not be repeated here.

[0143] In one exemplary embodiment, such as Figure 9 As shown, a fault prevention verification device based on distribution network self-healing control is provided, comprising: a first acquisition module 901, a first fault type determination module 902, a first fault type determination module 903, a second fault type determination module 904, and an execution module 905, wherein:

[0144] The first acquisition module 901 is used to acquire at least a first set of operating parameters and a second set of operating parameters of the target power grid line; wherein the first set of operating parameters is acquired based on a first time period; the second set of operating parameters is acquired based on a second time period; and the first time period is earlier than the second time period.

[0145] The first fault type determination module 902 is used to determine the first power grid fault type based on the first set of operating parameters.

[0146] The first strategy determination module 903 is used to determine the first target prevention strategy from the target prevention strategies based on the first power grid fault type.

[0147] The second fault type determination module 904 is used to determine the second power grid fault type based on the second set of operating parameters.

[0148] The execution module 905 is used to execute the first target prevention strategy if the second power grid fault type is the same as the first power grid fault type.

[0149] In one exemplary embodiment, a fault-prevention verification device based on distribution network self-healing control is provided, further comprising:

[0150] The second acquisition module is used to acquire a third set of operating parameters of the target power grid line if the second power grid fault type is different from the first power grid fault type. The third set of operating parameters is collected based on a third time period, which is later than the second time period.

[0151] The third fault type determination module is used to determine the third power grid fault type based on the third set of operating parameters.

[0152] The second strategy determination module is used to determine a second target prevention strategy based on at least one of the third power grid fault type and the second power grid fault type if the third power grid fault type is the same as the second power grid fault type, and to execute the second target prevention strategy.

[0153] The alarm module is used to output alarm information if the fault type of the third power grid is different from that of the second power grid.

[0154] In one exemplary embodiment, the first set of operating parameters for the target power grid line carries a type identifier; it also includes:

[0155] The target prevention strategy determination module is used to match at least one set of historical operating parameters of the target circuit line from the historical database according to the type identifier; compare the first set of operating parameters with the historical operating parameters, and determine the reference prevention strategy based on the comparison result, and execute the reference prevention strategy; and determine the target prevention strategy based on the execution effect of the reference prevention strategy.

[0156] In one exemplary embodiment, the historical operating parameters include at least two sets; the target prevention strategy determination module includes:

[0157] The curve generation unit is used to determine the first operating parameter curve based on the first set of operating parameters and the corresponding time sequence; and to determine the corresponding historical operating parameter curve based on each historical operating parameter and the corresponding time sequence.

[0158] The first acquisition unit is used to acquire the overall similarity between each historical operating parameter curve and the first operating parameter curve.

[0159] The target historical operating parameter curve determination unit is used to obtain each overall similarity greater than the overall similarity threshold as the target overall similarity, and select the target historical operating parameter curve corresponding to the largest target overall similarity from the target overall similarity.

[0160] The comparison unit is used to compare the first operating parameter curve with the target historical operating parameter curve.

[0161] In an exemplary embodiment, the comparison results include at least one point in the target's historical operating parameter curve where the local similarity is greater than a local similarity threshold; the target prevention strategy determination module includes:

[0162] The local similarity matching unit is used to select the point with the maximum local similarity from the points with local similarity greater than the local similarity threshold as the target point; to use the target historical operating curve within the target time period after the target point as the predicted curve of the first operating parameter curve; and to obtain the prevention strategy corresponding to the power grid fault type of the predicted curve as a reference prevention strategy.

[0163] In one exemplary embodiment, the target prevention strategy determination module includes:

[0164] The prevention strategy update unit is used to obtain the self-healing rate of the power grid line at the target time point after the implementation of the reference prevention strategy; delete the reference prevention strategies with a self-healing rate less than the self-healing rate threshold; and determine the target prevention strategy.

[0165] Each module in the aforementioned anti-misoperation verification device based on distribution network self-healing control can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0166] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 10As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores power distribution network operating parameters and preventative strategies. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a fault-checking method based on power distribution network self-healing control.

[0167] Those skilled in the art will understand that Figure 10 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0168] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0169] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0170] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0171] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0172] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0173] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for preventing errors in the self-healing control of a distribution network, characterized in that, The method comprises: Acquire at least a first set of operating parameters and a second set of operating parameters for the target power grid line; wherein the first set of operating parameters is collected based on a first time period; the second set of operating parameters is collected based on a second time period; and the first time period is earlier than the second time period. Based on the first set of operating parameters, the first power grid fault type is determined; The first target prevention strategy is determined from the target prevention strategy based on the first power grid fault type; Based on the second set of operating parameters, the second power grid fault type is determined; If the second power grid fault type is the same as the first power grid fault type, then the first target prevention strategy is executed; If the second power grid fault type is different from the first power grid fault type, then the third set of operating parameters of the target power grid line is obtained. The third set of operating parameters is collected based on a third time period, which is later than the second time period. Based on the third set of operating parameters, the third power grid fault type is determined; If the third power grid fault type is the same as the second power grid fault type, then a second target prevention strategy is determined based on at least one of the third power grid fault type and the second power grid fault type, and the second target prevention strategy is executed. If the third power grid fault type is different from the second power grid fault type, an alarm message is output.

2. The method according to claim 1, characterized in that, The first set of operating parameters of the target power grid line carries a type identifier; before determining the first target prevention strategy from the target prevention strategy based on the first power grid fault type, the method further includes: Match at least one set of historical operating parameters of the target circuit line from the historical database based on the type identifier; The first set of operating parameters is compared with the historical operating parameters, and a reference prevention strategy is determined based on the comparison results and the reference prevention strategy is executed. Based on the effectiveness of the reference prevention strategy, a target prevention strategy is determined.

3. The method according to claim 2, characterized in that, The historical operating parameters include at least two sets; comparing the first set of operating parameters with the historical operating parameters includes: The first operating parameter curve is determined based on the first set of operating parameters and the corresponding time sequence; the corresponding historical operating parameter curve is determined based on each of the historical operating parameters and the corresponding time sequence. Obtain the overall similarity between each of the historical operating parameter curves and the first operating parameter curve; Each overall similarity score greater than the overall similarity threshold is taken as the target overall similarity score, and the target historical running parameter curve corresponding to the largest target overall similarity score is selected from the target overall similarity scores. Compare the first operating parameter curve with the target historical operating parameter curve.

4. The method according to claim 2, characterized in that, The comparison results include at least one point in the target historical operating parameter curve where the local similarity is greater than a local similarity threshold; the determination of a reference prevention strategy based on the comparison results includes: Select the point with the highest local similarity from the points whose local similarity is greater than the local similarity threshold as the target point; The historical running curve of the target within the target time period after the target point is used as the predicted curve of the first running parameter curve. Obtain the prevention strategy corresponding to the power grid fault type of the predicted curve, and use it as a reference prevention strategy.

5. The method according to claim 2, characterized in that, The step of determining the target prevention strategy based on the implementation effect of the reference prevention strategy includes: Obtain the self-healing rate of the power grid lines at the target time point after implementing the reference prevention strategy; Delete the reference prevention strategies whose self-healing rate is less than the self-healing rate threshold, and determine the target prevention strategy.

6. A verification device for preventing errors based on self-healing control of power distribution networks, characterized in that, The device comprises: The first acquisition module is used to acquire at least a first set of operating parameters and a second set of operating parameters of the target power grid line; wherein, the first set of operating parameters is acquired based on a first time period; the second set of operating parameters is acquired based on a second time period; and the first time period is earlier than the second time period. The first fault type determination module is used to determine the first power grid fault type based on the first set of operating parameters; The first strategy determination module is used to determine a first target prevention strategy from the target prevention strategy based on the first power grid fault type; The second fault type determination module is used to determine the second power grid fault type based on the second set of operating parameters; An execution module is configured to execute the first target prevention strategy if the second power grid fault type is the same as the first power grid fault type. The second acquisition module is used to acquire a third set of operating parameters of the target power grid line if the second power grid fault type is different from the first power grid fault type. The third set of operating parameters is collected based on a third time period, which is later than the second time period. The third fault type determination module is used to determine the third power grid fault type based on the third set of operating parameters; The second strategy determination module is used to determine a second target prevention strategy based on at least one of the third power grid fault type and the second power grid fault type if the third power grid fault type is the same as the second power grid fault type, and to execute the second target prevention strategy. The alarm module is used to output alarm information if the third power grid fault type is different from the second power grid fault type.

7. The apparatus according to claim 6, characterized in that, The first set of operating parameters for the target power grid line carries a type identifier; it also includes: The target prevention strategy determination module is used to match at least one set of historical operating parameters of the target circuit line from the historical database according to the type identifier; compare the first set of operating parameters with the historical operating parameters, and determine a reference prevention strategy based on the comparison result, and execute the reference prevention strategy; and determine the target prevention strategy based on the execution effect of the reference prevention strategy.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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