Self-healing action analysis method based on power distribution automation terminal protection setting value

Abstract

The present invention relates to the technical field of electric power systems, and in particular to a self-healing action analysis method based on a power distribution automation terminal protection setting value, comprising: defining targets and standards of a self-healing action; by analyzing historical fault data, identifying a fault mode, and constructing a fault identification model; by combining historical data with real-time operation data, establishing a dynamic protection setting value model; constructing a multi-level decision tree structure on the basis of the identified fault mode and the dynamic protection setting value model; by simulating different fault scenarios and self-healing decision logic, performing a self-healing action feasibility test; by tracking each stage of a self-healing process, collecting and analyzing related data in a timely manner to form feedback information; by comparing operation data before and after self-healing, evaluating the actual effect of a self-healing measure; and establishing and optimizing a knowledge base. The present invention has the beneficial effects that a power distribution automation system can achieve real-time monitoring and intelligent response, thereby improving fault identification accuracy and self-healing capability.

Description

A self-healing action analysis method based on protection settings of distribution automation terminals Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a self-healing action analysis method based on the protection settings of distribution automation terminals. Background Technology

[0002] With the continuous growth of electricity demand, the reliability and intelligence level of power distribution systems urgently need to be improved. Existing methods for analyzing protection settings in power distribution automation terminals often rely on static data acquisition and processing, lacking dynamic adaptability and struggling to cope with complex power grid fault scenarios. Traditional methods lag behind in fault mode identification, protection setting adjustment, and self-healing decision-making logic, resulting in an inability to respond quickly and effectively to faults under extreme loads and severe weather conditions. Furthermore, the construction and updating of the knowledge base are insufficient to support real-time decision-making, limiting the intelligent development of the system. Summary of the Invention

[0003] In view of the problems existing in the above or prior art, the present invention is proposed.

[0004] Therefore, the purpose of this invention is to provide a self-healing action analysis method based on the protection settings of distribution automation terminals, which can improve the accuracy of fault identification and self-healing capability.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a self-healing action analysis method based on the protection settings of distribution automation terminals, which includes defining the target and standard of the self-healing action;

[0006] By analyzing historical fault data, fault patterns are identified, and fault identification models are constructed.

[0007] By combining historical data with real-time operational data, a dynamic protection setting model is established;

[0008] Based on the identified fault modes and dynamic protection setting models, a multi-level decision tree structure is constructed.

[0009] Feasibility tests of self-healing actions were conducted by simulating different fault scenarios and self-healing decision-making logic.

[0010] By tracking each stage of the self-healing process, relevant data can be collected and analyzed in a timely manner to generate feedback information;

[0011] The actual effectiveness of the self-healing measures is evaluated by comparing the operational data before and after self-healing.

[0012] Establish and optimize the knowledge base.

[0013] As a preferred embodiment of the self-healing action analysis method based on the protection settings of distribution automation terminals in this invention, the objectives of the self-healing action include: quickly restoring power supply, reducing the impact of faults on users, and improving system reliability.

[0014] The standards for self-healing actions include response time, fault detection accuracy, and automatic recovery rate.

[0015] As a preferred embodiment of the self-healing action analysis method based on the protection settings of distribution automation terminals in this invention, the following is included: constructing a fault identification model, including identifying fault modes such as short circuits, overloads, or equipment failures, to provide basic data support for self-healing analysis. The formula is as follows: F(x)=w1·I 2 +w2·V·R+w3·(P max -P current )+w4·e -λt

[0016] Where F(x) is the fault identification score, a higher value indicates a greater fault risk; I is the current currently flowing through the equipment; V is the equipment voltage; R is the equipment resistance; P max P is the rated power of the equipment. current The current power of the equipment; w1, w2, w3, and w4 are weighting parameters, representing the degree of influence of factors such as current, voltage, power, and equipment status on fault identification, respectively; λ is the attenuation factor, reflecting the impact of equipment aging or environmental factors on fault risk; t is the equipment running time.

[0017] The system uses a model to calculate the fault identification score of the current device. When the score exceeds a certain threshold, the system determines that the device is in a potential fault state. The threshold can be set autonomously according to the actual situation.

[0018] As a preferred embodiment of the self-healing action analysis method based on the protection settings of distribution automation terminals in this invention, the dynamic protection setting model includes automatically adjusting the protection settings according to the actual operating conditions of the power grid, environmental changes, and load characteristics, as shown in the following formula:

[0019] Where PD is the dynamic protection setting; P base The basic protection settings are based on equipment standards; α, β, and γ are weighting coefficients, representing the degree of influence of load, temperature, and environmental factors on the protection settings, respectively; L is the current load; L max T represents the maximum load of the equipment. current T represents the current temperature. max The maximum safe temperature for the equipment; H env This is an environmental change factor used to reflect the impact of the external environment on equipment.

[0020] As a preferred embodiment of the self-healing action analysis method based on the protection settings of distribution automation terminals in this invention, the decision logic includes response strategies under different fault conditions, such as selecting appropriate automatic reclosing, adjusting load, or segmented power supply, to ensure that power supply is restored to the maximum extent in the shortest time.

[0021] As a preferred embodiment of the self-healing action analysis method based on the protection settings of distribution automation terminals in this invention, the decision tree structure includes fault detection, dynamic protection setting evaluation, decision response strategy selection, and effect evaluation and feedback.

[0022] Based on the decision tree structure, the self-healing decision logic includes real-time monitoring, dynamic adjustment, and comprehensive evaluation during runtime.

[0023] As a preferred embodiment of the self-healing action analysis method based on the protection settings of distribution automation terminals in this invention, the feedback information includes whether the self-healing action is successful or not, as well as information on the above-mentioned standards such as the response time, fault detection accuracy, and automatic recovery rate of the successful self-healing action. Through continuous feedback analysis, the self-healing logic and decision tree structure can be continuously optimized to ensure the system's efficient self-healing capability.

[0024] As a preferred embodiment of the uninterruptible power detection current transformer secondary circuit open circuit system of the present invention, the self-healing effect evaluation includes: by comparing the operating data before and after self-healing, analyzing the power supply restoration time, user satisfaction, and system response effect, and evaluating the actual effect of the self-healing measures; based on the evaluation results, making optimization suggestions, including adjusting protection settings, improving decision-making logic, or improving monitoring methods, and combining them with actual operating conditions to achieve continuous improvement and dynamic adaptation of self-healing action analysis.

[0025] As a preferred embodiment of the uninterrupted power detection system for the open circuit of the secondary circuit of the current transformer of the present invention, the establishment and optimization of the knowledge base includes updating the knowledge base with information on fault types, pattern recognition methods, self-healing decision cases and evaluation results. Based on the knowledge base update model, it provides a reference for self-healing actions and provides strong support for future fault analysis and decision-making.

[0026] The knowledge base update model is as follows: K(t)=K(t-1)+α·E(t)+β·R(t)-γ·O(t)

[0027] Where K(t) is the amount of knowledge base content at time t-1; K(t-1) is the amount of knowledge base content at time t-1; E(t) is the number of newly acquired valid fault cases; R(t) is the number of self-healing decision cases after evaluation; O(t) is the number of outdated or invalid information; α, β, and γ are weight parameters, representing the degree of influence of valid cases, decision cases, and outdated information on the amount of knowledge base content, respectively.

[0028] As a preferred embodiment of the uninterrupted power supply detection system for the secondary circuit open circuit of the current transformer of the present invention, the knowledge base update model includes data collection, parameter setting, knowledge base update calculation, information filtering, knowledge base structure optimization, and a continuous feedback mechanism.

[0029] The beneficial effects of this invention are as follows: By establishing a dynamic protection setting model, designing self-healing decision logic, and constructing a comprehensive knowledge base update model, this invention enables the distribution automation system to achieve real-time monitoring and intelligent response, improving the accuracy of fault identification and self-healing capabilities. This not only ensures the reliability of power supply but also enhances the system's adaptability in complex environments, promoting the development of distribution networks towards greater intelligence and automation. Attached Figure Description

[0030] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0031] Figure 1 is a flowchart illustrating the self-healing action analysis method based on the protection settings of distribution automation terminals.

[0032] Figure 2 is a flowchart illustrating the fault type and pattern recognition process of the self-healing action analysis method based on the protection settings of distribution automation terminals.

[0033] Figure 3 is a flowchart illustrating the process of establishing a dynamic protection setting model based on the self-healing action analysis method of distribution automation terminal protection settings.

[0034] Figure 4 is a flowchart illustrating the knowledge base update model of the self-healing action analysis method based on the protection settings of distribution automation terminals. Detailed Implementation

[0035] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0036] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0037] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0038] Example 1

[0039] Referring to Figures 1 to 4, the first embodiment of the present invention provides a self-healing action analysis method based on the protection settings of distribution automation terminals, which can improve the accuracy of fault identification and self-healing capability.

[0040] Specifically, the goals and standards of self-healing actions are defined;

[0041] By analyzing historical fault data, fault patterns are identified, and fault identification models are constructed.

[0042] By combining historical data with real-time operational data, a dynamic protection setting model is established;

[0043] Based on the identified fault modes and dynamic protection setting models, a multi-level decision tree structure is constructed.

[0044] Feasibility tests of self-healing actions were conducted by simulating different fault scenarios and self-healing decision-making logic.

[0045] By tracking each stage of the self-healing process, relevant data can be collected and analyzed in a timely manner to generate feedback information;

[0046] The actual effectiveness of the self-healing measures is evaluated by comparing the operational data before and after self-healing.

[0047] Establish and optimize the knowledge base.

[0048] Furthermore, the goals of self-healing actions include rapidly restoring power supply, reducing the impact of failures on users, and improving system reliability.

[0049] The standards for self-healing actions include response time, fault detection accuracy, and automatic recovery rate.

[0050] It should be noted that the goals and standards for self-healing actions are defined as follows: the goals of self-healing actions must be specific and quantifiable, and the standards of self-healing actions must be quantifiable and testable. By setting specific and quantifiable goals, each subsequent step can be adjusted and optimized around these standards, thereby effectively guiding the implementation of self-healing actions.

[0051] Furthermore, a fault identification model is constructed, including the identification of fault modes such as short circuits, overloads, or equipment failures, to provide basic data support for self-healing analysis. The formula is as follows: F(x)=w1·I 2 +w2·V·R+w3·(P max -P current )+w4·e -λt

[0052] Where F(x) is the fault identification score, a higher value indicates a greater fault risk; I is the current currently flowing through the equipment; V is the equipment voltage; R is the equipment resistance; P max P is the rated power of the equipment. current The current power of the equipment; w1, w2, w3, and w4 are weighting parameters, representing the degree of influence of factors such as current, voltage, power, and equipment status on fault identification, respectively; λ is the attenuation factor, reflecting the impact of equipment aging or environmental factors on fault risk; t is the equipment running time.

[0053] The system uses a model to calculate the fault identification score of the current device. When the score exceeds a certain threshold, the system determines that the device is in a potential fault state. The threshold can be set autonomously according to the actual situation.

[0054] It should be noted that the fault identification model analyzes historical fault data to identify fault modes, such as short circuits, overloads, or equipment failures. This provides fundamental data support for self-healing analysis, constructing a fault identification model for real-time analysis and response to potential future fault types, thereby improving the accuracy and efficiency of fault mode identification. As shown in Figure 2, the fault identification model includes the following components during use:

[0055] Data acquisition: Collect real-time data such as current, voltage, power, and equipment operating time to provide a basis for model input;

[0056] Parameter settings: Based on historical fault data and expert experience, set the initial values ​​for weight parameters w1, w2, w3, and w4. The weights can be adjusted experimentally to achieve the best recognition results.

[0057] Fault score calculation: The fault identification score of the current device is calculated using model F(x). When the score is higher than a certain threshold (which can be set according to the actual situation), the system determines it to be a potential fault state.

[0058] Dynamic adjustment: Regularly analyze historical fault data and dynamically adjust weight parameters and decay factors based on new data to ensure that the model remains efficient as equipment status and environment change;

[0059] Decision support: Based on the scoring results, trigger self-healing actions, such as automatic reclosing and load transfer, to optimize the self-healing capability of the power system.

[0060] Furthermore, the dynamic protection setting model includes automatically adjusting the protection settings based on the actual operating conditions of the power grid, environmental changes, and load characteristics. The formula is shown below:

[0061] Where PD is the dynamic protection setting; P base The basic protection settings are based on equipment standards; α, β, and γ are weighting coefficients, representing the degree of influence of load, temperature, and environmental factors on the protection settings, respectively; L is the current load; L max T represents the maximum load of the equipment. current T represents the current temperature. max The maximum safe temperature for the equipment; H env This is an environmental change factor used to reflect the impact of the external environment on equipment.

[0062] It should be noted that, as shown in Figure 3, the dynamic protection setting model, when used, specifically includes the following process:

[0063] Real-time data collection of current load, temperature, and environmental changes provides the input basis for the model;

[0064] By analyzing historical data and consulting experts, initial values ​​for the weighting coefficients α, β, and γ are set to ensure that they reflect the importance of each factor.

[0065] The dynamic protection settings are calculated using the model PD. As real-time data changes, the settings will be automatically adjusted to ensure effectiveness under different conditions.

[0066] Monitor environmental changes and dynamically adjust H env The value, such as a negative value during extreme weather, reflects that the equipment needs to reduce the load protection setting.

[0067] Based on the calculated dynamic protection settings, real-time protection settings are implemented and their effectiveness is monitored. If a fault or abnormality is detected, self-healing actions are performed promptly.

[0068] Regularly assess the effectiveness of protection settings and adjust weighting coefficients based on fault data feedback to optimize model performance and ensure efficient self-healing capabilities in complex environments.

[0069] Furthermore, the decision-making logic includes response strategies for different fault conditions, such as selecting appropriate automatic reclosing, adjusting load, or segmenting power supply, to ensure maximum restoration of power supply in the shortest possible time.

[0070] Furthermore, the decision tree structure includes fault detection, dynamic protection setting evaluation, decision response strategy selection, and effect evaluation and feedback. Specifically, fault detection includes identifying fault types such as short-circuit faults, overload faults, and equipment faults, where the location of the fault includes the main line, branch line, and equipment terminal; dynamic protection setting evaluation includes checking the current protection setting PD to determine whether it is within the safe range. If so, monitoring continues; otherwise, self-healing logic is entered; decision response strategy selection includes short-circuit faults, overload faults, and equipment faults; effect evaluation and feedback includes checking whether power restoration was successful, collecting user feedback, and optimizing decision rules and response strategies based on the restoration effect and fault type.

[0071] Based on the decision tree structure, the self-healing decision logic includes real-time monitoring, dynamic adjustment, and comprehensive evaluation during runtime. Specifically, real-time monitoring includes continuously monitoring the power grid status, quickly detecting fault types and locations, and ensuring timely entry into the decision-making process; dynamic adjustment includes updating the strategies and thresholds in the decision tree in real time based on operational data and environmental changes; and comprehensive evaluation includes assessing the effectiveness and user satisfaction of each strategy after its implementation, so as to continuously optimize the decision logic in the future.

[0072] It should be noted that during a short-circuit fault, when automatic reclosing is performed, it is determined whether the number of reclosing attempts exceeds the threshold; otherwise, reclosing is performed, and if so, the system switches to the backup power supply. During partial isolation, the faulty section is isolated and power is restored to other parts.

[0073] In the event of an overload fault, during load adjustment, assess the loads that can be cut off and select non-critical users to reduce load; during automatic disconnection, disconnect the overloaded line when the load exceeds the protection setting.

[0074] In the event of equipment failure, during fault identification and assessment, determine whether the faulty equipment is repairable. If it is repairable, mark it and arrange for repair; if it is not repairable, switch to backup equipment. In the case of segmented power supply, isolate the power supply of the segment where the faulty equipment is located, and maintain normal power supply to other segments.

[0075] Furthermore, the feedback information includes whether the self-healing action was successful, as well as information on the above-mentioned standards such as the response time of a successful self-healing action, fault detection accuracy, and automatic recovery rate. Through continuous feedback analysis, the self-healing logic and decision tree structure can be continuously optimized to ensure the system's efficient self-healing capability.

[0076] Furthermore, the self-healing effect assessment includes comparing operational data before and after self-healing to analyze power supply restoration time, user satisfaction, and system response, and evaluating the actual effectiveness of self-healing measures; based on the assessment results, optimization suggestions are made, including adjusting protection settings, improving decision-making logic, or improving monitoring methods, and combined with actual operating conditions to achieve continuous improvement and dynamic adaptation of self-healing action analysis.

[0077] Furthermore, the establishment and optimization of the knowledge base includes updating the knowledge base with information on fault types, pattern recognition methods, self-healing decision cases, and evaluation results. Based on the knowledge base update model, it provides a reference for self-healing actions and also provides strong support for future fault analysis and decision-making.

[0078] The knowledge base update model is as follows: K(t)=K(t-1)+α·E(t)+β·R(t)-γ·O(t)

[0079] Where K(t) is the amount of knowledge base content at time t-1; K(t-1) is the amount of knowledge base content at time t-1; E(t) is the number of newly acquired valid fault cases; R(t) is the number of self-healing decision cases after evaluation; O(t) is the number of outdated or invalid information; α, β, and γ are weight parameters, representing the degree of influence of valid cases, decision cases, and outdated information on the amount of knowledge base content, respectively.

[0080] Furthermore, as shown in Figure 4, the knowledge base update model includes data collection, parameter setting, knowledge base update calculation, information filtering, knowledge base structure optimization, and a continuous feedback mechanism. Specifically, data collection includes continuously collecting fault cases, decision results, and evaluation data to provide the basis for model input; parameter setting includes setting initial values ​​for weight parameters α, β, and γ based on historical data and expert advice, and dynamically adjusting these parameters by analyzing their impact; knowledge base update calculation includes using model K(t) to calculate the amount of knowledge base content at time t, and triggering optimization or reconstruction of the knowledge base if K(t) exceeds a set threshold; information filtering includes periodically reviewing the information in the knowledge base based on evaluation results, removing outdated or invalid information O(t) to ensure the timeliness and effectiveness of the knowledge base; knowledge base structure optimization includes optimizing the structure and classification methods of the knowledge base based on feedback from newly acquired and outdated information to make it easier to query and use; and the continuous feedback mechanism includes collecting user feedback after each update, analyzing the usage and actual application effects of the knowledge base, and providing a basis for subsequent updates.

[0081] In summary, by establishing a dynamic protection setting model, designing self-healing decision logic, and constructing a comprehensive knowledge base update model, this invention enables the distribution automation system to achieve real-time monitoring and intelligent response, improving the accuracy of fault identification and self-healing capabilities. This not only ensures the reliability of power supply but also enhances the system's adaptability in complex environments, promoting the development of distribution networks towards greater intelligence and automation.

[0082] Example 2

[0083] Referring to Figures 1 to 4, the second embodiment of the present invention provides a self-healing action analysis method based on the protection settings of distribution automation terminals, which can improve the accuracy of fault identification and self-healing capability.

[0084] Specifically, the goals and standards of self-healing actions are defined;

[0085] By analyzing historical fault data, fault patterns are identified, and fault identification models are constructed.

[0086] By combining historical data with real-time operational data, a dynamic protection setting model is established;

[0087] Based on the identified fault modes and dynamic protection setting models, a multi-level decision tree structure is constructed.

[0088] Feasibility tests of self-healing actions were conducted by simulating different fault scenarios and self-healing decision-making logic.

[0089] By tracking each stage of the self-healing process, relevant data can be collected and analyzed in a timely manner to generate feedback information;

[0090] The actual effectiveness of the self-healing measures is evaluated by comparing the operational data before and after self-healing.

[0091] Establish and optimize the knowledge base.

[0092] Among them, the feasibility test of self-healing action is carried out by simulating different fault scenarios and self-healing decision logic, including the impact of variables such as different load conditions, climate factors and equipment status.

[0093] Specifically, the feasibility test includes two tests. The first feasibility test includes:

[0094] Test objective: To verify the effectiveness of the self-healing decision logic under different fault scenarios and to evaluate its response capability under various load conditions, climatic factors and equipment states.

[0095] Test scenarios: short circuit fault, overload fault, equipment failure.

[0096] Test variables: load conditions (low, medium, high), climate factors (normal, severe), equipment status (normal, aging, faulty).

[0097] The test results are shown in Table 1:

[0098] Table 1

[0099] The self-healing system exhibits a high success rate across various fault scenarios, particularly under low load and normal temperature conditions, with a self-healing success rate exceeding 90% for short-circuit and equipment faults. However, under high load and harsh environments, the fault response performance significantly declines, especially with a success rate of only 65% ​​for equipment faults, indicating that the system's ability to cope with extreme conditions still needs improvement. Therefore, further optimization of the decision-making logic and overload strategy is necessary to enhance response performance in complex environments.

[0100] Feasibility test two includes:

[0101] Test objective: To verify the performance of the self-healing decision logic under different fault types and environmental conditions, and to confirm the accuracy and recovery speed of the self-healing decision.

[0102] Test scenarios: short circuit fault, overload fault, equipment failure.

[0103] Test variables: load conditions (low, medium, high), temperature conditions (normal temperature, high temperature), equipment health status (normal, partially damaged).

[0104] The test results are shown in Table 2:

[0105] Table 2

[0106] Performance evaluations of the self-healing system under different temperatures and equipment health conditions show that while short-circuit and overload faults perform well under normal temperature conditions (success rates all above 90%), the success rate drops significantly under high temperatures and partial equipment damage, especially under high loads, where the success rate for equipment failure is only 62%. This indicates that temperature and equipment condition have a significant impact on self-healing decisions, and it is recommended to strengthen equipment monitoring and environmental adaptability analysis to improve the overall reliability and resilience of the system.

[0107] In summary, by establishing a dynamic protection setting model, designing self-healing decision logic, and constructing a comprehensive knowledge base update model, this invention enables the distribution automation system to achieve real-time monitoring and intelligent response, significantly improving fault identification accuracy and self-healing capabilities. This not only ensures the reliability of power supply but also enhances the system's adaptability in complex environments, promoting the development of distribution networks towards greater intelligence and automation.

[0108] It is important to note that the constructions and arrangements of this application shown in several different exemplary embodiments are merely illustrative. Although only a few embodiments are described in detail in this disclosure, those who consult this disclosure will readily understand that many modifications are possible (such as variations in installation arrangement, use of materials, color, orientation, etc.) without substantially departing from the novel teachings and advantages of the subject matter described in this application. For example, an element shown as integrally formed may be composed of multiple parts or elements, the position of the element may be inverted or otherwise changed, and the nature or number or position of the discrete elements may be altered or changed. Therefore, all such modifications are intended to be included within the scope of the invention. The order or sequence of any process or method steps may be changed or rearranged according to alternative embodiments. In the claims, any "support plus function" clause is intended to cover the structure performing the function described herein, and not only structurally equivalent but also equivalent in structure. Other substitutions, modifications, alterations, and omissions may be made in the design, operation, and arrangement of the exemplary embodiments without departing from the scope of the invention. Therefore, the invention is not limited to the particular embodiments but extends to a variety of modifications that still fall within the scope of the appended claims.

[0109] Furthermore, in order to provide a concise description of exemplary embodiments, not all features of actual embodiments may be omitted.

[0110] It should be understood that numerous specific implementation decisions can be made during the development of any practical implementation, such as in any engineering or design project. Such development efforts may be complex and time-consuming, but for those of ordinary skill in the art who benefit from this disclosure, the development effort will be a routine task in design, manufacturing, and production without requiring extensive experimentation.

[0111] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A self-healing action analysis method based on the protection settings of distribution automation terminals, characterized in that: include, Define the goals and standards of self-healing actions; By analyzing historical fault data, fault patterns are identified, and fault identification models are constructed. By combining historical data with real-time operational data, a dynamic protection setting model is established; Based on the identified fault modes and dynamic protection setting models, a multi-level decision tree structure is constructed. Feasibility tests of self-healing actions were conducted by simulating different fault scenarios and self-healing decision-making logic. By tracking each stage of the self-healing process, relevant data can be collected and analyzed in a timely manner to generate feedback information; The actual effectiveness of the self-healing measures is evaluated by comparing the operational data before and after self-healing. Establish and optimize the knowledge base.

2. The self-healing action analysis method based on the protection settings of distribution automation terminals as described in claim 1, characterized in that: The objectives of the self-healing action include quickly restoring power supply, reducing the impact of the fault on users, and improving system reliability. The criteria for the self-healing action include response time, fault detection accuracy, and automatic recovery rate.

3. The self-healing action analysis method based on the protection settings of distribution automation terminals as described in claim 2, characterized in that: The aforementioned fault identification model includes identifying fault modes such as short circuits, overloads, or equipment failures, providing basic data support for self-healing analysis. Its formula is as follows: F(x)=w1·I 2 +w2·V·R+w3·(P max -P current )+w4·e -λt Where F(x) is the fault identification score, a higher value indicates a greater fault risk; I is the current currently flowing through the equipment; V is the equipment voltage; R is the equipment resistance; P max P is the rated power of the equipment. current The current power of the equipment; w1, w2, w3, and w4 are weighting parameters, representing the degree of influence of factors such as current, voltage, power, and equipment status on fault identification, respectively; λ is the attenuation factor, reflecting the impact of equipment aging or environmental factors on fault risk; t is the equipment running time. The system uses a model to calculate the fault identification score of the current device. When the score exceeds a certain threshold, the system determines that the device is in a potential fault state. The threshold can be set autonomously according to the actual situation.

4. The self-healing action analysis method based on the protection settings of distribution automation terminals as described in claim 3, characterized in that: The dynamic protection setting model includes automatically adjusting the protection settings based on the actual operating conditions of the power grid, environmental changes, and load characteristics. The formula is shown below: Where PD is the dynamic protection setting; P base The basic protection settings are based on equipment standards; α, β, and γ are weighting coefficients, representing the degree of influence of load, temperature, and environmental factors on the protection settings, respectively; L is the current load; L max T represents the maximum load of the equipment. current T represents the current temperature. max The maximum safe temperature for the equipment; H env This is an environmental change factor used to reflect the impact of the external environment on equipment.

5. The self-healing action analysis method based on the protection settings of distribution automation terminals as described in claim 4, characterized in that: The decision logic includes response strategies for different fault conditions, such as selecting appropriate automatic reclosing, adjusting load, or segmenting power supply, to ensure that power supply is restored to the maximum extent in the shortest possible time.

6. The self-healing action analysis method based on the protection settings of distribution automation terminals as described in claim 5, characterized in that: The decision tree structure includes fault detection, dynamic protection setting evaluation, decision response strategy selection, and effect evaluation and feedback. Based on the decision tree structure, the self-healing decision logic includes real-time monitoring, dynamic adjustment, and comprehensive evaluation during runtime.

7. The self-healing action analysis method based on the protection settings of distribution automation terminals as described in claim 6, characterized in that: The feedback information includes whether the self-healing action was successful or not, as well as information on the above-mentioned standards such as the response time, fault detection accuracy, and automatic recovery rate of the successful self-healing action. Through continuous feedback analysis, the self-healing logic and decision tree structure can be continuously optimized to ensure the system's efficient self-healing capability.

8. The self-healing action analysis method based on the protection settings of distribution automation terminals as described in claim 7, characterized in that: The self-healing effect evaluation includes analyzing the power supply restoration time, user satisfaction, and system response effect by comparing the operating data before and after self-healing, and evaluating the actual effect of the self-healing measures; based on the evaluation results, optimization suggestions are made, including adjusting protection settings, improving decision-making logic, or improving monitoring methods, and combined with actual operating conditions to achieve continuous improvement and dynamic adaptation of self-healing action analysis.

9. The self-healing action analysis method based on the protection settings of distribution automation terminals as described in claim 8, characterized in that: The establishment and optimization of the knowledge base includes updating the knowledge base with information on fault types, pattern recognition methods, self-healing decision cases and evaluation results. Based on the knowledge base update model, it provides a reference for self-healing actions and provides strong support for future fault analysis and decision-making. The knowledge base update model is as follows: K(t)=K(t-1)+α·E(t)+β·R(t)-γ·O(t) Where K(t) is the amount of knowledge base content at time t-1; K(t-1) is the amount of knowledge base content at time t-1; E(t) is the number of newly acquired valid fault cases; R(t) is the number of self-healing decision cases after evaluation; O(t) is the number of outdated or invalid information; α, β, and γ are weight parameters, representing the degree of influence of valid cases, decision cases, and outdated information on the amount of knowledge base content, respectively.

10. The self-healing action analysis method based on the protection settings of distribution automation terminals as described in claim 9, characterized in that: The knowledge base update model includes data collection, parameter setting, knowledge base update calculation, information filtering, knowledge base structure optimization, and a continuous feedback mechanism.

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