A GIS equipment fault risk prediction method based on minimum cut set probability propagation

Abstract

This invention discloses a method for predicting GIS equipment failure risk based on minimum cut set probability propagation. The method includes acquiring GIS equipment structural information, constructing a fault tree model containing top events, intermediate events, and bottom events, and performing qualitative analysis to determine the minimum cut set corresponding to the top event. Combining historical failure information of the basic components corresponding to each bottom event, the method calculates the weighted failure rate and failure probability, and performs probability propagation based on the minimum cut set structure to obtain the occurrence probability of each minimum cut set and its risk contribution to the top event. An adaptive threshold is used to identify the dominant failure mode. Real-time operating status information of each basic component is acquired, a bottom event status deviation index is constructed, the failure probability of bottom events is dynamically corrected, and the occurrence probability of the dominant failure mode is updated to predict the real-time failure risk of the GIS equipment. This invention can integrate historical and real-time data to achieve dynamic assessment of equipment failure risk, improving prediction accuracy and operational reliability.

Description

Technical Field

[0001] This invention belongs to the field of GIS equipment failure prediction technology, specifically relating to a GIS equipment failure risk prediction method based on minimum cut set probability propagation. Background Technology

[0002] As a crucial component of high-voltage transmission systems, GIS (Gas Insulated Switchgear) directly impacts the safety and reliability of the power system. However, GIS equipment has a complex structure, including circuit breakers, instrument transformers, insulators, SF6 sealing elements, and other electrical components. These components are functionally dependent, connected in series and parallel, and their triggering conditions are determined by operating procedures or protection strategies. This complex system structure results in diverse fault propagation paths, and overall equipment failure is often caused by the coordinated failure of multiple underlying components.

[0003] In the prior art, CN108509290A discloses a data-driven fault tree analysis method, which generates a fault tree for equipment using historical fault data, calculates the failure probability of the base event and the Fussell-Vesely importance of the minimum cut set, and generates equipment condition evaluation data accordingly. This method can obtain the fault path corresponding to the minimum cut set and perform fault investigation or trigger early warning according to the importance. However, the existing method still has the following main problems: 1. Lack of consideration for structural coupling between basic events: When calculating the contribution of basic events or minimal cut sets to system failure risk, existing methods usually rely solely on historical statistical probabilities, ignoring the coupling effects of multiple basic events in complex system structures, resulting in inaccurate failure risk assessment.

[0004] 2. Lack of dynamic correction mechanism for the probability of basic event failure: Existing methods mainly rely on historical failure counts and empirical data to calculate the probability of basic events. They cannot combine real-time status information (such as temperature, running time, repair records, etc.) during equipment operation for dynamic correction, thus making it difficult to accurately reflect the risk level of the equipment in the current state.

[0005] 3. Inaccurate identification of dominant failure modes: In systems with multiple bottom events and multiple minimal cut sets, relying solely on Fussell-Vesely importance to rank cut sets cannot fully consider the interactions between bottom events and the complexity of the system structure, and is prone to missing critical failure paths that contribute significantly to the risk of the top event.

[0006] 4. Lack of real-time fault risk prediction capability: Existing fault tree analysis methods are mainly used for static assessment or historical data analysis, which makes it difficult to achieve real-time prediction and dynamic adjustment of fault risks during the operation of GIS equipment, thus limiting the timeliness and pertinence of operation and maintenance decisions.

[0007] In summary, existing technologies for GIS equipment failure risk assessment suffer from problems such as neglecting structural coupling effects, insufficient dynamic probability correction of underlying events, inaccurate identification of dominant failure modes, and lack of real-time prediction capabilities. There is an urgent need for more refined, dynamic, and real-time responsive failure risk analysis methods. Summary of the Invention

[0008] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a method for predicting GIS equipment failure risk based on minimum cut set probability propagation.

[0009] The objective of this invention can be achieved through the following technical solutions: This invention provides a method for predicting GIS equipment failure risk based on minimum cut set probability propagation, comprising the following steps: Obtain the structural information of the GIS equipment, and construct a corresponding GIS equipment fault tree model based on the structural information, wherein the fault tree model includes a top event, several intermediate events and several bottom events; Qualitative analysis is performed on the fault tree model to determine all minimum cut sets corresponding to the top event, and a set of minimum cut sets is formed, wherein each minimum cut set consists of at least one bottom event; Based on the historical fault information corresponding to each of the aforementioned bottom events, determine the failure probability of each bottom event; Based on the set of minimum cut sets and the failure probability of each underlying event, probability propagation calculation is performed on each minimum cut set to obtain the probability of each minimum cut set occurring. Calculate the risk contribution of each minimal cut set to the top event based on the probability of each minimal cut set occurring; Comparative analysis of the risk contribution of each minimal cut set is performed, and the minimal cut set whose risk contribution exceeds a preset threshold is taken as the dominant failure mode. The system acquires real-time operating status information of GIS equipment and, based on the dominant failure mode and its corresponding underlying event information, predicts the failure risk of GIS equipment to obtain failure risk prediction results.

[0010] Furthermore, the structural information includes the type, model, rated parameters, installation location, and logical connection relationships of each basic component of the GIS equipment; wherein the basic components include circuit breakers, instrument transformers, insulators, and SF6 sealing elements; the logical connection relationships include the functional dependencies, series or parallel electrical connections formed between the basic components during equipment operation, and triggering conditions determined by equipment operating procedures or protection strategies, used to determine the fault propagation path and the logical gate relationships between each bottom event, intermediate event, and top event in the fault tree model.

[0011] Furthermore, based on the structural information, a corresponding GIS equipment fault tree model is constructed, specifically including: The basic components of GIS equipment in the structural information are taken as base events. Introducing a fault tree model, where , This refers to the total number of basic components of the GIS equipment, i.e., the total number of base events; Based on the logical connections between the basic components in the structural information, intermediate events and logic gates are determined. These logic gates include AND and OR gates. Each bottom event is then connected to the intermediate and top events via these logic gates to form a complete fault tree structure. The top event... S This indicates a failure in the GIS equipment system; The structure function of the fault tree model is expressed as: in, For the top event in time The state variable takes a value of 1 to indicate that the top event has occurred, and 0 to indicate that the top event has not occurred. The vector of state variables for the underlying events; For the first The bottom line event in time The state variable takes a value of 1 to indicate that the event has occurred, and 0 to indicate that it has not occurred.

[0012] Furthermore, the qualitative analysis of the fault tree model to determine all minimal cut sets corresponding to the top event and form a set of minimal cut sets specifically includes: The top event, intermediate events, and bottom events in the fault tree model are encoded, where the bottom event is encoded as follows: ; Construct a fault tree structure matrix, and use the top event as the key. S Starting from the node, events are expanded layer by layer according to the logical gate relationships in the fault tree. When an AND gate is encountered, the input event code is written into the cut set matrix in a horizontal combination manner; when an OR gate is encountered, the input event code is written into the cut set matrix in a vertical expansion manner, thus obtaining all possible cut set combinations. The process involves iteratively replacing all the cut set combinations, replacing intermediate events with the corresponding next-level input event codes layer by layer, until all combinations contain only bottom event codes, thus obtaining the complete cut set set of the fault tree. Let the cut set be represented as: in, It is a cut set; The total number of cut sets; Indicates the first Each cut set is represented as: A set of events consisting of several underlying events; Indicates the first The first cut set The bottom line event; Minimize the set of cut sets when there exists When, then determine Non-minimum cut sets are removed, and cut sets that do not contain any subsets of other cut sets are retained as minimum cut sets, resulting in the set of minimum cut sets: in, It is the set of minimal cut sets. The total number of minimum cut sets.

[0013] Furthermore, the historical fault information corresponding to the bottom event includes the number of fault occurrences and the fault occurrence time of the corresponding faults recorded in the historical operating cycle related to the basic component corresponding to the bottom event; the relatedness indicates that there is a direct correspondence between the fault cause and the structural characteristics, function or operating status of the basic component, and the fault cause is the equipment abnormality or fault caused by the basic component's own fault, performance degradation or sealing failure, and the number of fault occurrences is used as the historical fault information of the corresponding bottom event.

[0014] Furthermore, based on the historical fault information corresponding to each of the aforementioned bottom events, the failure probability of each bottom event is determined, specifically including: Calculate the cumulative running time of the basic components corresponding to each bottom event within the historical operating cycle. And the number of failures in the historical fault information of each bottom event. Time of failure Calculate each base event Weighted failure rate : in, Indicates the first Weighted failure rate of individual events; Indicates the first Each basic event corresponds to the cumulative operating time of the basic component within the historical operating cycle; Indicates the first Each basic event corresponds to the number of times a basic component fails within its historical operating cycle; Indicates the first The bottom event The time weighting coefficient corresponding to each fault record; Indicates the first The bottom event The time of occurrence of the fault is recorded for each fault. This indicates the current statistical time, i.e., the current time. Indicates the time decay coefficient; Based on the weighted failure rate Calculate the base event Failure probability within the current statistical time period , is represented as: in, Indicates bottom event In time The probability of failure occurring within the system.

[0015] Furthermore, based on the set of minimal cut sets and the failure probability of each underlying event, probability propagation calculation is performed on each minimal cut set to obtain the probability of each minimal cut set occurring, specifically including: Based on the minimum cut set Minimal cut set in and the underlying events that constitute the minimum cut set. failure probability Calculate the first The basic probability of occurrence of a minimum cut set The formula is: in, Indicates the first The basic probability of occurrence of a minimum cut set; Indicates the first The number of base events contained in a minimum cut set; Indicates the first The first minimum cut set Individual events in statistical time Failure probability within; Based on the aforementioned basic occurrence probability, a minimum cut set structure complexity correction coefficient is introduced. The probability of occurrence of the basic occurrence is corrected by probability propagation to obtain the probability of occurrence of the minimum cut set. The formula is: Among them, the structural complexity correction coefficient Represented as: in, Indicates the first The probability of a minimum cut set occurring after probability propagation; This represents the minimum cut set structure complexity correction coefficient; This represents the structural coupling influence coefficient, which reflects the probability propagation attenuation effect between multiple bottom events in the minimum cut set due to the system's structural coupling.

[0016] Furthermore, the step of calculating the risk contribution of each minimal cut set to the top event based on the probability of each minimal cut set occurring specifically includes: Based on the minimum cut set The probability of each minimal cut set occurring Calculate the first The probability sensitivity coefficient of the minimum cut set The formula is: in, Indicates the first The probability sensitivity coefficient of a minimum cut set; Indicates the first The probability of a minimum cut set occurring after probability propagation; Indicates the first The first minimum cut set Individual events in statistical time Failure probability within; Indicates the first The number of base events contained in a minimum cut set; It represents the partial derivative of the probability of occurrence of the minimum cut set with respect to the probability of failure of the underlying event, and is used to reflect the degree of influence of the change in the probability of failure of the underlying event on the probability of the minimum cut set; Based on the probability sensitivity coefficient, a minimum cut set structure coupling coefficient is introduced. Calculate the first The risk contribution of a minimum cut set to the top event The formula is: Among them, the structural coupling coefficient Represented as: in, Indicates the first The number of base events contained in a minimum cut set; This represents the total number of minimal cut sets in the set of minimal cut sets; This represents the probability coupling adjustment coefficient, used to adjust the weight of the impact of the probability of bottom-event failure on structural coupling.

[0017] Furthermore, the comparative analysis of the risk contribution of each minimal cut set, identifying minimal cut sets with risk contribution exceeding a preset threshold as the dominant failure mode, specifically includes: Based on the minimum cut set Risk contribution of each minimum cut set Calculate the standard deviation of risk contribution. The formula is: in, This represents the total number of minimal cut sets in the set of minimal cut sets; This represents the average risk contribution of all minimal cut sets in the minimal cut set set; An adaptive risk discrimination threshold is constructed based on the mean and standard deviation. The formula is: in, This represents the risk amplification factor; When the minimum cut set satisfies the following condition: Then determine the minimum cut set. This is the dominant failure mode in GIS equipment systems.

[0018] Furthermore, the step of acquiring real-time operating status information of the GIS equipment and predicting the failure risk of the GIS equipment based on the dominant failure mode and its corresponding underlying event information, to obtain the failure risk prediction result of the GIS equipment, specifically includes: Acquire real-time operational status information of GIS equipment, including the status information of all basic components corresponding to all underlying events, including operating temperature. Number of repairs and running time ; Based on the real-time operating status information, construct the first... Individual event state deviation index The formula is: in, Indicates the first The fundamental event at the current moment State deviation index; Indicates the first Each basic event corresponds to the basic component at the current moment. Operating temperature; Indicates the first Each bottom event corresponds to the rated operating temperature of the basic components; Indicates the first Each basic event corresponds to the basic component at the current moment. The cumulative number of repairs; Indicates the first Each basic event corresponds to a reference repair frequency threshold for the basic component; Indicates the first Each basic event corresponds to the basic component at the current moment. Cumulative running time; Indicates the first Each basic event corresponds to the design operating life of the fundamental components; Preset weighting coefficients; Indicates the current moment; Based on the aforementioned state deviation index, a bottom event state correction coefficient is constructed. The formula is: in, Indicates the first The state correction coefficient of each basic event in real-time running state; Based on the aforementioned state correction coefficient, the probability of failure of the bottom event... After making corrections, the failure probability of the underlying event in real-time is obtained. The formula is: in, Indicates the first The probability of failure of a basic event in real-time operation; Indicates the first Weighted failure rate of individual events; Failure probability based on bottom events in real-time operation Calculate the real-time occurrence probability of the minimum cut set corresponding to the dominant failure mode. The formula is: in, Indicates the first The probability of a minimum cut set occurring in real-time; Indicates the first The number of base events contained in a minimum cut set; Indicates the first The first minimum cut set The probability of failure of a basic event in real time; Based on the probability of occurrence of the minimum cut set corresponding to the dominant failure mode in real-time operation, the real-time fault risk prediction value of the GIS equipment system is calculated. The formula is: in, Represents the set of minimal cut sets corresponding to the dominant failure modes; Real-time fault risk prediction value As a result of fault risk prediction for GIS equipment.

[0019] Compared with the prior art, the present invention has the following advantages: (1) In the existing technology, the failure risk assessment method for GIS equipment lacks consideration of the structural coupling effect between basic events. It usually only calculates the risk contribution of basic events or minimum cut sets to the top event based on historical statistical probabilities, which leads to inaccurate failure risk assessment under the synergistic effect of multiple basic events. This invention introduces a structural complexity correction coefficient in the calculation of the probability of occurrence of minimum cut sets and a minimum cut set structural coupling coefficient in the calculation of risk contribution. It incorporates the system coupling effect between basic events into the probability propagation and risk contribution analysis, realizes accurate correction of the probability of minimum cut sets and true reflection of the risk contribution of the top event, and improves the accuracy and reliability of failure risk assessment of GIS equipment systems.

[0020] (2) In the prior art, the failure probability of a bottom event relies solely on historical fault data and lacks a dynamic correction method that incorporates the equipment's operating status, making it difficult to accurately reflect the risk level of the equipment under its current operating state. This invention obtains real-time operating status information of the basic components corresponding to each bottom event, including operating temperature, number of repairs, and cumulative operating time, and constructs a state deviation index and a state correction coefficient to correct the failure probability of the bottom event, thereby achieving dynamic adjustment of the failure probability of the bottom event and enabling a more accurate fault risk assessment based on the real-time operating status of the equipment.

[0021] (3) In the prior art, the identification of dominant failure modes relies on the Fussell-Vesely importance ranking, which cannot fully consider the interaction of multiple events and the complexity of the system structure, and is prone to missing key failure paths. This invention calculates the risk contribution of each minimal cut set, combines the probability sensitivity coefficient and the structural coupling coefficient, and then adaptively generates a risk discrimination threshold based on the mean and standard deviation. It identifies the minimal cut sets whose risk contribution exceeds the threshold as the dominant failure modes, thereby achieving accurate identification of key failure paths in GIS equipment systems and improving the pertinence and effectiveness of failure risk control.

[0022] (4) In the prior art, the calculation of the probability of failure of the lowest event does not take into account the decay weight of the failure time, which leads to the deviation of the assessment of the contribution of historical failure data to the current risk. The present invention introduces a time decay coefficient into the calculation of the weighted failure rate of the lowest event, and corrects the historical failure events according to the time weight, so that the recent failures have a higher impact on the probability of failure of the lowest event, while the impact of the more distant historical failures gradually decays, thereby improving the timeliness and accuracy of the assessment of the probability of failure of the lowest event. Attached Figure Description

[0023] Figure 1 This is a flowchart of the GIS equipment fault risk prediction method according to an embodiment of the present invention; Figure 2 This is a model diagram of the GIS equipment failure risk prediction system according to an embodiment of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0025] Example 1: This embodiment provides a method for predicting GIS equipment failure risks based on minimum cut set probability propagation, such as... Figure 1 As shown, it includes the following steps: Step S1: Obtain the structural information of the GIS equipment and construct the corresponding GIS equipment fault tree model based on the structural information; The fault tree model includes a top event, several intermediate events, and several bottom events. Obtain structural information about the GIS equipment, including the type, model, rated parameters, installation location, and logical connections between each basic component. Basic components include circuit breakers, instrument transformers, insulators, and SF6 sealing elements. The logical connections between components include not only series or parallel electrical connections but also functional dependencies formed during equipment operation and triggering conditions determined by equipment operating procedures or protection strategies. This structural information is used to clarify the propagation path of faults within the equipment and to determine the logical gate relationships between bottom, intermediate, and top events in the fault tree model, providing foundational data for subsequent fault probability calculations and risk assessments.

[0026] In this embodiment, step S1 specifically includes: The basic components of GIS equipment in the structural information are taken as base events. Introducing a fault tree model, where , This represents the total number of basic components of GIS equipment, i.e., the total number of base events. Based on the logical connections between the basic components in the structural information, intermediate events and logic gates are determined. Logic gates include AND gates and OR gates. Each bottom event is then connected to the intermediate events and the top event through logic gates to form a complete fault tree structure. The top event... S This indicates that the entire GIS equipment system has failed.

[0027] The structure function of the fault tree model is represented as: in, For the top event in time The state variable takes a value of 1 to indicate that the top event has occurred, and 0 to indicate that the top event has not occurred. The vector of state variables for the underlying events; For the first The bottom line event in time The state variable takes a value of 1 to indicate that the bottom event has occurred, and 0 to indicate that it has not occurred. This structure function can map changes in the bottom event state to the top event state, thus providing a mathematical description of the overall system failure.

[0028] The reason for using this structural function is that it can simultaneously consider the combined influence of multiple bottom events on the top event, through the bottom event state vector. This allows the system to reflect its operational status at any given time. The technical advantage of this setup lies in establishing a quantitative mapping relationship from components to the system, providing a unified data foundation for subsequent qualitative analysis using minimal cut sets, probability propagation calculations, and risk contribution analysis. Compared to traditional methods for calculating the failure probability of a single component, this method can accurately describe the possibility of system failure under the synergistic effect of multiple components, thereby achieving a more scientific and accurate risk assessment of GIS equipment failures.

[0029] Step S2: Perform qualitative analysis on the fault tree model to determine all minimum cut sets corresponding to the top event and form a set of minimum cut sets; Each minimal cut set consists of at least one base event; In this embodiment, step S2 specifically includes: The top, intermediate, and bottom events in the fault tree model are encoded, with the bottom event corresponding to the following encoding: ; During the analysis, a fault tree structure matrix is ​​constructed, with the top event... S Starting with a node, events are expanded layer by layer according to the logic gate relationships in the fault tree. When an AND gate is encountered, the input event codes are written into the cut-set matrix in a horizontal combination manner. This accurately represents the combination relationship where the AND gate logic requires all input events to occur simultaneously to cause the output event. When an OR gate is encountered, the input event codes are written into the cut-set matrix in a vertical expansion manner, representing the logical characteristic that the occurrence of any input event will cause the output event. Through this matrix processing method, all possible cut-set combinations can be systematically obtained, ensuring that the analysis process is comprehensive and without omissions.

[0030] After obtaining the initial cutset combinations, the intermediate events are iteratively replaced, with the intermediate events being replaced layer by layer with the corresponding input event codes of the next level, until all combinations contain only the bottom event codes, thus obtaining the complete cutset set of the fault tree. C : in, It is a cut set; The total number of cut sets; Indicates the first Each cut set is represented as: A set of events consisting of several underlying events; Indicates the first The first cut set The bottom line event; To obtain the minimum cut set, the set of cut sets needs to be minimized. This is necessary when there exists... When, then determine Non-minimum cut sets are removed, and cut sets that do not contain any subsets of other cut sets are retained as minimum cut sets, resulting in the set of minimum cut sets: in, It is the set of minimal cut sets. The total number of minimum cut sets.

[0031] The principle behind matrix coding and iterative replacement processing lies in its ability to systematically and automatically parse the logical relationships within complex fault trees, ensuring the completeness and accuracy of minimal cut set calculations. The minimal cut set obtained in this way reflects all potential critical paths to failure in the GIS equipment system, providing a precise qualitative basis for subsequent low-level event probability propagation and risk contribution analysis. The technical advantage of this method is its ability to efficiently and accurately identify the combination of low-level events that contributes the most to the risk of the top event in the system, providing a scientific basis for subsequent risk prediction and dominant failure mode determination, while avoiding potential omissions and misjudgments in traditional manual analysis.

[0032] Step S3: Determine the failure probability of each bottom event based on the historical fault information corresponding to each bottom event; Historical fault information for bottom-level events includes the number of faults recorded for the corresponding basic component during its historical operating cycle, as well as the time of each fault. This fault information is directly related to the structural characteristics, functional role, or operating status of the basic component. The causes of faults are usually equipment anomalies or malfunctions caused by the basic component itself, performance degradation, or seal failure. In the analysis, the number of fault occurrences and the time are used as the core data of historical fault information for bottom-level events to reflect the actual operational risk characteristics of the basic component.

[0033] In this embodiment, step S3 specifically includes: Calculate the cumulative running time of the basic components corresponding to each basic event within the historical operating cycle. And the number of failures in the historical fault information of each bottom event. Time of failure Calculate each base event Weighted failure rate : in, Indicates the first Weighted failure rate of individual events; Indicates the first Each basic event corresponds to the cumulative operating time of the basic component within the historical operating cycle; Indicates the first Each basic event corresponds to the number of times a basic component fails within its historical operating cycle; Indicates the first The bottom event The time weighting coefficient corresponding to each fault record; Indicates the first The bottom event The time of occurrence of the fault is recorded for each fault. This indicates the current statistical time, i.e., the current time. Indicates the time decay coefficient; Time weighting coefficient The design principle is that the impact of historical failures decreases over time, while more recent failures have a greater impact on current risks. Using an exponential decay function can reasonably reflect this timeliness. Weighted failure rate By accumulating the time weights of all failures and dividing by the running time, the failure rate takes into account both the frequency of failures and the length of the operating cycle, thus more accurately reflecting the health status of basic components.

[0034] Based on weighted failure rate Calculate the base event Failure probability within the current statistical time period , is represented as: in, Indicates bottom event In time The probability of failure occurring within the given timeframe. This formula uses an exponential failure model, converting the weighted failure rate into an actual failure probability, which reflects the magnitude of the probability of failure occurring at the current moment due to the underlying event.

[0035] The bottom-event failure probability obtained through this method fully considers the impact of historical failure frequency, failure time, and operating cycle on failure risk, enabling a quantitative assessment of the bottom-event health status. The technical advantage lies in the fact that the bottom-event failure probability not only reflects the overall reliability of the component but also dynamically reflects the contribution of recent failures to risk, providing an accurate probabilistic basis for subsequent minimum cutset probability propagation calculations and risk contribution analysis. Compared to traditional simple failure rate statistical methods, this weighted failure rate and exponential probability model can more scientifically and accurately assess the potential failure risk of GIS equipment, while improving the reliability and sensitivity of failure risk prediction.

[0036] Step S4: Based on the set of minimal cut sets and the failure probability of each underlying event, perform probability propagation calculations on each minimal cut set to obtain the probability of each minimal cut set occurring. Specifically, this includes: Based on the minimum cut set Minimal cut set in and the base events that constitute the minimal cut set. failure probability Calculate the first The basic probability of occurrence of a minimum cut set The formula is: in, Indicates the first The basic probability of occurrence of a minimum cut set; Indicates the first The number of base events contained in a minimum cut set; Indicates the first The first minimum cut set Individual events in statistical time The formula is based on the independent event assumption. It multiplies the failure probabilities of each bottom event within the minimum cut set to obtain the joint probability of the minimum cut set occurring, reflecting the basic probability relationship that the occurrence of multiple bottom events simultaneously leads to the occurrence of the top event.

[0037] Considering the potential structural coupling effect among the underlying events in a minimal cut set in a real-world system—meaning that multiple underlying events are not completely independent—and that the system structure and logical relationships may cause attenuation in probability propagation, a minimal cut set structural complexity correction coefficient is introduced. The basic occurrence probability is corrected to obtain the corrected minimum cut set occurrence probability. The formula is: Among them, the structural complexity correction coefficient Represented as: in, Indicates the first The probability of a minimum cut set occurring after probability propagation; This represents the minimum cut set structure complexity correction coefficient; This represents the structural coupling influence coefficient, used to reflect the probability propagation attenuation effect among multiple basic events in a minimal cut set due to system structural coupling. This correction reduces the overestimation of joint probabilities in complex minimal cut sets, reflecting the actual impact of structural coupling on risk propagation.

[0038] The design principle of this method lies in using the product of the failure probabilities of bottom events as the basic occurrence probability, which directly reflects the contribution of the combination of bottom events within the minimal cut set to the occurrence of the top event. The structural complexity correction coefficient takes into account the inhibitory effect of the coupling relationship between events in the actual system on probability propagation. By introducing the correction coefficient, the intuitiveness of the basic probability calculation is preserved while the accuracy of the probability calculation is improved. It can more accurately reflect the risk magnitude of the top event caused by the minimal cut set under actual operating conditions, avoiding the risk overestimation that may be caused by the traditional simple multiplication method. Advantages include: effectively identifying the true risk contribution of structurally complex minimal cut sets; and, combined with the failure probability of the bottom event and the characteristics of the system structure, providing a reliable quantitative basis for subsequent risk contribution analysis and dominant failure mode identification.

[0039] Step S5: Based on the probability of each minimal cut set occurring, calculate the risk contribution of each minimal cut set to the top event, specifically including: Based on the minimum cut set The probability of each minimal cut set occurring Calculate the first The probability sensitivity coefficient of the minimum cut set The formula is: in, Indicates the first The probability sensitivity coefficient of a minimum cut set; Indicates the first The probability of a minimum cut set occurring after probability propagation; Indicates the first The first minimum cut set Individual events in statistical time Failure probability within; Indicates the first The number of base events contained in a minimum cut set; The partial derivative of the probability of occurrence of the minimum cut set with respect to the probability of failure of the underlying event is used to reflect the degree of influence of the change in the probability of failure of the underlying event on the probability of the minimum cut set. This calculation method can reflect the degree of influence of the change in the probability of failure of the underlying event on the probability of the minimum cut set, thereby identifying the underlying events most sensitive to system risk and providing a basis for accurate risk assessment.

[0040] Based on this, a minimum cut set structure coupling coefficient is introduced. Considering the combined effect of the relative size of the minimum cut set in the system and the failure probability of the bottom event, calculate the... The final risk contribution of each minimal cut set to the top event The formula is: Among them, the structural coupling coefficient Represented as: in, Indicates the first The number of base events contained in a minimum cut set; This represents the total number of minimal cut sets in the set of minimal cut sets; This represents the probability coupling adjustment coefficient, used to adjust the weight of the impact of the probability of bottom-event failure on structural coupling.

[0041] Combining the occurrence probability of minimal cut sets with sensitivity and structural coupling effects allows for a comprehensive consideration of both internal and external influencing factors. Probabilistic sensitivity reflects the impact of basic events on minimal cut sets, while the structural coupling coefficient reflects the relative importance of minimal cut sets within the system and their probabilistic coupling effect. It can accurately quantify the risk contribution of each minimal cutset to the top event of the system, providing a reliable basis for the identification of dominant failure modes. Its advantages are: it can identify critical minimal cutsets that are both highly probable and sensitive to the system, avoiding misjudgments caused by relying solely on a single probability or cutset size, and providing a scientific basis for subsequent risk management, maintenance decisions, and operational optimization.

[0042] Step S6: Compare and analyze the risk contribution of each minimal cut set, and identify the minimal cut sets whose risk contribution exceeds a preset threshold as the dominant failure mode. Specifically, this includes: Based on the minimum cut set Risk contribution of each minimum cut set Calculate the standard deviation of risk contribution. The formula is: in, This represents the total number of minimal cut sets in the set of minimal cut sets; This represents the average risk contribution of all minimal cut sets in the minimal cut set set; An adaptive risk discrimination threshold is constructed based on the mean and standard deviation. The formula is: in, This represents the risk amplification factor; this threshold comprehensively considers the average risk contribution and dispersion of the minimum cut set, so that the judgment can focus on high-risk cut sets while excluding occasional or low-impact cut sets.

[0043] When the minimum cut set satisfies the following condition: Then determine the minimum cut set This is the dominant failure mode in GIS equipment systems.

[0044] By combining statistical analysis with adaptive thresholding, cutsets with high risk contribution and significant impact on the system are identified. The technical effect is the accurate screening of critical failure paths, facilitating subsequent targeted maintenance and fault prevention. The advantage lies in the fact that, compared to fixed thresholding methods, adaptive thresholding can adapt to different equipment structures and operating conditions, improving the reliability and accuracy of dominant failure mode identification.

[0045] Step S7: Obtain real-time operating status information of the GIS equipment, and based on the dominant failure mode and its corresponding underlying event information, predict the failure risk of the GIS equipment to obtain the failure risk prediction result of the GIS equipment, specifically including: Acquire real-time operational status information of GIS equipment. This real-time operational status information includes the status information of all basic components corresponding to all underlying events, including operating temperature. Number of repairs and running time ; Based on real-time operational status information, construct the first... Individual event state deviation index The formula is: in, Indicates the first The fundamental event at the current moment State deviation index; Indicates the first Each basic event corresponds to the basic component at the current moment. Operating temperature; Indicates the first Each bottom event corresponds to the rated operating temperature of the basic components; Indicates the first Each basic event corresponds to the basic component at the current moment. The cumulative number of repairs; Indicates the first Each basic event corresponds to a reference repair frequency threshold for the basic component; Indicates the first Each basic event corresponds to the basic component at the current moment. Cumulative running time; Indicates the first Each basic event corresponds to the design operating life of the fundamental components; Preset weighting coefficients; This indicates the current moment; this formula quantifies the degree of deviation in the health status of components corresponding to each underlying event, providing a basis for real-time risk correction. This indicator can dynamically reflect the impact of multiple factors such as temperature rise, wear, and maintenance status, avoiding the inadequacy of a single parameter to reflect the true risk.

[0046] Construct a bottom-event state correction coefficient based on the state deviation index. The formula is: in, Indicates the first The state correction coefficient of each basic event in real-time running state; Based on the state correction coefficient, the probability of failure of the bottom event. After making corrections, the failure probability of the underlying event in real-time is obtained. The formula is: in, Indicates the first The probability of failure of a basic event in real-time operation; Indicates the first The formula is a weighted failure rate of individual events; it couples historical statistical failure probabilities with real-time operating status, enabling predictions to reflect the true risk level under current operating conditions, thus improving the accuracy and sensitivity of failure prediction.

[0047] Failure probability based on bottom events in real-time operation Calculate the real-time occurrence probability of the minimum cut set corresponding to the dominant failure mode. The formula is: in, Indicates the first The probability of a minimum cut set occurring in real-time; Indicates the first The number of base events contained in a minimum cut set; Indicates the first The first minimum cut set The probability of failure of a basic event in real time; Based on the probability of occurrence of the minimum cut set corresponding to the dominant failure mode under real-time operating conditions, the real-time fault risk prediction value of the GIS equipment system is calculated. The formula is: in, Represents the set of minimal cut sets corresponding to the dominant failure modes; Real-time fault risk prediction value As a result of fault risk prediction for GIS equipment.

[0048] By introducing state deviation index and state correction coefficient, dynamic correction of historical statistical probability is achieved. The technical effect is that it can reflect the impact of equipment operating status on failure risk in real time. The advantage is that the prediction results are more accurate and sensitive, which can support equipment operation management and preventive maintenance decisions, while avoiding the shortcomings of a single index or the overall equipment status being insufficient to characterize local risks.

[0049] Example 2: This embodiment provides a GIS equipment failure risk prediction system based on minimum cut set probability propagation, such as... Figure 2 As shown, the system includes a data acquisition module, a fault tree construction module, a minimum cut set analysis module, a bottom event failure probability calculation module, a probability propagation calculation module, a risk contribution assessment module, a dominant failure mode identification module, and a real-time risk prediction module. These modules work together to achieve dynamic prediction and real-time assessment of GIS equipment failure risks.

[0050] The data acquisition module is used to obtain the structural information and real-time operating status information of the GIS equipment. Structural information includes the type, model, rated parameters, installation location, and logical connections of each basic component, including circuit breakers, instrument transformers, insulators, and SF6 sealing elements. Logical connections include functional dependencies, series or parallel electrical connections, and triggering conditions determined by operating procedures or protection strategies. Real-time operating status information includes the operating status of all basic events corresponding to each basic component, reflecting the current health status of each component.

[0051] The fault tree construction module builds a GIS equipment fault tree model based on the structural information, including the top event, several intermediate events and the bottom event. The state of each bottom event is represented as a state variable, and the top event is represented as the system failure state, which is used to describe the overall risk.

[0052] The minimum cut set analysis module performs qualitative analysis on the fault tree, expands the logic gate relationships through the fault tree structure matrix to obtain all cut set combinations, and obtains the minimum cut set set through iterative replacement and minimization, providing a foundation for subsequent probabilistic analysis.

[0053] The bottom event failure probability calculation module uses historical fault information of each bottom event to statistically analyze the operation status and fault records, calculates the bottom event failure probability, and provides input data for probability propagation.

[0054] The probability propagation calculation module calculates the occurrence probability of each minimal cut set based on the set of minimal cut sets and the failure probability of the underlying event, and corrects the results for structural complexity to reflect the impact of system structural coupling on risk.

[0055] The risk contribution assessment module calculates the probability sensitivity based on the occurrence probability of each minimal cut set, and combines it with structural coupling factors to obtain the risk contribution of the minimal cut set to the top event, which is used to quantify the impact of each cut set on the system risk.

[0056] The dominant failure mode identification module calculates the average value and standard deviation based on the risk contribution, constructs an adaptive risk discrimination threshold, and determines the minimum cut set that exceeds the threshold as the dominant failure mode, thus providing a focus for real-time risk prediction.

[0057] The real-time risk prediction module obtains the real-time operating status of the components corresponding to each basic event, constructs a status deviation index and correction coefficient, corrects the failure probability of the basic event, calculates the real-time minimum cut set occurrence probability of the dominant failure mode, and finally obtains the real-time fault risk prediction value of the GIS equipment system, realizing dynamic monitoring and early warning of equipment fault risk.

[0058] The entire system, through modular design, achieves closed-loop prediction from historical data analysis to real-time status correction, accurately reflecting the impact of the status of each component on system risk, supporting reliability management and preventive maintenance, and improving equipment operation safety and maintenance efficiency.

[0059] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0060] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for predicting GIS equipment failure risk based on minimum cut set probability propagation, characterized in that, Includes the following steps: Obtain the structural information of the GIS equipment, and construct a corresponding GIS equipment fault tree model based on the structural information, wherein the fault tree model includes a top event, several intermediate events and several bottom events; Qualitative analysis is performed on the fault tree model to determine all minimum cut sets corresponding to the top event, and a set of minimum cut sets is formed, wherein each minimum cut set consists of at least one bottom event; Based on the historical fault information corresponding to each of the aforementioned bottom events, determine the failure probability of each bottom event; Based on the set of minimum cut sets and the failure probability of each underlying event, probability propagation calculation is performed on each minimum cut set to obtain the probability of each minimum cut set occurring. Calculate the risk contribution of each minimal cut set to the top event based on the probability of each minimal cut set occurring; Comparative analysis of the risk contribution of each minimal cut set is performed, and the minimal cut set whose risk contribution exceeds a preset threshold is taken as the dominant failure mode. The system acquires real-time operating status information of GIS equipment and, based on the dominant failure mode and its corresponding underlying event information, predicts the failure risk of GIS equipment to obtain failure risk prediction results.

2. The GIS equipment failure risk prediction method based on minimum cut set probability propagation according to claim 1, characterized in that, The structural information includes the type, model, rated parameters, installation location, and logical connection relationships of each basic component of the GIS equipment; wherein the basic components include circuit breakers, instrument transformers, insulators, and SF6 sealing elements; the logical connection relationships include the functional dependencies, series or parallel electrical connections formed between the basic components during equipment operation, and triggering conditions determined by equipment operating procedures or protection strategies, used to determine the fault propagation path and the logical gate relationships between each bottom event, intermediate event, and top event in the fault tree model.

3. The GIS equipment failure risk prediction method based on minimum cut set probability propagation according to claim 2, characterized in that, Based on the structural information, a corresponding GIS equipment fault tree model is constructed, specifically including: The basic components of GIS equipment in the structural information are taken as base events. Introducing a fault tree model, where , This refers to the total number of basic components of the GIS equipment, i.e., the total number of base events; Based on the logical connections between the basic components in the structural information, intermediate events and logic gates are determined. These logic gates include AND and OR gates. Each bottom event is then connected to the intermediate and top events via these logic gates to form a complete fault tree structure. The top event... S This indicates a failure in the GIS equipment system; The structure function of the fault tree model is expressed as: in, The top event in time The state variable takes a value of 1 to indicate that the top event has occurred, and 0 to indicate that the top event has not occurred. The vector of state variables for the underlying events; For the first The bottom line event in time The state variable takes a value of 1 to indicate that the event has occurred, and 0 to indicate that it has not occurred.

4. The GIS equipment failure risk prediction method based on minimum cut set probability propagation according to claim 1, characterized in that, The qualitative analysis of the fault tree model, determining all minimal cut sets corresponding to the top event, and forming a set of minimal cut sets, specifically includes: The top event, intermediate events, and bottom events in the fault tree model are encoded, where the bottom event is encoded as follows: ; Construct a fault tree structure matrix, and use the top event as the key. S Starting from the node, events are expanded layer by layer according to the logical gate relationships in the fault tree. When an AND gate is encountered, the input event code is written into the cut set matrix in a horizontal combination manner; when an OR gate is encountered, the input event code is written into the cut set matrix in a vertical expansion manner, thus obtaining all possible cut set combinations. The process involves iteratively replacing all the cut set combinations, replacing intermediate events with the corresponding next-level input event codes layer by layer, until all combinations contain only bottom event codes, thus obtaining the complete cut set set of the fault tree. Let the cut set be represented as: in, It is a cut set; The total number of cut sets; Indicates the first Each cut set is represented as: A set of events consisting of several underlying events; Indicates the first The first cut set The bottom line event; Minimize the set of cut sets when there exists When, then determine Non-minimum cut sets are removed, and cut sets that do not contain any subsets of other cut sets are retained as minimum cut sets, resulting in the set of minimum cut sets: in, It is the set of minimal cut sets. The total number of minimum cut sets.

5. The GIS equipment failure risk prediction method based on minimum cut set probability propagation according to claim 1, characterized in that, The historical fault information corresponding to the bottom event includes the number of faults recorded in the historical operating cycle and the time of occurrence of the corresponding faults, which are related to the basic components corresponding to the bottom event. The related indication indicates that there is a direct correspondence between the cause of the fault and the structural characteristics, function, or operating status of the basic component, and the cause of the fault is an equipment abnormality or fault caused by the fault of the basic component itself, performance degradation, or sealing failure. The number of fault occurrences is used as the historical fault information of the corresponding basic event.

6. The GIS equipment failure risk prediction method based on minimum cut set probability propagation according to claim 5, characterized in that, Based on the historical fault information corresponding to each of the aforementioned bottom events, the failure probability of each bottom event is determined, specifically including: Calculate the cumulative running time of the basic components corresponding to each bottom event within the historical operating cycle. And the number of failures in the historical fault information of each bottom event. Time of failure Calculate each base event Weighted failure rate : in, Indicates the first Weighted failure rate of individual events; Indicates the first Each basic event corresponds to the cumulative operating time of the basic component within the historical operating cycle; Indicates the first Each basic event corresponds to the number of times a basic component fails within its historical operating cycle; Indicates the first The bottom event The time weighting coefficient corresponding to each fault record; Indicates the first The bottom event The time of occurrence of the fault is recorded for each fault. This indicates the current statistical time, i.e., the current time. Indicates the time decay coefficient; Based on the weighted failure rate Calculate the base event Failure probability within the current statistical time period , is represented as: in, Indicates bottom event In time The probability of failure occurring within the system.

7. The GIS equipment failure risk prediction method based on minimum cut set probability propagation according to claim 6, characterized in that, Based on the set of minimum cut sets and the failure probabilities of each underlying event, probability propagation calculations are performed on each minimum cut set to obtain the probability of each minimum cut set occurring, specifically including: Based on the minimum cut set Minimal cut set in and the underlying events that constitute the minimum cut set. failure probability Calculate the first The basic probability of occurrence of a minimum cut set The formula is: in, Indicates the first The basic probability of occurrence of a minimum cut set; Indicates the first The number of base events contained in a minimum cut set; Indicates the first The first minimum cut set Individual events in statistical time Failure probability within; Based on the aforementioned basic occurrence probability, a minimum cut set structure complexity correction coefficient is introduced. The probability of occurrence of the basic occurrence is corrected by probability propagation to obtain the probability of occurrence of the minimum cut set. The formula is: Among them, the structural complexity correction coefficient Represented as: in, Indicates the first The probability of a minimum cut set occurring after probability propagation; This represents the minimum cut set structure complexity correction coefficient; This represents the structural coupling influence coefficient, which reflects the probability propagation attenuation effect between multiple bottom events in the minimum cut set due to the system's structural coupling.

8. The GIS equipment failure risk prediction method based on minimum cut set probability propagation according to claim 7, characterized in that, Based on the probability of each minimal cut set occurring, the risk contribution of each minimal cut set to the top event is calculated, specifically including: Based on the minimum cut set The probability of each minimal cut set occurring Calculate the first The probability sensitivity coefficient of the minimum cut set The formula is: in, Indicates the first The probability sensitivity coefficient of a minimum cut set; Indicates the first The probability of a minimum cut set occurring after probability propagation; Indicates the first The first minimum cut set Individual events in statistical time Failure probability within; Indicates the first The number of base events contained in a minimum cut set; It represents the partial derivative of the probability of occurrence of the minimum cut set with respect to the probability of failure of the underlying event, and is used to reflect the degree of influence of the change in the probability of failure of the underlying event on the probability of the minimum cut set; Based on the probability sensitivity coefficient, a minimum cut set structure coupling coefficient is introduced. Calculate the first The risk contribution of a minimum cut set to the top event The formula is: Among them, the structural coupling coefficient Represented as: in, Indicates the first The number of base events contained in a minimum cut set; This represents the total number of minimal cut sets in the set of minimal cut sets; This represents the probability coupling adjustment coefficient, used to adjust the weight of the impact of the probability of bottom-event failure on structural coupling.

9. The GIS equipment failure risk prediction method based on minimum cut set probability propagation according to claim 8, characterized in that, A comparative analysis of the risk contribution of each minimal cut set was conducted, and minimal cut sets whose risk contribution exceeded a preset threshold were identified as the dominant failure modes, specifically including: Based on the minimum cut set Risk contribution of each minimum cut set Calculate the standard deviation of risk contribution. The formula is: in, This represents the total number of minimal cut sets in the set of minimal cut sets; This represents the average risk contribution of all minimal cut sets in the minimal cut set set; An adaptive risk discrimination threshold is constructed based on the mean and standard deviation. The formula is: in, This represents the risk amplification factor; When the minimum cut set satisfies the following condition: Then determine the minimum cut set. This is the dominant failure mode in GIS equipment systems.

10. The GIS equipment failure risk prediction method based on minimum cut set probability propagation according to claim 1, characterized in that, The process of acquiring real-time operating status information of GIS equipment and predicting the failure risk of GIS equipment based on the dominant failure mode and its corresponding underlying event information, to obtain the failure risk prediction result of GIS equipment, specifically includes: Acquire real-time operational status information of GIS equipment, including the status information of all basic components corresponding to all underlying events, including operating temperature. Number of repairs and running time ; Based on the real-time operating status information, construct the first... Individual event state deviation index The formula is: in, Indicates the first The fundamental event at the current moment State deviation index; Indicates the first Each basic event corresponds to the basic component at the current moment. Operating temperature; Indicates the first Each bottom event corresponds to the rated operating temperature of the basic components; Indicates the first Each basic event corresponds to the basic component at the current moment. The cumulative number of repairs; Indicates the first Each basic event corresponds to a reference repair frequency threshold for the basic component; Indicates the first Each basic event corresponds to the basic component at the current moment. The cumulative running time; Indicates the first Each basic event corresponds to the design operating life of the fundamental components; Preset weighting coefficients; Indicates the current moment; Based on the aforementioned state deviation index, a bottom event state correction coefficient is constructed. The formula is: in, Indicates the first The state correction coefficient of each basic event in real-time running state; Based on the aforementioned state correction coefficient, the probability of failure of the bottom event... After making corrections, the failure probability of the underlying event in real-time is obtained. The formula is: in, Indicates the first The probability of failure of a basic event in real-time operation; Indicates the first Weighted failure rate of individual events; Failure probability based on bottom events in real-time operation Calculate the real-time occurrence probability of the minimum cut set corresponding to the dominant failure mode. The formula is: in, Indicates the first The probability of a minimum cut set occurring in real-time; Indicates the first The number of base events contained in a minimum cut set; Indicates the first The first minimum cut set The probability of failure of a basic event in real time; Based on the probability of occurrence of the minimum cut set corresponding to the dominant failure mode in real-time operation, the real-time fault risk prediction value of the GIS equipment system is calculated. The formula is: in, Represents the set of minimal cut sets corresponding to the dominant failure modes; Real-time fault risk prediction value As a result of fault risk prediction for GIS equipment.

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