Scheduling automation master station system function operation state evaluation method

Abstract

The invention discloses a method for evaluating the function operation state of a dispatching automation master station system, and solves the problems that the efficiency is low and the operation stability and reliability of a power grid are influenced after the function detection of the dispatching automation master station system in the prior art occurs after a fault occurs, and the method comprises the following steps: constructing a function model of the dispatching automation master station system; determining an influence factor set of the function failure of the master station system; determining the weight value of the influence factor set by using an analytic hierarchy process and checking the consistency of the influence factor set; constructing a fuzzy comprehensive evaluation matrix by using a single-factor fuzzy evaluation method; the fuzzy evaluation matrix and the influence factor set weight value are combined to evaluate the operation state of the main station system function; and constructing a main station system function failure fault tree model, solving a minimum cut set and a minimum path set, and tracing the reason of the main station system function failure. The function failure probability of the dispatching automation master station system can be evaluated, the function failure reason can be traced, and safe and stable operation of a power grid is ensured.

Description

Technical Field

[0001] The invention relates to the technical field of fault detection of a dispatching automation master station system, and in particular to a method for evaluating the functional operation status of a dispatching automation master station system. Background Art

[0002] The dispatching automation master station system is a software system that provides services for power grid operation, which includes multiple functional modules such as data acquisition, monitoring and control. The system can collect the operation data of the power grid system in real time, and generate relevant operation information and forecast data through data processing and data analysis, providing decision support and control means for power dispatching agencies and operators at all levels. This system is one of the core systems of power system dispatching automation, and plays an important role in the planning, operation and management of power systems. It is conducive to improving the operation efficiency of power systems and optimizing the supply and demand relationship of the power market, and contributing to the development of power enterprises and social economy.

[0003] For a long time, the management and operation of power grids have been basically at the level of experience dispatching. This traditional management mode has led to insufficient management level and work efficiency. In addition, the production operation and daily maintenance operations of power grids mostly adopt manual inspection and on-site operation. This traditional operation mode also has certain limitations. Therefore, the dispatching automation master station system needs to have the characteristics of high efficiency, safety, reliability, and intelligence to ensure the safe and stable operation of the power grid, improve energy utilization efficiency, and promote the sustainable development of social economy.

[0004] The existing information analysis and processing speed of the dispatching automation master station system is relatively slow, which is difficult to meet the efficient operation requirements of the power grid. In order to improve the performance and efficiency of the dispatching automation system, it is necessary to establish common functional modules to share information and resources between different power grids and improve the overall efficiency of the system. However, due to the influence of various factors, the functions of the dispatching automation master station system may fail. In actual production work, the abnormal functions and fault identification of the dispatching automation master station system are often discovered only after the abnormality of the dispatching automation master station system occurs, and then the staff conducts analysis and identification, which often delays the fault diagnosis and recovery of the dispatching automation master station system, affecting the stability and reliability of the power grid operation. Summary of the invention

[0005] The purpose of the present invention is to overcome the problem in the prior art that the function detection of the dispatching automation master station system occurs after a fault occurs, which is inefficient and affects the stability and reliability of the power grid operation. A method for evaluating the functional operation status of the dispatching automation master station system is provided, which can evaluate the functional failure probability of the dispatching automation master station system and trace the cause of the functional failure in advance, improve the fault diagnosis and recovery efficiency of the dispatching automation master station system, improve the performance and efficiency of the dispatching automation system, ensure the reliability and stability of the dispatching automation master station system, and thus ensure the safe and stable operation of the power grid.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a method for evaluating the function operation status of a dispatching automation master station system, comprising the following steps:

[0007] S1: construct a function model of the dispatching automation master station system and determine a set of influencing factors for failure of the master station system function;

[0008] S2: Use the analytic hierarchy process to determine the weight values ​​of the influencing factor set and check the consistency of the influencing factor set;

[0009] S3: Use the single factor fuzzy evaluation method to construct a fuzzy comprehensive evaluation matrix;

[0010] S4: Combine the fuzzy evaluation matrix and the weight value of the influencing factor set to evaluate the operating status of the master station system function;

[0011] S5: Construct a fault tree model of the main station system function failure, solve the minimum cut set and the minimum path set, and trace the cause of the main station system function failure.

[0012] The influencing factor set includes data acquisition and monitoring function status, data storage function status, fault alarm and processing function status, visual management function status, remote control function status, etc. In order to ensure the reliability and stability of the power grid system, it is crucial to evaluate the functional failure probability of the dispatching automation master station system and trace the functional failure causes in the power grid operation. The present invention uses the hierarchical analysis method and the fuzzy comprehensive evaluation method to determine the weight value of the functional failure influencing factor set of the dispatching automation master station system on the premise of constructing the functional model of the dispatching automation master station system, and then judge the functional failure probability of the dispatching automation master station system. And when the main station system function fails, the three factors of equipment, personnel and environment are considered, and the minimum cut set and the minimum warp set are solved by the fault tree model to trace the cause of the failure. It is convenient for the staff to handle the fault in time, and the emergency response capability of the dispatching automation master station system is improved. Improve the fault diagnosis and recovery efficiency of the dispatching automation master station system, improve the performance and efficiency of the dispatching automation system, ensure the reliability and stability of the dispatching automation master station system, and then ensure the safe and stable operation of the power grid.

[0013] Preferably, the step S3 further comprises:

[0014] S3.1: Establish the master station system function operation status level evaluation set V = {v1, v2, ..., v m},v m Indicates the function operation status level of the master station system;

[0015] S3.2: For the i-th factor u in the influencing factor set i The functional operation status level is evaluated to obtain a comment set;

[0016] S3.3: Calculate the state v in the review set i The membership degree r ij , j = 1, 2, ..., m, using the membership set R i = {r i1 ,r i2 ,…,r im} represents factor u i The results of the assessment;

[0017] S3.4: The evaluation results of all factors constitute the fuzzy judgment matrix R.

[0018] Through the generalized fuzzy operator evaluation, the maximum membership method is adopted to take the evaluation element corresponding to the maximum evaluation value as the evaluation result.

[0019] Preferably, the constructing of the master station system function failure fault tree model comprises:

[0020] Determine the top event, analyze the various factors that cause the top event, and obtain the intermediate event;

[0021] Analyze various failure causes under intermediate events as basic events;

[0022] The intermediate events are combined with the basic events to obtain the structure function, and the fault tree model of the main station system functional failure is constructed. The fault tree events are organized into a directed acyclic graph, and the occurrence of each fault event is explained by the combination of other fault events.

[0023] The top event is the object event to be analyzed. Considering the intermediate events such as equipment, environment and personnel factors, each intermediate event is composed of the following basic events. The cause of the main station system failure is determined by the fault tree analysis method, which facilitates the staff to handle the failure in time and improves the emergency response capability of the dispatching automation main station system.

[0024] Preferably, solving the minimum cut set and the minimum path set includes:

[0025] Expand the intermediate events in the structure function, and use each item of the expanded structure function as a minimum cut set;

[0026] Using duality, the AND gate of the structure function is converted into an OR gate, and the OR gate is converted into an AND gate to obtain a successful tree;

[0027] Expand the intermediate events in the success tree, and then use the duality of the expanded success tree to convert the AND gate into an OR gate, and then convert the OR gate into an AND gate, and make each item of the converted success tree a minimum path set.

[0028] The minimum cut set is a cut set that contains the least number of necessary bottom events. The complete set of all minimum cut sets represents all given faults. The minimum cut set reflects the risk level of the master station system. The more cut sets there are, the more likely the master station system is to have top events, and the lower the safety. The minimum path set refers to the set of the minimum basic events required for the top events to occur. The minimum path set reflects the safety level of the master station system. The more minimum path sets there are, the safer the master station system is. By analyzing the minimum cut set, we can determine the possible risk factors in the master station system and take measures to deal with these risk factors, thereby improving the safety and stability of the master station system.

[0029] Preferably, the step S5 further comprises determining the structural importance according to the minimum cut set solution:

[0030] Solve for the minimum cut set:

[0031]

[0032] Get the structural importance of each factor in the influencing factor set;

[0033] Sort the various factors in the influencing factor set according to the obtained structural importance, and analyze the cause of the main station system function failure based on the sorting results;

[0034] In the formula, I (i) For the basic event X (i) Approximate discriminant value of structural importance; X (i) ∈K (j) For the basic event X (i) Belong to K (j) Minimum cut set; n t For basic event X (i) The number of basic events in the minimum cut set.

[0035] Structural importance is to analyze the impact of basic events on top events. It is an important basis for formulating safety measures. Structural importance is solved based on the minimum cut set.

[0036] Preferably, in step S2, determining the weight value of the influencing factor set by using the hierarchical analysis method further comprises:

[0037] S2.1: Set the judgment matrix scale value comparison table and construct the judgment matrix U of the influencing factor set;

[0038] S2.2: Use the analytic hierarchy process to solve the eigenvalues ​​and eigenvectors of the judgment matrix U;

[0039] S2.3: Normalize all eigenvectors using the maximum eigenvalue to obtain a weight vector.

[0040] The judgment matrix U consists of the maximum eigenvalue and eigenvector; the element u in the judgment matrix U ij Indicates the relative importance of the i-th influencing factor to the j-th factor, i = 1, 2, ..., n, j = 1, 2, ..., n, u ij =1 / u ji .

[0041] Preferably, in step S2, checking the consistency of the influencing factor set includes:

[0042] According to the maximum eigenvalue and order of the judgment matrix U, the consistency index CI is calculated;

[0043] Set the average random consistency index RI table, and determine the average random consistency index RI in combination with the order of the judgment matrix U;

[0044] The consistency ratio CR of the judgment matrix U is calculated using the ratio of the consistency index CI to the average random consistency index RI, and whether the judgment matrix U meets the consistency requirements is determined based on the consistency ratio.

[0045] Preferably, the step S4 further comprises:

[0046] The fuzzy comprehensive evaluation matrix R and the weight value of each factor in the influencing factor set are multiplied to obtain a fuzzy vector representing the probability of the master station system functioning normally.

[0047] Sort the fuzzy vectors of all evaluation objects to obtain the final evaluation result.

[0048] The fuzzy vector is composed of two probability values: the probability that the automation master station system is normal and the probability that it is failed. If the probability that the fuzzy vector belongs to normal reaches the threshold, it means that the comprehensive evaluation result of the dispatching automation master station system is normal.

[0049] Preferably, checking the consistency of the influencing factor set includes:

[0050] If the judgment matrix U satisfies consistency, the weight corresponding to each factor in the influencing factor set is determined according to the weight vector;

[0051] If the judgment matrix U does not satisfy consistency, the judgment matrix is ​​checked and corrected, and the weight vector is recalculated.

[0052] Therefore, the present invention has the following beneficial effects:

[0053] 1. It includes the probability analysis of the function failure of the dispatching automation master station system and the tracing of the cause of the function failure of the dispatching automation master station system. It can evaluate the functional operation status of the dispatching automation master station system, improve the emergency response capability of the dispatching automation master station system, ensure the operation of the dispatching automation master station system equipment, improve the power supply reliability and distribution automation benefits, and thus ensure the stable and reliable operation of the power grid;

[0054] 2. Use the analytic hierarchy process and fuzzy comprehensive evaluation method to analyze the operation status of the data collection and monitoring function, data storage function, fault alarm and processing function, visual management function, remote control function and other functions of the dispatching automation master station system, determine the weight value of the factor set affecting the failure of the dispatching automation master station system, and thus determine the normal working probability of the dispatching automation master station system, with high accuracy of the calculation results;

[0055] 3. When a system function failure is detected, the fault tree model is constructed by considering equipment, environment and personnel factors. The minimum cut set and minimum path set are solved through the fault tree, the cause-effect relationship between each fault event is analyzed, the possibility and impact of the fault are determined, and the safety and reliability of the dispatching automation master station system are protected. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 The figure is a flow chart of the steps of the method of the present invention.

[0057] Figure 2 This is a flow chart of the functional failure analysis of the dispatching automation master station system in the present invention.

[0058] Figure 3 This is the fault tree model of the dispatching automation master station system in the present invention. DETAILED DESCRIPTION

[0059] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments:

[0060] Embodiment 1:

[0061] like Figure 1In the illustrated embodiment, a method for evaluating the functional operation status of a dispatching automation master station system can be seen, and its operation process is as follows: step one, constructing a functional model of the dispatching automation master station system, and determining a set of influencing factors for the failure of the master station system function; step two, using a hierarchical analysis method to determine the weight value of the influencing factor set, and checking the consistency of the influencing factor set; step three, using a single-factor fuzzy evaluation method, constructing a fuzzy comprehensive evaluation matrix of the dispatching automation master station system; step four, combining the fuzzy evaluation matrix with the weight value of the influencing factor set to evaluate the operating status of the master station system function; step five, constructing a fault tree model for the failure of the master station system function, solving the minimum cut set and the minimum path set, and tracing the cause of the master station system function failure.

[0062] Analytic Hierarchy Process (AHP) is a commonly used decision-making method. It decomposes decision-related elements into multiple levels, such as goals, criteria, and plans, and then conducts qualitative and quantitative analysis on this basis to help decision makers make more reasonable choices. Fuzzy Comprehensive Evaluation Method (FCEM) is a method that combines fuzzy mathematical theory with multi-factor comprehensive evaluation to solve the problem of uncertain and difficult to quantify information of the evaluation object. Therefore, combining the hierarchical analysis method and the fuzzy comprehensive evaluation method is an effective strategy for evaluating the functional operation status of the dispatching automation master station system.

[0063] Fault Tree Analysis (FTA) is one of the most commonly used methods for analyzing system reliability. It is used to determine the cause of system failure and formulate corresponding preventive measures. By constructing a fault tree, solving the minimum cut set and the minimum path set, analyzing the causal relationship between various fault events, determining the possibility and impact of the fault, and safeguarding the safety and reliability of the system.

[0064] This embodiment uses the analytic hierarchy process and the fuzzy comprehensive evaluation method to determine the weight value of the factor set affecting the failure of the dispatching automation master station system function under the premise of constructing the functional model of the dispatching automation master station system, and then judge the probability of failure of the dispatching automation master station system function. When the master station system function fails, the three factors of equipment, personnel and environment are considered, and the minimum cut set and the minimum warp set are solved by the fault tree model to trace the cause of the failure. It is convenient for the staff to handle the fault in time and improve the emergency response capability of the dispatching automation master station system. Improve the fault diagnosis and recovery efficiency of the dispatching automation master station system, improve the performance and efficiency of the dispatching automation system, ensure the reliability and stability of the dispatching automation master station system, and then ensure the safe and stable operation of the power grid.

[0065] The solution of this embodiment is further described in detail below in conjunction with specific processes.

[0066] The first step is to construct a functional model of the dispatching automation master station system and determine the influencing factor set of the master station system function failure.

[0067] First, a functional model of the dispatching automation master station system is constructed to determine a set of influencing factors for system function failure. In this embodiment, the influencing factor set is determined as {data acquisition and monitoring function status, data storage function status, fault alarm and processing function status, visualization management function status, and remote control function status}.

[0068] Step 2: Use the hierarchical analysis method to determine the weight value of the influencing factor set and check the consistency of the influencing factor set.

[0069] (1) Construct the judgment matrix U of the influencing factor set and use the hierarchical analysis method to determine the weight value.

[0070] A matrix scale value comparison table is set up to use a digital scale to indicate the importance of the elements. The matrix scale value comparison data in this embodiment is shown in the following table:

[0071]

[0072]

[0073] According to the above table, the judgment matrix U of the influencing factor set is constructed:

[0074]

[0075] In the judgment matrix U, u ij Indicates the relative importance of the i-th influencing factor to the j-th factor, i = 1, 2, ..., n, j = 1, 2, ..., n, u ij =1 / u ji , where the numbers 1-9 and their reciprocals are used as scales to define the matrix U = (u ij ) n×n .

[0076] According to the composition structure of the judgment matrix U:

[0077] U=λ max ω'

[0078] Solve for the eigenvalues ​​and eigenvectors of the judgment matrix U.

[0079] Where: max is the maximum eigenvalue of the judgment matrix U, and ω' is the eigenvector of the judgment matrix U.

[0080] After normalizing the obtained eigenvector ω', we get the weight vector:

[0081] ω=(ω1,ω2,...,ω n ).

[0082] In the weight vector, each element is the weight of the factor in its corresponding influencing factor set.

[0083] (2) Calculate consistency.

[0084] Calculate the consistency index CI:

[0085]

[0086] n represents the order of the judgment matrix U.

[0087] Set the average random consistency index RI table:

[0088]

[0089] According to the above table and the order of the judgment matrix U, determine the average random consistency index RI and calculate the consistency ratio CR:

[0090] CR=CI / RI.

[0091] The consistency ratio CR is used to determine whether the judgment matrix U meets the consistency requirement.

[0092] If the consistency requirement is met, the weights corresponding to each factor are determined according to the obtained weight vector; if not, the judgment matrix U is checked and corrected, and the weights are recalculated according to the steps in (1).

[0093] Step 3: Use the single-factor fuzzy evaluation method to construct the fuzzy comprehensive evaluation matrix of the dispatching automation master station system.

[0094] (1) Establish a functional operation status level evaluation set.

[0095] When constructing the fuzzy comprehensive evaluation model of functional operation status, we first establish the main station system functional operation status level judgment set V = {v1, v2, ..., v m},v m Indicates the level of the function operation status of the master station system. In this embodiment, two states, "normal" and "failure", are used, namely: V = {normal, failure} = {v1, v2}.

[0096] (2) Through single factor fuzzy evaluation, construct the fuzzy comprehensive evaluation matrix R m×n .

[0097] The i-th factor u of the set of factors influencing the function operation of the dispatching automation master station system iEvaluate the operating status level of each function to obtain a comment set;

[0098] For the state v in the comment set i The membership degree is r ij (j=1,2), then the membership set R can be used i = {r i1 ,r i2} represents factor u i The results of the evaluation.

[0099] Therefore, all factors of the influencing factor set constitute its fuzzy evaluation matrix R 5×2 :

[0100]

[0101] Step 4: Combine the fuzzy evaluation matrix and the weight value of the influencing factor set to evaluate the operating status of the main station system function.

[0102] Establish a comprehensive evaluation model, determine the factor weight vector ω in the second step and the fuzzy comprehensive evaluation matrix R in the third step m×n Afterwards, the fuzzy comprehensive evaluation matrix is ​​multiplied by the weight of each factor to obtain a fuzzy vector B = ω × R = {b1, b2}, and the fuzzy vector is used as the comprehensive evaluation value.

[0103] The comprehensive evaluation values ​​of all evaluation objects (factors in the influencing factor set) are sorted to obtain the final evaluation results.

[0104] Step 5: Construct a fault tree model of the functional failure of the dispatching automation master station system, solve the minimum cut set and the minimum path set, and trace the cause of the functional failure of the master station system.

[0105] (1) Construct a fault tree model of the master station system functional failure.

[0106] Determine the top event of the failure of the dispatching automation master station system. The top event is the object event to be analyzed; analyze the various causes of system failure from the perspectives of equipment, environment, and human factors to obtain intermediate events. Analyze the various causes of failure under the intermediate events to obtain basic events.

[0107] According to the intermediate events and basic events obtained by analysis, a fault tree is constructed, and the fault tree events are organized into a directed acyclic graph, so that the occurrence of each fault event can be explained by the combination of other fault events.

[0108] (2) Solve the minimum cut set and the minimum path set, and determine the possible dangerous factors in the master station system through the minimum cut set.

[0109] The minimum cut set is the cut set that contains the least number of necessary bottom events. The complete set of all minimum cut sets represents all given faults. The minimum cut set reflects the danger level of the master station system. The more cut sets there are, the more likely the master station system is to have top events, and the lower the security.

[0110] The minimum path set refers to the set of the minimum basic events required for the top event to occur. The minimum path set reflects the security of the master station system. The more minimum path sets there are, the safer the master station system is.

[0111] Therefore, by analyzing the minimum cut set, we can determine the possible dangerous factors in the master station system and take measures to deal with these dangerous factors, thereby improving the safety and stability of the master station system. Finally, we can obtain the cause of the failure of the dispatching automation master station system and deal with the failure in time, thus improving the emergency response capability of the dispatching automation master station system.

[0112] This embodiment uses the hierarchical analysis method and fuzzy comprehensive evaluation method to analyze the operation status of the data collection and monitoring function, data storage function, fault alarm and processing function, visual management function, and remote control function of the dispatching automation master station system to determine whether the dispatching automation master station system function has failed. When a failure in the dispatching automation master station system function is detected, a fault tree model is constructed considering equipment, environment, and personnel factors, and the cause of system function failure is determined by the fault tree analysis method. The system fault emergency response capability is improved, the operation of system equipment is guaranteed, and the power supply reliability and distribution automation benefits are improved.

[0113] Embodiment 2:

[0114] Based on the first embodiment, this embodiment further illustrates in detail the method for evaluating the function operation status of the dispatching automation master station system in combination with specific examples and data.

[0115] like Figure 2 As shown, this embodiment divides the dispatching automation master station system function operation status evaluation system into two parts, namely, system function failure probability analysis and system function failure cause tracing. It aims to improve the performance and efficiency of the dispatching automation system, establish common functional modules, so as to share information and resources between different power grids and improve the overall efficiency of the system. Due to the influence of various factors, the function of the dispatching automation master station system may fail, so evaluating the probability of function failure of the dispatching automation master station system and tracing the cause of function failure ensures the reliability and stability of the dispatching automation master station system.

[0116] The first part, functional failure probability analysis, aims to determine the weight value of the system functional failure influencing factor set based on the construction of the dispatching automation master station system functional model through the hierarchical analysis method and the fuzzy comprehensive evaluation method. The operating state of the dispatching automation master station system is divided into "normal" and "failure". According to the data analysis of a certain power grid in a certain area, a fuzzy evaluation matrix is ​​constructed. Through the generalized fuzzy operator evaluation, the maximum membership method is used to obtain the maximum evaluation value b max =max{b j |j=1,2,3} corresponding to the evaluation element is taken as the evaluation result.

[0117] The second part detects the failure of system functions in the functional failure probability analysis, considers three factors: equipment, personnel and environment, analyzes the possible causes of system failure, and traces the cause of the failure by solving the minimum cut set and minimum path set through the fault tree model.

[0118] Specific:

[0119] 1: Analysis on the probability of functional failure of dispatching automation master station system.

[0120] In this embodiment, the average random consistency index RI table is determined in advance, and the results are shown in the following table:

[0121]

[0122] In order to ensure the reliability of the results, this embodiment integrates the opinions of the review experts of the 5 automated master station systems when constructing the judgment matrix U, and determines the values ​​of each value in the judgment matrix U by means of expert scoring. The judgment matrix filled in by the 5 experts is used for calculation to obtain the weight value of each factor determined by each expert, and the weighted average of the values ​​of each factor is performed to finally obtain the weight value of each factor.

[0123] Taking the judgment matrix U scored by an expert as an example, the expert scoring results are shown in the following table:

[0124]

[0125] By calculating the judgment matrix, we can get the weights and eigenvalues ​​of each factor. The calculation results are shown in the following table:

[0126]

[0127] The above table is the AHP hierarchical analysis result table.

[0128] The calculation results using the hierarchical analysis method show that the maximum eigenvalue of the judgment matrix is ​​5.258. According to the average random consistency index RI table, the corresponding average random consistency index RI is 1.11, so:

[0129] CR=CI / RI=0.064<0.1,

[0130] Then the judgment matrix U passes the one-time test. Therefore, the error of the expert scoring is within the credible range, and the weights of each factor are the calculation results.

[0131] The judgment matrices given by the five experts are calculated in the same way as above, and the results are weighted averaged. The results are shown in the following table:

[0132]

[0133] The above table is the consistency test result table.

[0134] The weight value of each factor in the influencing factor set is obtained, and the results are shown in the following table:

[0135]

[0136] Taking a certain power grid dispatching automation master station system as an example, when determining the single factor evaluation matrix, the ascending semi-trapezoidal membership model is used to determine the positive factor of the functional state. It can be seen that the fuzzy evaluation matrix R of the dispatching automation master station system is 5×2 , as shown in the following table:

[0137]

[0138] Therefore, the fuzzy vector B is obtained:

[0139] B=ω×R=(0.998983, 0.001017)

[0140] The calculated fuzzy vector shows that the probability of the automation master station system being normal is 99.8983%, and the probability of being failed is 0.1017%. Therefore, the comprehensive evaluation result of the dispatching automation master station system is considered normal.

[0141] 2. Analysis of reasons for failure of dispatching automation master station system.

[0142] In view of possible failures of the dispatching automation master station system, the intermediate events that cause the master station system to fail are determined. The intermediate events and basic events in this embodiment are shown in the following table:

[0143]

[0144] According to these intermediate events, a fault tree model can be constructed. Figure 3In the figure, T represents the top event; A1 represents the OR gate of the intermediate event "equipment factor" and "inadequate maintenance"; A2 represents the OR gate of the intermediate event "personnel factor" and "inadequate management"; A3 represents the OR gate of the intermediate event "environmental factor" and "not handled in time".

[0145] In the fault tree model of the automation master station system, the equipment factors, human factors and environmental factors have different positions and have different impacts on the failure of the automation master station system. Therefore, the minimum cut set and the minimum path set are used to directly arrange the order of structural importance.

[0146] The structure function is derived from the intermediate events:

[0147] T=A1A2A3

[0148] in:

[0149] A1=X1X2X3X4X5+X 11

[0150] A2=X6X7X8X9+X 10

[0151] A3=X 12 X 13 X 14 +X9

[0152] Therefore, the expanded structure function is:

[0153] T=X1X2X3X4X5X6X7X8X9X 12 X 13 X 14 +X1X2X3X4X5X6X7X8X9+X1X2X3X4X5X 10 X 12 X 13 X 14 +X1X2X3X4X5X9X 10 +X6X7X8X9X 11 X 12 X 13 X 14 +X6X7X8X9X 11 +X 10 X 11 X 12 X 13 X 14 +X9X 10 X 11

[0154] According to the structure function, the minimum cut set is obtained:

[0155] {X1X2X3X4X5X6X7X8X9X12 X 13 X 14}, {X1X2X3X4X5X6X7X8X9}, {X1X2X3X4X5X 10 X 12 X 13 X 14}, {X1X2X3X4X5X9X 10}、{X6X7X8X9X 11 X 12 X 13 X 14}、{X6X7X8X9X 11}、{X 10 X 11 X 12 X 13 X 14}、{X9X 10 X 11}.

[0156] When solving the minimum path set, first use the duality to convert the structure function:

[0157] T=A1A2A3

[0158] The AND gate in is converted to an OR gate, and the OR gate is converted to an AND gate, thus forming a success tree;

[0159] Next, the minimum cut set of the success tree is solved, that is, the intermediate events in the success tree are expanded to obtain the expanded success tree:

[0160]

[0161] By using duality, we can convert the AND gates in the expanded success tree into OR gates, and the OR gates into AND gates, and then we can get the minimum path set:

[0162]

[0163] Therefore, the minimum path set is:

[0164] {X1,X 11}{X2,X 11}{X3,X 11}{X4,X 11}{X5,X 11}{X6,X 10}

[0165] {X7,X 10}{X8,X 10}{X9,X 10}{X 12 ,X9}{X 13 ,X9}{X14 ,X9}

[0166] Structural importance is to analyze the impact of basic events on top events, and is an important basis for formulating safety measures. Structural importance is solved based on the minimum cut set, and the solution formula is:

[0167]

[0168] In the formula, I (i) For the basic event X (i) Approximate discriminant value of structural importance; X (i) ∈K (j) For the basic event X (i) Belong to K (j) Minimum cut set; n t For the basic event X (i) The number of basic events in the minimum cut set.

[0169] According to the above solution formula, the structural importance of each factor is solved:

[0170]

[0171]

[0172]

[0173]

[0174]

[0175] It can be seen that the structural importance of each factor is ranked as follows:

[0176] I(10)=I(11)>I(9)>I(6)=I(7)=I(8)=I(12)=I(13)=I(14)>I(1)=I(2)=I(3)=I(4)=I(5).

[0177] According to the above calculation, there are 8 minimum cut sets and 12 minimum path sets in this embodiment, which means that there are 8 dangers and 12 ways to prevent the failure of the dispatching automation master station system. In the structural importance ranking, the structural importance of the two basic events "inadequate management" and "inadequate maintenance" is the largest. Therefore, these two factors are most likely to cause the failure of the dispatching automation master station system.

[0178] The above-described embodiment is only a preferred solution of the present invention and does not limit the present invention in any form. There are other variations and modifications without exceeding the technical solution described in the claims.

Claims

1. A method for evaluating the functional operation status of a dispatching automation master station system, characterized in that: The following steps are involved: S1: construct a function model of the dispatching automation master station system and determine a set of influencing factors for failure of the master station system function; S2: Use the analytic hierarchy process to determine the weight value of the influencing factor set and check the consistency of the influencing factor set; S3: Using the single factor fuzzy evaluation method, the fuzzy comprehensive evaluation matrix of the dispatching automation master station system is constructed; S4: Combine the fuzzy evaluation matrix and the weight value of the influencing factor set to evaluate the operating status of the master station system function; S5: Construct a fault tree model of the main station system function failure, solve the minimum cut set and the minimum path set, and trace the cause of the main station system function failure.

2. A method for evaluating the function operation status of a dispatching automation master station system according to claim 1, characterized in that: The step S3 further comprises: S3.1: Establish the master station system function operation status level evaluation set V = {v1, v2, ..., v m },v m Indicates the function operation status level of the master station system; S3.2: For the i-th factor u in the influencing factor set i The functional operation status level is evaluated to obtain a comment set; S3.3: Calculate the state v in the review set i The membership degree r ij , j = 1, 2, ..., m, using the membership set R i = {r i1 ,r i2 ,…,r im } represents factor u i The results of the assessment; S3.4: The evaluation results of all factors constitute the fuzzy judgment matrix R.

3. A method for evaluating the function operation status of a dispatching automation master station system according to claim 1, characterized in that: The construction of the master station system function failure fault tree model includes: Determine the top event, analyze the various factors that cause the top event, and obtain the intermediate event; Analyze various failure causes under intermediate events as basic events; The intermediate events are combined with the basic events to obtain the structure function, and the fault tree model of the main station system functional failure is constructed. The fault tree events are organized into a directed acyclic graph, and the occurrence of each fault event is explained by the combination of other fault events.

4. A method for evaluating the function operation status of a dispatching automation master station system according to claim 3, characterized in that: The solving of the minimum cut set and the minimum path set includes: Expand the intermediate events in the structure function, and use each item of the expanded structure function as a minimum cut set; By using the duality, the AND gate of the structure function is converted into an OR gate, and the OR gate is converted into an AND gate to obtain a success tree; the intermediate events in the success tree are expanded, and the expanded success tree is then converted into an OR gate and the OR gate is converted into an AND gate by using the duality, and each item of the converted success tree is a minimum path set.

5. A method for evaluating the function operation status of a dispatching automation master station system according to claim 1, 3 or 4, characterized in that: The step S5 further includes determining the structural importance according to the minimum cut set solution: Solve for the minimum cut set: Get the structural importance of each factor in the influencing factor set; Sort the various factors in the influencing factor set according to the obtained structural importance, and analyze the cause of the main station system function failure based on the sorting results; In the formula, I (i) For basic event X (i) Approximate discriminant value of structural importance; X (i) ∈K (j) For the basic event X (i) Belong to K (j) Minimum cut set; n t For the basic event X (i) The number of basic events in the minimum cut set.

6. A method for evaluating the function operation status of a dispatching automation master station system according to claim 1, characterized in that: In step S2, determining the weight value of the influencing factor set by using the hierarchical analysis method further includes: S2.1: Set the judgment matrix scale value comparison table and construct the judgment matrix U of the influencing factor set; S2.2: Use the analytic hierarchy process to solve the eigenvalues ​​and eigenvectors of the judgment matrix U; S2.3: Normalize all eigenvectors using the maximum eigenvalue to obtain a weight vector.

7. A method for evaluating the function operation status of a dispatching automation master station system according to claim 6, characterized in that: In step S2, checking the consistency of the influencing factor set includes: According to the maximum eigenvalue and order of the judgment matrix U, the consistency index CI is calculated; Set the average random consistency index RI table, and determine the average random consistency index RI in combination with the order of the judgment matrix U; The consistency ratio CR of the judgment matrix U is calculated using the ratio of the consistency index CI to the average random consistency index RI, and whether the judgment matrix U meets the consistency requirements is determined based on the consistency ratio.

8. A method for evaluating the function operation status of a dispatching automation master station system according to claim 1 or 2 or 3 or 4 or 6, characterized in that: The step S4 further comprises: The fuzzy comprehensive evaluation matrix and the weight values ​​of each factor in the influencing factor set are multiplied to obtain a fuzzy vector representing the probability of the master station system functioning in a normal state; Sort the fuzzy vectors of all evaluation objects to obtain the final evaluation result.

9. A method for evaluating the function operation status of a dispatching automation master station system according to claim 1, 6 or 7, characterized in that: The checking of consistency of the influencing factor set includes: If the judgment matrix U satisfies consistency, the weight corresponding to each factor in the influencing factor set is determined according to the weight vector; If the judgment matrix U does not satisfy consistency, the judgment matrix is ​​checked and corrected, and the weight vector is recalculated.

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