Maintenance strategy making method and system for improving reliability of stability control system

Abstract

The invention discloses a maintenance strategy making method and system for improving the reliability of a stability control system, and the method comprises the following steps: firstly, collecting system-level abnormal fault data in the stability control system, building a fault tree model according to the fault data, and analyzing the influence of the potential fault of each component in the stability control system on the reliability of the whole stability control system; secondly, based on the constructed fault tree model, using an improved Apriori algorithm to analyze influence factors of potential faults in the stability control system, constructing a strong association rule between the influence factors and the potential faults, and providing a theoretical basis for a maintenance strategy so as to reduce the fault rate; and finally, optimizing the maintenance strategy by using a Markov model method so as to improve the fault repair rate, and reducing the fault repair time of the stability control system through a finite time optimization control algorithm. The method aims to reduce the fault rate and the fault repair time of the stability control system so as to improve the reliability of the stability control system.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system safety and stability control, and specifically relates to a maintenance strategy formulation method and system for improving the reliability of a stability control system. Background Art

[0002] The stability control system is a vital component of the power system, which is responsible for maintaining the stability and reliability of the power system under various operating conditions. With the continuous expansion of the scale of the power system, the increasing number of equipment, and the access of new energy and distributed generation resources, the challenges faced by the stability control system are also increasing.

[0003] Traditional reliability assessment methods for stability control systems mainly rely on static models, which usually assume that the state of system components is constant and do not consider factors such as equipment aging, maintenance activities, and changes in the external environment. Static models cannot accurately capture dynamic changes, limiting the accuracy and practicality of the assessment results.

[0004] Fault root cause analysis is a key link in power system operation and maintenance. It involves identifying the initial signs of faults from a large amount of data, understanding the causal relationship between faults and other system behaviors, and inferring the direct and indirect causes of faults. Traditional fault analysis methods rely on expert experience and rule-driven logic. These methods work well when dealing with simple faults, but when faced with complex faults and large-scale data, and when the system fails, the repair time often cannot meet user needs.

[0005] Therefore, a new maintenance strategy for the stability control system is urgently needed to capture the dynamic changes of the stability control system, quickly identify the cause of the fault, and shorten the repair time of the system fault. Summary of the invention

[0006] In order to solve the problem in the prior art that static models cannot accurately capture dynamic changes and the repair time is too long when facing complex faults, the present invention discloses a maintenance strategy formulation method and system for improving the reliability of a stabilization and control system.

[0007] Technical solution:

[0008] A maintenance strategy formulation method for improving the reliability of a stabilization control system includes:

[0009] Step 1: Collect system-level fault data in the stabilization control system, including intra-station communication anomalies, system-level logic error prevention anomalies, and inter-station / inter-device communication anomalies, and perform data preprocessing;

[0010] Step 2: Establish a fault tree model based on the preprocessed fault data, and form a hierarchical system with the upper station of the stabilization control system and the lower station controlled by the upper station. According to the fault tree model, the potential faults of each component in the upper station and the lower station and the corresponding top-level events are inferred; the top-level event is the most serious fault state in the fault tree model.

[0011] Step 3: Use the improved Apriori algorithm to analyze the influencing factors of potential faults in the stability control system, determine whether there is a strong association rule between the influencing factors and the potential faults, and formulate a maintenance strategy based on the influencing factors with strong association rules, potential faults and the top-level events corresponding to step 2;

[0012] Step 4: Develop a maintenance strategy. When the maintenance strategy does not meet the requirements, use the Markov model to construct a finite-time optimization control algorithm to develop the optimal repair time.

[0013] Furthermore, the data preprocessing in step 1 is to map the fault data into a Boolean matrix B containing only 0 and 1. a×b :

[0014]

[0015] In the formula, C cd =0 or 1, c = 1, 2, ..., a, d = 1, 2, ..., b are Boolean matrix elements, representing a total of b fault records, a represents the length of the union of abnormal items generated by b faults, each column of the matrix represents the stability control system fault and its influencing factors and potential faults, and each row represents a piece of stability control system fault data and information; if B a×b =1 means fault b appears in the ath data. If B a×b =0 means fault b does not appear in the ath data.

[0016] Furthermore, in step 2, a fault tree model is established by using fault tree symbols to establish a tree structure to form a top-down system fault identification mode; the undesirable fault state of the system is set as the core goal of fault analysis, and these top events are connected with the bottom events through logic gates to construct a fault tree model.

[0017] Furthermore, the Boolean structure expression of the logic gate is:

[0018] T=M 1 +M 2 +M 3 +M 4 +M 5 +M 6 +M 7 +...

[0019] =[(X 1+X 2 )+(X 3 X 4 )]+{[(X 5 +X 6 +X 7 )X 8 ]+(X 7 X 9 )}+

[0020] (X 5 +X 6 )+(X 10 X 11 )+X 12 +(X 13 +X 14 )+X 5 +...

[0021] Simplifying the above formula, we get:

[0022] T=X 1 +X 2 +(X 3 X 4 )+(X 7 X 8 )+(X 7 X 9 )+

[0023] X 5 +X 6 +(X 10 X 11 )+X 12 +X 13 +X 14 +...

[0024] Where T is the top event; M i , i=1,2,3...are the main events; X i ,i=1,2,3...is the underlying event, also known as the cut set; the cut set contains the failure events of the components, and a cut set may contain multiple component failure events.

[0025] Furthermore, the top-level events include main events, and the main events include bottom-level events; any fault tree model contains a finite number of minimum cut sets, and the minimum cut sets are unique to the top-level events.

[0026] Furthermore, the improved Apriori algorithm process described in step 3 is as follows:

[0027] The union of the abnormal items in each column of the Boolean matrix B of the stabilization and control device fault generates a candidate frequent Z-1 item set, and the number of occurrences of the "1" element in each column of the Boolean matrix B is counted S d, determine whether the number of occurrences meets the minimum threshold α, and perform pruning. If the number of occurrences of the "1" element in column d meets S d ≤b×α%, delete the Boolean matrix and generate frequent L-1 item sets;

[0028] Perform a logical "AND" operation on the potential fault item column of the Boolean matrix B to generate a candidate frequent Z-2 item set; compare the number of "1" elements in each subset of the candidate frequent Z-2 fault item set with b×α%, if the element "1" in the subset is less than b×α%, delete the subset to obtain a frequent L-2 item set; count the number of single potential fault anomaly items in the frequent L-2 item set, if the number is less than 2, delete the subset containing the single anomaly item, and update the fault frequent L-2 item set;

[0029] Perform a logical "AND" operation on the potential fault item column of the Boolean matrix B again to generate a candidate frequent Z-3 fault item set; compare the number of "1" elements in each subset of the candidate frequent Z-3 fault item set with b×α%, if the element "1" in the subset is less than b×α%, delete the subset to obtain a frequent L-3 item set; count the number of single potential fault anomaly items in the frequent L-3 item set, if the number is less than 3, delete the subset containing the single anomaly item, and update the fault frequent L-3 item set;

[0030] Similarly, update the fault frequent item set Lk, k>3, and determine whether L is an empty set, then output the fault frequent item set, otherwise continue to perform logical "and" operations on each subset in the fault frequent item set until the algorithm termination condition is met.

[0031] Furthermore, the step 3 of determining whether there is a strong association rule between the influencing factors and the potential faults includes:

[0032] (3.1) Each safety and stability control system fault data is an item set Y, denoted as:

[0033] Y={y 1 ,y 2 ,...,y n}

[0034] In the formula, y i ,i=1,2,...,n represents each influencing factor and potential fault in the safety and stability control system fault data;

[0035] (3.2) Influencing factor A and potential fault B are two sub-item sets in Y. For an association rule The support degree S(A) of influencing factor A and the association rules of potential failures caused by influencing factors Support Confidence for:

[0036]

[0037] In the formula, C(A) is the number of influencing factor A item set elements appearing in the stable control system fault data set, C(A∪B) represents the number of influencing factor A item set elements and potential fault B item set elements appearing at the same time; CD(dateset) is the number of all stable control system fault data; P(A) is the probability of influencing factor A item set appearing in the stable control system fault data set;

[0038] (3.3) Set the minimum threshold S for support and confidence min , C min , the support of an m-item set satisfies the minimum support S min It is called a frequent m-item set, and the association rule The support and confidence of both meet the minimum threshold S min , C min , then it is called This set of association rules is a strong association.

[0039] Furthermore, the maintenance strategy is to formulate a subsequent operation plan for the stabilization control system based on the influencing factors and potential faults with strong association rules obtained in step 3, including:

[0040] Monitor influencing factors, set warning thresholds, issue warning signals when monitoring indicators reach the warning thresholds, and arrange maintenance;

[0041] Set up backup components for corresponding components with potential failures and switch them for use when failures occur.

[0042] Furthermore, the maintenance strategy is optimized by using the Markov model. The maintenance strategy is optimized by using the Markov model based on the maintenance strategy formulated in step 3, and the failure rate is kept low while the failure repair time is shortened. The steps include:

[0043] (4.1) Draw the state space diagram and establish the state transfer matrix Q based on the state space diagram:

[0044]

[0045] In the formula, the non-diagonal element q ij (i≠j) represents the probability of transitioning from state i to state j; the diagonal element represents the self-transition probability of state i, and the formula is:

[0046]

[0047] (4.2) Solve the state probability matrix P of the stable control system at time t t , according to the probability of the system being in each state, solve the probability of the system operating state at the next moment:

[0048] P t =[p 1 p 2 …p n ]

[0049]

[0050] (4.3) The repair rate refers to the probability that a component that is in failure at time t will be repaired in the subsequent unit time, usually expressed as μ:

[0051]

[0052] Where T is the time when the component is repaired; P is the state probability matrix of the stable control system.

[0053] The fault repair time T is expressed as:

[0054]

[0055] In the formula, ξ is a random variable greater than 0 and less than 1. When the fault repair rate μ is increased, the fault repair time T can be effectively reduced. Based on this, the maintenance strategy is optimized;

[0056] (4.4) Based on the data and information of the fault state of the stability control system and the state information of normal operation, a stable positive time-varying function ρ(t) is constructed:

[0057]

[0058] In the formula, ρ(0) = 1 and lim t→T ρ(t)=ρ T And 0<ρ T ≤1 is a constant; when t≥T is consistent with the predetermined time T, ρ(t)=ρ T ρ T , λ and T are positive constants;

[0059] (4.5) Design a performance function of a finite-time control algorithm that is independent of the initial conditions, combining the characteristics of ρ(t):

[0060]

[0061] In the formula, δ is a positive constant; is a monotonically decreasing function; for When t≥T holds

[0062] (4.6) Define tracking error e = yyd To determine the operating state error of the stabilization control system after time T, When limiting the tracking error, the tracking error should satisfy And satisfy the following characteristics:

[0063]

[0064] (4.7) Construct performance function:

[0065]

[0066] ρ 1 (t) = ρ(t) + γ(t)tanh(Λ(t))

[0067] Where γ(t) is continuously differentiable, γ(0) = 0 and 0 ≤ γ(t) < 1 - ρ(t); Λ(t) and Λ(0) = 0 are corrected by the following formula:

[0068]

[0069] In the formula, m 1 >0 and m 2 >0 is a constant; Γ i (t), i = 1, 2 represents the gain and is non-negative; v is the expected fault level; This is an unexpected serious failure.

[0070] The present invention also discloses a computer method for formulating a maintenance strategy for improving the reliability of a stabilization control system, comprising a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above method.

[0071] The beneficial effects described in the present invention are: constructing a fault tree model based on the fault data of the stability control system, using the improved Apriori algorithm to analyze the influencing factors of potential faults in the stability control system, constructing strong association rules between the influencing factors and potential faults, providing a theoretical basis for the maintenance strategy to reduce the failure rate, improve the fault repair rate, and reduce the fault repair time of the stability control system through a finite time optimization control algorithm.

[0072] The present invention optimizes the maintenance strategy by using the Markov model method, simulates the random changes in equipment status, and takes into account the impact of equipment aging and maintenance activities, thereby providing a more accurate reliability assessment, better predicting the behavior of the system in the future, and providing more reliable decision support for power grid planning and operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 It is a flow chart of a maintenance strategy formulation method for improving the reliability of a stabilization control system according to the present invention;

[0074] Figure 2 is a flow chart of the improved Apriori algorithm of the present invention;

[0075] Figure 3 It is a schematic diagram of the logical operation of the fault tree model in the embodiment of the present invention. DETAILED DESCRIPTION

[0076] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent forms of modifications to the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0077] The present invention provides a maintenance strategy formulation method for improving the reliability of a stabilization control system, such as Figure 1 Shown is a flow chart of the method of the present invention, comprising the following steps:

[0078] Step 1: Collect system-level fault data such as abnormal communication between devices within the station, abnormal system-level logic error prevention, abnormal communication between stations / devices, etc. in the stabilization control system, and perform data preprocessing.

[0079] As the second line of defense for power grid security, the safety and stability control system ensures the safe and stable operation of the power system. The safety and stability control system obtains system-level abnormal fault data such as communication anomalies between devices within the station, communication anomalies between devices between stations, and system-level logic error prevention anomalies through self-inspection and pre-processes them, screening out the abnormal fault data of the main events and underlying events corresponding to each abnormal fault.

[0080] First, the fault data of the stability control system is preprocessed and mapped into a Boolean matrix B containing only 0 and 1 a×b :

[0081]

[0082] In the formula, C cd =0 or 1, c = 1, 2, ..., a, d = 1, 2, ..., b are Boolean matrix elements, representing a total of b fault records, a represents the length of the union of abnormal items generated by b faults, each column of the matrix represents the stability control system fault and its influencing factors and potential faults, and each row represents a piece of stability control system fault data and information. a×b =1 means fault b appears in the ath data. If B a×b =0 means fault b does not appear in the ath data.

[0083] Step 2: Establish a fault tree model based on the preprocessed fault data, and form a hierarchical system with the upper station of the stabilization control system and the lower station controlled by the upper station. According to the fault tree model, the potential faults of each component in the upper station and the lower station and the corresponding top-level events are inferred; the top-level event is the most serious fault state in the fault tree model.

[0084] The components refer to the various devices or units that constitute the stabilization and control system, including relay protection devices, communication modules within the station, communication links between stations, main control systems, etc.; component failures will affect system operation step by step.

[0085] The construction of the fault tree model uses fault tree symbols to establish a tree structure and form a top-down system fault identification mode. The undesirable fault state of the system is set as the core goal of fault analysis. These top events are connected with the bottom events through logic gates to build a fault tree. Through fault tree analysis, the specific top-level events corresponding to the potential faults of each component can be obtained to avoid the potential faults that cause hierarchical system failure, communication abnormalities, and malfunctions, thereby affecting the reliability of the stability control system.

[0086] The steps of fault tree model analysis are to first determine the top-level event, then determine the main event that causes the top-level event, then find the minimum cut set and determine the bottom-level event, and finally connect the event set through Boolean logic gates for quantitative analysis. The Boolean structure expression is:

[0087] T=M 1 +M 2 +M 3 +M 4 +M 5 +M 6 +M 7 +...

[0088] =[(X 1 +X 2 )+(X 3 X 4 )]+{[(X 5 +X 6 +X 7 )X 8 ]+(X 7 X 9 )}+

[0089] (X 5 +X 6 )+(X 10 X 11 )+X 12 +(X 13 +X 14 )+X 5 +...

[0090] Simplifying the above formula, we get:

[0091] T=X 1 +X 2 +(X 3 X 4 )+(X 7 X 8 )+(X 7 X 9 )+

[0092] X 5 +X 6 +(X 10 X 11 )+X 12 +X 13 +X 14 +...

[0093] Where T is the top event; M i , i=1,2,3...are the main events; X i ,i=1,2,3... are the bottom-level events, i.e., cut sets. The cut sets contain component failure events, and a cut set may contain multiple component failure events. The top-level events include the main events, and the main events include the bottom-level events. Therefore, the top-level events are equal to the sum of all bottom-level events. When all components in the bottom-level events fail, the top-level events will inevitably occur. Any fault tree model contains a finite number of minimum cut sets, and the minimum cut set is unique for the top-level events.

[0094] like Figure 3 The fault tree model in this embodiment is shown, specifically:

[0095] A top event T contains two main events M 1 and M 2 , and the main event M 1 Contains three underlying events X 1 , X 4 and X 5 , the main event M 2 Contains two underlying events X 2 and X 3 , and are all connected using OR gates.

[0096] When a component failure causes the underlying event X 1 , X 4 and X 5 When one or more of them occur, they will inevitably lead to the main event M 1 When a component failure causes an underlying event X 2 and X 3 When one or more of them occur, they will inevitably lead to the main event M 2If it happens, it will inevitably lead to the occurrence of the top event T.

[0097] For example, a failure in a communication component in a lower-level station will result in the upper-level station being unable to obtain real-time data, thereby causing system failure. That is, in this case, the fault tree model in the present invention outputs the fault component as "communication component of the lower-level station", the potential fault is "the upper-level station is unable to obtain real-time data", and the corresponding top-level event is "system failure".

[0098] Step 3: Use the improved Apriori algorithm to analyze the influencing factors of potential faults in the stability control system, determine whether there is a strong association rule between the influencing factors and potential faults, and formulate a maintenance strategy based on the influencing factors with strong association rules, potential faults and the top-level events corresponding to step 2.

[0099] The traditional Apriori algorithm mainly relies on two things: 1) if transaction A is added to the itemset of non-potential fault frequent potential fault itemset Y, the new potential fault itemset must not be a frequent itemset; 2) a non-empty subset of potential fault frequent itemset must also be a potential fault frequent itemset. However, the algorithm process has shortcomings. When screening frequent fault itemsets, it is necessary to frequently scan the stable control system fault data, resulting in high time complexity.

[0100] By improving the Apriori algorithm, the number of times the fault data of the stability control system is scanned is reduced, thereby reducing the time complexity. Figure 2 shown.

[0101] The union of the abnormal items in each column of the Boolean matrix B of the stabilization and control device fault generates a candidate frequent Z-1 item set, and the number of occurrences of the "1" element in each column of the Boolean matrix B is counted S d , determine whether the number of occurrences meets the minimum threshold α, and perform pruning. If the number of occurrences of the "1" element in column d meets S d ≤b×α%, delete the Boolean matrix and generate frequent L-1 item sets.

[0102] Perform a logical "AND" operation on the potential fault item column of the Boolean matrix B to generate a candidate frequent Z-2 item set; compare the number of "1" elements in each subset of the candidate frequent Z-2 fault item set with b×α%. If the element "1" in the subset is less than b×α%, delete the subset to obtain a frequent L-2 item set; count the number of single potential fault anomaly items in the frequent L-2 item set. If the number is less than 2, delete the subset containing the single anomaly item and update the fault frequent L-2 item set.

[0103] Perform a logical "AND" operation on the potential fault item column of the Boolean matrix B again to generate a candidate frequent Z-3 fault item set; compare the number of "1" elements in each subset of the candidate frequent Z-3 fault item set with b×α%. If the element "1" in the subset is less than b×α%, delete the subset to obtain a frequent L-3 item set; count the number of single potential fault anomaly items in the frequent L-3 item set. If the number is less than 3, delete the subset containing the single anomaly item and update the fault frequent L-3 item set.

[0104] Similarly, update the fault frequent item set Lk, and determine whether L is an empty set, then output the fault frequent item set, otherwise continue to perform logical "and" operations on each subset in the fault frequent item set until the algorithm termination condition is met. Finally, the strong association rules between the influencing factors and potential faults are obtained. The strong association rules provide a theoretical basis for the maintenance strategy, thereby reducing the failure rate.

[0105] Association rules can reflect the interdependence and correlation between potential failures of the safety and stability control system and influencing factors. If there is a certain correlation between the potential failure of the system and one or more influencing factors, the most likely influencing factors of the potential failure can be quickly found through data mining, or the potential failure can be predicted from the occurrence of influencing factors. If each safety and stability control system failure data is an item set, it is recorded as:

[0106] Y={y 1 ,y 2 ,...,y n}

[0107] In the formula, y i , i=1,2,...,n represents each influencing factor and potential fault in the safety and stability control system fault data. For a safety and stability control system fault data item set Y, if the influencing factor A and the potential fault B are two sub-item sets, the association rule between the factor and the potential fault can be expressed as The support is the percentage of all safety and stability control system failure data that contain A∪B, and the confidence is the percentage of safety and stability control system failure data that contain both A and B.

[0108] For an association rule The support of influencing factor A is:

[0109]

[0110] Where C(A) is the number of items of influencing factor A appearing in the stability control system failure data set; CD(dateset) is the number of all stability control system failure data; P(A) is the probability of influencing factor A appearing in the stability control system failure data set.

[0111] Association rules of potential failures caused by influencing factors The support is:

[0112]

[0113] In the formula, C(A∪B) represents the number of simultaneous occurrences of the influencing factor A item set elements and the potential fault B item set elements.

[0114] Association rules of potential failures caused by influencing factors The confidence level is:

[0115]

[0116] Set the minimum support and confidence thresholds. If the minimum support and confidence thresholds are S min , C min , the support of an m-item set satisfies the minimum support S min It is called a frequent m-item set, and the association rule The support and confidence of both meet the minimum threshold S min , C min , then it is called This set of association rules is a strong association.

[0117] The maintenance strategy process of the communication link priority formulated in this embodiment is as follows. First, through the fault tree model and the improved Apriori algorithm, it is obtained that the potential fault "intra-station communication component failure" and the influencing factor "inter-station communication link interruption" are strongly correlated, and both may cause the top-level event "system-level communication interruption".

[0118] Based on this analysis result, the priority maintenance strategy formulated includes: installing temperature sensors to monitor the temperature of communication equipment within the station in real time, and issuing warnings and arranging maintenance when the temperature exceeds the standard; setting up redundant paths for inter-station communication links so that they can quickly switch to the backup link when the main link is interrupted.

[0119] Optionally, formulate additional maintenance strategies to regularly check the signal transmission stability and delay of the inter-station communication links; dynamically adjust the plan based on monitoring data, such as expanding or upgrading the communication link capacity in advance when the communication link load increases. The implementation of this strategy significantly reduces the probability of communication link interruption, and responds quickly when a fault occurs, ensuring the reliability and stability of the stabilization system.

[0120] Step 4: When the maintenance strategy does not meet the demand, a finite time optimization control algorithm is constructed using the Markov model to optimize the maintenance strategy and improve the repair rate. The repair rate refers to the probability that a component that is in failure at time t will be repaired in the subsequent unit time, usually expressed as μ:

[0121]

[0122] Where T is the time when the component is repaired; P is the state probability matrix of the stable control system.

[0123] The Markov model is used to analyze the possible states of the operating system, and then determine the various states of the stable control system caused by it, draw the state space diagram, and establish the state transfer matrix Q according to the state space diagram:

[0124]

[0125] In the formula, the non-diagonal element q ij (i≠j) represents the probability of transitioning from state i to state j; the diagonal element represents the self-transition probability of state i, and the formula is:

[0126]

[0127] Solve the state probability matrix P of the stable control system at time t t , according to the probability of the system being in each state, solve the probability of the system operating state at the next moment:

[0128] P t =[p 1 p 2 …p n ]

[0129]

[0130] The Markov model is used to analyze the impact of different maintenance strategies on the state transition of the stability control system, so as to find the optimal strategy to transfer the stability control system from the fault state to the normal operation state, thereby improving the fault repair rate μ. The fault repair time T can be expressed as:

[0131]

[0132] Where ξ is a random variable greater than 0 and less than 1. When the fault repair rate μ is increased, the fault repair time T can be effectively reduced.

[0133] The finite time optimization control algorithm is constructed. When the finite time control algorithm is performed, a stable positive time-varying function ρ(t) is constructed based on the data of the fault state of the stable control system and the state information of the normal operation:

[0134]

[0135] In the formula, ρ(0) = 1 and lim t→T ρ(t)=ρ T And 0<ρ T≤1 is a constant; when t≥T is consistent with the predetermined time T, ρ(t)=ρ T ρ T , λ and T are positive constants, designed according to user requirements. To ensure that the repair time of the stabilization control system meets the needs of users and the power grid, the appropriate repair time T is determined based on historical data, user needs, and power grid needs. T It means that the system state converges to the expected performance level within a fixed time, which is set as the performance requirement when the stable control system is working normally.

[0136] Design a performance function for a finite-time control algorithm that is independent of the initial conditions, incorporating the properties of ρ(t):

[0137]

[0138] In the formula, δ is a positive constant; is a monotonically decreasing function; for When t≥T holds

[0139] Finally, based on the normal operation and fault operation data of the stabilization control system, the tracking error e=yy is defined d To determine the operating state error of the stabilization control system after time T, When limiting the tracking error, the tracking error should satisfy And satisfy the following characteristics:

[0140]

[0141] The initial value of the tracking error e is arbitrary. After a predetermined time T, the tracking error enters a performance boundary consisting of constant values, so that the stabilization control system meets the stable operation conditions.

[0142] Furthermore, in order to prevent unexpected failures of the stability control system that may cause the failure of the preset performance, a comparison A more conservative performance function with bounds greater than The transient performance is sacrificed only when the failure of the stability control system is more serious and beyond expectations, so that the tracking error will never reach the performance boundary. The correction signal Λt is introduced on the basis of the finite time control algorithm:

[0143]

[0144] ρ 1 (t) = ρ(t) + γ(t)tanh(Λ(t))

[0145] Where γ(t) is continuously differentiable, γ(0) = 0 and 0 ≤ γ(t) < 1-ρ(t). Λ(t) and Λ(0) = 0 are corrected by the following formula:

[0146]

[0147] In the formula, m 1 >0 and m 2 >0 is a constant; Γ i (t), i = 1, 2 represents the gain and is non-negative; v is the expected fault level; The addition of Λ(t) realizes the unexpected serious fault The constraint feedback of the modified finite time control algorithm is introduced. By taking the derivative of the first differential equation in the above formula, it can be obtained that Γ(0)=0, Γ(t)≥0, and γ(t) is selected to satisfy ρ 1 (t)≤1 and ρ 1 (0) = 1. i (t), i=1,2 means when When the fault is within the expected range, the modified finite time control algorithm will be restored to the finite time control algorithm with preset performance, otherwise the modified finite time control algorithm with wider boundary will be used.

[0148] Based on the fault status data and information of the stability control system, the status information during normal operation, and the repair time T that meets user needs and system requirements, a finite-time optimization control algorithm is used, and unexpected faults are taken into consideration. A correction signal is introduced so that the corrected finite-time control algorithm has a wider performance boundary, ensuring that the stability control system can meet the required performance boundary within the finite time T in the event of expected faults or unexpected serious faults, thereby reducing the fault repair time of the stability control system.

Claims

1. A maintenance strategy formulation method for improving the reliability of a stabilization control system, characterized in that: include: Step 1: Collect system-level fault data in the stabilization control system, including intra-station communication anomalies, system-level logic error prevention anomalies, and inter-station / inter-device communication anomalies, and perform data preprocessing; Step 2: Establish a fault tree model based on the preprocessed fault data, construct the upper station of the stability control system and the lower station controlled by the upper station into a hierarchical system, and derive the potential faults of each component in the upper station and the lower station and the corresponding top-level events according to the fault tree model; The top-level event is the most serious fault state in the fault tree model; Step 3: Use the improved Apriori algorithm to analyze the influencing factors of potential faults in the stability control system, determine whether there is a strong association rule between the influencing factors and the potential faults, and formulate a maintenance strategy based on the influencing factors with strong association rules, potential faults and the top-level events corresponding to step 2; Step 4: When the maintenance strategy does not meet the demand, a finite time optimization control algorithm is constructed using the Markov model to optimize the maintenance strategy.

2. The maintenance strategy formulation method according to claim 1, characterized in that: Step 1: Data preprocessing is to map the fault data into a Boolean matrix B containing only 0 and 1. a×b : In the formula, C cd =0 or 1, c = 1, 2, ..., a, d = 1, 2, ..., b are Boolean matrix elements, representing a total of b fault records, a represents the length of the union of abnormal items generated by b faults, each column of the matrix represents the stability control system fault and its influencing factors and potential faults, and each row represents a piece of stability control system fault data and information; if B a×b =1 means fault b appears in the ath data. If B a×b =0 means fault b does not appear in the ath data.

3. The maintenance strategy formulation method according to claim 2, characterized in that: In step 2, a fault tree model is established, and a tree structure is established by using a fault tree symbol to form a top-down system fault identification mode; The undesirable fault state of the system is set as the core goal of fault analysis, and these top events are connected with the bottom events through logic gates to build a fault tree model.

4. The maintenance strategy formulation method according to claim 3, characterized in that: The expression of the logic gate is: T=X1+X2+(X3X4)+(X7X8)+(X7X9)+ X5+X6+(X 10 X 11 )+X 12 +X 13 +X 14 +... Where T is the top event; X i ,i=1,2,3...is the underlying event, also known as the cut set; the cut set contains the failure events of the components, and a cut set may contain multiple component failure events.

5. The maintenance strategy formulation method according to claim 4, characterized in that: The top-level events include the main events, and the main events include the bottom-level events; any fault tree model contains a finite number of minimum cut sets, and the minimum cut sets are unique to the top-level events.

6. The maintenance strategy formulation method according to claim 1, characterized in that: The improved Apriori algorithm process described in step 3 is: The union of the abnormal items in each column of the Boolean matrix B of the stabilization and control device fault generates a candidate frequent Z-1 item set, and the number of occurrences of the "1" element in each column of the Boolean matrix B is counted S d , determine whether the number of occurrences meets the minimum threshold α, and perform pruning operations. If the number of occurrences of the "1" element in column d meets S d ≤b×α%, delete the Boolean matrix and generate frequent L-1 item sets; Perform logical "AND" operation on the potential fault item column of Boolean matrix B to generate candidate frequent Z-2 fault item set; Compare the number of "1" elements in each subset of the candidate frequent Z-2 fault item set with b×α%. If the number of "1" elements in the subset is less than b×α%, delete the subset to obtain the frequent L-2 item set. Count the number of single potential fault anomaly items in the frequent L-2 item set. If the number is less than 2, delete the subset containing the single anomaly item and update the frequent fault L-2 item set. Perform a logical "AND" operation on the potential fault item column of the Boolean matrix B again to generate a candidate frequent Z-3 fault item set; Compare the number of "1" elements in each subset of the candidate frequent Z-3 fault item set with b×α%. If the number of "1" elements in the subset is less than b×α%, delete the subset to obtain the frequent L-3 item set. Count the number of single potential fault anomaly items in the frequent L-3 item set. If the number is less than 3, delete the subset containing the single anomaly item and update the frequent fault L-3 item set. Similarly, update the fault frequent item set Lk, k>3, and determine whether L is an empty set, then output the fault frequent item set, otherwise continue to perform logical "and" operations on each subset in the fault frequent item set until the algorithm termination condition is met.

7. The maintenance strategy formulation method according to claim 6, characterized in that: Step 3 determines whether there is a strong association rule between the influencing factors and the potential faults, specifically including: (3.1) Each safety and stability control system fault data is an item set Y, denoted as: Y={y1,y2,...,y n } In the formula, y i ,i=1,2,...,n represents each influencing factor and potential fault in the safety and stability control system fault data; (3.2) Influencing factor A and potential fault B are two sub-item sets in Y. For an association rule The support degree S(A) of influencing factor A and the association rules of potential failures caused by influencing factors Support Confidence for: In the formula, C(A) is the number of influencing factor A item set elements appearing in the stable control system fault data set, C(A∪B) represents the number of influencing factor A item set elements and potential fault B item set elements appearing at the same time; CD(dateset) is the number of all stable control system fault data; P(A) is the probability of influencing factor A item set appearing in the stable control system fault data set; (3.3) Set the minimum threshold S for support and confidence min , C min , the support of an m-item set satisfies the minimum support S min It is called a frequent m-item set, and the association rule The support and confidence of both meet the minimum threshold S min , C min , then it is called This set of association rules is a strong association.

8. The maintenance strategy formulation method according to claim 7, characterized in that: The maintenance strategy is formulated based on the influencing factors with strong association rules, potential faults and top-level events corresponding to step 2, including: Monitor influencing factors, set warning thresholds, issue warning signals when monitoring indicators reach the warning thresholds, and arrange maintenance; Set up backup components for corresponding components with potential failures and switch them for use when failures occur.

9. The maintenance strategy formulation method according to claim 1, characterized in that: The maintenance strategy optimized by using the Markov model is based on the maintenance strategy formulated in step 3, which maintains a low failure rate while shortening the fault repair time, specifically including: (4.1) Draw the state space diagram and establish the state transfer matrix Q based on the state space diagram: In the formula, the non-diagonal element q ij (i≠j) represents the probability of transitioning from state i to state j; the diagonal element represents the self-transition probability of state i, and the formula is: (4.2) Solve the state probability matrix P of the stable control system at time t t , according to the probability of the system being in each state, solve the probability of the system operating state at the next moment: P t =[p1 p2…p n ] (4.3) The repair rate refers to the probability that a component that is in failure at time t will be repaired in the subsequent unit time, usually expressed as μ: Where T is the time when the component is repaired; P is the state probability matrix of the stable control system; The fault repair time T is expressed as: In the formula, ξ is a random variable greater than 0 and less than 1. When the fault repair rate μ is increased, the fault repair time T can be effectively reduced. Based on this, the maintenance strategy is optimized; (4.4) Based on the data and information of the fault state of the stability control system and the state information of normal operation, a stable positive time-varying function ρ(t) is constructed: In the formula, ρ(0) = 1 and lim t→T ρ(t)=ρ T And 0<ρ T ≤1 is a constant; when t≥T is consistent with the predetermined time T, ρ(t)=ρ T ρ T , λ and T are positive constants; (4.5) Design a performance function of a finite-time control algorithm that is independent of the initial conditions, combining the characteristics of ρ(t): In the formula, δ is a positive constant; is a monotonically decreasing function; for When t≥T holds (4.6) Define tracking error e = yy d To determine the operating state error of the stabilization control system after time T, When limiting the tracking error, the tracking error should satisfy And satisfy the following characteristics: (4.7) Construct performance function: ρ1(t)=ρ(t)+γ(t)tanh(Λ(t)) Where γ(t) is continuously differentiable, γ(0) = 0 and 0 ≤ γ(t) < 1 - ρ(t); Λ(t) and Λ(0) = 0 are corrected by the following formula: Where m1>0 and m2>0 are constants; Γ i (t), i = 1, 2 represents the gain and is non-negative; v is the expected fault level; This is an unexpected serious failure.

10. A maintenance strategy formulation system for improving the reliability of a stabilization control system, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method according to claims 1 to 9.

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