Toughness evaluation method for multi-source fault of floating platform

Abstract

The invention provides a toughness evaluation method for a multi-source fault of a floating platform. The toughness evaluation method mainly comprises the following steps: 1) constructing a degradation process model of the floating platform; 2) constructing a floating platform recovery process model; 3) determining corresponding toughness indexes of the system; and 4) evaluating the toughness grade of the system. The floating platform toughness evaluation framework constructed by the method disclosed by the invention more pays more attention to the adaptive capacity and the quick restoring force of the system when the system is disturbed by an external environment, and is a more comprehensive evaluation index for the safety and the reliability of the system; the concept of toughness is introduced into evaluation of the full life cycle of the floating platform, a decision maker can be helped to formulate a more reasonable operation and maintenance strategy for the platform with the low toughness level, the overall operation and maintenance cost of the platform is reduced, and the economical efficiency and competitiveness of the platform are improved; a toughness evaluation process and a frame are established based on the degradation characteristic and the recovery characteristic of the floating platform, a toughness factor is proposed to measure the toughness level, and reference significance can be provided for establishing a toughness evaluation system for other ocean structures.

Description

Technical Field

[0001] The present invention relates to the technical field of resilience assessment of floating platforms of offshore wind power systems, and in particular to a resilience assessment method for multi-source faults of floating platforms. Background Art

[0002] In the development and progress of social economy, energy is its strong backing. In the current global energy structure, fossil energy has always occupied a mainstream position. Although fossil energy has played an important role in social progress, the pollution problems it produces should not be underestimated. Under the background of global "low carbon", in order to respond to the goal of "carbon peak and carbon neutrality", the development of renewable energy is imminent. As one of the key technologies for carbon emission reduction in the energy system, offshore wind power system occupies an important position. Offshore wind power system stands out from other new energy sources because of its rich resources, large available space, no water consumption, and high economic performance. Offshore wind power system is becoming the commanding heights of global green and low-carbon technology.

[0003] The operation site of the floating platform is in the deep sea, with strong winds and waves and many uncertain factors, resulting in low safety and reliability of its operation. In addition, due to the particularity of the deep-sea environment, poor logistics performance, weather uncertainty, and high maintenance costs, the maintenance process faces unprecedented challenges. In order to deal with this problem, it is necessary to establish an evaluation mechanism to help decision makers formulate more reasonable operation and maintenance strategies.

[0004] Resilience has become a hot topic for domestic and foreign scholars, and relevant research has made some progress and achievements. Although China has begun to pay attention to the research and application of resilience, the current research foundation is weak, and the relevant theoretical knowledge is still in the exploratory stage. The research on specific systems and specific projects is not sufficient, and there is no application and support of examples. In addition, the research field of resilience in China is relatively narrow, and the literature on the resilience assessment of floating platforms is even more scarce. Summary of the invention

[0005] In view of the technical problems existing in the above-mentioned background technology, the present invention proposes a resilience assessment method for multi-source failures of floating platforms. Its resilience measurement indicators, resilience assessment framework and process for multi-source failures of floating platforms are of great significance for further optimizing the operation and maintenance strategy of floating platforms during service and improving system resilience.

[0006] In order to solve the above technical problems, the present invention provides a method for evaluating the resilience of a floating platform against multi-source faults, which mainly includes the following steps:

[0007] 1) Constructing a floating platform degradation process model

[0008] First, according to the functionality of the floating platform, it is subdivided into six subsystems: mooring system, hydraulic system, gearbox, generator, yaw system and braking system. The intermediate events that may cause failures in each subsystem and the bottom events that cause the intermediate events are analyzed to build a fault tree model. The minimum cut set is determined by the ascending method, and then the minimum cut set of the fault is scored according to the risk standard to obtain the probability of occurrence of the minimum cut set. Then, the failure probability of the floating platform is calculated to generate the corresponding degradation curve.

[0009] 2) Constructing a floating platform recovery process model

[0010] According to the characteristics of the overall stage from the failure of the floating platform to the recovery, the recovery process is divided into three parts, namely the fault detection stage, the resource allocation stage and the maintenance stage. The uncertainty factors in each stage are analyzed, and the impact of the uncertainty factors is discussed by classification. The dynamic Bayesian network is used to establish the floating platform recovery process model, and the system performance value of the floating platform in the recovery stage is obtained.

[0011] 3) Determine the corresponding resilience indicators of the system

[0012] The resilience index includes absorption capacity, adaptability and recovery capacity. The absorption capacity, adaptability and recovery capacity are quantitatively analyzed. The specific data is determined by the degradation curve value and recovery capacity value determined in the above steps 1) and 2). The resilience factor value is calculated by comprehensively calculating the absorption capacity, adaptability and recovery capacity.

[0013] 4) Evaluation of system resilience level

[0014] The resilience levels of floating platform systems are divided and the levels are determined by measuring the size of the resilience factor. The specific expressions of different levels of resilience and the relationship between the size of the resilience factor and the level are established.

[0015] The method for assessing resilience of a floating platform with multiple source faults, wherein the specific process of step 1) is as follows:

[0016] 1.1) Determine the minimum cut set of failures and their occurrence probability of the floating platform system

[0017] A fault tree model is constructed for the degradation process of the floating platform system. The specific process is as follows: the failure of the floating platform system is set as the top event, the direct cause of the failure of the top event is analyzed, and the failure of the mooring system, hydraulic system, gearbox, generator, yaw system and braking system of the floating platform system is set as the intermediate event; the causes of sub-component failures in the above six subsystems are analyzed in detail, and the sub-component failure events are listed as bottom events;

[0018] The basic events of the floating platform system are complex and numerous. The fault tree model value is calculated by establishing the minimum cut set. The minimum cut set is determined by the upward method. The specific process is: based on the established fault tree model, according to the OR gate logic of each event in the fault tree, that is, when an event in the fault tree structure fails, the upward event fails, and the top event expression is obtained:

[0019]

[0020] In the above formula, T refers to the system failure event of the floating platform, i.e., the top event of the fault tree, and X i represents the determined minimum cut set, i = 1, 2, ... 54, A m ,m=1,2,...6 represents the six subsystem failure events of the floating platform;

[0021] The probability of occurrence of the minimum cut set event is determined by dividing safety into six risk levels according to the DNV standard, and the corresponding relationship between the probability value of the minimum cut set event of the fault tree and the fault level is formulated based on the divided risk levels;

[0022] 1.2) Solve the probability of occurrence of the top event of the fault tree

[0023] In the established fault tree, the probability of occurrence of the top event represents the failure probability of the floating platform system. Based on the fault tree model established in step 1.1) and the minimum cut set determined in step 1.1), the calculation process of the probability of occurrence of the top event is given, and its formula is as follows:

[0024] P(T)=P(A1+A2+A3+A4+A5+A6);

[0025] When solving the probability of occurrence of the top event, the sum of independent events is used to solve the probability of the intermediate event. The formula is as follows:

[0026]

[0027] In the above formula, P(T) refers to the system failure probability of the floating platform, P z Refers to the probability of failure of intermediate events, P(X i ) means the probability of occurrence of a minimal cut set event;

[0028] 1.3) Generate degradation curve

[0029] Assume that the number of components in the floating platform system is n, and the number of failures that occur in the subcomponents in the time interval between 0 and t is recorded as N(t). The floating platform system is composed of six subsystems, including a total of 53 subcomponents. Under the condition of minimum repair of subcomponents, it is modeled as a homogeneous Poisson process, and its calculation formula is as follows:

[0030]

[0031] R(t)=e -pt ;

[0032] In the above formula, p represents the failure probability of floating platform failure; λ t It refers to the fault intensity, which specifically means the number of times the fault occurs; R(t) is the toughness margin; where the determination of the λ value depends on the analysis and calculation of the types and situations of the floating platform faults.

[0033] The method for assessing resilience of a floating platform with multiple source faults, wherein the specific process of step 2) is as follows:

[0034] 2.1) Building a fault detection capability model

[0035] The fault detection stage is affected by fault diagnosis, fault detection time and the system monitoring capability of the floating platform. The expression of fault detection capability is as follows:

[0036]

[0037] In the above formula, S refers to the fault detection capability value, ρ s refers to the accuracy of fault diagnosis, m refers to the fault monitoring capability value, i is the coefficient of the utility function, t s Represents the detection time of the fault;

[0038] And the relationship between fault monitoring capability and fault diagnosis accuracy is expressed by the following formula:

[0039] m=log 10 (11-x);

[0040] ρ s =ρ m ;

[0041] In the above formula, m represents the influence of monitoring capability on the accuracy of fault diagnosis, x represents the level of monitoring capability x=(1,2,3...), ρ refers to the accuracy of fault diagnosis, ρ s Refers to the accuracy of fault diagnosis after taking monitoring capabilities into consideration;

[0042] 2.2) Build a resource allocation capability model

[0043] According to the factors affecting resource allocation, resource capacity is quantified as the reasonable rate of resource allocation and the time taken to obtain resources:

[0044]

[0045] In the above formula, C refers to the resource allocation capability value, μ c represents the reasonable rate of resource allocation, λ is the coefficient of the utility function, tc Refers to the total time that resources are allocated;

[0046] 2.3) Build a maintenance capability model

[0047] Maintenance capability refers to the ability to restore the system of a floating platform to a state where it can maintain normal operation through existing maintenance means during the maintenance phase. It can be quantified as maintenance time and maintenance success rate, and the formula is as follows:

[0048]

[0049] In the above formula, K(t k ) refers to the maintenance capability value, I r refers to the successful maintenance rate, ω is the utility coefficient, t k The time it takes to complete the repair.

[0050] The resilience assessment method for multi-source failures of floating platforms, wherein, in step 2), when constructing the floating platform recovery process model, the contribution rate of the failure of the subcomponent to the system failure of the floating platform needs to be taken into account, and the given correction formula is as follows;

[0051]

[0052] In the above formula, E(t MT ) represents the corrected maintenance time; (t M ) i Represents the maintenance time of different maintenance methods; P i (t MT ) represents the ratio of the faulty components to the overall failure rate.

[0053] The method for assessing resilience of a floating platform with multiple source faults, wherein the specific process of step 3) is as follows:

[0054] 3.1) Determination of toughness factor

[0055] According to the degradation characteristics and recovery characteristics of the floating platform, the resilience level of the floating platform system is measured by the resilience factor α. The given resilience factor expression formula is as follows:

[0056]

[0057] In the above formula, α represents the toughness factor, l p represents the system recovery rate of the floating platform, F0 refers to the original stable system performance level of the floating platform, and F f Refers to the performance level achieved after the interruption, F t Refers to the system performance level value of the floating platform after the recovery process is completed and the performance is in a new stable state;

[0058] 3.2) Absorption capacity is defined as the ability of the floating platform system to minimize the consequences after being affected by a disturbance. From the degradation curve simulated in step (1), it can be seen that the degradation process of the floating platform system after being affected by an external interruption event is divided into three situations, namely: the system performance of the floating platform drops sharply and cannot withstand the impact of the interruption event, the system performance of the floating platform gradually drops and finally stabilizes in this state, and the system performance of the floating platform drops slowly and can be restored to the initial state after a quick response. The calculation formula of the absorption capacity is given as follows:

[0059]

[0060] In the above formula, F0 refers to the original stable system performance level; F f Refers to the performance level value achieved after the interruption; F f / F0 is the absorption capacity value, which refers to the ratio of the system performance level of the floating platform after the failure to the initial state of the floating platform system, which means the resilience loss of the floating platform system after the failure; λ min Refers to the minimum failure probability of the system subcomponents of the floating platform; λ max Refers to the maximum probability of failure in the system subcomponents of a floating platform;

[0061] 3.3) Adaptability refers to the ability of the floating platform system to adapt to the impact of the external environment through some changes when facing disturbances. It mainly examines the floating platform system's ability to deal with risk events and its learning ability. Based on the fault detection stage in step 2.1) and the resource allocation stage in step 2.2), the calculation formula for adaptability is as follows:

[0062]

[0063] Among them, F in the above formula t / F0 is the adaptability value, which refers to the ratio of the state of the floating platform system after repair and restoration to the initial state; F t It refers to the new stable state value of performance after the recovery process is completed; C represents the resource allocation capability value; K(t k ) means the maintenance capability value;

[0064] 3.4) Recovery capability refers to the ability of the floating platform system to quickly recover from a fault state to an initial state or to improve the reliability of the floating platform system; the determination of the recovery capability parameter depends on the maintenance phase in step 2.3). The recovery rate of the floating platform system is used as a standard to measure the recovery capability. The recovery rate calculation formula is given as follows:

[0065]

[0066] In the above formula, lp represents the system recovery rate of the floating platform, E(t MT ) represents the mean maintenance time, t ft Represents the average downtime after a system failure on a floating platform.

[0067] The method for assessing the toughness of a floating platform with multiple sources of failure, wherein: the toughness levels in step 4) include high toughness, medium-high toughness, medium toughness and low toughness.

[0068] The present invention has the following beneficial effects by adopting the above technical solution:

[0069] The resilience assessment method for multi-source failures of floating platforms of the present invention is reasonably conceived, and its characteristics and advantages are mainly reflected in the following aspects:

[0070] (1) The constructed floating platform resilience assessment framework is different from traditional evaluation indicators. It pays more attention to the system's adaptability and rapid recovery when it is disturbed by the external environment. It is a more comprehensive assessment indicator for system safety and reliability.

[0071] (2) Introducing the concept of resilience into the life cycle assessment of floating platforms can help decision makers formulate more reasonable operation and maintenance strategies for platforms with low resilience levels, reduce the overall operation and maintenance costs of the platforms, and improve the economic efficiency and competitiveness of the platforms;

[0072] (3) The contribution rate of sub-component failure rate to the system failure of the floating platform is taken into account in the resilience assessment, making the evaluation more accurate.

[0073] (4) Based on the degradation and recovery characteristics of floating platforms, a resilience assessment process and framework are established, and a resilience factor is proposed to measure the resilience level; this can provide a reference for establishing a resilience assessment system for other marine structures. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0075] Figure 1 This is a flow chart of a method for assessing the resilience of a floating platform system in one embodiment of the present invention;

[0076] Figure 2 A schematic diagram of a fault tree model established according to the characteristics of the floating platform degradation process in one embodiment of the present invention;

[0077] Figure 3 A floating platform system degradation curve generated based on a reliability formula and a fault tree model in one embodiment of the present invention;

[0078] Figure 4 It is a schematic diagram of a dynamic Bayesian model established according to a physical model of a floating platform system recovery process in one embodiment of the present invention. DETAILED DESCRIPTION

[0079] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0080] The present invention is further explained below in conjunction with specific implementation modes.

[0081] like Figure 1 As shown, this embodiment provides a resilience measurement index, resilience assessment framework and process for multi-source faults of a floating platform. Now the resilience assessment process of the present invention is applied to a certain platform, including the following steps:

[0082] S100, Construction of degradation model

[0083] The floating platform system is huge and has many subcomponents, and the causes of failure are multi-layered. Therefore, it is subdivided into six major systems according to the functionality of the platform system: mooring system, hydraulic system, gearbox, generator, yaw system and braking system; the intermediate events that may cause failures in each subsystem and the bottom events that cause the intermediate events are analyzed to construct a fault tree model; the ascending method is used to determine the minimum cut set, and then the minimum cut set of failures is scored according to the risk standard to obtain the probability of occurrence of the minimum cut set; then the failure probability of the floating platform is calculated to generate the corresponding degradation curve.

[0084] S101. Determine the minimum cut set of faults of the floating platform system and their occurrence probability

[0085] A fault tree model is constructed for the degradation process of the floating platform system. The specific steps are as follows: the failure of the floating platform system is set as the top event, the direct cause of the failure of the top event is analyzed, and the failure of the mooring system, hydraulic system, gearbox, generator, yaw system and braking system of the floating platform system is set as the intermediate event; the causes of sub-component failures in the above six subsystems are analyzed in detail, and the sub-component failure events are listed as bottom events; according to the above steps, a fault tree model is established, such as Figure 2 shown.

[0086] Since the basic events of the floating platform system are complex and numerous, the minimum cut set is established to facilitate the calculation of the fault tree model value; the minimum cut set is composed of bottom events. When all bottom events in the minimum cut set occur at the same time, the intermediate event must occur; the minimum cut set is determined by the upward method. The specific steps are: based on the fault tree model established above, according to the OR gate logic of each event in the fault tree, that is, when an event in the fault tree structure fails, the upward event fails, and the top event expression is obtained:

[0087]

[0088] In the above formula, T refers to the system failure event of the floating platform, i.e., the top event of the fault tree, and X i represents the minimum cut set in the fault tree, i=1,2,...54, A m , m=1,2,...6 represent the six subsystem failure events of the floating platform; the calculation process of the top event is shown in Table 2 and Table 3:

[0089] Table 2 Fault types of floating wind turbines

[0090]

[0091] Table 3 Fault event composition

[0092] Fault event components Fault event components <![CDATA[S=A1+A2+A3+A4+A5+A6]]> <![CDATA[M9=X 20 +X 21 +X 22 ]]> <![CDATA[A1=M2+M3+M4+M5+M6+M7]]> <![CDATA[M 10 =X 23 +X 24 ]]> <![CDATA[A2=X 25 +X 26 +X 27 +X 28 ]]> <![CDATA[M 11 =M 16 +M 17 ]]> <![CDATA[A3=M 11 +M 12 +M 13 ]]> <![CDATA[M 12 =X 32 +X 33 ]]> <![CDATA[A4=M 18 +M 19 ]]> <![CDATA[M 13 =X 29 +X 30 +X 31 ]]> <![CDATA[A5=M 20 +M 21 +M 22 ]]> <![CDATA[M 14 =X 40 +X 41 ]]> <![CDATA[A6=M 14 +M 15 ]]> <![CDATA[M 15 =X 38 +X 39 ]]> <![CDATA[M2=X1+M8+X2]]> <![CDATA[M 16 =X 34 +X 35 ]]> <![CDATA[M3=X3+M9+X4]]> <![CDATA[M 17 =X 36 +X 37 ]]> <![CDATA[M4=X5+X6+M 10 +X7]]> <![CDATA[M 18 =X 47 +X 48 +X 49 +M 24 ]]> <![CDATA[M5=X8+X9+X 10 +X 11 ]]> <![CDATA[M 19 =X 50 +X 53 ]]> <![CDATA[M6=X 12 +X 13 +X 14 +X 15 ]]> <![CDATA[M 20 =X 42 +X 43 <!-- 7 -->]]> <![CDATA[M7=X 16 +X 17 ]]> <![CDATA[M 21 =X 44 +X 45 ]]> <![CDATA[M8=X 18 +X 19 ]]> <![CDATA[M 22 =X 51 +X 52 +X 54 ]]>

[0093] The determination of the probability of occurrence of the minimum cut set event is based on the DNV standard, which divides safety into six risk levels. Based on the divided risk levels, the corresponding relationship between the probability value of the minimum cut set event of the fault tree and the fault level is formulated; see Table 4 and Table 5 for details:

[0094] Table 4 Probability values ​​corresponding to the minimum cut set

[0095]

[0096]

[0097] Table 5 Minimum cut set event probability

[0098]

[0099]

[0100]

[0101] S102. Calculate the probability of occurrence of the top event in the fault tree

[0102] The probability of occurrence of the top event in the established fault tree represents the failure probability of the floating platform system. The calculation process of the probability of occurrence of the top event is given according to the fault tree model established in step S101 and the minimum cut set determined in step S102, and its formula is as follows:

[0103] P(T)=P(A1+A2+A3+A4+A5+A6)

[0104] When solving the probability of the top event, the sum of independent events is used to solve the probability of the intermediate event; the formula is as follows:

[0105]

[0106] In the above formula, P(T) refers to the system failure probability of the floating platform, P z Refers to the probability of failure of intermediate events, P(X i ) refers to the probability of occurrence of the minimum cut set event; the intermediate event failure probability and the system failure probability of the floating platform are obtained by sorting;

[0107] S103. Generate degradation curve

[0108] Assume that the number of components in the system is n. The number of failures that occur in a subcomponent in the time interval between 0 and t can be recorded as N(t). The floating platform system consists of six subsystems and contains 53 subcomponents. The number of failures N(t) of a subcomponent in the time interval between 0 and t is usually modeled as a homogeneous Poisson process in the case of minimal repair of the subcomponent, and its calculation formula is as follows:

[0109]

[0110] R(t)=e -pt ;

[0111] In the above formula, p represents the failure probability of floating platform failure; λ t It refers to the fault intensity, which specifically means the number of faults; R(t) is the toughness margin; where the determination of the λ value depends on the analysis and calculation of the types and situations of floating platform faults mentioned above, p(S) = 2.84E-03, when the time is one year, that is, t = 365, the degradation curve is as follows: Figure 3 shown.

[0112] S200, establishing a recovery process model and determining specific values

[0113] According to the characteristics of the overall stage from the failure of the floating platform to the recovery, the recovery process is divided into three parts: fault detection stage, resource allocation stage and maintenance stage; the uncertainty factors in each stage are analyzed, the impact of uncertain variables is discussed by classification, and a dynamic Bayesian network model is established to obtain the system performance value in the recovery stage. The dynamic Bayesian structure model is constructed as follows: the nodes of the Bayesian network are used to represent physical variables, the arcs of the Bayesian network are used to represent the causal relationship between variables, and the mathematical models of each stage are used to represent the mutual relationship between the sub-model nodes; the recovery process model, such as Figure 4 As shown, the specific construction process is as follows:

[0114] S201. Build a fault detection capability model

[0115] Fault detection capability refers to the ability of the platform to correctly identify faulty components and fault types when a fault occurs. It is the first step in maintenance activities. The fault detection stage is affected by fault diagnosis, fault detection time and the system monitoring capability of the floating platform. The expression of fault detection capability is as follows:

[0116]

[0117] In the above formula, S refers to the fault detection capability value, ρ s refers to the accuracy of fault diagnosis, m refers to the fault monitoring capability value; i is the coefficient of the utility function; t s Represents the detection time of the fault;

[0118] Among them, the fault diagnosis rate is related to the capacity of the monitoring system; the monitoring system plays an important role in sensing the operating status of the platform, identifying risk events, and accurately locating fault points during platform operation. The relationship between monitoring capability and fault diagnosis rate is expressed by the following formula:

[0119] m=log 10 (11-x);

[0120] ρ s =ρ m ;

[0121] Where m represents the influence of monitoring capability on fault diagnosis accuracy; x represents the monitoring capability level x = (1, 2, 3...); ρ refers to the fault diagnosis accuracy; ρs refers to the fault diagnosis accuracy after taking monitoring capability into consideration;

[0122] Among them, the fault detection capability mainly considers the influence of different detection methods and monitoring capabilities; in the fault detection activities, the fault diagnosis methods of floating wind turbine platforms are divided into ROV detection and artificial underwater detection. Artificial underwater detection is completed by divers. Its advantages are strong ability to respond to emergencies and can perform detection operations on complex nodes. The accuracy can reach 98%, but the efficiency is low and the operation time is long; while ROV detection can well improve the defects of high time consumption and low efficiency of operations, but the operation is relatively difficult, resulting in a low accuracy rate of only 78%. The values ​​of fault detection capabilities under different schemes are shown in Table 6:

[0123] Table 6 Fault detection capability

[0124] Monitoring level ROV Inspection Artificial underwater inspection 5 0.807 0.946 6 0.832 0.965 7 0.843 0.967

[0125] S202. Construct resource allocation capability model

[0126] Resource capacity refers to the ability to allocate sufficient resources after the system correctly diagnoses the fault and to allow the resources to reach the fault point in the shortest time. For the maintenance process of floating platforms, reasonable resource allocation can reduce resource redundancy and thus reduce maintenance costs, while improving maintenance efficiency. According to the factors affecting resource allocation, resource capacity is quantified as the reasonable rate of resource allocation and the time used to obtain resources:

[0127]

[0128] In the above formula, C refers to the value of resource allocation capability; μ c represents the reasonable rate of resource allocation; λ is the coefficient of the utility function; t c Refers to the total time that resources are allocated.

[0129] Among them, resource capacity mainly considers resource allocation capacity; because the offshore wind power system is in the deep sea, maintenance personnel can only reach the maintenance site by operation and maintenance ship. At present, the operation and maintenance ships of offshore wind farms are mainly divided into two categories: small maintenance ships and main maintenance ships; according to the data summary, the allocation time of general resource capacity is within 12 hours, and the utility function coefficient λ of resource allocation is 0.005. The resource capacity values ​​under different schemes are shown in Table 7:

[0130] Table 7 Resource capabilities under different resource availability

[0131] Resource availability Resource Capacity 93.91％ 0.884 91.32％ 0.86 90.72％ 0.854

[0132] S203. Build a maintenance capability model

[0133] Maintenance capability refers to the ability to restore the system of a floating platform to a state where it can maintain normal operation through existing maintenance means during the maintenance phase; maintenance capability can be quantified as maintenance time and maintenance success rate, and the formula is as follows:

[0134]

[0135] In the above formula, K(t k ) means the maintenance capability value; r refers to the successful maintenance rate; ω is the utility coefficient; t k The time it takes to complete the repair.

[0136] Among them, under normal circumstances, the maintenance success rate obeys the normal distribution; in the normal distribution, the value range of the random variable basically falls within the interval (μ-3σ,μ+3σ), and the area within the interval is as follows:

[0137]

[0138] The above formula shows that the repair success rate can reach at least 99%; the repair capacity mainly depends on the repair time, and the repair time mainly depends on the type of repair; there are mainly three situations for offshore wind farm repair:

[0139] ①Some minor repairs due to damage to non-major components;

[0140] ② Damage to major components may result in longer downtime and repair time;

[0141] ③Replace and repair damaged and unusable parts;

[0142] However, in actual situations, the repair time of components with high failure rates is relatively short. Therefore, simply adding up the repair time will produce a large error from the actual situation. When constructing the floating platform recovery process model, the contribution rate of sub-component failures to the system failure of the floating platform must also be taken into account. The given correction formula is as follows:

[0143]

[0144] In the above formula, E(t MT ) represents the corrected maintenance time; (t M ) i Represents the maintenance time of different maintenance methods; P i (t MT ) represents the ratio of the faulty components to the overall failure rate.

[0145] The maintenance capability values ​​under different schemes are shown in Table 8:

[0146] Table 8 Maintenance time and maintenance capability of different maintenance methods

[0147]

[0148] S300. Determine corresponding resilience indicators of the system.

[0149] The three characteristics of resilience are absorption capacity, adaptability and recovery capacity. The three capacities are quantitatively analyzed. The specific data is determined by the degradation curve and recovery curve determined in step 1 and step 2. The value of the resilience factor is calculated by combining the three capacities. The specific implementation process is as follows:

[0150] S301. Determination of toughness factor

[0151] According to the degradation characteristics and recovery characteristics of the floating platform, the resilience level of the floating platform system is measured by the resilience factor α. The given resilience factor expression formula is as follows:

[0152]

[0153] In the above formula, α represents the toughness factor; l p represents the system recovery rate of the floating platform; F0 refers to the system performance level value of the original stable floating platform, F f Refers to the performance level achieved after the interruption, F t It refers to the system performance level of the floating platform after the recovery process is completed. The determination of the variables in the formula depends on the quantitative analysis of absorption capacity, adaptability and recovery capacity.

[0154] S302, absorption capacity is defined as the ability of the system to minimize the consequences after being affected by a disturbance; from the degradation curve simulated in the above step S100, it can be seen that the degradation process of the system after being affected by an external interruption event is divided into three situations: (1) the system performance drops sharply and cannot withstand the impact of the interruption event, (2) the system performance gradually drops and eventually stabilizes in this state, (3) the system performance slowly drops and can be restored to the initial state after a quick response. The absorption capacity calculation formula is given as follows:

[0155]

[0156] In the above formula, F0 refers to the original stable system performance level; F f Refers to the performance level value achieved after the interruption; F f / F0 is the absorption capacity value, which refers to the ratio of the system performance level of the floating platform after the failure to the initial state of the floating platform system, which means the resilience loss of the floating platform system after the failure; λ min Refers to the minimum failure probability of the system subcomponents of the floating platform; λ max Refers to the maximum probability of failure in the system sub-components of a floating platform.

[0157] S303, adaptability refers to the ability of the floating platform system to adapt to the impact of the external environment through some changes when facing disturbances. It mainly examines the system's ability to deal with risk events and its learning ability; the floating platform must also go through the stages of fault detection and resource allocation when waiting for repairs in the process of sudden failure; the adaptability calculation formula is given based on the fault detection stage in step S201 and the resource allocation stage in step S202 as follows:

[0158]

[0159] In the above formula, F t / F0 is the adaptability value, which refers to the ratio of the state of the floating platform system after repair and restoration to the initial state; F t It refers to the new stable state value of performance after the recovery process is completed; C represents the resource allocation capability value; K(t k ) means the maintenance capability value.

[0160] S304 The resilience of the system refers to the ability of the floating platform system to quickly recover from a fault state to an initial state or to improve the reliability of the floating platform system; according to the maintenance phase in step S203 above, the recovery rate of the floating platform system is used as a standard to measure the recovery capability, and the recovery rate calculation formula is given as follows:

[0161]

[0162] In the above formula, l p represents the system recovery rate of the floating platform, E(t MT ) represents the mean maintenance time, t ft Represents the average downtime after a system failure on a floating platform.

[0163] The above step S300 is to evaluate the system resilience level by determining the size of the system resilience factor; according to the degradation curve determined in the above step S100 and the recovery model data obtained in step S200, the specific values ​​of the three capabilities can be obtained according to the calculation formulas of absorption capacity, adaptability and recovery capacity, thereby calculating the resilience factor of the floating platform.

[0164] S400, system resilience level assessment, the resilience level of the floating platform system is divided into 1-4 levels, namely high resilience (level 4), medium-high resilience (level 3), medium resilience (level 2) and low resilience (level 1), and the level is determined by the size of the resilience factor. The specific description of different levels of resilience and the relationship between the size of the resilience factor and the level are shown in Table 1:

[0165] Table 1 Toughness grade explanation

[0166]

[0167] According to the above toughness level table 1, the toughness level of the floating platform system under different schemes can be found out:

[0168] Table 9 Resilience factors of different schemes (monitoring capabilities and detection methods)

[0169] plan Monitoring capabilities Detection method Absorption capacity Adaptability Resilience Resilience Factor 1 5 ROV Inspection 0.615 0.694 1.12 0.478 2 6 ROV Inspection 0.615 0.715 1.12 0.492 3 7 ROV Inspection 0.615 0.724 1.12 0500 4 5 Manual inspection 0.615 0.814 1.12 0.560 5 6 Manual inspection 0.615 0.830 1.12 0.571 6 7 Manual inspection 0.615 0.831 1.12 0.572

[0170] As shown in Table 9, the enhancement of monitoring capability has improved the resilience of the floating wind turbine platform under both detection schemes; and monitoring has a smaller impact on the system resilience of the manual detection scheme, but a greater impact on the system resilience of the ROV detection scheme. Therefore, in the maintenance plan, the adaptability under the 7-level monitoring capability is adopted for the ROV detection method, and the 6-level detection capability is adopted for the manual detection method. The specific situation is shown in Table 10:

[0171] Table 10 Resilience factor size of different schemes (maintenance means)

[0172] Detection method Maintenance method Absorption capacity Adaptability Resilience Resilience Factor ROV Inspection Minor repair 0.615 0.724 1.63 0.725 ROV Inspection Overhaul 0.615 0.724 1.23 0.547 ROV Inspection Replacement Parts 0.615 0.724 1.05 0.468 Manual inspection Minor repair 0.615 0.830 1.63 0.832 Manual inspection Overhaul 0.615 0.830 1.23 0.627 Manual inspection Replacement Parts 0.615 0.830 1.05 0.553

[0173] As shown in Table 10, the overall resilience level of the three maintenance methods of the floating platform using manual inspection all reached level 3, the system resilience performance was high, and the probability of failure was low. If an interruption event occurred, the system could quickly identify and locate the fault point and fault type, allocate resources more reasonably, and have good recovery capabilities. The system recovered to the normal production state in a short time.

[0174] The overall resilience level of the main maintenance method and minor repair method of the floating platform using ROV inspection has reached level 3, but the resilience level of the maintenance method of replacing parts has only reached level 2. The overall resilience performance is medium, and the probability of failure is average. If an interruption event occurs, the system's ability to identify and locate the fault point and fault type is average, and the recovery ability is medium. The system takes a certain amount of time to return to normal production. Therefore, for the platform system using ROV inspection, a more reasonable operation and maintenance plan should be established to deal with risk challenges, focusing on improving the system's recovery capabilities, thereby improving the overall resilience level.

[0175] The present invention has a reasonable conception. The constructed floating platform resilience assessment framework is different from the traditional evaluation indicators. It pays more attention to the adaptability and rapid recovery of the system when it is disturbed by the external environment. It is a more comprehensive assessment indicator for system safety and reliability. The concept of resilience is introduced into the assessment of the entire life cycle of the floating platform. For platforms with low resilience levels, it can help decision makers formulate more reasonable operation and maintenance strategies, reduce the overall operation and maintenance costs of the platform, and improve the platform's economy and competitiveness. Based on the degradation characteristics and recovery characteristics of the floating platform, a resilience assessment process and framework are established, and it is proposed to use resilience factors to measure the resilience level, which can provide a reference for establishing a resilience assessment system for other marine structures.

[0176] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A resilience assessment method for multi-source failures of floating platforms, characterized in that , mainly including the following steps: 1) Constructing a floating platform degradation process model First, according to the functionality of the floating platform, it is subdivided into six subsystems: mooring system, hydraulic system, gearbox, generator, yaw system and braking system. The intermediate events that may cause failures in each subsystem and the bottom events that cause the intermediate events are analyzed to build a fault tree model. The minimum cut set is determined by the ascending method, and then the minimum cut set of failures is scored according to the risk standard to obtain the probability of occurrence of the minimum cut set. Then, the failure probability of the top event, that is, the floating platform system, is calculated to generate the corresponding degradation curve. 2) Constructing a floating platform recovery process model According to the characteristics of the overall stage from the failure of the floating platform to the recovery, the recovery process is divided into three parts, namely the fault detection stage, the resource allocation stage and the maintenance stage. The uncertainty factors in each stage are analyzed, and the impact of the uncertainty factors is discussed by classification. The dynamic Bayesian network is used to establish the floating platform recovery process model, and the system performance value of the floating platform in the recovery stage is obtained. 3) Determine the corresponding resilience indicators of the system The resilience index includes absorption capacity, adaptability and recovery capacity. The absorption capacity, adaptability and recovery capacity are quantitatively analyzed. The specific data is determined by the degradation curve value and recovery capacity value determined in the above steps 1) and 2). The resilience factor value is calculated by comprehensively calculating the absorption capacity, adaptability and recovery capacity. 4) Evaluation of system resilience level The resilience levels of floating platform systems are divided and the levels are determined by measuring the size of the resilience factor. The specific expressions of different levels of resilience and the relationship between the size of the resilience factor and the level are established.

2. The method for assessing resilience of a floating platform against multi-source failures according to claim 1, characterized in that: The specific process of step 1) is as follows: 1.1) Determine the minimum cut set of failures and their occurrence probability of the floating platform system A fault tree model is constructed for the degradation process of the floating platform system. The specific process is as follows: the failure of the floating platform system is set as the top event, the direct cause of the failure of the top event is analyzed, and the failure of the mooring system, hydraulic system, gearbox, generator, yaw system and braking system of the floating platform system is set as the intermediate event; the causes of sub-component failures in the above six subsystems are analyzed in detail, and the sub-component failure events are listed as bottom events; The basic events of the floating platform system are complex and numerous. The fault tree model value is calculated by establishing the minimum cut set. The minimum cut set is determined by the upward method. The specific process is: based on the established fault tree model, according to the OR gate logic of each event in the fault tree, that is, when an event in the fault tree structure fails, the upward event fails, and the top event expression is obtained: In the above formula, T refers to the system failure event of the floating platform, i.e., the top event of the fault tree, and X i represents the determined minimum cut set, i = 1, 2, ... 54, A m ,m=1,2,...6 represents the six subsystem failure events of the floating platform; The probability of occurrence of the minimum cut set event is determined by dividing safety into six risk levels according to the DNV standard, and the corresponding relationship between the probability value of the minimum cut set event of the fault tree and the fault level is formulated based on the divided risk levels; 1.2) Solve the probability of occurrence of the top event of the fault tree In the established fault tree, the probability of occurrence of the top event represents the failure probability of the floating platform system. Based on the fault tree model established in step 1.1) and the minimum cut set determined in step 1.1), the calculation process of the probability of occurrence of the top event is given, and its formula is as follows: P(T)=P(A1+A2+A3+A4+A5+A6); When solving the probability of occurrence of the top event, the sum of independent events is used to solve the probability of the intermediate event. The formula is as follows: In the above formula, P(T) refers to the system failure probability of the floating platform, P z Refers to the probability of failure of intermediate events, P(X i ) means the probability of occurrence of a minimal cut set event; 1.3) Generate degradation curve Assume that the number of components in the floating platform system is n, and the number of failures that occur in the subcomponents in the time interval between 0 and t is recorded as N(t). The floating platform system is composed of six subsystems, including a total of 53 subcomponents. Under the condition of minimum repair of subcomponents, it is modeled as a homogeneous Poisson process, and its calculation formula is as follows: R(t)=e -pt ; In the above formula, p represents the failure probability of floating platform failure; λ t It refers to the fault intensity, which specifically means the number of times the fault occurs; R(t) is the toughness margin; where the determination of the λ value depends on the analysis and calculation of the types and situations of the floating platform faults.

3. The method for assessing resilience of a floating platform against multi-source failures according to claim 1, characterized in that: The specific process of step 2) is as follows: 2.1) Building a fault detection capability model The fault detection stage is affected by fault diagnosis, fault detection time and the system monitoring capability of the floating platform. The expression of fault detection capability is as follows: In the above formula, S refers to the fault detection capability value, ρ s refers to the accuracy of fault diagnosis, m refers to the fault monitoring capability value, i is the coefficient of the utility function, t s Represents the detection time of the fault; And the relationship between fault monitoring capability and fault diagnosis accuracy is expressed by the following formula: m=log 10 (11-x); r s =ρ m ; In the above formula, m represents the influence of monitoring capability on the accuracy of fault diagnosis, x represents the level of monitoring capability x=(1,2,3...), ρ refers to the accuracy of fault diagnosis, ρ s Refers to the accuracy of fault diagnosis after taking monitoring capabilities into consideration; 2.2) Build a resource allocation capability model According to the factors affecting resource allocation, resource capacity is quantified as the reasonable rate of resource allocation and the time taken to obtain resources: In the above formula, C refers to the resource allocation capability value, μ c represents the reasonable rate of resource allocation, λ is the coefficient of the utility function, t c Refers to the total time that resources are allocated; 2.3) Build a maintenance capability model Maintenance capability refers to the ability to restore the system of a floating platform to a state where it can maintain normal operation through existing maintenance means during the maintenance phase. It can be quantified as maintenance time and maintenance success rate, and the formula is as follows: In the above formula, K(t k ) refers to the maintenance capability value, I r refers to the successful maintenance rate, ω is the utility coefficient, t k The time it takes to complete the repair.

4. The method for assessing resilience of a floating platform against multi-source failures according to claim 1 or 3, characterized in that ,In step 2), when constructing the floating platform recovery process model, it is also necessary to take into account the contribution rate of the failure of the subcomponent to the system failure of the floating platform, and the given correction formula is as follows; In the above formula, E(t MT ) represents the corrected maintenance time; (t M ) i Represents the maintenance time of different maintenance methods; P i (t MT ) represents the ratio of the faulty components to the overall failure rate.

5. The method for assessing resilience of a floating platform against multi-source failures according to claim 1, characterized in that: The specific process of step 3) is as follows: 3.1) Determination of toughness factor According to the degradation characteristics and recovery characteristics of the floating platform, the resilience level of the floating platform system is measured by the resilience factor α. The given resilience factor expression formula is as follows: In the above formula, α represents the toughness factor, l p represents the system recovery rate of the floating platform, F0 refers to the original stable system performance level of the floating platform, and F f Refers to the performance level achieved after the interruption, F t Refers to the system performance level value of the floating platform after the recovery process is completed and the performance is in a new stable state; 3.2) Absorption capacity is defined as the ability of the floating platform system to minimize the consequences after being affected by a disturbance; from the degradation curve simulated in step 1), it can be seen that the degradation process of the floating platform system after being affected by an external interruption event is divided into three situations, namely: the system performance of the floating platform drops sharply and cannot withstand the impact of the interruption event, the system performance of the floating platform gradually drops and finally stabilizes in this state, and the system performance of the floating platform slowly drops and can be restored to the initial state after a quick response; the absorption capacity calculation formula is given as follows: In the above formula, F0 refers to the original stable system performance level; F f Refers to the performance level value achieved after the interruption; F f / F0 is the absorption capacity value, which refers to the ratio of the system performance level of the floating platform after the failure to the initial state of the floating platform system, which means the resilience loss of the floating platform system after the failure; λ min Refers to the minimum failure probability of the system subcomponents of the floating platform; λ max Refers to the maximum probability of failure in the system subcomponents of a floating platform; 3.3) Adaptability refers to the ability of the floating platform system to adapt to the impact of the external environment through some changes when facing disturbances. It mainly examines the floating platform system's ability to deal with risk events and its learning ability. Based on the fault detection stage in step 2.1) and the resource allocation stage in step 2.2), the calculation formula for adaptability is as follows: Among them, F in the above formula t / F0 is the adaptability value, which refers to the ratio of the state of the floating platform system after repair and restoration to the initial state; F t It refers to the new stable state value of performance after the recovery process is completed; C represents the resource allocation capability value; K(t k ) means the maintenance capability value; 3.4) Recovery capability refers to the ability of the floating platform system to quickly recover from a fault state to an initial state or to improve the reliability of the floating platform system; the determination of the recovery capability parameter depends on the maintenance phase in step 2.3). The recovery rate of the floating platform system is used as a standard to measure the recovery capability. The recovery rate calculation formula is given as follows: In the above formula, l p represents the system recovery rate of the floating platform, E(t MT ) represents the mean maintenance time, t ft Represents the average downtime after a system failure on a floating platform.

6. The method for assessing resilience of a floating platform against multi-source failures according to claim 1, characterized in that: The toughness grades in step 4) include high toughness, medium-high toughness, medium toughness and low toughness.

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