CN111091292B - Nuclear power station real-time risk dynamic modeling analysis system - Google Patents
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Abstract
The invention discloses a real-time risk dynamic modeling analysis system of a nuclear power station, which comprises four modules: a component/device risk contribution analyzer, a data source analyzer, a real-time risk monitoring modeler, and a real-time risk monitoring model analyzer. The component/device risk contribution analyzer employs a risk contribution ranking method to calculate and rank risk contributions for all components/devices involved in a real-time risk monitoring model of a given system. The data source analyzer classifies and sorts according to device type, time characteristics and period. The real-time risk monitoring modeler combines the risk contribution and the data source to determine a final sequencing result, comprehensively adopts various methods such as common-cause failure model reconstruction, model extension sub-library, multiple boundary condition sets and the like, and rapidly constructs, stores and dynamically updates an actual risk monitoring model according to the final sequencing according to the actual configuration and the component state of the system so as to support the real-time risk monitoring model analyzer to accurately evaluate the real-time risk of the system.
Description
Technical Field
The invention relates to the field of modeling and analysis of real-time risk monitoring of nuclear energy systems, in particular to a dynamic modeling and analysis system of real-time risk of a nuclear power station, which is also suitable for safety analysis of general complex systems.
Background
The nuclear energy system (such as a nuclear power station, an experimental stack and the like) needs to evaluate and analyze the overall risk of the system in real time in the operation and other stages, so that weak links in the system are found in time, possible anomalies or potential accidents of the system are judged, and the system is maintained and improved. Finally, on the premise of controlling the system risk within a certain range, the system task is completed.
Currently, a PRA (Probabilistic Risk Assessment probabilistic risk assessment) technology is generally adopted in a nuclear power plant to evaluate and analyze the reliability and risk of a power plant system, and the most common methods in the PRA technology are fault tree analysis (Fault Tree Analysis, FTA) and event tree analysis (Event Tree Analysis, ETA) methods. Meanwhile, a small event tree-large fault tree method and a Top logic (Top-logic) large fault tree method are generally adopted to integrate fault trees and event trees in a risk evaluation model, and multiple modeling analysis methods and technologies including a common cause failure model, a boundary condition set, a fault tree conversion page and the like are inevitably involved. In the later twentieth century, researchers developed a risk-aware decision (RID, risk Informed Decision) based on PRA, and integrated probabilistic risk assessment with the traditional decision-making safety analysis method, so that the shortcoming that the decision-making method is too dependent on engineering experience judgment and conservation of calculation results is eliminated by using probability theory, and finally the purpose of scientific decision is achieved. After the implementation of risk decision-making is promoted by the American nuclear pipe, the American nuclear power station not only improves the safety obviously, but also improves the economy greatly.
The method and the technology for monitoring the real-time risk of the nuclear power station are important reflecting and supporting tools for knowing risk decisions, and according to the real situation of the operation of the nuclear power station, the developed nuclear power station risk monitor can establish a dynamic real-time risk monitoring model, calculate the real-time risk of the nuclear power station, and rapidly evaluate the daily operation of the nuclear power station, including power operation modes, out-of-service/re-service of components, maintenance activities, periodic tests, operation/standby state switching and the like. And according to the evaluation result, proposing operation management to operators and managers of the nuclear power station. The real-time risk calculated by the risk monitor based on the real-time risk monitoring model can more accurately evaluate the real risk level of the nuclear power plant in a specific configuration state at a certain operation time compared with the average risk and the reference risk calculated by the normal PRA. Because of the effectiveness of the risk monitoring concept on system state evaluation, the risk monitoring concept is widely applied to a plurality of nuclear power countries at home and abroad at present, and even is expanded to the related fields beyond nuclear power.
The following important problems can be found to exist in the current research situation of real-time risk monitoring at home and abroad, but the important problems are not solved: the real-time of the risk monitoring model has become a main trend, and the existing method adopts the uniform frequency update to all the component data of the real-time risk monitoring model at the same time. However, because the real-time performance and the accuracy are mutually restricted, especially, the object of real-time risk monitoring is often a typical large-scale complex system of the nuclear power station, and real-time update of all information in the real-time risk monitoring model of the nuclear power station is difficult to realize, which leads to the inaccurate problem of risk evaluation. The dynamic construction of the model and the data sources updated in real time are various, but the type of information updated by the model is not complete, and the accuracy of the data is not enough. In addition, in the dynamic modeling process of the real-time risk monitoring of the nuclear power plant, the risk contribution of the components to the nuclear power plant is related, and the component importance ranking is performed only according to a single configuration state of the nuclear power plant in the conventional method, for example, the RAW importance representing the degree of increase of the risk of the nuclear power plant after the component is failed is adopted for ranking, or the FV importance representing the degree of importance of the component in the real-time risk monitoring model structure is adopted for ranking, which causes inaccurate ranking under the condition of the configuration state change of the nuclear power plant.
Disclosure of Invention
The invention solves the technical problems: the utility model provides a real-time risk dynamic modeling analysis system of nuclear power station, utilize part/equipment to the pre-order of nuclear energy system (here, "nuclear energy system" includes but is not limited to nuclear power station, nuclear energy heating reactor, reactor for research, and fuel circulation facility that generate electricity) comprehensive risk contribution under multiple nuclear energy system running state to and the difference of multiple data source update time characteristic in the nuclear energy system, carry out the dynamic construction of real-time risk monitoring model according to the actual influence of part/equipment to nuclear energy system risk, have quick, accurate advantage.
The technical scheme of the invention is as follows: a real-time risk dynamic modeling analysis system of a nuclear power plant, as shown in fig. 1, comprises:
a component/device risk contribution analyzer, a data source analyzer, a real-time risk monitoring modeler, and a real-time risk monitoring model analyzer;
component/device risk contribution analyzer: calculating and sequencing risk contributions of all components/devices related to a real-time risk monitoring model of a given nuclear energy system by adopting a risk contribution sequencing method to obtain a size sequencing list of the influence of normal operation or faults of each component/device on the risk level of the whole nuclear energy system; for use by a real-time risk monitoring modeler;
data source analyzer: classifying and sequencing the data sources according to the equipment types, the inherent time characteristics and the period of the data sources in the real-time risk monitoring model for a given nuclear energy system to obtain a size sequencing list of the update time required by each data source; for use by a real-time risk monitoring modeler;
real-time risk monitoring modeler: integrating the fault tree and the event tree in the real-time risk monitoring model based on a small event tree-big fault tree method and a Top logic (Top-logic) big fault tree method; the method comprises the steps of integrating risk contribution sorting and data source sorting results, constructing, storing and dynamically modifying and updating a real-time risk monitoring model by adopting a method comprising common cause failure model reconstruction, recovery analysis, model expansion sub-library, multiple boundary condition sets and multiple fault tree conversion pages, so as to obtain the real-time risk monitoring model of a given nuclear energy system; for use by a real-time risk monitoring model analyzer;
real-time risk monitoring model analyzer: aiming at a real-time risk monitoring model, firstly, carrying out single valued processing on multiple boundary condition sets, and completing the processing of multiple fault tree conversion pages and an expansion model sub-library in a dynamic link mode; performing fault tree simplification, and simplifying the conversion from various complex logic gates to AND gates and OR gates, the deletion and combination of repeated logic gates and basic events and the integration of similar branch structures; and finally, coding and modularization of the fault tree model, calculation of a minimum cut set, calculation of risk indexes including the allowable out-of-service time and the periodic test interval of the component/equipment, calculation of importance and sensitivity of the component/equipment, and calculation of uncertainty distribution of system risk acquisition.
In the component/equipment risk contribution analyzer, the component/equipment risk contribution calculation and sequencing of the nuclear energy system are performed in the following manner:
(1) Firstly, RAW importance, FV importance and RTS importance of all components/devices in a real-time risk monitoring model are calculated, and the calculation method is as follows:
RAW i ＝P (i1) /P (R) ，FV i ＝(P (R) -P (i0) )/P (R) ，RTS i ＝(P (i1) -P (R) )/P (i1) wherein P is (R) Representing the top event failure probability of a top-level logic big fault tree of the real-time risk monitoring model; p (P) (i1) The top event failure probability of the top-level logical large fault tree representing the case where the failure probability of the component i is set to 1, or the top-level logical large fault tree failure probability of all the components that cause the failure of the device i in real time is set to 1; p (P) (i0) The top event failure probability of the top-level logical large fault tree representing the case where the failure probability of the component i is set to 0, or the top-level logical large fault tree failure probability where the failure probabilities of all the components that lead to the failure of the device i in real time are set to 0;
(2) Then, according to the calculation results of RAW importance, FV importance and RTS importance, carrying out risk importance ranking on the components/devices in the real-time risk monitoring model, and marking the components or devices with the importance larger than a critical value as risk important components or devices; wherein the range of the critical value RI of the RAW importance is [0.5,8], the range of the critical value FI of the FV importance is [0.001,0.9], and the range of the critical value RI of the RTS importance is [0.001,0.9];
(3) And finally, calculating according to a calculation formula of the comprehensive importance TI to obtain final risk contribution ranking, wherein the final risk contribution ranking is as follows:
TI i ＝α*FV i +β*RTS i +γ*RAW i wherein alpha, beta and gamma are weight factors, and the value range is [0,1]The method comprises the steps of carrying out a first treatment on the surface of the For an apparatus including a plurality of parts, its RAW importance is the maximum value of the RAW importance of the included parts, its FV importance is the sum of all the parts FV importance, and its RTS importance is the sum of all the parts RTS importance; the components/devices with large overall importance TI value are the components/devices with large risk contribution, and the components/devices are ranked at the front.
The classification and sequencing of the data sources by the data source analyzer are performed in the following manner:
(1) All data sources for a given nuclear power system are classified into 5 classes by their device type, including: operator action and equipment monitoring data, fault diagnosis and reliability correction data, maintenance and test-based equipment information data, modification of nuclear power system design or operating procedures, and other data affecting the risk level of the nuclear power system including environmental weather in which the nuclear power system is located and technical specifications of the nuclear power system;
(2) According to the real-time risk influence of the 5 types of data sources on the nuclear energy system, sequencing the data sources, wherein the specific sequence is as follows: design or operation procedure change of the nuclear energy system, operator action and equipment monitoring data, equipment information data based on maintenance and test, fault diagnosis and reliability correction data, and other data influencing the risk level of the nuclear energy system including environmental weather where the nuclear energy system is located and technical specifications of the nuclear energy system; judging priority according to the ranking of risk contribution in each type of data source;
(3) The data sources with the same risk contribution of the same equipment type are ordered according to the inherent time characteristics and the cycle, and the specific sequence is in the order of seconds, minutes, hours to days and months to years.
The real-time risk monitoring modeler comprehensively considers the risk contribution sorting and the data source sorting results, and the priority is judged in each type of data according to the sorting of the risk contribution; the real-time risk monitoring model is constructed, stored and dynamically modified and updated in the following sequence and mode:
(1) The equipment type is a data source for nuclear energy system design or operation regulation change, the priority of the related model structure and part reliability data is highest in the data sources of all equipment types, and the operation of constructing, storing and dynamically modifying and updating the data is performed preferentially; the equipment information data based on maintenance and test belongs to the risk important parts, and the priority is arranged at the 2 nd position;
(2) The priority of the equipment state and reliability data of the risk important parts is higher than that of the equipment state and reliability data of the non-risk important parts, and in the data source of the same equipment type, the equipment state and reliability data of the risk important parts are preferentially constructed, stored and dynamically modified and updated;
(3) For the modification of the nuclear power system design or operating protocol belonging to the risk critical component, and the operator action and equipment monitoring data belonging to the risk critical component, the update period is 1 to 30 seconds; for fault diagnosis and reliability correction data belonging to the risk important components, the update period is 5 seconds to 1 minute; the update period is 10 seconds to 2 minutes for the modification of the nuclear energy system design or operation procedure of the non-risk important component, the operator action and equipment monitoring data of the non-risk important component, the fault diagnosis and reliability correction data of the non-risk important component, and the maintenance and test-based equipment information data of the non-risk important component; the update period of the other data of the risk important component is 30 seconds to 2 minutes, and the update period of the other data of the non-risk important component is 2 minutes to 5 minutes;
(4) When the real-time risk monitoring model is constructed, stored and dynamically modified and updated, the adopted method comprises one or more methods including a common-cause failure model reconstruction method, a recovery analysis method, an extended model sub-library method, a multiple boundary condition set method and a multiple fault tree conversion page method.
Compared with the prior art, the invention has the advantages that:
(1) Aiming at the problem that in the existing real-time risk monitoring of the nuclear power station, the state information of all system components/equipment of the nuclear power station is difficult to update rapidly, the method arranges the update frequency and sequence of the components/equipment in the model according to the contribution of the components/equipment to the real-time risk of the nuclear power station, overcomes the defect that the existing method updates the data of all components of the real-time risk monitoring model at the same time in a unified frequency, and can effectively improve the modeling and analysis speed of the real-time risk monitoring of the nuclear power station.
(2) The invention comprehensively adopts various risk contribution measurement indexes, particularly comprehensively considers RTS importance indexes, can be suitable for the correct sequencing of the components/equipment for the overall risk contribution of the complex systems such as the nuclear power station and the like in the real-time running state, and solves the problem of inaccuracy of the existing method. In addition, the multiple data sources are classified according to the equipment types, and the risk contribution is combined to order, so that the parts/equipment information with large influence on the nuclear energy system risk in the real-time risk model can be updated preferentially; the same applies to data updates for generally complex systems that are non-nuclear.
(3) According to the invention, various methods such as common-cause failure model reconstruction, model sub-library expansion, multiple boundary condition sets and the like are comprehensively adopted according to the characteristics of the real-time risk monitoring model, so that the real-time risk monitoring model is quickly constructed, stored and dynamically updated according to the actual configuration and the component state of the nuclear energy system, and the scale of the real-time risk monitoring model is limited; to support accurate monitoring of real-time risk of nuclear power systems.
(4) The invention synthesizes a plurality of data sources related to dynamic construction and real-time updating of the real-time risk monitoring model of the nuclear power station, and overall, the model updating information is more complete in type and better in data accuracy; the method is beneficial to higher automation degree of an updating mode, and establishes a foundation for real-time online risk monitoring.
Drawings
FIG. 1 is a flow chart of an implementation of the present invention;
FIG. 2 is a fault tree model of a reactor pressure relief system;
FIG. 3 is a fault tree model of the main water supply system;
FIG. 4 is a fault tree model of the 3 rd AC electronic system in the main water supply system;
FIG. 5 is a general diagram of the main watering system after the pump number 1 fault tree model has been developed.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings and examples.
As shown in fig. 1, the method aims at the problem that the real-time performance and accuracy are mutually restricted in the real-time risk monitoring of complex systems such as a nuclear power station and the like, and aims at rapidly and accurately carrying out dynamic modeling and analysis on a real-time risk monitoring model, firstly, sorting contribution of components or equipment of the nuclear power station to the overall risk in different configuration states, secondly, comprehensively adopting various data sources related to the real-time risk monitoring of the nuclear power station and contribution of the components or equipment to the overall risk of the nuclear power station and different time updating characteristics of the components or equipment according to the real-time risk monitoring of the nuclear power station, and carrying out high-efficiency dynamic construction and modification on the model in various modes such as rapid reconstruction of a common-cause failure group, multi-boundary condition set nested processing, multi-database and the like according to the characteristics of the real-time risk monitoring model, and finally, carrying out analysis on the real-time risk monitoring model. The invention provides a new thought for the development of the risk evaluation of the nuclear power station in China, and promotes the real-time risk monitoring and management work in the actual operation of the nuclear power station. And a certain reference can be provided for a risk evaluation modeling analysis method of the complex system in various working modes.
Example 1: given the real-time risk monitor of a nuclear power plant, which, due to its large model size, typically involves thousands of subsystems/components, one of its key systems is now: a fault tree model of a reactor pressure relief system is taken as an example to illustrate a specific embodiment of the present invention, as shown in fig. 2. FIG. 2 includes 6 automatic relief valves, DPS-VS01-A, DPS-VS02-A, DPS-VS03-A, DPS-VS04-A, DPS-VS05-A, DPS-VS06-A, and one manual relief valve DPS-MAN-H, wherein 6 automatic relief valve components form a redundant system of 6-out-of-2 and are subject to common cause failure.
The dynamic modeling analysis system for real-time risk monitoring provided by the invention carries out the following processing:
(1) And (3) performing risk contribution calculation and sequencing of each component by adopting the following risk contribution sequencing method:
firstly, RAW importance, FV importance and RTS importance of all components/devices in the real-time risk monitoring model are calculated. The reactor pressure relief system involves 7 automatic pressure relief valves, DPS-VS01-A, DPS-VS02-A, DPS-VS03-A, DPS-VS04-A, DPS-VS05-A, DPS-VS06-A, a manual pressure relief valve DPS-MAN-H, and an off-line component DPS-VS07-A (because the configuration state of the component is off-line, the component is not displayed in the real-time risk model, and the original fault tree position is beside DPS-VS 06-A). The redundant system formed by the 6 automatic components involves common cause failures, and is therefore represented by failure conversion pages of the common cause failures.
Taking DPS-MAN-H components as an example, the RAW importance, FV importance and RTS importance calculation method comprises the following steps: raw=p (1) /P (R) ，FV＝(P (R) -P (0) )/P (R) ，RTS＝(P (1) -P (R) )/P (1) Wherein P is (R) Representing the top event failure probability of a top-level logic big fault tree of the real-time risk monitoring model; p (P) (1) Representing the top event failure probability of the top-level logical large failure tree when the failure probability of the component DPS-MAN-H is set to 1 in the failure tree model; p (P) (0) Representing the top event failure probability of the top-level logical large failure tree when the failure probability of the component DPS-MAN-H is set to 0 in the failure tree model. The calculation result of the full model of the real-time risk monitoring according to the nuclear power station is shown in the following table:
(2) Then, according to the calculation results of RAW importance, FV importance and RTS importance, carrying out risk importance ranking on the components/devices in the real-time risk monitoring model, and marking the components or devices with the importance larger than a critical value as risk important components or devices; in this embodiment, the value of the critical value RI of the RAW importance is 8, the value of the critical value FI of the fv importance is 0.1, and the value of the critical value RI of the rts importance is 0.1; since the RAW importance calculation result of the component DPS-MAN-H is 10.5, the DPS-MAN-H is judged to be a risk important component, and the importance of the rest 7 components is smaller than the critical value, thus belonging to a non-risk important component.
(3) Finally, according to the calculation formula of the comprehensive importance TI, calculating and sequencing to obtain final risk contribution priority, the method comprises the following steps:
TI i ＝α*FV i +β*RTS i +γ*RAW i wherein alpha, beta and gamma are weight factors, and the values are 0.4,0.4,0.4 respectively; the present example does not relate to an apparatus including a plurality of components, so the RAW importance, FV importance, RTS importance are the importance of the components themselves, respectively; the components or devices with high risk contribution are ranked at the front of the comprehensive importance TI. The overall importance TI size ordering of the 7 components is DPS-MAN- -H, DPS-VS01-A, DPS-VS02-A, DPS-VS03-A, DPS-VS04-A, DPS-VS05-A, DPS-VS06-A, DPS-VS07-A, i.e., DPS-MAN- -H, with the greatest risk contribution, DPS-VS01-A times, and so on, calculated according to the formulas and the tables above.
(4) The data sources are classified and ranked in the following manner:
all data sources are classified into 5 classes by their device type, including: operator action and equipment monitoring data, fault diagnosis and reliability correction data, equipment information data based on maintenance and testing, nuclear power system design or modification of operating procedures. Wherein DPS-MAN-H belongs to the class of operator action and device monitoring data; assuming DPS-VS01-A is being maintained, the DPS-VS01-A belongs to equipment information data class based on maintenance and test; the failure probability of DPS-VS02-A is changed by the latest operation rule change, and belongs to the change class of the nuclear energy system design or operation rule; if the DPS-VS03-A needs to perform fault diagnosis and reliability correction, the DPS-VS04-A, DPS-VS05-A, DPS-VS06-A belongs to fault diagnosis and reliability correction data types, and the rest DPS-VS04-A, DPS-VS05-A, DPS-VS06-A is monitored, and belongs to operator action and equipment monitoring data types; DPS-VS07-A belongs to other data.
(5) According to the real-time risk influence of the 5 types of data sources on the nuclear energy system, sequencing the data sources, wherein the specific sequence is as follows: design or operating protocol modification of nuclear power systems, operator action and equipment monitoring data, equipment information data based on maintenance and testing, fault diagnosis and reliability correction data, other data; judging priority according to the ranking of risk contribution in each type of data source; thus, the sequencing of the 7 parts is: DPS-VS02-A, DPS-MAN- -H, DPS-VS04-A, DPS-VS05-A, DPS-VS06-A, DPS-VS01-A, DPS-VS03-A, DPS-VS07-A.
(6) The data sources with the same risk contribution of the same equipment type are ordered according to the inherent time characteristics and the cycle, and the specific sequence is the order of seconds, minutes, hours to days and months to years; the risk contributions of the 7 components in this example are different and do not involve the determination of the inherent temporal characteristics.
The dynamic modeling method for real-time risk monitoring is characterized by comprising the following steps of: combining the risk contribution sorting and the data source sorting results, and judging the priority in each type of data according to the sorting of the risk contribution; the real-time risk monitoring model is constructed, stored and dynamically modified and updated in the following sequence and mode:
the equipment type is a data source for nuclear energy system design or operation regulation change, the priority of the related model structure and part reliability data is highest in the data sources of all equipment types, and the operation of constructing, storing and dynamically modifying and updating the data is performed preferentially; the equipment information data based on maintenance and test belongs to the risk important parts, and the priority is arranged at the 2 nd position; therefore, the component DPS-VS02-A has the highest priority. Although the component DPS-VS01-A is being serviced, it does not belong to a risk-critical component, so the ordering is not applicable to this judgment.
The priority of the equipment state and reliability data of the risk important parts is higher than that of the equipment state and reliability data of the non-risk important parts, and in the data source of the same equipment type, the equipment state and reliability data of the risk important parts are preferentially constructed, stored and dynamically modified and updated; thus, DPS-MAN- -H is ranked in preference to DPS-VS04-A, DPS-VS05-A, DPS-VS06-A. Finally, the result of comprehensive judgment and sequencing is: DPS-VS02-A, DPS-MAN- -H, DPS-VS04-A, DPS-VS05-A, DPS-VS06-A, DPS-VS01-A, DPS-VS03-A, DPS-VS07-A.
(7) The data update period of all the components/devices is judged as follows: for the modification of the nuclear power system design or operating protocol belonging to the risk critical component, and the operator action and equipment monitoring data belonging to the risk critical component, the update period is 1 to 30 seconds; for fault diagnosis and reliability correction data belonging to the risk important components, the update period is 5 seconds to 1 minute; the update period is 10 seconds to 2 minutes for the modification of the nuclear energy system design or operation procedure of the non-risk important component, the operator action and equipment monitoring data of the non-risk important component, the fault diagnosis and reliability correction data of the non-risk important component, and the maintenance and test-based equipment information data of the non-risk important component; the update period of the other data of the risk important component is 30 seconds to 2 minutes, and the update period of the other data of the non-risk important component is 2 minutes to 5 minutes; for each type of update period, the lower limit is selected in this example to improve the real-time performance of risk monitoring.
Thus, the update periods for the 7 components are DPS-VS02-A (10 seconds), DPS-MAN- -H (1 second), DPS-VS04-A (10 seconds), DPS-VS05-A (10 seconds), DPS-VS06-A (10 seconds), DPS-VS01-A (10 seconds), DPS-VS03-A (10 seconds), DPS-VS07-A (2 minutes), respectively.
(8) Finally, when the real-time risk monitoring model is constructed, stored and dynamically modified and updated, the adopted method comprises one or more methods including a common cause failure model reconstruction method, a recovery analysis method, an extended model sub-library method, a multiple boundary condition set method and a multiple fault tree conversion page method. The example relates to 2 processing methods of common cause failure model reconstruction and multiple fault tree conversion pages for 6 components such as DPS-VS01-A, DPS-VS02-A, DPS-VS03-A, DPS-VS04-A, DPS-VS05-A, DPS-VS06-A and the like, and an expansion model sub-base corresponding to the 6 components is built; meanwhile, the components DPS-MAN-H, DPS-VS01-A, DPS-VS02-A need to be subjected to recovery analysis, and according to the real-time state of the components, the components are judged when to be listed in the fault tree of the real-time risk monitoring model at a certain moment, for example, the DPS-VS01-A which is being maintained does not need to be listed, the failure probability of the components does not need to be updated, in addition, the components 2 have been actually operated, the DPS-VS02-A also needs to be listed, and the failure probability after the operation procedure is changed needs to be updated in the model.
(9) For the established real-time risk monitoring model, firstly, single valued processing is carried out on multiple boundary condition sets of the whole model, for example: the part DPS-VS01-A being repaired needs to discard its original failure probability and set it to 1 in the fault tree model.
The processing of multiple fault tree conversion pages and an expansion model sub-library is completed in a dynamic link mode, wherein the multiple fault tree conversion pages and the expansion model sub-library comprise common-cause failure fault tree pages corresponding to the valves;
and secondly, fault treelimizing and simplification is carried out, wherein the fault treelimizing and the fault treelimizing are carried out, and the fault treelimizing are used for converting various complex logic gates into AND gates and OR gates, for example, voting gates with 6-th 2 in the example. Pruning and merging duplicate logic gates and basic events, and integration of similar branch structures;
and finally, coding and modularization are carried out on the fault tree model, and qualitative analysis, namely, the minimum cut set is solved, the cut set is processed and quantitatively analyzed, including RAW, FV importance calculation, sensitivity analysis and uncertainty analysis, is completed.
Example 2:
assuming a fault Tree model of the main water supply system (Main feed water system) of a nuclear power plant, as shown in fig. 3, the PUMP No. 1 @ MFW-PUMP-2-Gate, PUMP No. 2 @ MFW-PUMP-3-Gate, PUMP No. 3 @ MFW-PUMP-4-Gate form a redundant configuration of 3, while the isolation valve No. 1 @ MFW-isul-3-Gate, isolation valve No. 2 @ MFW-isul-2-Gate of the main water supply system form a configuration that is redundant with each other, and the ac electronic system No. 3 ACP-3-Tree of the main water supply system (the fault Tree model thereof is shown in fig. 4). The 3 rd way ac electronic system fails if and only if 2 events occur: one is a Loss of offsite power and the other is a power failure (Standby power supply from Gas Turbine fail) provided by the steam turbine.
Assuming that the fault tree model of PUMP No. 1 @ MFW-PUMP-2-Gate in FIG. 3 is developed, the fault tree overall model of the main water supply system after development is shown in FIG. 5, it can be seen that the developed section includes 2 fault conversion pages representing common cause failure of the water supply PUMP components, PUMPs 1MFW-PM01-D-CCFP and 2MFW-PM01-A-CCFP, and a house type event MFW-P1 represents whether PUMP 1 is running; the value of the house type event is 0 or 1, and the importance calculation is not participated. The other PUMPs in FIG. 3 are not developed, and therefore, the embodiment of the present invention is similar to example 1 for PUMP # 1 @ MFW-PUMP-2-Gate, 3 rd AC electronic system ACP-3-Tree, and will not be described in detail herein. The importance calculation result of the full model of the real-time risk monitoring of the nuclear power station is assumed to be shown in the following table:
| part name | RAW importance | Importance of FV | Importance of RTS |
| ACP-GT01-A | 6.3 | 0.005 | NA |
| ACP-GT01-M | 6.3 | 0.005 | NA |
| OFFSITE-POWER | 10.8 | 0.08 | NA |
| MFW-PM01-D-CCFP | 5.7 | 0.004 | NA |
| MFW-PM01-A-CCFP | 5.7 | 0.004 | NA |
In this embodiment, the value of the critical value RI of the RAW importance is 9, the value of the critical value FI of the fv importance is 0.07, and the value of the critical value RI of the rts importance is 0.1; since the RAW importance calculation result of the component offset-POWER is 10.8, it is determined that offset-POWER is a risk important component, and the importance of the remaining components is smaller than the critical value, and thus belongs to a non-risk important component.
The differences are described in detail in the case of PUMP number 2 @ MFW-PUMP-3-Gate, PUMP number 3 @ MFW-PUMP-4-Gate, isolation valve number 1 @ MFW-isul-3-Gate, isolation valve number 2 @ MFW-isul-2-Gate, which belong to a device comprising a plurality of components, and are different from those of embodiment 1, namely, when the final risk contribution priority is obtained by calculating the ranking according to the calculation formula of the overall importance TI, the specific method is as follows:
TI i ＝α*FV i +β*RTS i +γ*RAW i where α, β, and γ are weight factors, and in this embodiment, assume a value of 0.3; for an apparatus including a plurality of parts, its RAW importance is the maximum value of the RAW importance of the included parts, its FV importance is the sum of all the parts FV importance, and its RTS importance is the sum of all the parts RTS importance.
In this embodiment, it is assumed that PUMP No. 2 @ MFW-PUMP-3-Gate contains 2 parts (MFW-PM 02-D-CCFP, MFW-PM 02-A-CCFP), PUMP No. 3 @ MFW-PUMP-4-Gate contains 2 parts (MFW-PM 03-D-CCFP, MFW-PM 03-A-CCFP), isolation valve No. 1 @ MFW-ISOL-3-Gate contains 2 parts (MFW-VCO 1-D, MFW-VCO 1-A), isolation valve No. 2 @ MFW-ISOL-2-Gate contains 2 parts (MFW-VCO 2-D, MFW-VCO 2-A). The importance degree calculation result of the full model of the real-time risk monitoring of the nuclear power station is shown in the following table:
| part name | RAW importance | Importance of FV | Importance of RTS |
| MFW-PM02-D-CCFP | 5.3 | 0.005 | NA |
| MFW-PM02-A-CCFP | 5.4 | 0.006 | NA |
| MFW-PM03-D-CCFP | 5.5 | 0.006 | NA |
| MFW-PM03-A-CCFP | 5.5 | 0.005 | NA |
| MFW-VCO1-D | 4.1 | 0.005 | NA |
| MFW-VCO1-A | 4.2 | 0.004 | NA |
| MFW-VCO2-D | 3.9 | 0.003 | NA |
| MFW-VCO2-A | 3.9 | 0.004 | NA |
Thus, PUMP No. 2 @ MFW-PUMP-3-Gate has a RAW importance of 5.4 and FV importance of 0.011. The No. 3 PUMP @ MFW-PUMP-4-Gate has a RAW importance of 5.5 and a FV importance of 0.011. The isolation valve No. 1 @ MFW-ISOL-3-Gate has a RAW importance of 4.2 and a FV importance of 0.009. The isolation valve No. 2 @ MFW-ISOL-2-Gate has a RAW importance of 3.9 and a FV importance of 0.007.
Other implementation steps comprise the following comprehensive risk contribution sorting and data source sorting results, wherein the priority of the model update is judged according to the sorting of the risk contribution in each type of data; the time period for determining the real-time risk monitoring model construction, storage and dynamic modification update, etc., and the final real-time risk model analysis method are similar to those of embodiment 1, and will not be described in detail.
The invention is not described in detail in part as is known in the art.
While the invention has been described with respect to certain preferred embodiments, it will be apparent to those skilled in the art that various changes and substitutions can be made herein without departing from the scope of the invention as defined by the appended claims.
Claims (3)
1. A real-time risk dynamic modeling analysis system for a nuclear power plant, comprising: a component/device risk contribution analyzer, a data source analyzer, a real-time risk monitoring modeler, and a real-time risk monitoring model analyzer;
component/device risk contribution analyzer: calculating and sequencing risk contributions of all components/devices related to a real-time risk monitoring model of a given nuclear energy system by adopting a risk contribution sequencing method to obtain a size sequencing list of the influence of normal operation or faults of each component/device on the risk level of the whole nuclear energy system; for use by a real-time risk monitoring modeler;
data source analyzer: classifying and sequencing the data sources according to the equipment types, the inherent time characteristics and the period of the data sources in the real-time risk monitoring model for a given nuclear energy system to obtain a size sequencing list of the update time required by each data source; for use by a real-time risk monitoring modeler;
real-time risk monitoring modeler: integrating the fault tree and the event tree in the real-time risk monitoring model based on a small event tree-big fault tree method and a Top logic (Top-logic) big fault tree method; the method comprises the steps of integrating risk contribution sorting and data source sorting results, constructing, storing and dynamically modifying and updating a real-time risk monitoring model by adopting a method comprising common cause failure model reconstruction, recovery analysis, model expansion sub-library, multiple boundary condition sets and multiple fault tree conversion pages, so as to obtain the real-time risk monitoring model of a given nuclear energy system; for use by a real-time risk monitoring model analyzer;
real-time risk monitoring model analyzer: aiming at a real-time risk monitoring model, firstly, carrying out single valued processing on multiple boundary condition sets, and completing the processing of multiple fault tree conversion pages and an expansion model sub-library in a dynamic link mode; the fault tree simplification comprises the conversion from various complex logic gates to AND gates and OR gates, the deletion and combination of repeated logic gates and basic events and the integration of similar branch structures; finally, coding and modularization are carried out on the fault tree model, a minimum cut set is calculated, risk indexes including the allowable out-of-service time and the regular test interval of the component/equipment are calculated, the importance and the sensitivity of the component/equipment are calculated, and uncertainty distribution of system risks is calculated and acquired;
in the component/equipment risk contribution analyzer, the component/equipment risk contribution calculation and sequencing of the nuclear energy system are performed in the following manner:
(1) Firstly, RAW importance, FV importance and RTS importance of all components/devices in a real-time risk monitoring model are calculated, and the calculation method is as follows:
RAW i ＝P (i1) /P (R) ，FV i ＝(P (R) -P (i0) )/P (R) ，RTS i ＝(P (i1) -P (R) )/P (i1) wherein P is (R) Representing the top event failure probability of a top-level logic big fault tree of the real-time risk monitoring model; p (P) (i1) The top event failure probability of the top-level logical large fault tree representing the case where the failure probability of the component i is set to 1, or the top-level logical large fault tree failure probability of all the components that cause the failure of the device i in real time is set to 1; p (P) (i0) The failure probability of the representative component i is set toThe top event failure probability of the top-level logical large fault tree at 0, or the failure probability of the top-level logical large fault tree when the failure probability of all components that lead to the failure of the device i in real time is set to 0;
(2) Then, according to the calculation results of RAW importance, FV importance and RTS importance, carrying out risk importance ranking on the components/devices in the real-time risk monitoring model, and marking the components or devices with the importance larger than a critical value as risk important components or devices; wherein the range of the critical value RI of the RAW importance is [0.5,8], the range of the critical value FI of the FV importance is [0.001,0.9], and the range of the critical value RI of the RTS importance is [0.001,0.9];
(3) And finally, calculating according to a calculation formula of the comprehensive importance TI to obtain final risk contribution ranking, wherein the final risk contribution ranking is as follows:
TI i ＝α*FV i +β*RTS i +γ*RAW i wherein alpha, beta and gamma are weight factors, and the value range is [0,1]The method comprises the steps of carrying out a first treatment on the surface of the For an apparatus including a plurality of parts, its RAW importance is the maximum value of the RAW importance of the included parts, its FV importance is the sum of all the parts FV importance, and its RTS importance is the sum of all the parts RTS importance; the components/devices with large overall importance TI value are the components/devices with large risk contribution, and the components/devices are ranked at the front.
2. The system for dynamic modeling analysis of real-time risk of nuclear power plant according to claim 1, wherein: the classification and sequencing of the data sources by the data source analyzer are performed in the following manner:
(1) All data sources for a given nuclear power system are classified into 5 classes by their device type, including: operator action and equipment monitoring data, fault diagnosis and reliability correction data, maintenance and test-based equipment information data, modification of nuclear power system design or operating procedures, and other data affecting the risk level of the nuclear power system including environmental weather in which the nuclear power system is located and technical specifications of the nuclear power system;
(2) According to the real-time risk influence of the 5 types of data sources on the nuclear energy system, sequencing the data sources, wherein the specific sequence is as follows: design or operation procedure change of the nuclear energy system, operator action and equipment monitoring data, equipment information data based on maintenance and test, fault diagnosis and reliability correction data, and other data influencing the risk level of the nuclear energy system including environmental weather where the nuclear energy system is located and technical specifications of the nuclear energy system; judging priority according to the ranking of risk contribution in each type of data source;
(3) The data sources with the same risk contribution of the same equipment type are ordered according to the inherent time characteristics and the cycle, and the specific sequence is in the order of seconds, minutes, hours to days and months to years.
3. The system for dynamic modeling analysis of real-time risk of nuclear power plant according to claim 1, wherein: the real-time risk monitoring modeler comprehensively considers the risk contribution sorting and the data source sorting results, and the priority is judged in each type of data according to the sorting of the risk contribution; the real-time risk monitoring model is constructed, stored and dynamically modified and updated in the following sequence and mode:
(1) The equipment type is a data source for nuclear energy system design or operation regulation change, the priority of the related model structure and part reliability data is highest in the data sources of all equipment types, and the operation of constructing, storing and dynamically modifying and updating the data is performed preferentially; the equipment information data based on maintenance and test belongs to the risk important parts, and the priority is arranged at the 2 nd position;
(2) The priority of the equipment state and reliability data of the risk important parts is higher than that of the equipment state and reliability data of the non-risk important parts, and in the data source of the same equipment type, the equipment state and reliability data of the risk important parts are preferentially constructed, stored and dynamically modified and updated;
(3) For the modification of the nuclear power system design or operating protocol belonging to the risk critical component, and the operator action and equipment monitoring data belonging to the risk critical component, the update period is 1 to 30 seconds; for fault diagnosis and reliability correction data belonging to the risk important components, the update period is 5 seconds to 1 minute; the update period is 10 seconds to 2 minutes for the modification of the nuclear energy system design or operation procedure of the non-risk important component, the operator action and equipment monitoring data of the non-risk important component, the fault diagnosis and reliability correction data of the non-risk important component, and the maintenance and test-based equipment information data of the non-risk important component; the update period of the other data of the risk important component is 30 seconds to 2 minutes, and the update period of the other data of the non-risk important component is 2 minutes to 5 minutes;
(4) When the real-time risk monitoring model is constructed, stored and dynamically modified and updated, the adopted method comprises one or more methods including a common-cause failure model reconstruction method, a recovery analysis method, an extended model sub-library method, a multiple boundary condition set method and a multiple fault tree conversion page method.
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