CN111091292A - Real-time risk dynamic modeling analysis system for nuclear power station - Google Patents
Real-time risk dynamic modeling analysis system for nuclear power station Download PDFInfo
- Publication number
- CN111091292A CN111091292A CN201911309656.0A CN201911309656A CN111091292A CN 111091292 A CN111091292 A CN 111091292A CN 201911309656 A CN201911309656 A CN 201911309656A CN 111091292 A CN111091292 A CN 111091292A
- Authority
- CN
- China
- Prior art keywords
- risk
- real
- equipment
- time
- importance
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000004458 analytical method Methods 0.000 title claims abstract description 23
- 238000012544 monitoring process Methods 0.000 claims abstract description 100
- 238000000034 method Methods 0.000 claims abstract description 44
- 238000012163 sequencing technique Methods 0.000 claims abstract description 21
- 238000004364 calculation method Methods 0.000 claims description 18
- 238000006243 chemical reaction Methods 0.000 claims description 16
- 238000013461 design Methods 0.000 claims description 16
- 238000012360 testing method Methods 0.000 claims description 16
- 230000009471 action Effects 0.000 claims description 14
- 238000012937 correction Methods 0.000 claims description 14
- 238000003745 diagnosis Methods 0.000 claims description 14
- 238000012423 maintenance Methods 0.000 claims description 13
- 230000008859 change Effects 0.000 claims description 11
- 238000012545 processing Methods 0.000 claims description 9
- 238000012986 modification Methods 0.000 claims description 6
- 230000004048 modification Effects 0.000 claims description 6
- 238000011084 recovery Methods 0.000 claims description 5
- 230000007613 environmental effect Effects 0.000 claims description 4
- 230000010354 integration Effects 0.000 claims description 3
- 238000012217 deletion Methods 0.000 claims description 2
- 230000037430 deletion Effects 0.000 claims description 2
- 230000035945 sensitivity Effects 0.000 claims description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 10
- 238000011156 evaluation Methods 0.000 description 7
- 238000002955 isolation Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 4
- 238000010276 construction Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 238000003672 processing method Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000013210 evaluation model Methods 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000011017 operating method Methods 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 238000012805 post-processing Methods 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000013138 pruning Methods 0.000 description 1
- 238000004451 qualitative analysis Methods 0.000 description 1
- 238000004445 quantitative analysis Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012502 risk assessment Methods 0.000 description 1
- 238000010206 sensitivity analysis Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000002123 temporal effect Effects 0.000 description 1
- 238000013076 uncertainty analysis Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/067—Enterprise or organisation modelling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Quality & Reliability (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Educational Administration (AREA)
- Health & Medical Sciences (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Monitoring And Testing Of Nuclear Reactors (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The invention discloses a real-time risk dynamic modeling analysis system for a nuclear power station, which comprises four modules: a component/equipment risk contribution analyzer, a data source analyzer, a real-time risk monitoring modeler, and a real-time risk monitoring model analyzer. The component/equipment risk contribution analyzer calculates and orders the risk contribution of all components/equipment involved in the real-time risk monitoring model of the given system by adopting a risk contribution ordering method. The data source analyzer performs classification and sorting according to the device type, time characteristics and period. The real-time risk monitoring modeler determines a final sequencing result by combining risk contribution and a data source, comprehensively adopts various methods such as common-cause failure model reconstruction, model sublibrary expansion, multiple boundary condition sets and the like, and quickly constructs, stores and dynamically updates a real-time risk monitoring model according to the final sequencing according to the actual configuration and component state of the system so as to support a 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 analyzing of real-time risk monitoring of a nuclear power system, in particular to a real-time risk dynamic modeling analysis system of a nuclear power station, which is also suitable for safety analysis of a general complex system.
Background
The nuclear energy system (such as a nuclear power station, an experimental reactor and the like) needs to evaluate and analyze the overall risk of the system in real time in the operation and other stages, so as to find weak links in the system in time and judge possible abnormity or potential accidents of the system, and further maintain and improve the system. And finally, completing system tasks on the premise of controlling the system risk within a certain range.
At present, the reliability and Risk of a power plant system are generally evaluated and analyzed by using a PRA (Probabilistic Risk Assessment) technology in a nuclear power plant, and the most commonly used methods in the PRA technology are a Fault Tree Analysis (FTA) and an Event Tree Analysis (ETA) method. Meanwhile, a method based on a small event tree-large fault tree and a method based on a Top-level logic (Top-logic) large fault tree are generally adopted to integrate the fault tree and the event tree in the risk evaluation model, which inevitably involves various modeling analysis methods and technologies including a common cause failure model, a boundary condition set, a fault tree conversion page and the like. In the late twentieth century, researchers developed a known Risk Decision (RID) based on PRA, and synthesized probabilistic Risk evaluation with the conventional deterministic theory security analysis method, so that the disadvantage of relying too much on engineering experience judgment and calculation result conservation in the deterministic theory method is eliminated by using the probabilistic theory, and the purpose of scientific Decision is finally achieved. Since the U.S. nuclear power plant will promote risk decision making, the U.S. nuclear power plant not only significantly improves safety, but also greatly improves economy.
The real-time risk monitoring method and the technology of the nuclear power station are important embodying and supporting tools for knowing risk decision, a dynamic real-time risk monitoring model can be established according to the real condition of the operation of the nuclear power station by the developed risk monitor of the nuclear power station, the real-time risk of the nuclear power station is calculated, and the daily operation of the nuclear power station is rapidly evaluated, wherein the evaluation comprises a power operation mode, component ex-service/re-service, maintenance activities, periodic tests, operation/standby train state switching and the like. And according to the evaluation result, operation management suggestions are provided for nuclear power plant operators and managers. Compared with the average risk and the benchmark risk calculated by the common PRA, 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 under a specific configuration state at a certain operation moment. Due to the effectiveness of the risk monitoring concept on system state evaluation, the risk monitoring concept is widely applied to many nuclear power countries at home and abroad at present, and even is expanded to related fields beyond nuclear power.
In view of the current situation of real-time risk monitoring at home and abroad, the following important problems can be found and are not solved effectively: the real-time risk monitoring model is the main trend, and the existing method adopts the unified frequency updating of all 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, objects of real-time risk monitoring are often large complex systems such as nuclear power plants, and it is difficult to update all information in a real-time risk monitoring model of the nuclear power plant in real time, which leads to the inaccuracy of risk evaluation. In addition, although the data sources for dynamic building and real-time updating of the model are various, the information type of model updating is not complete and the data accuracy is not sufficient in general. In addition, in the dynamic modeling process of real-time risk monitoring of the nuclear power plant, the risk contribution of components to the nuclear power plant is related, and the existing method only performs component importance ranking according to a single nuclear power plant configuration state, for example, ranking is performed by using RAW importance representing the increase degree of the risk of the nuclear power plant after the components fail, or ranking is performed by using FV importance representing the importance degree of the components in a real-time risk monitoring model structure, which causes inaccurate ranking under the condition that the nuclear power plant configuration state changes.
Disclosure of Invention
The invention solves the problems: the system has the advantages of being fast and accurate, and carrying out dynamic construction of a real-time risk monitoring model according to actual influence of components/equipment on the risk of the nuclear energy system by utilizing the pre-sequencing of comprehensive risk contribution of the components/equipment to the nuclear energy system (the 'nuclear energy system' comprises but is not limited to a nuclear power station for power generation, a nuclear energy heat supply reactor, a reactor for research and a fuel circulation facility) under various nuclear energy system operation states and the difference of updating time characteristics of various data sources in the nuclear energy system.
The technical scheme of the invention is as follows: a real-time risk dynamic modeling analysis system for a nuclear power plant, as shown in fig. 1, includes:
a component/equipment risk contribution analyzer, a data source analyzer, a real-time risk monitoring modeler and a real-time risk monitoring model analyzer;
component/equipment risk contribution analyzer: calculating and sorting risk contributions of all components/equipment related to a real-time risk monitoring model of a given nuclear energy system by adopting a risk contribution sorting method to obtain a sorted list of the influence of normal operation or fault of each component/equipment on the risk level of the whole nuclear energy system; for use by a real-time risk monitoring modeler;
a data source analyzer: classifying and sorting the data sources of a given nuclear energy system according to the equipment types, inherent time characteristics and periods of the data sources related to the real-time risk monitoring model to obtain a sorted list of the updating 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-large fault tree method and a Top-level logic (Top-logic) large fault tree method; integrating risk contribution sequencing and data source sequencing results, and 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 sublibrary expansion, multiple boundary condition sets and multiple fault tree conversion pages to obtain the real-time risk monitoring model of the 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, single value-taking processing is carried out on multiple boundary condition sets, and processing of multiple fault tree conversion pages and an expansion model sub-library is completed in a dynamic link mode; then, fault tree simplification is carried out, wherein the 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; and finally, coding and modularizing the fault tree model, calculating a minimum cut set, calculating a risk index including the allowable outage time of the component/equipment and a regular test interval, calculating the importance and sensitivity of the component/equipment, and calculating the uncertainty distribution of the acquired system risk.
In the component/equipment risk contribution analyzer, the risk contribution of the components/equipment to the nuclear energy system is calculated and sequenced in the following way:
(1) firstly, calculating RAW importance, FV importance and RTS importance of all components/devices in a real-time risk monitoring model, wherein the calculation method comprises the following steps:
RAWi＝P(i1)/P(R)，FVi＝(P(R)-P(i0))/P(R)，RTSi＝(P(i1)-P(R))/P(i1)in which P is(R)Representing the top event failure probability of a top-level logic large fault tree of the real-time risk monitoring model; p(i1)Representing the probability of failure of the top event of the top-level logically large fault tree when the probability of failure of component i is set to 1, or the loss of all components causing the failure of device i in real timeThe failure probability of the top-level logic large fault tree when the effectiveness probabilities are all set to 1; p(i0)Representing the top event failure probability of the top-level logic large fault tree when the failure probability of the component i is set to 0, or the top-level logic large fault tree failure probability when the failure probabilities of all components causing the failure of the device i in real time are set to 0;
(2) then, according to the calculation results of the RAW importance, the FV importance and the RTS importance, sorting the risk importance of the components/equipment in the real-time risk monitoring model, and marking the components or equipment with the importance greater than a critical value as important risk components or equipment; wherein, the value range of the critical value RI of the RAW importance is [0.5,8], the value range of the critical value FI of the FV importance is [0.001,0.9], and the value 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 a final risk contribution ranking, which is as follows:
TIi＝α*FVi+β*RTSi+γ*RAWiwherein α, β and gamma are weight factors, and the value range is [0, 1%](ii) a Aiming at equipment comprising a plurality of parts, the RAW importance of the equipment is the maximum value of the RAW importance of the contained parts, the FV importance of the equipment is the sum of the FV importance of all the parts, and the RTS importance of the equipment is the sum of the RTS importance of all the parts; the components/devices with large values of the overall importance TI are the components/devices with large risk contributions, and are ranked in the top.
The data source analyzer classifies and sorts the data sources in the following way:
(1) all data sources of a given nuclear power system are classified into 5 classes according to their device types, including: the system comprises operator action and equipment monitoring data, fault diagnosis and reliability correction data, equipment information data based on maintenance and test, nuclear energy system design or operation regulation change, and other data which influence the risk level of the nuclear energy system, including environmental weather where the nuclear energy system is located and the technical specification of the nuclear energy system;
(2) according to the magnitude of the real-time risk influence of the 5 types of data sources on the nuclear energy system, the data sources are sequenced, and the specific sequence is as follows: design or operation regulation 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 which affect the risk level of the nuclear energy system, including environmental weather where the nuclear energy system is located and the technical specification of the nuclear energy system; judging the priority in each type of data source according to the ranking of risk contributions;
(3) data sources with the same risk contribution of the same equipment type are sorted according to inherent time characteristics and periods, 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 risk contribution sequencing and data source sequencing results, and the priority is judged in each type of data according to the risk contribution sequencing; 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 the reliability data of the components is the highest among the data sources of all the equipment types, and the operation of constructing, storing and dynamically modifying and updating is preferentially carried out on the data sources; the equipment information data which belongs to the important risk components and is based on maintenance and test is prioritized to be the 2 nd bit;
(2) the priority of the equipment state and the reliability data of the risk important component is higher than that of the equipment state and the reliability data of the non-risk important component;
(3) for the modification of the nuclear energy system design or operation rules belonging to the risk important parts, and the operator action and equipment monitoring data belonging to the risk important parts, the updating period is 1-30 seconds; for fault diagnosis and reliability correction data belonging to risk important components, the updating period is 5 seconds to 1 minute; for the change of the nuclear energy system design or operation rules of the non-risk important components, the operator action and equipment monitoring data of the non-risk important components, the fault diagnosis and reliability correction data of the non-risk important components and the equipment information data based on the maintenance and test of the non-risk important components, the updating period is 10 seconds to 2 minutes; the update period of other data of the risk important part is 30 seconds to 2 minutes, and the update period of other data of the non-risk important part 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, a model subbase expansion method, a multiple boundary condition set method and a multiple fault tree page conversion method.
Compared with the prior art, the invention has the advantages that:
(1) aiming at the problem that the state information of all system components/equipment of the nuclear power station is difficult to update simultaneously and rapidly in the existing nuclear power station real-time risk monitoring, the updating frequency and the updating sequence of the components/equipment in the model are arranged according to the contribution of the components/equipment to the nuclear power station real-time risk, the defect that the unified frequency updating is carried out on all component data of a real-time risk monitoring model at the same time in the existing method is overcome, and the modeling and analyzing speed of the nuclear power station real-time risk monitoring can be effectively improved.
(2) The method comprehensively adopts multiple risk contribution measurement indexes, particularly comprehensively considers RTS importance indexes, can be suitable for correctly sequencing the total risk contribution of components/equipment of complex systems such as nuclear power stations and the like in a real-time running state, and solves the problem of inaccuracy of the existing method. In addition, various data sources are classified according to the equipment types, and are sorted by combining risk contributions, so that the priority updating of component/equipment information which has large influence on the nuclear energy system risk in the real-time risk model is facilitated; the idea is also applicable to data updating of general complex systems without nuclear energy.
(3) According to the real-time risk monitoring model, various methods such as common-cause failure model reconstruction, model sublibrary 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 a nuclear energy system, and the scale of the real-time risk monitoring model is limited; to support accurate monitoring of real-time risks of nuclear energy systems.
(4) The invention integrates various data sources related to the dynamic construction and real-time update of the real-time risk monitoring model of the nuclear power station, and generally, the information type of model update is more complete, and the data accuracy is better; the method is beneficial to higher automation degree of the 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 a principal water system;
FIG. 4 is a fault tree model of the 3 rd AC electrical system in the primary water system;
FIG. 5 is a general diagram of the principal water system after the development of the tree model for Pump failure number 1.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and examples.
As shown in FIG. 1, aiming at the problem that real-time performance and accuracy are mutually restricted in real-time risk monitoring of complex systems such as a nuclear power station and the like, the dynamic modeling and analysis of a real-time risk monitoring model are planned to be rapidly and accurately carried out, firstly, the contribution of components or equipment of the nuclear power station to the overall risk is sequenced in different configuration states, secondly, the model is efficiently and dynamically constructed and modified in various modes such as rapid reconstruction of a common-factor failure group, nested processing of multiple boundary condition sets, multiple data sub-libraries and the like according to various data sources related to the real-time risk monitoring of the nuclear power station and the contribution of the various data sources to the overall risk of the nuclear power station and different time updating characteristics of the various data sources, the rapid reconstruction of the common-factor failure group, the nested processing of multiple boundary condition sets, the. The invention provides a new idea for the development of 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. Certain reference can be provided for a risk evaluation modeling analysis method of a complex system under multiple working modes.
Example 1: given that there is a real-time risk monitor for a nuclear power plant, which, due to its large model size, typically involves thousands of subsystems/components, now one of its key systems: a fault tree model of a reactor pressure relief system is an example to illustrate an embodiment of the present invention, as shown in fig. 2. FIG. 2 includes 6 automatic pressure relief valves, DPS-VS01-A, DPS-VS02-A, DPS-VS03-A, DPS-VS04-A, DPS-VS05-A, DPS-VS06-A, and a manual pressure relief valve DPS-MAN-H, wherein the 6 automatic pressure relief valve components form a redundant system of 6 to 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) calculating and sequencing the risk contribution of each part by adopting the following risk contribution sequencing method:
firstly, RAW importance, FV importance and RTS importance of all components/devices in a real-time risk monitoring model are calculated. The reactor pressure relief system relates to 7 automatic pressure relief valves, namely 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 offline component DPS-VS07-A (because the configuration state of the component is offline, the component is not displayed in a real-time risk model and the original fault tree position is beside DPS-VS 06-A). The redundant system formed by 6 automatic components involves common cause failures, and therefore, fault conversion pages of the common cause failures are respectively adopted for representation.
Taking DPS-MAN-H part as an example, the calculation methods of RAW importance, FV importance and RTS importance are as follows: RAW ═ P(1)/P(R)，FV＝(P(R)-P(0))/P(R)，RTS＝(P(1)-P(R))/P(1)In which P is(R)Representing the top event failure probability of a top-level logic large fault tree of the real-time risk monitoring model; p(1)Representing the loss of the part DPS-MAN-H in the fault tree modelA top event failure probability of the top level logical large fault tree when the probability of effectiveness is set to 1; p(0)Representing the probability of failure of the top event of the top level logically large fault tree in the fault tree model with the probability of failure of the component DPS-MAN-H set to 0. The calculation results according to the nuclear power plant real-time risk monitoring full model are assumed to be shown in the following table:
(2) then, according to the calculation results of the RAW importance, the FV importance and the RTS importance, sorting the risk importance of the components/equipment in the real-time risk monitoring model, and marking the components or equipment with the importance greater than a critical value as important risk components or equipment; 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; as the RAW importance calculation result of the part DPS-MAN-H is 10.5, the part DPS-MAN-H is judged to be a risk important part, and the importance of the rest 7 parts is less than the critical value, thus belonging to a non-risk important part.
(3) And finally, calculating and sequencing according to a calculation formula of the comprehensive importance TI to obtain a final risk contribution priority, which is as follows:
TIi＝α*FVi+β*RTSi+γ*RAWithe total importance TI of the 7 parts is calculated according to a formula and the table, the total importance TI of the 7 parts is ranked in the order of DPS-MAN- -H, DPS-VS01-A, DPS-VS02-A, DPS-VS03-A, DPS-VS04-A, DPS-VS 05-VS 06-A, DPS-VS07-A, namely the risk contribution of DPS-MAN- -H is the largest, DPS-VS01-A is the same, and the rest is analogized.
(4) Data sources are classified and ordered as follows:
all data sources are classified into 5 types according to the device types, 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. Wherein DPS-MAN- -H belongs to the class of operator action and equipment monitoring data; DPS-VS01-A is assumed to be servicing, and belongs to the equipment information data class based on servicing and testing; the failure probability of the DPS-VS02-A is changed by the change of the recent operation rule, and belongs to the modification class of the nuclear energy system design or the operation rule; DPS-VS03-A needs to carry out fault diagnosis and reliability correction, and belongs to the fault diagnosis and reliability correction data class, and the rest DPS-VS04-A, DPS-VS05-A, DPS-VS06-A are monitored and belong to the operator action and equipment monitoring data class; DPS-VS07-A belongs to other data.
(5) According to the magnitude of the real-time risk influence of the 5 types of data sources on the nuclear energy system, the data sources are sequenced, and the specific sequence is as follows: design or operating protocol changes for nuclear power systems, operator actions and equipment monitoring data, maintenance and test-based equipment information data, fault diagnosis and reliability correction data, and other data; judging the priority in each type of data source according to the ranking of risk contributions; thus, the precedence ordering of the 7 components is: DPS-VS02-A, DPS-MAN- -H, DPS-VS04-A, DPS-VS05-A, DPS-VS06-A, DPS-VS01-A, DPS-VS03-A, DPS-VS 07-A.
(6) Data sources with the same risk contribution of the same equipment type are sorted according to inherent time characteristics and periods, and the specific sequence is in the order of seconds, minutes, hours to days and months to years; the risk contribution of the 7 components of this example is different and does not involve the determination of the intrinsic temporal characteristics.
The dynamic modeling method for real-time risk monitoring is characterized by comprising the following steps: integrating the risk contribution ordering and data source ordering results, and judging the priority in each type of data according to the ordering of the risk contributions; 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 the reliability data of the components is the highest among the data sources of all the equipment types, and the operation of constructing, storing and dynamically modifying and updating is preferentially carried out on the data sources; the equipment information data which belongs to the important risk components and is based on maintenance and test is prioritized to be the 2 nd bit; thus, component DPS-VS02-A has the highest priority. Although the component DPS-VS01-A is being serviced, it is not a risk critical component and therefore the ranking is not applicable to this determination.
The priority of the equipment state and the reliability data of the risk important component is higher than that of the equipment state and the reliability data of the non-risk important component; thus, DPS-MAN- -H is ranked in preference to DPS-VS04-A, DPS-VS05-A, DPS-VS 06-A. Finally, the result of comprehensive judgment sequencing is as follows: DPS-VS02-A, DPS-MAN- -H, DPS-VS04-A, DPS-VS05-A, DPS-VS06-A, DPS-VS01-A, DPS-VS03-A, DPS-VS 07-A.
(7) The data update cycle of all components/devices is judged as follows: for the modification of the nuclear energy system design or operation rules belonging to the risk important parts, and the operator action and equipment monitoring data belonging to the risk important parts, the updating period is 1-30 seconds; for fault diagnosis and reliability correction data belonging to risk important components, the updating period is 5 seconds to 1 minute; for the change of the nuclear energy system design or operation rules of the non-risk important components, the operator action and equipment monitoring data of the non-risk important components, the fault diagnosis and reliability correction data of the non-risk important components and the equipment information data based on the maintenance and test of the non-risk important components, the updating period is 10 seconds to 2 minutes; the update period of other data of the risk important part is 30 seconds to 2 minutes, and the update period of other data of the non-risk important part is 2 minutes to 5 minutes; for each type of update period, the lower limit is chosen in this example to improve the real-time performance of risk monitoring.
Therefore, the update periods corresponding to 7 parts 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) and DPS-VS07-A (2 minutes), respectively.
(8) And 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, a model sub-base expansion method, a multiple boundary condition set method and a multiple fault tree page conversion method. The example relates to the steps of carrying out common cause failure model reconstruction and 2 processing methods of multiple fault tree conversion pages on 6 components such as DPS-VS01-A, DPS-VS02-A, DPS-VS03-A, DPS-VS04-A, DPS-VS05-A and DPS-VS06-A, and establishing an extension model sub-library corresponding to the 6 components; meanwhile, the components DPS-MAN-H, DPS-VS01-A, DPS-VS02-A need to be recovered and analyzed, and the time when the components need to be listed in a fault tree of the real-time risk monitoring model at a certain moment is judged according to the real-time state of the components, for example, the DPS-VS01-A which is being maintained does not need to be listed, the failure probability does not need to be updated, the other 2 components need to be listed after being actually operated, and the DPS-VS02-A needs to update the failure probability after the operation rule is changed into the model.
(9) Aiming at the established real-time risk monitoring model, firstly, single value-taking processing is carried out on a multiple boundary condition set of the integral model, for example: the servicing component DPS-VS01-A, which needs to discard its original failure probability, is set to 1 in the fault tree model.
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 comprise common cause failure fault tree pages corresponding to the valves;
and secondly, fault tree simplification is carried out, and conversion from various complex logic gates to AND gates and OR gates is included, for example, a voting gate of which the number is 6 and the number is 2 in the example. Pruning and merging duplicate logic gates and base events, and integration of similar branch structures;
and finally, coding and modularizing the fault tree model, and completing qualitative analysis, namely solving a minimum cut set, post-processing the cut set and quantitative analysis, including RAW and FV importance calculation, sensitivity analysis and uncertainty analysis.
Example 2:
assume that there is a fault Tree model of the Main water supply system (Main feed water system) of the nuclear power plant, as shown in fig. 3, wherein the Main water supply system number 1 PUMP @ MFW-PUMP-2-Gate, number 2 PUMP @ MFW-PUMP-3-Gate, number 3 PUMP @ MFW-PUMP-4-Gate constitute a redundant configuration of 3 taking 2, and the Main water supply system number 1 isolation valve @ MFW-ISOL-3-Gate, number 2 isolation valve @ MFW-ISOL-2-Gate form a redundant configuration each other, and the Main water supply system number 3 ac electronic system ACP-3-Tree (the fault Tree model of which is shown in fig. 4). The 3 rd ac subsystem fails if and only if 2 events occur: one is Loss of off-site power, and the other is Loss of power supplied by the steam Turbine (Standby power supply from Gas Turbine fail).
Assuming that the fault tree model of PUMP # 1 @ MFW-PUMP-2-Gate in FIG. 3 is developed, the general fault tree model of the main water supply system after being developed is shown in FIG. 5, it can be seen that the developed part includes 2 fault conversion pages representing common cause failure of water supply PUMP components, a PUMP 1MFW-PM01-D-CCFP and a PUMP 2MFW-PM01-A-CCFP, and a house type event MFW-P1 represents whether the PUMP 1 is in operation or not; the value of the house type event is 0 or 1, and the house type event does not participate in the importance calculation. The other PUMPs in fig. 3 are not expanded, so the specific implementation of the present invention is similar to that of example 1 for PUMP No. 1 @ MFW-PUMP-2-Gate and channel 3 ac electronic system ACP-3-Tree, and will not be described in detail here. The calculation results of the importance of the nuclear power plant real-time risk monitoring full model are assumed to be shown in the following table:
| component partName (R) | RAW importance | FV importance | 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 calculation result of the RAW importance of the offset-POWER component is 10.8, the offset-POWER component is judged to be a risk important component, and the importance of the rest components is less than the critical value, so that the offset-POWER component belongs to a non-risk important component.
The processing method of the device is different from that of embodiment 1, and the difference is explained in detail here, namely when the final risk contribution priority is obtained by calculating and sorting according to the calculation formula of the comprehensive importance degree TI, the specific method is as follows:
TIi＝α*FVi+β*RTSi+γ*RAWiwherein α, β and gamma are weighting factors, and the value is assumed to be 0.3 in the embodiment, for a device comprising a plurality of components, the RAW importance of the device is the maximum value of the RAW importance of the included components, the FV importance of the device is the sum of the FV importance of all the components, and the RTS importance of the device is the sum of the RTS importance of all the components.
In this embodiment, it is assumed that the No. 2 PUMP @ MFW-PUMP-3-Gate comprises 2 components (MFW-PM02-D-CCFP, MFW-PM02-A-CCFP), the No. 3 PUMP @ MFW-PUMP-4-Gate comprises 2 components (MFW-PM03-D-CCFP, MFW-PM03-A-CCFP), the No. 1 isolation valve @ MFW-ISOL-3-Gate comprises 2 components (MFW-VCO1-D, MFW-VCO1-A), and the No. 2 isolation valve @ MFW-ISOL-2-Gate comprises 2 components (MFW-VCO2-D, MFW-VCO 2-A). The calculation result according to the importance of the nuclear power station real-time risk monitoring full model is shown in the following table:
| name of component | RAW importance | FV importance | 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, the RAW importance of Pump # 2 @ MFW-PUMP-3-Gate is 5.4 and the FV importance is 0.011. The RAW importance of the PUMP # 3 @ MFW-PUMP-4-Gate is 5.5, and the FV importance is 0.011. The RAW importance of the No. 1 isolation valve @ MFW-ISOL-3-Gate is 4.2, and the FV importance is 0.009. The RAW importance of the No. 2 isolation valve @ MFW-ISOL-2-Gate is 3.9, and the FV importance is 0.007.
Other implementation steps comprise subsequent comprehensive risk contribution sequencing and data source sequencing results, and the priority of each type of data during updating is judged according to the sequencing of the risk contributions; determining time periods for real-time risk monitoring model construction, storage, dynamic modification and updating, and the like, and the final real-time risk model analysis method are similar to those of embodiment 1, and are not described in detail herein.
The present invention has not been described in detail so as not to obscure the present invention.
The above description is only a part of the embodiments of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (4)
1. A real-time risk dynamic modeling analysis system for a nuclear power plant is characterized by comprising: a component/equipment risk contribution analyzer, a data source analyzer, a real-time risk monitoring modeler and a real-time risk monitoring model analyzer;
component/equipment risk contribution analyzer: calculating and sorting risk contributions of all components/equipment related to a real-time risk monitoring model of a given nuclear energy system by adopting a risk contribution sorting method to obtain a sorted list of the influence of normal operation or fault of each component/equipment on the risk level of the whole nuclear energy system; for use by a real-time risk monitoring modeler;
a data source analyzer: classifying and sorting the data sources of a given nuclear energy system according to the equipment types, inherent time characteristics and periods of the data sources related to the real-time risk monitoring model to obtain a sorted list of the updating 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-large fault tree method and a Top-level logic (Top-logic) large fault tree method; integrating risk contribution sequencing and data source sequencing results, and 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 sublibrary expansion, multiple boundary condition sets and multiple fault tree conversion pages to obtain the real-time risk monitoring model of the 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, single value-taking processing is carried out on multiple boundary condition sets, and processing of multiple fault tree conversion pages and an expansion model sub-library is completed in a dynamic link mode; then, fault tree simplification is carried out, wherein the 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; and finally, coding and modularizing the fault tree model, calculating a minimum cut set, calculating a risk index including the allowable outage time of the component/equipment and a regular test interval, calculating the importance and sensitivity of the component/equipment, and calculating the uncertainty distribution of the acquired system risk.
2. The nuclear power plant real-time risk dynamic modeling analysis system of claim 1, wherein: in the component/equipment risk contribution analyzer, the risk contribution of the components/equipment to the nuclear energy system is calculated and sequenced in the following way:
(1) firstly, calculating RAW importance, FV importance and RTS importance of all components/devices in a real-time risk monitoring model, wherein the calculation method comprises the following steps:
RAWi＝P(i1)/P(R)，FVi＝(P(R)-P(i0))/P(R)，RTSi＝(P(i1)-P(R))/P(i1)in which P is(R)Representing the top event failure probability of a top-level logic large fault tree of the real-time risk monitoring model; p(i1)Representing the top event failure probability of the top-level logic large fault tree when the failure probability of the component i is set to 1, or the top-level logic large fault tree failure probability when the failure probabilities of all components causing the failure of the device i in real time are set to 1; p(i0)Representing the top event failure probability of the top-level logic large fault tree when the failure probability of the component i is set to 0, or the top-level logic large fault tree failure probability when the failure probabilities of all components causing the failure of the device i in real time are set to 0;
(2) then, according to the calculation results of the RAW importance, the FV importance and the RTS importance, sorting the risk importance of the components/equipment in the real-time risk monitoring model, and marking the components or equipment with the importance greater than a critical value as important risk components or equipment; wherein, the value range of the critical value RI of the RAW importance is [0.5,8], the value range of the critical value FI of the FV importance is [0.001,0.9], and the value 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 a final risk contribution ranking, which is as follows:
TIi＝α*FVi+β*RTSi+γ*RAWiwherein α, β and gamma are weight factors, and the value range is [0, 1%](ii) a Aiming at equipment comprising a plurality of parts, the RAW importance of the equipment is the maximum value of the RAW importance of the contained parts, the FV importance of the equipment is the sum of the FV importance of all the parts, and the RTS importance of the equipment is the sum of the RTS importance of all the parts; the components/devices with large values of the overall importance TI are the components/devices with large risk contributions, and are ranked in the top.
3. The nuclear power plant real-time risk dynamic modeling analysis system of claim 1, wherein: the data source analyzer classifies and sorts the data sources in the following way:
(1) all data sources of a given nuclear power system are classified into 5 classes according to their device types, including: the system comprises operator action and equipment monitoring data, fault diagnosis and reliability correction data, equipment information data based on maintenance and test, nuclear energy system design or operation regulation change, and other data which influence the risk level of the nuclear energy system, including environmental weather where the nuclear energy system is located and the technical specification of the nuclear energy system;
(2) according to the magnitude of the real-time risk influence of the 5 types of data sources on the nuclear energy system, the data sources are sequenced, and the specific sequence is as follows: design or operation regulation 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 which affect the risk level of the nuclear energy system, including environmental weather where the nuclear energy system is located and the technical specification of the nuclear energy system; judging the priority in each type of data source according to the ranking of risk contributions;
(3) data sources with the same risk contribution of the same equipment type are sorted according to inherent time characteristics and periods, and the specific sequence is in the order of seconds, minutes, hours to days and months to years.
4. The nuclear power plant real-time risk dynamic modeling analysis system of claim 1, wherein: the real-time risk monitoring modeler comprehensively considers risk contribution sequencing and data source sequencing results, and the priority is judged in each type of data according to the risk contribution sequencing; 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 the reliability data of the components is the highest among the data sources of all the equipment types, and the operation of constructing, storing and dynamically modifying and updating is preferentially carried out on the data sources; the equipment information data which belongs to the important risk components and is based on maintenance and test is prioritized to be the 2 nd bit;
(2) the priority of the equipment state and the reliability data of the risk important component is higher than that of the equipment state and the reliability data of the non-risk important component;
(3) for the modification of the nuclear energy system design or operation rules belonging to the risk important parts, and the operator action and equipment monitoring data belonging to the risk important parts, the updating period is 1-30 seconds; for fault diagnosis and reliability correction data belonging to risk important components, the updating period is 5 seconds to 1 minute; for the change of the nuclear energy system design or operation rules of the non-risk important components, the operator action and equipment monitoring data of the non-risk important components, the fault diagnosis and reliability correction data of the non-risk important components and the equipment information data based on the maintenance and test of the non-risk important components, the updating period is 10 seconds to 2 minutes; the update period of other data of the risk important part is 30 seconds to 2 minutes, and the update period of other data of the non-risk important part 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, a model subbase expansion method, a multiple boundary condition set method and a multiple fault tree page conversion method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911309656.0A CN111091292B (en) | 2019-12-18 | 2019-12-18 | Nuclear power station real-time risk dynamic modeling analysis system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911309656.0A CN111091292B (en) | 2019-12-18 | 2019-12-18 | Nuclear power station real-time risk dynamic modeling analysis system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111091292A true CN111091292A (en) | 2020-05-01 |
| CN111091292B CN111091292B (en) | 2023-08-18 |
Family
ID=70396152
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911309656.0A Active CN111091292B (en) | 2019-12-18 | 2019-12-18 | Nuclear power station real-time risk dynamic modeling analysis system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111091292B (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114136354A (en) * | 2021-09-28 | 2022-03-04 | 国网山东省电力公司营销服务中心(计量中心) | Fault diagnosis method and system for platform area measurement equipment based on positioning analysis |
| CN114694869A (en) * | 2022-03-24 | 2022-07-01 | 广东核电合营有限公司 | Nuclear power station instrument control system transformation risk control method, electronic equipment and storage medium |
| CN114997696A (en) * | 2022-06-21 | 2022-09-02 | 岭澳核电有限公司 | Nuclear power unit shutdown and shutdown risk dynamic assessment method and device and computer equipment |
| CN117196321A (en) * | 2023-11-03 | 2023-12-08 | 福建福清核电有限公司 | Nuclear power plant planning task probability risk calculation method and calculation device thereof |
| JP7570311B2 (en) | 2021-12-03 | 2024-10-21 | 三菱重工業株式会社 | Reliability evaluation method, reliability evaluation device, and program |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4632802A (en) * | 1982-09-16 | 1986-12-30 | Combustion Engineering, Inc. | Nuclear plant safety evaluation system |
| CN103400246A (en) * | 2013-08-23 | 2013-11-20 | 中国科学院合肥物质科学研究院 | System and method for monitoring risks of nuclear power plant based on cloud architecture |
| CN103400193A (en) * | 2013-08-21 | 2013-11-20 | 中国科学院合肥物质科学研究院 | Three-day risk rolling monitoring and management system and management method used for nuclear power station |
| CN103700025A (en) * | 2013-11-22 | 2014-04-02 | 浙江大学 | Power system equipment importance assessing and sorting method based on risk analysis |
| CN104318110A (en) * | 2014-10-28 | 2015-01-28 | 中国科学院合肥物质科学研究院 | Method for improving risk design and maintenance efficiency of large complex system on basis of importance and sensibility complex sequence |
| CN105469186A (en) * | 2014-11-28 | 2016-04-06 | 上海核工程研究设计院 | Risk monitoring system capable of realizing self-monitoring and self-monitoring method |
| CN105631578A (en) * | 2015-12-10 | 2016-06-01 | 浙江大学 | Risk assessment-orientated modeling method of power transmission and transformation equipment failure probability model |
| CN106407216A (en) * | 2015-07-31 | 2017-02-15 | 国网能源研究院 | Clue tracing audition system developed on basis of semantic net construction path and construction method of clue tracing audition system |
| CN108416526A (en) * | 2018-03-15 | 2018-08-17 | 哈尔滨工程大学 | A kind of line duration relevant risk monitoring system and method for the nuclear power station production schedule |
| CN109002959A (en) * | 2018-06-11 | 2018-12-14 | 北京石油化工学院 | A kind of harmful influence risk in transit forecasting system based on big data |
-
2019
- 2019-12-18 CN CN201911309656.0A patent/CN111091292B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4632802A (en) * | 1982-09-16 | 1986-12-30 | Combustion Engineering, Inc. | Nuclear plant safety evaluation system |
| CN103400193A (en) * | 2013-08-21 | 2013-11-20 | 中国科学院合肥物质科学研究院 | Three-day risk rolling monitoring and management system and management method used for nuclear power station |
| CN103400246A (en) * | 2013-08-23 | 2013-11-20 | 中国科学院合肥物质科学研究院 | System and method for monitoring risks of nuclear power plant based on cloud architecture |
| CN103700025A (en) * | 2013-11-22 | 2014-04-02 | 浙江大学 | Power system equipment importance assessing and sorting method based on risk analysis |
| CN104318110A (en) * | 2014-10-28 | 2015-01-28 | 中国科学院合肥物质科学研究院 | Method for improving risk design and maintenance efficiency of large complex system on basis of importance and sensibility complex sequence |
| CN105469186A (en) * | 2014-11-28 | 2016-04-06 | 上海核工程研究设计院 | Risk monitoring system capable of realizing self-monitoring and self-monitoring method |
| CN106407216A (en) * | 2015-07-31 | 2017-02-15 | 国网能源研究院 | Clue tracing audition system developed on basis of semantic net construction path and construction method of clue tracing audition system |
| CN105631578A (en) * | 2015-12-10 | 2016-06-01 | 浙江大学 | Risk assessment-orientated modeling method of power transmission and transformation equipment failure probability model |
| CN108416526A (en) * | 2018-03-15 | 2018-08-17 | 哈尔滨工程大学 | A kind of line duration relevant risk monitoring system and method for the nuclear power station production schedule |
| CN109002959A (en) * | 2018-06-11 | 2018-12-14 | 北京石油化工学院 | A kind of harmful influence risk in transit forecasting system based on big data |
Non-Patent Citations (3)
| Title |
|---|
| ZHANG HAO; ZHANG LIQIANG; QIN YANFENG; JIANG TAO; CAO NING; ZHOU CHUANGBIN; ET ALL: "Modeling and simulation of loss of power in nuclear power plant", 《2018 INTERNATIONAL CONFERENCE ON POWER SYSTEM TECHNOLOGY (POWERCON)》 * |
| 戴婧;毕军;马宗伟;: "风险排序及其在环境风险管控中的应用", no. 01 * |
| 陈珊琦: "故障树和事件树建模与分析的关键算法研究" * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114136354A (en) * | 2021-09-28 | 2022-03-04 | 国网山东省电力公司营销服务中心(计量中心) | Fault diagnosis method and system for platform area measurement equipment based on positioning analysis |
| JP7570311B2 (en) | 2021-12-03 | 2024-10-21 | 三菱重工業株式会社 | Reliability evaluation method, reliability evaluation device, and program |
| CN114694869A (en) * | 2022-03-24 | 2022-07-01 | 广东核电合营有限公司 | Nuclear power station instrument control system transformation risk control method, electronic equipment and storage medium |
| CN114997696A (en) * | 2022-06-21 | 2022-09-02 | 岭澳核电有限公司 | Nuclear power unit shutdown and shutdown risk dynamic assessment method and device and computer equipment |
| CN117196321A (en) * | 2023-11-03 | 2023-12-08 | 福建福清核电有限公司 | Nuclear power plant planning task probability risk calculation method and calculation device thereof |
| CN117196321B (en) * | 2023-11-03 | 2024-03-01 | 福建福清核电有限公司 | Nuclear power plant planning task probability risk calculation method and calculation device thereof |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111091292B (en) | 2023-08-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107358366B (en) | Distribution transformer fault risk monitoring method and system | |
| CN111091292A (en) | Real-time risk dynamic modeling analysis system for nuclear power station | |
| CN108375715B (en) | Power distribution network line fault risk day prediction method and system | |
| CN106384210B (en) | A kind of power transmission and transforming equipment maintenance prioritization method based on maintenance risk income | |
| CN110320892A (en) | The sewage disposal device fault diagnosis system and method returned based on Lasso | |
| CN114035468B (en) | Method and system for predictively monitoring overhaul flow of fan based on XGBoost algorithm | |
| CN110320893B (en) | Thermal power generating unit equipment maintenance method based on RCM | |
| CN110688389B (en) | Cloud management system for defects of secondary equipment of transformer substation | |
| CN113537523A (en) | Substation equipment state maintenance and decision-making assisting method | |
| CN113780375B (en) | Digital twinning-based virtual-real interaction wind power plant wind power monitoring system and application | |
| CN118313519B (en) | Electromechanical full life cycle prediction modeling method and system | |
| CN112016806A (en) | Method, system, medium and device for overhauling state of power station equipment | |
| Ma et al. | Economic operation evaluation of active distribution network based on fuzzy Borda method | |
| CN112085108A (en) | Photovoltaic power station fault diagnosis algorithm based on automatic encoder and K-means clustering | |
| CN118261562A (en) | Intelligent operation system and method based on large hydropower station equipment analysis | |
| CN110781206A (en) | Method for predicting whether electric energy meter in operation fails or not by learning meter-dismantling and returning failure characteristic rule | |
| Jin et al. | Adaptive age replacement using on-line monitoring | |
| CN117578440A (en) | Power grid load prediction method based on neural network | |
| CN114861973B (en) | Wind power generation system risk assessment method considering multiple source factors | |
| Shopeju et al. | A comprehensive review of thermal power plants reliability using stochastic methods | |
| Li et al. | Two-stage multi-level equipment grey state prediction model and application | |
| CN116307838A (en) | Accurate equivalence method, system and medium for power distribution network in power grid reliability evaluation | |
| CN115936663A (en) | Maintenance method and device for power system | |
| CN114897262A (en) | Rail transit equipment fault prediction method based on deep learning | |
| CN112070335A (en) | Assessment method and system for planned outage availability of nuclear power plant |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |