CN113837535B - Method for backtracking severe accident process of nuclear power plant - Google Patents
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Abstract
The invention relates to a method for backtracking the progress of serious accidents in a nuclear power plant. By adopting the method provided by the invention, when a serious accident occurs in the nuclear power plant, the type of the originating event, the parameter of the originating event and the system response information of the serious accident can be rapidly diagnosed. And inputting the retrospectively obtained diagnosis information of the serious accident process into a serious accident integrated analysis program to obtain prediction information of further development of the accident process, thereby providing positive and negative surface influence evaluation for the intervention operation of operators. The method provided by the invention also realizes automatic diagnosis without manual intervention. Meanwhile, the method of the invention also provides a diagnosis of the time of the head event in the serious accident process of the accident sequence, namely a diagnosis of the response time of the system, and the influence of the delay running time of each head event of the accident sequence on the serious accident process is analyzed.
Description
Technical Field
The invention belongs to the field of implementation of severe accident management guidelines of nuclear power plants, and relates to a method for backtracking the progress of severe accidents of a nuclear power plant.
Background
The severe accident management Guidelines (SEVERE ACCIDENT MANAGEMENT guides, SAMG) are Guidelines for the staff of the main control room and the technical support center to take measures to alleviate the consequences of core damage accidents when severe accidents occur in the nuclear power plant. A typical practice for many decisions by serious accident management guidelines is to evaluate the potential negative impact of the action to be taken. SAMG one of the key factors for successful implementation is that technical support personnel rapidly diagnose the serious accident process, diagnose the serious accident starting event, starting event parameters and the response of the nuclear power plant system. But in emergency auxiliary decision-making systems, there is a lack of responsive action information of the systems in the nuclear power plant system, such as action signals of high pressure safety injection pumps, low pressure safety injection pumps, safety shower pumps, main steam line safety valves, etc. This results in that in the event of a serious accident, the response of the nuclear power plant system can only be recorded manually, and no automated evaluation can be achieved.
In the prior art, some methods for backtracking the progress of serious accidents appear, which generally comprise the following methods:
(1) Emergency operation protocol: the main method is that an operator identifies the accident cause according to the optimal recovery procedure and guides the operator to enter the appointed accident recovery procedure, thereby rapidly and efficiently relieving the consequences of the specific accident.
(2) Intelligent diagnosis: the main method is to record key meter data by using a machine learning algorithm, and diagnose the starting event and starting event parameters of the serious accident process according to the time sequence characteristics of the meter data.
(3) Manual operation log: the main method is that an operator performs manual recording when operating according to an emergency running rule, and the starting time of equipment such as pumps, valves and the like is recorded.
However, the existing methods for backtracking the progress of the serious accidents have some defects: 1) The emergency operation rules can only aim at part of specific accidents, namely, the accidents of the accident cause can be diagnosed, the application range is narrow, and the accidents of which the accident cause cannot be identified cannot be processed; and only the category of the originating event can be diagnosed, and the parameters of the originating event cannot be specifically diagnosed. 2) The intelligent diagnosis method can diagnose the type of the initial event and the parameters thereof, has stronger processing capacity than the emergency running regulation, but does not diagnose the response of the nuclear power plant system. 3) The manual recording can only record the system response in the serious accident process, and the diagnosis of the originating event cannot be carried out; and because of being isolated data, the method is difficult to fuse with an electronic informatization system.
Therefore, it is necessary to develop a method for tracing back the serious accident process of the nuclear power plant, which can quickly trace back the serious accident process after the serious accident occurs, provide intervention operation advice in a shorter time, and improve the serious accident management level of the nuclear power plant.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention aims to provide a method for backtracking the progress of a severe accident of a nuclear power plant, which can quickly backtrack the progress of the severe accident in the process of transferring SAMG from an emergency operation rule after the severe accident occurs, and further deduce the positive and negative side effects of various intervention means through computer program simulation calculation; thereby, intervention operation advice can be provided in a short time, and the serious accident management level of the nuclear power plant is improved.
To achieve the object, the invention provides a method for backtracking the progress of a severe accident in a nuclear power plant, which comprises the following steps:
s1, diagnosing the category and parameters of an initial event by adopting a deep learning algorithm, wherein the diagnosis of the category of the initial event refers to a probability safety analysis report;
S2, referring to an event tree of a probability security analysis report, and judging an event tree corresponding to the originating event category diagnosed in the step S1;
S3, referring to an event tree of the probability security analysis report, and diagnosing an accident sequence of a serious accident in the event tree of the probability security analysis report by adopting a deep learning algorithm; the accident sequence comprises a plurality of head events, and a deep learning algorithm is adopted to diagnose the time of the head events;
And S4, automatically generating a serious accident integrated program input card according to the diagnosis result of the step S3, writing the information of the diagnosis result into the input card, and calculating by the serious accident integrated program to obtain the future development trend of the serious accident.
Further, in the step S1, the deep learning algorithm uses a long-short-term memory network algorithm in the recurrent neural network, the long-short-term memory network algorithm is good at processing time series data, and the input data is historical data of a plurality of key meters of the nuclear power plant.
Further, in the probabilistic security analysis report, including detailed analysis of the originating event and classification thereof; for operating power operating condition probability safety analysis reports, the categories of originating events include, but are not limited to, loss of reactor coolant, loss of hot traps, loss of feedwater, loss of external power supply, feedwater pipe breach, steam generator heat transfer pipe breach, steam pipe breach superimposed steam generator heat transfer pipe breach.
Further, in the probabilistic safety analysis report, an accident sequence analysis is performed on each type of the originating event, so as to determine that after the originating event occurs, the nuclear power plant generates an event tree through an automatic response and an artificial response model.
Further, in the step S3, each event tree reported by the probabilistic safety analysis includes a plurality of accident sequences, where the accident sequences include accident sequences that are successfully alleviated and accident sequences that cause damage to the core, and each accident sequence represents a progress of a serious accident.
Further, the categories of the header event include, but are not limited to, required security functions, investment in the system, occurrence of a base event, or operator behavior.
Further, the header event is a nuclear power plant system response including, but not limited to, initiation of an injection system pump, and operator performed operations.
Further, in the step S3, the deep learning algorithm diagnoses a nuclear power plant system response in a serious accident process by adopting a long-term memory network algorithm in a cyclic neural network, and determines an accident sequence of the serious accident in the event tree of the probability security analysis report.
Further, in the step S3, the method for diagnosing the time of the header event includes: calculating each header event delay run-time dataset conforming to the accident sequence features using a severe accident integration analysis program with reference to the diagnosed accident sequence of the severe accident in the event tree of the probabilistic safety analysis report; dividing the delayed running time data set of each head event into a training set and a testing set, and diagnosing the delayed running time of each head event by adopting a long-and-short-term memory network algorithm in a cyclic neural network; and summarizing the delayed running time of each head event to obtain the diagnosis of the head event time in the serious accident process of the accident sequence.
Further, in step S4, the severe accident integration analysis program includes, but is not limited to MAAP developed by Fauske & Associates, MELCOR developed by Sandia national laboratory, usa, and ASTEC developed by the GRS combination of IRSN in france and germany.
The method for backtracking the severe accident process of the nuclear power plant has the beneficial effects that when the severe accident happens to the nuclear power plant, the time sequence obtained through monitoring of the available key instrument is adopted to diagnose the type of the initiating event, the parameters of the initiating event and the response information of the system of the severe accident. The diagnosis information is input into a severe accident integrated analysis program to obtain prediction information of accident progress, and the severe accident progress is diagnosed, so that positive and negative surface influence evaluation is provided for the intervention operation of operators. The method provided by the invention also realizes automatic diagnosis without manual intervention. Meanwhile, the method provided by the invention reduces the number of parameters required by diagnosis, and can quickly diagnose without the start-stop signals of the pump and the valve, so that the diagnosis function can be realized when the start-stop signals of the pump, the valve and other equipment cannot be obtained. In addition, the method provided by the invention analyzes the influence of the starting running time of each head event of the accident sequence on the serious accident process, and the deep learning input data set comprises a plurality of running times of each head event, so that the influence of the delayed running time of the head event on the serious accident process is considered.
Drawings
Fig. 1 is a schematic main flow chart of a method for backtracking a severe accident process of a nuclear power plant according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of an accident sequence time diagnosis flow provided in an embodiment of the present invention.
FIG. 3 is a flow chart of a break reactor coolant loss (i.e., BI 1A) event tree in the hot leg of the operating power operating regime provided by an embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the drawings and detailed description. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, the main flow diagram of an embodiment of the method for tracing back the progress of a serious accident in a nuclear power plant according to the present invention mainly includes the following steps:
S1, diagnosing the type and parameters of an originating event by adopting a long-short-term memory network algorithm (LSTM algorithm) in a cyclic neural network. In the probabilistic security analysis report (PSA report), some of the originating events and parameters are shown in table 1; for example, a hypothetical event is diagnosed from an originating event in a PSA report as a medium break reactor coolant loss event (medium LOCA).
S2, judging an event tree of the PSA report corresponding to the originating event category diagnosed in the step S1. For example, according to the accident conclusion of LOCA in the diagnosis of step S1, it is determined that the assumed accident is a break reactor coolant loss accident in the hot leg (i.e., BI 1A), the event tree of BI1A is found from the PSA report, and the event tree of BI1A is shown in fig. 3.
S3, diagnosing an accident sequence of the assumed accident in the event tree by adopting an LSTM algorithm. As shown in FIG. 3, there are a total of 8 accident sequences in the BI1A event tree, and the LSTM algorithm is used to diagnose that the hypothetical accident belongs to the accident sequence 2.
In the PSA report BI1A event tree shown in fig. 3, the header event includes safe shutdown, containment direct spray, high pressure safety injection pump direct injection via pipeline, operator execution of a1.2 protocol, safety injection tank injection, low pressure safety injection pump direct injection via cold leg injection pipeline, low pressure safety injection pump recycle injection via cold leg pipeline, safety injection pump recycle operation, etc. If a header event is successfully executed, the column of the header event is represented as a horizontal line in fig. 3; if execution fails, the column in which the header event is located is denoted as a polyline in FIG. 3. In fig. 3, the accident sequence 1 is that after the break accident occurs, the accident is relieved and no serious accident occurs because each head event is successfully executed; the other 7 accident sequences are all serious accidents of core damage caused by failure of execution of a header event. And diagnosing whether each head event in the event tree is successfully executed by adopting an LSTM algorithm, and diagnosing accident sequence numbers which cause the damage of the reactor core.
The expression of accident sequence 2 in PSA report is: after a water loss accident occurs at a break in a hot section under a power operation condition, emergency shutdown is successful, containment is successfully sprayed directly, a high-pressure injection pump is successfully injected directly through a pipeline, an operator executes A1.2 rule to quickly reduce temperature and pressure of a loop, an injection box is successfully injected, a low-pressure injection system is successful, and under the condition that the injection system and the injection box are successfully operated, the waste heat of a reactor core can be taken away through the break, but the injection pump fails to recycle operation, so that the reactor core fails to carry heat for a long time, and the reactor core is finally damaged.
In addition to diagnosing the header event, the LSTM algorithm is also used to diagnose the time of the header event. For example, the high pressure safety injection pump in BI1A accident sequence 2 is diagnosed via the pipeline direct injection start time.
More specific embodiments of event tree header event time diagnostics in PSA reports are as follows:
Step P1, referring to a LOCA event tree in a PSA report, diagnosing that an accident sequence which occurs by adopting a deep learning LSTM algorithm belongs to an accident sequence 2 in the LOCA event tree;
Step P2, referring to PSA report incident sequence 2, using severe incident integration analysis program MAAP, calculates a header event delay run-time dataset that fits the characteristics of the incident sequence 2. For example, for the direct injection of the high pressure safety injection pump via the pipeline, the delay time of the actual injection behavior of the high pressure safety injection pump via the pipeline under various conditions such as 0 minutes, 2 minutes, 5 minutes, 10 minutes and the like after the high pressure safety injection starting condition is reached is calculated, and the calculated data are used as the direct injection delay operation time data set of the high pressure safety injection pump of the LSTM algorithm via the pipeline.
For other header events, such as injection of an injection tank, failure of a low-pressure injection pump in a recirculation injection stage of a cold section pipeline, and the like, the same method is adopted to calculate by MAAP so as to obtain a corresponding delayed running time data set of the header event.
And P3, dividing the header event delay running time data set obtained in the step P2 into a training set and a testing set, and diagnosing the injection time of direct injection of the high-pressure safety injection by adopting a deep learning LSTM algorithm. And (3) summarizing the delay running time of each header event obtained by diagnosis in the step (P2) to obtain the diagnosis of the time of the header event in the serious accident process of the accident sequence (2), namely the diagnosis of the response time of the system.
And S4, automatically generating a serious accident integrated analysis program MAAP input card according to the diagnosis result of the step, writing diagnosis information into the MAAP input card, generating a MAAP input card conforming to the BI1A accident sequence 2, calculating MAAP to obtain the development trend of the serious accident process, and diagnosing the serious accident process, thereby providing positive and negative surface influence evaluation for the intervention operation of operators.
TABLE 1 originating event type and parameter Table
The above embodiments are merely illustrative of the present invention and various modifications and variations may be made thereto by those skilled in the art without departing from the spirit and scope of the present invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (9)
1. A method for backtracking a severe accident process in a nuclear power plant, the method comprising the steps of:
S1, diagnosing the category and parameters of an originating event by adopting a deep learning algorithm, wherein the category of the originating event refers to a probability security analysis report;
S2, referring to an event tree of a probability security analysis report, and judging an event tree corresponding to the originating event category diagnosed in the step S1;
S3, referring to an event tree of the probability security analysis report, and diagnosing an accident sequence of a serious accident in the event tree of the probability security analysis report by adopting a deep learning algorithm; the accident sequence comprises a plurality of head events, and a deep learning algorithm is adopted to diagnose the time of the head events;
In the step S3, the method for diagnosing the time of the header event includes: calculating each header event delay run-time dataset conforming to the accident sequence features using a severe accident integration analysis program with reference to the diagnosed accident sequence of the severe accident in the event tree of the probabilistic safety analysis report; dividing the delayed running time data set of each head event into a training set and a testing set, and diagnosing the delayed running time of each head event by adopting a long-and-short-term memory network algorithm in a cyclic neural network; summarizing the delayed running time of each head event to obtain the diagnosis of the head event time in the serious accident process of the accident sequence;
And S4, automatically generating a serious accident integrated program input card according to the diagnosis result of the step S3, writing the information of the diagnosis result into the input card, and calculating by the serious accident integrated program to obtain the future development trend of the serious accident.
2. The method according to claim 1, wherein in the step S1, the deep learning algorithm uses a long-short-term memory network algorithm in a recurrent neural network, the long-short-term memory network algorithm is good at processing time series data, and the input data is history data of a plurality of key meters in the nuclear power plant.
3. A method for the backtracking of a severe accident process in a nuclear power plant according to claim 1, wherein in the probabilistic safety analysis report, there are included an originating event analysis and classification; probabilistic safety analysis reports for power operating conditions, originating event categories include, but are not limited to, loss of reactor coolant, loss of hot traps, loss of feedwater, loss of external power, feedwater pipe breach, steam generator heat transfer pipe breach, steam pipe breach superimposed steam generator heat transfer pipe breach.
4. A method for the backtracking of the progress of a severe accident in a nuclear power plant according to claim 1, wherein in the probabilistic security analysis report, each type of originating event is subjected to an accident sequence analysis in order to determine that the nuclear power plant generates an event tree by means of an automatic response and an artificial response model after the occurrence of the originating event.
5. The method according to claim 1, wherein in step S3, each event tree reported by the probabilistic safety analysis includes a plurality of accident sequences, wherein the accident sequences include accident sequences successfully alleviated and accident sequences causing core damage, and each accident sequence represents the progress of a serious accident.
6. A method for the backtracking of a severe accident process in a nuclear power plant according to claim 1, wherein the categories of the header events include, but are not limited to, required safety functions, investment in systems, occurrence of basic events or operator actions.
7. A method for backtracking a severe accident process in a nuclear power plant according to claim 1, wherein the header event is a nuclear power plant system response including, but not limited to, priming of an injection system pump, and operator performed operations.
8. The method according to any one of claims 1-7, wherein in step S3, the deep learning algorithm uses a long and short term memory network algorithm in a recurrent neural network to diagnose the response of the nuclear power plant system in the severe accident process and determine the accident sequence of the severe accident in the event tree reported by the probabilistic safety analysis.
9. A method for the retrospective of severe accident progression in nuclear power plants according to claim 1, characterized in that in step S4, the severe accident integration analysis procedure includes, but is not limited to, MAAP developed by Fauske & Associates, MELCOR developed by Sandia national laboratory, usa, and ASTEC developed by GRS union in IRSN and germany, france.
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| US11551106B2 (en) * | 2019-08-07 | 2023-01-10 | Saudi Arabian Oil Company | Representation learning in massive petroleum network systems |
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| CN103399820A (en) * | 2013-08-21 | 2013-11-20 | 中国科学院合肥物质科学研究院 | Fault diagnosis system and fault diagnosis method based on sequence and consequence analysis of event tree |
| CN107548497A (en) * | 2015-03-10 | 2018-01-05 | 医科达有限公司 | Adapted treatments management system with Workflow Management engine |
| CN104915768A (en) * | 2015-06-02 | 2015-09-16 | 中科华核电技术研究院有限公司 | Method and system for diagnosing and responding and supporting nuclear power plant severe accident |
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