Power plant severe accident progression prediction system based on severe accident analysis database

Abstract

The present invention relates to a system for predicting the progression of a power plant severe accident on the basis of a severe accident analysis database, the system comprising: a power plant state diagnosis module for diagnosing and defining a power plant state and an initiating event that distinguish the progression of a severe accident; a severe accident progression prediction module, which provides first prediction information for predicting the progression of the severe accident on the basis of an accident scenario analysis result extracted from a severe accident analysis database; and a severe accident detailed prediction module, which provides second prediction information for predicting the progression of the severe accident on the basis of an uncertainty calculation result value for each accident scenario stored in a severe accident uncertainty analysis database.

Description

Power plant severe accident progression prediction system based on severe accident analysis database

[0001] The present invention relates to a power plant severe accident progression prediction system, and more specifically, to a power plant severe accident progression prediction system based on a severe accident analysis database.

[0002] Extensive information related to severe accident phenomena at power plants is databased and provided to power plant personnel with limited knowledge in the field or to experts requiring accumulated data. The severe accident analysis database organizes the analysis results of potential severe accident phenomena and radiation sources at power plants, and provides the necessary information to users through a database management system.

[0003] In a power plant, a severe accident refers to an incident in which the nuclear fuel inside the reactor vessel is damaged in large quantities, exceeding design standards and causing significant damage to the core. In the event of a severe accident, nuclear power plant operators respond using the Severe Accident Management Guidelines (SAMG) documented by the Technical Support Center (TSC); however, there are limitations due to the uncertainty of the accident phenomenon and environmental constraints in actual situations. Consequently, the response is based on qualitative expert judgment and symptom-based judgment rather than prediction, which presents a disadvantage in that it is difficult to establish rapid accident mitigation strategies and respond effectively.

[0004] [Prior Art Literature]

[0005] [Patent Literature]

[0006] (Patent Document 1) Republic of Korea Registered Patent 1657642 (System and method for predicting the integrity of a reactor building in the event of a severe accident, Korea Hydro & Nuclear Power Co., Ltd.)

[0007] (Patent Document 2) Republic of Korea Published Patent 2024-0055621 (Device for diagnosing and predicting severe accidents in nuclear power plants using artificial intelligence, method for diagnosing and predicting severe accidents, Korea Atomic Energy Research Institute)

[0008] (Patent Document 3) European Published Patent 3702861 (PLANT OPERATION ASSISTANCE DEVICE AND OPERATION ASSISTANCE METHOD, Hitachi)

[0009] The technical objective of the present invention to solve the aforementioned problems is to provide a system that predicts the progression of a severe accident by utilizing a database of analysis results built in advance using information distinguishing the characteristics of the progression process of a severe accident at a power plant and a severe accident analysis code (MAAP, etc.).

[0010] However, the problem to be solved by the present invention is not limited thereto and may be expanded in various ways without departing from the spirit and scope of the present invention.

[0011] To achieve the above objectives, a system for predicting the progression of a severe accident in a power plant based on a severe accident analysis database according to one embodiment may include: a power plant status diagnosis module that diagnoses and defines the power plant status and initial events that distinguish the progression of the severe accident; a severe accident progression prediction module that provides first prediction information for predicting the progression of the severe accident based on accident scenario analysis results extracted from the severe accident analysis database; and a severe accident precision prediction module that provides second prediction information for predicting the progression of the severe accident based on uncertainty calculation result values ​​for each accident scenario stored in the severe accident uncertainty analysis database.

[0012] The above power plant status may include the power plant operating mode prior to the occurrence of the above severe accident, initial events that could lead to the above severe accident, and the status of major safety systems.

[0013] The above power plant status diagnosis module defines the path of occurrence of the above severe accident, and the above power plant status diagnosis module can determine the above operating mode, the above initial events, and the path of occurrence of the above severe accident through the accident circumstances prior to entry into the above severe accident.

[0014] The above-mentioned power plant status diagnosis module can verify the availability of safety systems located along the severe accident path in the event of a severe accident by utilizing a pre-prepared equipment availability table based on the Severe Accident Management Guideline (SAMG).

[0015] The above-mentioned device availability table may include a plurality of means composed of a combination of safety systems related to a major accident, items and variables of the safety system, the status of the safety system, and the availability of each of the plurality of means.

[0016] The above severe accident analysis database can store analysis results based on the power plant operating mode, initial event, and availability of safety systems, based on the Modular Accident Analysis Program (MAPP).

[0017] The above severe accident analysis database may consist of codes by power plant operating mode, codes by initial event, and availability codes by safety system.

[0018] The above severe accident analysis database can store analysis results by accident scenario using severe accident analysis codes (MAAP, etc.).

[0019] The above accident scenarios are classified based on the operating mode, initial event, and availability of safety systems that can determine the progression of a severe accident, and the above accident scenarios can be classified by a combination of codes for each power plant operating mode, codes for each initial event, and codes indicating the availability of each safety system.

[0020] The above-mentioned major accident progression prediction module can compare the accident scenario analysis results extracted from the above-mentioned major accident analysis database with safety variables monitored in the main control room and visually display the above-mentioned first prediction information, which is the result of predicting the progression of the above-mentioned major accident, to the operator.

[0021] The above-mentioned severe accident uncertainty analysis database may include the results of the uncertainty calculation for each accident scenario stored in the above-mentioned severe accident analysis database and the analysis results for the values ​​representing the phenomenological uncertainty of the above-mentioned severe accident.

[0022] The above severe accident uncertainty analysis database includes results based on uncertainty variables of the model inherent in the severe accident analysis code (MAPP) in the accident scenario of the above severe accident analysis database, and the uncertainty variables may include variables that reflect the phenomenological uncertainty characteristics of the heat transfer coefficient, melt behavior, and hydrogen generation correlation coefficient models.

[0023] The above-mentioned major accident precision prediction module can compare the uncertainty calculation result value for each accident scenario with the safety variable monitored in the main control room to extract the above-mentioned second prediction information, which is prediction information with the minimum error, and display it visually.

[0024] The disclosed technology may have the following effects. However, this does not mean that a specific embodiment must include all of the following effects or only the following effects; therefore, the scope of the rights of the disclosed technology should not be understood as being limited by this.

[0025] According to the system for predicting the progression of a severe accident at a power plant based on a severe accident analysis database according to the embodiments of the present invention described above, the severe accident analysis results are output from the severe accident analysis database based on available information at the time of a severe accident at the power plant, namely the initial accident and the availability of safety systems, and the progression of the severe accident reflecting real-time safety variable information from the main control room is predicted, thereby significantly improving the severe accident response capabilities of power plant operators and the technical support room.

[0026] In addition, it can significantly contribute to enhancing power plant safety by providing information for rapid decision-making in the event of an actual major accident.

[0027] FIG. 1 is an overall block diagram of a system for predicting the progression of a severe accident in a power plant based on a severe accident analysis database according to an embodiment of the present invention.

[0028] FIG. 2 is a figure showing an example of a device availability table according to an embodiment of the present invention.

[0029] FIG. 3 is a figure showing an example of keywords included in a major accident analysis database according to an embodiment of the present invention.

[0030] FIG. 4 is an example figure showing the main control room safety variables and the results of major accident progression prediction according to an embodiment of the present invention.

[0031] Figure 5 is an example figure showing the results of a precise prediction of a major accident according to an embodiment of the present invention.

[0032] The present invention is capable of various modifications and may have various embodiments, and specific embodiments are illustrated in the drawings and described in detail.

[0033] However, this is not intended to limit the invention to specific embodiments, and it should be understood that it includes all modifications, equivalents, and substitutions that fall within the spirit and scope of the invention.

[0034] Terms such as "first," "second," etc., may be used to describe various components, but said components should not be limited by said terms. These terms are used solely for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, the first component may be named the second component, and similarly, the second component may be named the first component.

[0035] When it is stated that one component is "connected" or "connected" to another component, it should be understood that while it may be directly connected or connected to that other component, there may also be other components in between. On the other hand, when it is stated that one component is "directly connected" or "directly connected" to another component, it should be understood that there are no other components in between.

[0036] The terms used in this application are used merely to describe specific embodiments and are not intended to limit the invention. Singular expressions include plural expressions unless the context clearly indicates otherwise. In this application, terms such as "comprising" or "having" are intended to indicate the presence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0037] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which the present invention pertains. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this application.

[0038] Hereinafter, preferred embodiments of the present invention will be described clearly and in detail with reference to the attached drawings so that a person skilled in the art can easily practice the present invention.

[0039] FIG. 1 is an overall block diagram of a system for predicting the progression of a severe accident in a power plant based on a severe accident analysis database according to an embodiment of the present invention.

[0040] The power plant severe accident progression prediction system (100) illustrated in FIG. 1 may be composed of a power plant condition diagnosis module (110), a severe accident progression prediction module (130), and a severe accident precision prediction module (150).

[0041] The power plant condition diagnosis module (110) is a module that diagnoses the safety system status and designates an operating mode and initial event that distinguish the progression of a serious accident when a diagnosis procedure to determine the type of serious accident is performed first upon the occurrence of a serious accident. In order to diagnose whether the safety system is available, the operator can create an equipment availability table (see 200 in FIG. 2) and check and select available means. The operator can use the equipment availability table to quickly check the means available in an emergency situation.

[0042] The power plant status diagnosis module (110) can define the power plant operating state (operating mode) prior to the occurrence of a severe accident, initial events that may lead to a severe accident (e.g., loss of coolant accident, complete loss of power plant AC power, complete loss of final heat removal source, etc.), and the state of major safety systems. Additionally, it can define the path of occurrence of a severe accident. Here, the power plant operating state and initial events are determined through the accident circumstances prior to entering the severe accident, and the state of major safety systems can be diagnosed by checking the availability of safety systems located in the path of occurrence of the severe accident when a severe accident occurs, using an equipment availability table (see 200 in FIG. 2) prepared in advance according to the Severe Accident Management Guideline (SAMG).

[0043] The severe accident analysis database (120, see 300 in FIG. 3) is a database in which severe accident analysis results based on the power plant operating mode, initial event, and whether the safety system is operating are established using the Modular Accident Analysis Program (MAPP). It may be composed of codes for each power plant operating mode, codes for each initial event, and codes indicating the availability of each safety system, and the database may be constructed by naming it with keyword combinations according to each condition of the power plant operating mode, initial event, and safety system.

[0044] A severe accident analysis database (120, see 300 in FIG. 3) is constructed by storing the results of an analysis of various conditions and possible scenarios that may unfold when an accident occurs using a severe accident analysis code (MAAP). Accident scenarios are classified according to the operating mode, initial event, and availability of safety systems, which determine the progression of the severe accident, and the database can be constructed by matching keywords that can distinguish accident scenarios.

[0045] The severe accident progression prediction module (130) can extract severe accident progression prediction analysis results including the predicted progression path of a severe accident by using a keyword search method within the severe accident analysis database (120, see 300 in FIG. 3) for the operating mode, initial event, and safety system availability diagnosed by the power plant status diagnosis module (110).

[0046] The major accident progress prediction module (130) can provide information or a graph (see FIG. 4) that can predict the progress of the accident so that the operator can visually check it by comparing the accident scenario analysis results extracted from the major accident analysis database (120, see 300 in FIG. 3) with safety variables monitored in the main control room.

[0047] The severe accident progression prediction module (130) extracts analysis results by searching keywords in the severe accident analysis database (120, see 300 in FIG. 3) based on the results diagnosed by the power plant status diagnosis module (110) (e.g., operating mode, initial event, availability of safety system). The extracted analysis results can predict the progression of the severe accident by comparing them with safety variables monitored in the main control room so that the power plant operator can visually confirm them.

[0048] The major accident uncertainty analysis database (140) includes the results of uncertainty calculations for each accident scenario stored in the major accident analysis database (120), and may include analysis results for values ​​that can represent the phenomenological uncertainty of a major accident. The result with the lowest error can be extracted by comparing the safety variables being monitored in real-time in the main control room with the uncertainty analysis results (see 550 in FIG. 5).

[0049] The severe accident uncertainty analysis database (140) may include results based on uncertainty variables of the model inherent in the severe accident analysis code (MAPP) in the accident scenario of the severe accident analysis database (120). Here, the uncertainty variables may include variables that reflect the phenomenological uncertainty characteristics of the model, such as various heat transfer coefficients, melt behavior, and hydrogen generation correlation coefficients.

[0050] The severe accident precision prediction module (150) can extract the analysis result with the lowest error by comparing the analysis result of the severe accident uncertainty analysis database (140) with the safety variable being monitored in the main control room, and can provide information to the operator that can predict the precise course of a severe accident according to the power plant situation based on the analysis result.

[0051] The severe accident precision prediction module (150) can expect a more precise prediction by predicting the progression of a severe accident similar to the actual power plant behavior that reflects uncertainty.

[0052] A system (100) for predicting the progression of a severe accident in a power plant based on a severe accident analysis database uses data collected from a power plant status diagnosis module (110), namely the current operating mode, identified initial events, and information on the availability of safety systems, as input values, searches the severe accident analysis database (120) according to a predefined keyword system, extracts analysis results for scenarios related to the severe accident based on the severe accident analysis database (120) and the severe accident uncertainty analysis database, and can provide the extracted analysis results to an operator in the form of a visualized chart or graph so that they can be compared with real-time safety variable data being monitored in the main control room. Based on the comparison results, the operator can accurately predict the progression of the severe accident.

[0053] Here, the severe accident analysis database (120) can support the operator in making quick and accurate judgments in the event of a power plant accident. The severe accident analysis database (120) predicts the progression of a severe accident in advance so that the operator can establish an appropriate response strategy.

[0054] FIG. 2 is a figure showing an example of a device availability table according to an embodiment of the present invention.

[0055] The power plant condition diagnosis module (110) of FIG. 1 can use the equipment availability table (200) of FIG. 2 to evaluate the condition of major safety systems related to major accidents and confirm and diagnose the availability of said safety systems located in the path of major accident occurrence.

[0056] As illustrated in FIG. 2, the device availability table (200) may include a plurality of means (210) showing a combination of safety systems related to a major accident, items and variables (220) of the main safety system, the status (230) of the main safety system, and the availability (240) of each of the plurality of means.

[0057] Examples of the conditions (230) of the main safety system may include damage to the pump, power supply, outlet pressure, and motor cooling condition, and may include the condition of the tank water level in the suction source and the power condition of the valve in the flow path.

[0058] FIG. 3 is a figure showing an example of keywords included in a major accident analysis database according to an embodiment of the present invention.

[0059] The keyword table (300) included in the major accident analysis database of FIG. 3 may include keywords indicating the power plant operating mode (310), initial event (320), and the availability / inability status of the safety system.

[0060] The major accident progression prediction module (130) of FIG. 1 can extract analysis results through keyword search indicating the power plant operating mode (310), initial event (320), and the availability / inability status of the safety system based on the results diagnosed by the power plant status diagnosis module (110) (e.g., operating mode, initial event, availability of the safety system).

[0061] For example, when the power plant status diagnosis module (110) of FIG. 1 transmits a diagnosis result of a serious accident that occurred as “a small loss of coolant accident occurred in operation mode 1 and all safety systems other than the containment sprinkler system are inoperable,” the serious accident progress prediction module (130) searches for keywords (300) included in the serious accident analysis database to extract the “M1SLH0L0C0S1A0” code, and uses the code to quickly search for and extract the analysis result of the serious accident from the serious accident analysis database (120) of FIG. 1.

[0062] FIG. 4 is an example figure showing the main control room safety variables and the results of major accident progression prediction according to an embodiment of the present invention.

[0063] FIG. 4 is an example of a visualization graph (400) of information output by the critical accident progress prediction module (130) of FIG. 1, and may be composed of a graph showing safety variables (410) monitored in the main control room and prediction results (420) of the main control room vs. the critical accident progress prediction module (130).

[0064] A graph showing the prediction result (420) of the main control room vs. the major accident progress prediction module (130) can simultaneously display the main control room monitoring data (440) and the major accident progress prediction graph (43).

[0065] Figure 5 is an example figure showing the results of a precise prediction of a major accident according to an embodiment of the present invention.

[0066] FIG. 5 is an example of a visualization graph (500) of information output by the major accident precision prediction module (150) of FIG. 1, which can show control room monitoring data (510), an expected graph (520) before reflecting the uncertainty calculation result value, a minimum uncertainty range (530) and a maximum uncertainty range (540), and an expected graph (550) with the uncertainty calculation result value reflected.

[0067] It can be seen that the predicted graph (550) reflecting the result of the uncertainty calculation has the most similar directionality to the direction of progress of the safety variable of the main control room monitoring data (510). In other words, it can be seen that it shows the most similar predicted progress to the actual progress of the major accident.

[0068] [Explanation of the symbol]

[0069] 100: A system for predicting the progression of severe power plant accidents based on a severe accident analysis database

[0070] 110: Power Plant Condition Diagnosis Module

[0071] 120: Major Accident Analysis Database

[0072] 130: Major Incident Progression Prediction Module

[0073] 140: Major Incident Uncertainty Analysis Database

[0074] 150: Major Accident Prediction Module

[0075] 200: Device Availability Table

[0076] 300: Keyword table included in the major accident analysis database

[0077] 400: Visualization graph of information output by the Severe Incident Progression Prediction Module

[0078] 500: Visualization graph of information output by the major accident precision prediction module

[0079] 430: Major Accident Course Prediction Graph

[0080] 440, 510: Control room monitoring data

[0081] 520: Graph before reflecting uncertainty calculation results

[0082] 530: Uncertainty range (minimum)

[0083] 540: Uncertainty range (max)

[0084] 550: Graph reflecting the uncertainty calculation result value

Claims

1. As a system for predicting the progression of severe accidents at power plants based on a severe accident analysis database, A power plant status diagnosis module that diagnoses and defines power plant status and initial events to distinguish the progression of a major accident; A severe accident progression prediction module that provides first prediction information for predicting the progression of the said severe accident based on accident scenario analysis results extracted from a severe accident analysis database; and A system for predicting the progression of a severe accident at a power plant, comprising a severe accident precision prediction module that provides second prediction information for predicting the progression of the severe accident based on the result of uncertainty calculations for each accident scenario stored in a severe accident uncertainty analysis database.

2. In Paragraph 1, A system for predicting the progression of a severe accident in a power plant, wherein the above-mentioned power plant state includes the power plant operating mode prior to the occurrence of the severe accident, initial events that may develop into the severe accident, and the state of major safety systems.

3. In Paragraph 1, A system for predicting the progression of a severe accident in a power plant, wherein the power plant status diagnosis module defines the path of occurrence of the severe accident, and the power plant status diagnosis module determines the operating mode, the initial events, and the path of occurrence of the severe accident through the accident circumstances prior to entry into the severe accident.

4. In Paragraph 1, The above-mentioned power plant status diagnosis module is a system for predicting the progression of a severe accident in a power plant, which uses a pre-prepared equipment availability table based on the Severe Accident Management Guideline (SAMG) to verify the availability of safety systems located along the path of the severe accident when the severe accident occurs.

5. In Paragraph 4, A system for predicting the progression of a severe accident in a power plant, wherein the above-mentioned equipment availability table comprises a plurality of means composed of a combination of safety systems related to the severe accident, items and variables of the safety systems, the status of the safety systems, and the availability of each of the plurality of means.

6. In Paragraph 1, The above severe accident analysis database is a system for predicting the progression of severe accidents in a power plant, in which analysis results based on the power plant operating mode, initial event, and availability of safety systems are stored based on the Modular Accident Analysis Program (MAPP).

7. In Paragraph 6, The above severe accident analysis database is a system for predicting the progression of severe accidents in a power plant, comprising codes by power plant operating mode, codes by initial event, and availability codes by safety system.

8. In Paragraph 1, The above severe accident analysis database is a system for predicting the progression of severe accidents in power plants, in which analysis results for each accident scenario using severe accident analysis codes (MAAP, etc.) are stored.

9. In Paragraph 8, A system for predicting the progression of a severe accident in a power plant, wherein the above accident scenarios are classified based on operating modes, initial events, and the availability of safety systems, which can determine the progression of the severe accident, and the above accident scenarios are classified by a combination of codes for each power plant operating mode, codes for each initial event, and codes indicating the availability of each safety system.

10. In Paragraph 9, The above-mentioned severe accident progression prediction module is a system for predicting the progression of a severe accident in a power plant, which compares the accident scenario analysis results extracted from the above-mentioned severe accident analysis database with safety variables monitored in the main control room and visually displays the above-mentioned first prediction information, which is the result of predicting the progression of the above-mentioned severe accident, to the operator.

11. In Paragraph 10, A system for predicting the progression of a severe accident at a power plant, wherein the above severe accident uncertainty analysis database includes the results of an analysis of the uncertainty calculation values ​​for each accident scenario stored in the above severe accident analysis database and the values ​​representing the phenomenological uncertainty of the above severe accident.

12. In Paragraph 11, A system for predicting the progression of a severe accident at a power plant, wherein the above severe accident uncertainty analysis database includes results based on uncertainty variables of a model inherent in the above severe accident analysis code (MAPP) in the above accident scenario of the above severe accident analysis database, and the above uncertainty variables include variables reflecting the phenomenological uncertainty characteristics of the heat transfer coefficient, melt behavior, and hydrogen generation correlation coefficient models.

13. In Paragraph 12, The above-mentioned severe accident precision prediction module is a system for predicting the progression of a severe accident in a power plant, which compares the result of the uncertainty calculation for each accident scenario with the safety variable monitored in the main control room to extract the above-mentioned second prediction information, which is prediction information with the minimum error, and visually displays it.

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