Core automated monitoring system

• US 5,392,320 A
• Filed: 10/07/1993
• Issued: 02/21/1995
• Est. Priority Date: 07/06/1992
• Status: Expired due to Term

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Chao, Fred C.
• Primary Examiner(s): Walsh, Donald P.
• Assistant Examiner(s): VOSS, FREDERICK
• Application Number:
  US08/132,945
• Time in Patent Office: 502 Days
• Field of Search: 376/215, 376/216, 376/245, 376/254, 376/277
• US Class Current: 376/215
• CPC Class Codes: G21C 17/00 Monitoring; Testing measuring in general G01; Maintaining
• CPC Class Codes: G21D 3/001 Computer implemented control
• CPC Class Codes: Y02E 30/00 Energy generation of nuclear origin
• CPC Class Codes: Y02E 30/30 Nuclear fission reactors
• CPC Class Codes: Y10S 706/90 Fuzzy logic

Abstract

An automated monitoring system and method of operation is provided for a nuclear reactor having a pressure vessel containing a reactor core for boiling water to generate steam. A plurality of monitors provide signals for respective monitoring parameters for monitoring operation of the reactor, and a computer includes a data base therein containing predetermined setpoints for the monitoring parameters. The computer identifies abnormal and normal behavior of the monitoring parameters based on the data base, and determines the cause of abnormal behavior of the monitoring parameters using artificial intelligence. A warning is also provided to identify the cause of the monitoring parameter abnormal behavior. And, automatic mitigation action may also be effected.

Claims

1. A system for receiving monitoring parameter input signals from a plurality of monitors monitoring operation of a nuclear reactor said system comprising:

a data base containing predetermined setpoints;

means for identifying abnormal behavior of said nuclear reactor by utilizing said setpoints and said input signals from said monitors;

means for determining a cause of said identified abnormal behavior including predetermined artificial intelligence rules associated with each primary one of said monitoring parameters and based on performance of preselected related secondary ones of said monitoring parameters; and

means for determining a control parameter to mitigate said abnormal behavior.

2. A system according to claim 1 further comprising means for providing a warning identifying said abnormal behavior cause.

3. A system according to claim 2 wherein said secondary monitoring parameters are preselected for each primary monitoring parameter to determine said abnormal behavior cause based on all three of:

predetermined analytical transient and accident operating performance of said reactor;

actual operating records of transient and accident operating performance of said reactor; and

interrelationship of said primary and secondary monitoring parameters based on predetermined reactor physics and thermal hydraulic principles.

4. A system according to claim 2 wherein said secondary monitoring parameters are preselected for each primary monitoring parameter to determine said abnormal behavior cause based on at least one of:

predetermined analytical transient and accident operating performance of said reactor;

actual operating records of transient and accident operating performance of said reactor; and

interrelationship of said primary and secondary monitoring parameters based on predetermined reactor physics and thermal hydraulic principles.

5. A system according to claim 4 wherein said primary monitoring parameter is neutron flux and said secondary monitoring parameters include core flowrate and control rod density change rate;

and

said artificial intelligence rules identify a recirculation flow control system problem as a cause of abnormal behavior of said neutron flux exceeding a given setpoint wherein said core flowrate also exceeds a given setpoint without normal demand therefor and said control rod density change rage is substantially zero.

6. A system according to claim 4 further comprising means for adjusting said control parameter to mitigate said abnormal behavior.

7. A system according to claim 5 wherein said behavior identifying means determine rate of said abnormal behavior including a slow transient, a fast transient relative to said slow transient, and a severe transient faster than said fast transient, with said slow, fast, and severe transients effecting different levels of mitigation.

8. A system for controlling the operation of a nuclear reactor in dependence on monitoring parameter input signals received from a plurality of monitors placed inside the nuclear reactor, comprising:

a data base containing at least a scram setpoint and a pre-scram setpoint for a primary one of said plurality of monitoring parameter input signals, said scram setpoint indicating relatively more severe abnormal behavior and said pre-scram setpoint indicating relatively less severe abnormal behavior of said reactor;

means for comparing said primary one of said plurality of monitoring parameter input signals to said scram setpoint;

means for triggering reactor scram in response to said primary one of said plurality of monitoring parameter input signals reaching said scram setpoint;

means for comparing said primary one of said plurality of monitoring parameter input signals to said pre-scram setpoint;

artificial intelligence means for diagnosing the cause of said primary one of said plurality of monitoring parameter input signals reaching said pre-scram setpoint, said artificial intelligence means comprising means for checking at least a secondary one of said plurality of monitoring parameter input signals for a predetermined relationship to said primary one of said plurality of monitoring parameter input signals; and

means for triggering mitigation of said relatively less severe abnormal behavior of said reactor before the occurrence of said relatively more severe abnormal behavior of said reactor in response to the existence of said predetermined relationship.

9. The system as defined in claim 8, wherein said mitigation triggering means comprising means for identifying at least one control parameter to be adjusted and means for adjusting said at least one control parameter.

10. The system as defined in claim 9, wherein said means for identifying at least one control parameter to be adjusted comprising fuzzy logic processing means.

11. The system as defined in claim 8, wherein said mitigation triggering means selects a mitigation action in dependence on the rate at which a transient event is proceeding.

12. The system as defined in claim 8, wherein said primary one of said plurality of monitoring parameter input signals is neutron flux.

13. The system as defined in claim 12, wherein said checking means checks first and second secondary ones of said plurality of monitoring parameter input signals for a predetermined relationship to said primary one of said plurality of monitoring parameter input signals, said first secondary one of said plurality of monitoring parameter input signals being core flowrate and said second secondary one of said plurality of monitoring parameter input signals being control rod density change rate.

14. The system as defined in claim 13, wherein said artificial intelligence means comprise means for identifying a recirculation flow control system malfunction as a cause of the neutron flux exceeding said pre-scram setpoint when the core flowrate also exceeds a corresponding pre-scram setpoint without normal demand therefor and the control rod density change rate is substantially zero.

15. The system as defined in claim 8, further comprising means for displaying a warning identifying the cause of said primary one of said plurality of monitoring parameter input signals reaching said pre-scram setpoint.

16. The system as defined in claim 8, wherein said artificial intelligence means comprise a knowledge base including at least one of the following:

(a) predetermined analytical transient and accident operating performances of said reactor;

(b) actual operating records of transient and accident operating performances of said reactor; and

(c) the interrelationship of said primary and secondary ones of said plurality of monitoring parameter input signals based on predetermined reactor physics and thermal hydraulic principles.

17. A method for controlling the operation of a nuclear reactor in dependence on monitoring parameter input signals received from a plurality of monitors placed inside the nuclear reactor, comprising the steps of:

storing a data base containing at least a scram setpoint and a pre-scram setpoint for a primary one of said plurality of monitoring parameter input signals, said scram setpoint indicating relatively more severe abnormal behavior and said pre-scram setpoint indicating relatively less severe abnormal behavior of said reactor;

comparing said primary one of said plurality of monitoring parameter input signals to said scram setpoint;

triggering reactor scram in response to said primary one of said plurality of monitoring parameter input signals reaching said scram setpoint;

comparing said primary one of said plurality of monitoring parameter input signals to said pre-scram setpoint;

diagnosing the cause of said primary one of said plurality of monitoring parameter input signals reaching said pre-scram setpoint using artificial intelligence rules which include at least a rule for checking at least a secondary one of said plurality of monitoring parameter input signals for a predetermined relationship to said primary one of said plurality of monitoring parameter input signals; and

triggering mitigation of said relatively less severe abnormal behavior of said reactor before the occurrence of said relatively more severe abnormal behavior of said reactor in response to the existence of said predetermined relationship.

18. The method as defined in claim 17, wherein said step of triggering mitigation comprises the steps of identifying at least one control parameter to be adjusted and adjusting said at least one control parameter.

19. The method as defined in claim 18, wherein said step of identifying at least one control parameter to be adjusted is performed using fuzzy logic.

20. The method as defined in claim 17, wherein said step of triggering mitigation comprises the step of selecting a mitigation action in dependence on the rate at which a transient event is proceeding.