DE19517104A1 - Procedure for monitoring the status of dynamic noise processes - Google Patents

Procedure for monitoring the status of dynamic noise processes

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Publication number
DE19517104A1
DE19517104A1 DE19517104A DE19517104A DE19517104A1 DE 19517104 A1 DE19517104 A1 DE 19517104A1 DE 19517104 A DE19517104 A DE 19517104A DE 19517104 A DE19517104 A DE 19517104A DE 19517104 A1 DE19517104 A1 DE 19517104A1
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DE
Germany
Prior art keywords
monitoring
signal
diagnosis
measured
values
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.)
Withdrawn
Application number
DE19517104A
Other languages
German (de)
Inventor
Joachim Dipl Phys Pohlus
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
ISTEC GmbH
Original Assignee
ISTEC GmbH
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by ISTEC GmbH filed Critical ISTEC GmbH
Priority to DE19517104A priority Critical patent/DE19517104A1/en
Priority to PCT/EP1996/001998 priority patent/WO1996035981A1/en
Publication of DE19517104A1 publication Critical patent/DE19517104A1/en
Withdrawn legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G21—NUCLEAR PHYSICS; NUCLEAR ENGINEERING
    • G21D—NUCLEAR POWER PLANT
    • G21D3/00—Control of nuclear power plant
    • G21D3/001—Computer implemented control
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B21/00—Systems involving sampling of the variable controlled
    • G05B21/02—Systems involving sampling of the variable controlled electric
    • G—PHYSICS
    • G21—NUCLEAR PHYSICS; NUCLEAR ENGINEERING
    • G21C—NUCLEAR REACTORS
    • G21C17/00—Monitoring; Testing ; Maintaining
    • G—PHYSICS
    • G21—NUCLEAR PHYSICS; NUCLEAR ENGINEERING
    • G21D—NUCLEAR POWER PLANT
    • G21D3/00—Control of nuclear power plant
    • G21D3/08—Regulation of any parameters in the plant
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E30/00—Energy generation of nuclear origin
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E30/00—Energy generation of nuclear origin
    • Y02E30/30—Nuclear fission reactors

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Plasma & Fusion (AREA)
  • High Energy & Nuclear Physics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Health & Medical Sciences (AREA)
  • Monitoring And Testing Of Nuclear Reactors (AREA)

Abstract

Disclosed is a method of monitoring the reliable operation and improving diagnosis of state of controlled stochastic processes, in particular of the energy-producing process in nuclear reactors, using process signals measured with the aid of suitable sensors (detectors) for measured values, e.g. neutron flux, temperature and pressure, including the constant components of said values (process values) and the fluctuating components (process noise). Specifically, measurements and analysis of the process signals from the stochastic process are used to calculate characteristic values based on statistical procedures, using statistical parameter estimation procedures of sequential analysis; variations in the calculated model parameters (characteristic vectors) are evaluated for monitoring and diagnosis of the wideband fluctuating components in the frequency range with the aid of characteristic values such as the pulse response function. The calculated residual function in the time range and the power density spectrum of the signal residue are used to improve diagnosis of the narrow-band fluctuating components in the frequency range such as deterministic oscillations and, with the aid of multivariate parameter models, the effect of fluctuations of process values of a particular process variable vector on the measured individual signal is separated for monitoring purposes and, using multivariate parameter models, signal and system transfer functions and signal and noise contributory factors for the signals of a measured process variable vector are calculated and used to diagnose process characteristics, in particular in feedback systems such as those in a nuclear reactor.

Description

Die Erfindung betrifft ein Verfahren zu Überwachung der betrieblichen Arbeitsweise und der verbesserten Zustandsdiagnose von stochastischen Prozessen, wie der Prozesse in Kernreaktoren.The invention relates to a method for monitoring the operational mode and improved state diagnosis of stochastic processes, such as the processes in Nuclear reactors.

Eine Vielzahl der stofflichen Umwandlungsprozesse in der Industrie besitzen stochasti­ schen Charakter. Der energieerzeugende Prozeß im Kernreaktor ist stochastisch begrün­ det durch den statistischen Charakter der Kernspaltung. Dieser Prozeß wird über ein rück­ gekoppeltes System der Prozeßgrößen Neutronenfluß, Temperatur und Druck beeinflußt. Die Prozeßgrößen selbst zeigen statistische Fluktuationen und stellen somit Rauschquellen dar.A large number of material conversion processes in industry have stochasti character. The energy-generating process in the nuclear reactor is stochastically green det by the statistical character of nuclear fission. This process is going back coupled system of the process variables neutron flow, temperature and pressure influences. The process variables themselves show statistical fluctuations and thus represent sources of noise represents.

Für die Prozeßüberwachung und Prozeßsteuerung in einem Kernkraftwerk werden die Pro­ zeßgrößen Neutronenfluß, Temperatur und Druck gemessen und bewertet. Für die Diagno­ se und Zustandsüberwachung der Prozesse im Reaktorkern stehen allein diese Signale zur Verfügung.For process monitoring and process control in a nuclear power plant, the Pro Neutron flux, temperature and pressure are measured and evaluated. For the diagnosis These signals are only available for monitoring the condition of the processes in the reactor core Available.

Während eines Betriebszyklus (Brennelementzyklus) verändert sich das Übertragungsver­ halten zwischen den Prozeßgrößen sowie der charakteristische Schwankungsanteil dieser Prozeßgrößen dynamisch. Infolge des Abbrandverhaltens der Brennelemente wird wäh­ rend eines Betriebszyklus die Borkonzentration im Reaktor und damit der Reaktivitätskoeffi­ zient der Kühlmitteltemperatur verändert. Die Folge ist eine dynamische Erhöhung des Neutronenflußrauschens.The transmission ver changes during an operating cycle (fuel element cycle) keep between the process variables as well as the characteristic fluctuation component of these Process variables dynamic. As a result of the burning behavior of the fuel assemblies, it is selected During an operating cycle, the boron concentration in the reactor and thus the reactivity coefficient the coolant temperature changes. The result is a dynamic increase in Neutron flux noise.

Die Veränderungen in den dynamischen Prozeßgrößen Neutronenfluß, Temperatur und Druck werden in der Anlage überwacht und durch Grenzwerte kontrolliert. Dabei können auch Ansprechwerte durch die der Prozeßgröße überlagerten Fluktuationen erreicht werden.The changes in the dynamic process variables neutron flux, temperature and Pressure is monitored in the system and controlled by limit values. You can response values are also achieved through the fluctuations superimposed on the process variable will.

Die Analyse der Schwankungsanteile der Prozeßgrößen wird bisher für die Überwachung und Diagnose von Schwingungen der Komponenten und Kerneinbauten mit Hilfe von FFT- Prozeduren durchgeführt.The analysis of the fluctuation components of the process variables has so far been used for monitoring and diagnosis of vibrations of components and core internals with the help of FFT Procedures performed.

Das in der Erfindung beschriebene Verfahren der statistischen Parametermodellierung er­ möglicht die Separation des breitbandigen Prozeßrauschens vom gemessenen Gesamtsi­ gnal. Dabei werden über rekursive Algorithmen die Parameter des statistischen Modells aus dem Prozeßsignal und aus den berechneten statistischen Funktionen ermittelt und als Filterkoeffizienten verwendet.The method of statistical parameter modeling described in the invention enables the separation of the broadband process noise from the measured total Si gnal. The parameters of the statistical model are determined using recursive algorithms determined from the process signal and from the calculated statistical functions and as Filter coefficients used.

Damit können im Zeitbereich on-line die gemessenen Signale gefiltert und der breitbandige Rauschanteil separiert werden. Dieses Verfahren ist auch für die Filterung von Signalen mit überlagertem Rauschen im Bereich außerhalb der Kernenergie einsetzbar. This allows the measured signals to be filtered online and the broadband in the time domain Noise component can be separated. This method is also used for filtering signals superimposed noise can be used in the area outside of nuclear energy.  

Der separierte breitbandige Rauschanteil kann für Diagnosezwecke genutzt werden. Die dynamische Erhöhung des Neutronenflußrauschens über den Brennelement-Zyklus kann mit Hilfe von charakteristischen Kenngrößen auf der Basis der ermittelten Modellparameter überwacht werden.The separated broadband noise component can be used for diagnostic purposes. The dynamic increase in neutron flux noise over the fuel cycle can with the help of characteristic parameters based on the determined model parameters be monitored.

Die Residualfunktion stellt das über das Parametermodell gefilterte Signal dar. Dieses Si­ gnal enthält die deterministischen Schwankungsanteile und wird für die Schwingungsdia­ gnose genutzt. Dabei können mit Hilfe der multivariaten Analyse die Einflüsse der Prozeß­ größen und ihrer Schwankungen auf das Einzelsignal separiert werden.The residual function represents the signal filtered via the parameter model. This Si gnal contains the deterministic fluctuation components and is used for the vibration slide used. With the help of multivariate analysis, the influences of the process can sizes and their fluctuations are separated on the individual signal.

Die Berechnung der Übertragungsfunktionen zwischen den Prozeßgrößen Neutronenfluß und Temperatur ermöglicht die Überwachung der dynamischen Änderungen wichtiger Re­ aktorparameter, wie des Moderator-Temperatur-Koeffizienten der Reaktivität, sowie die De­ tektion von Prozeßanomalien, z. B. im Reaktorkern, direkt aus den Schwankungssignalen.The calculation of the transfer functions between the neutron flux process variables and temperature enables the dynamic changes of important Re to be monitored actuator parameters, such as the moderator temperature coefficient of reactivity, and the De tection of process anomalies, e.g. B. in the reactor core, directly from the fluctuation signals.

Zur Erleichterung der Veranschaulichung sind in den Fig. 1-3 Autoleistungs­ dichtespektren des AR-Residuals des Excore-Neutronenflußsignals X10 in linearer Darstellung und in den Fig. 4-6 Autoleistungsdichtespektren des Excore-Neutronenflußsignals X10 und des AR-Residuals dargestellt.For ease of illustration, FIGS . 1-3 show auto power density spectra of the AR residual of the excore neutron flux signal X10 in a linear representation and FIGS . 4-6 auto power density spectra of the excore neutron flux signal X10 and the AR residual.

Claims (1)

Verfahren zur Überwachung der betriebssicheren Arbeitsweise und der verbesserten Zustandsdiagnose von kontrollierten stochastischen Prozessen, insbesondere des energieerzeugenden Prozesses in Kernreaktoren, unter Verwendung der mit geeigne­ ten Meßwertaufnehmern (Detektoren) gemessenen Prozeßsignale, wie z. B. Neutro­ nenfluß, Temperatur und Druck, einschließlich deren Gleichanteile (Prozeßgrößen) und Schwankungsanteile (Prozeßrauschen),
  • a) in Messungen und Analysen der Prozeßsignale des stochastischen Prozesses charakteristische Kenngrößen auf der Basis statistischer Verfahren (wie z. B. der Berechnung der Kovarianzfunktion bzw. Korrelationsfunktion im Zeitbereich und des Leistungsdichtespektrums im Frequenzbereich) ermittelt und in einer definier­ ten Datenstruktur abgespeichert werden,
  • b) unter Verwendung statistischer Parameterschätzverfahren der Zeitreihenanalyse, wie der autoregressiven Modellbildung, eine Separation bestimmter Signalanteile mit unterschiedlichen statistischen Eigenschaften durchgeführt wird (autoregres­ sive Filterfunktion),
  • c) die Änderungen in den berechneten Modellparametern (Merkmalsvektoren) für die Überwachung und Diagnose der im Frequenzbereich breitbandigen Schwan­ kungsanteile mit Hilfe von Kennwerten, wie der Impulsantwort-Funktion, bewertet werden,
  • d) die berechnete Residualfunktion im Zeitbereich bzw. das Leistungsdichtespektrum des Signalresiduals für die Verbesserung der Diagnose der im Frequenzbereich schmalbandigen Schwankungsanteile, wie deterministische Schwingungen, ver­ wendet wird,
  • e) mit Hilfe der Anwendung multivariater Parametermodelle, wie der multivariaten au­ toregressiven Modellbildung, der Einfluß der Schwankungen der Prozeßgrößen einer bestimmten Prozeßvariablenvektors auf das gemessene Einzelsignal für Überwachungszwecke separiert wird,
  • f) auf der Basis multivariater Parametermodelle Signal- sowie Systemübertragungs­ funktionen und Signal- sowie Rauschquellen-Beitragsverhältnisse für die Signale eines gemessenen Prozeßvariablenvektors berechnet und für die Diagnose des Prozeßverhaltens, insbesondere in rückgekoppelten Systemen, wie in einem Kernreaktor, verwendet werden,
  • g) eine für die Prozeßüberwachung und Prozeßführung relevante Information zur Unterstützung erforderlicher Maßnahmen zur Verfügung gestellt wird.
Method for monitoring the reliable operation and the improved condition diagnosis of controlled stochastic processes, in particular the energy-generating process in nuclear reactors, using the process signals measured with suitable transducers (detectors), such as, for. B. neutron flow, temperature and pressure, including their DC components (process variables) and fluctuation components (process noise),
  • a) characteristic parameters in measurements and analyzes of the process signals of the stochastic process are determined on the basis of statistical methods (such as the calculation of the covariance function or correlation function in the time domain and the power density spectrum in the frequency domain) and are stored in a defined data structure,
  • b) using statistical parameter estimation methods of time series analysis, such as the autoregressive modeling, a separation of certain signal components with different statistical properties is carried out (autoregressive filter function),
  • c) the changes in the calculated model parameters (feature vectors) for the monitoring and diagnosis of the broadband fluctuation components in the frequency range are evaluated with the aid of characteristic values, such as the impulse response function,
  • d) the calculated residual function in the time domain or the power density spectrum of the signal residual is used to improve the diagnosis of the narrow-band fluctuation components in the frequency domain, such as deterministic vibrations,
  • e) using the use of multivariate parameter models, such as multivariate autoregressive modeling, the influence of the fluctuations in the process variables of a specific process variable vector on the measured individual signal is separated for monitoring purposes,
  • f) on the basis of multivariate parameter models, signal and system transmission functions and signal and noise source contribution ratios for the signals of a measured process variable vector are calculated and used for the diagnosis of the process behavior, in particular in feedback systems, such as in a nuclear reactor,
  • g) information relevant to process monitoring and process control to support necessary measures is made available.
DE19517104A 1995-05-10 1995-05-10 Procedure for monitoring the status of dynamic noise processes Withdrawn DE19517104A1 (en)

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PCT/EP1996/001998 WO1996035981A1 (en) 1995-05-10 1996-05-10 Method of monitoring the state of dynamic noise processes

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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE19635033A1 (en) * 1996-08-29 1998-03-12 Siemens Ag Process for analyzing a process status of a technical system
DE19732046A1 (en) * 1997-07-25 1999-01-28 Abb Patent Gmbh Process diagnostic system and method for diagnosing processes and states of a technical process
DE19737404A1 (en) * 1997-08-27 1999-03-11 Siemens Ag Process for the non-destructive testing of the immunity to accidents of an electrical component of a nuclear power plant
DE10044402A1 (en) * 2000-09-08 2002-04-04 Tobias P Kurpjuhn Parameter estimation method e.g. for frequency estimation adjusting spatial pre-filter by feeding back coarse parameter estimation and optimizing data of transformed, virtual array processing using fed back, estimated parameters
FR2818427A1 (en) * 2000-12-15 2002-06-21 Framatome Anp Determining time of doubling neutron flow in nuclear reactor core comprises use of chamber with coating emitting particles in response to neutron flow
EP1605730A1 (en) * 2004-06-08 2005-12-14 Electrolux Home Products Corporation N.V. A method to automate cooking processes

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117109953B (en) * 2023-10-16 2024-01-02 唐智科技湖南发展有限公司 Sound and vibration collaborative diagnosis method, system, device and medium for train

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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE19635033A1 (en) * 1996-08-29 1998-03-12 Siemens Ag Process for analyzing a process status of a technical system
DE19732046A1 (en) * 1997-07-25 1999-01-28 Abb Patent Gmbh Process diagnostic system and method for diagnosing processes and states of a technical process
DE19737404A1 (en) * 1997-08-27 1999-03-11 Siemens Ag Process for the non-destructive testing of the immunity to accidents of an electrical component of a nuclear power plant
DE10044402A1 (en) * 2000-09-08 2002-04-04 Tobias P Kurpjuhn Parameter estimation method e.g. for frequency estimation adjusting spatial pre-filter by feeding back coarse parameter estimation and optimizing data of transformed, virtual array processing using fed back, estimated parameters
FR2818427A1 (en) * 2000-12-15 2002-06-21 Framatome Anp Determining time of doubling neutron flow in nuclear reactor core comprises use of chamber with coating emitting particles in response to neutron flow
EP1605730A1 (en) * 2004-06-08 2005-12-14 Electrolux Home Products Corporation N.V. A method to automate cooking processes

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