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WO1996035981A1 - Method of monitoring the state of dynamic noise processes - Google Patents

Method of monitoring the state of dynamic noise processes Download PDF

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Publication number
WO1996035981A1
WO1996035981A1 PCT/EP1996/001998 EP9601998W WO9635981A1 WO 1996035981 A1 WO1996035981 A1 WO 1996035981A1 EP 9601998 W EP9601998 W EP 9601998W WO 9635981 A1 WO9635981 A1 WO 9635981A1
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WIPO (PCT)
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.)
Ceased
Application number
PCT/EP1996/001998
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German (de)
French (fr)
Inventor
Joachim 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.)
Institut fur Sicherheitstechnologie (istec) GmbH
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Institut fur Sicherheitstechnologie (istec) GmbH
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Publication of WO1996035981A1 publication Critical patent/WO1996035981A1/en
Anticipated expiration legal-status Critical
Ceased 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

Definitions

  • the invention relates to a method for monitoring the operational mode of operation and the improved state diagnosis of stochastic processes, such as the processes in nuclear reactors.
  • the energy-generating process in the nuclear reactor is stochastically based on the statistical nature of nuclear fission. This process is influenced by a feedback system of the process variables neutron flow, temperature and pressure.
  • the process variables themselves show statistical fluctuations and thus represent sources of noise.
  • the process variables neutron flux, temperature and pressure are measured and evaluated for process monitoring and process control in a nuclear power plant. Only these signals are available for the diagnosis and status monitoring of the processes in the reactor core.
  • ERS ⁇ TZBL ⁇ rT (RULE 26) cycle changes the boron concentration in the reactor and thus the reactivity coefficient of the coolant temperature. The result is a dynamic increase in neutron flux noise.
  • the method of statistical parameter modeling described in the invention enables the broadband process noise to be separated from the measured overall signal.
  • the parameters of the statistical model from the process signal and from the calculated statistical functions are determined using recursive algorithms and used as filter coefficients.
  • the measured signals can be filtered on-line in the time domain and the broadband noise component can be separated.
  • This method can also be used for filtering signals with superimposed noise in the area outside of nuclear energy.
  • the separated broadband noise component can be used for diagnostic purposes.
  • the dynamic increase of the separated broadband noise component can be used for diagnostic purposes.
  • EBS ⁇ TZBLA ⁇ (REGEL26)
  • Neutron flux noise over the fuel element cycle can be monitored using characteristic parameters on the basis of the model parameters determined.
  • the residual function represents the signal filtered via the parameter model.
  • This signal contains the deterministic fluctuation components and is used for the vibration diagnosis. With the help of multivariate analysis, the influences of the process variables and their fluctuations on the individual signal can be separated.
  • the calculation of the transfer functions between the process variables neutron flux and temperature enables the dynamic changes in important reactor parameters, such as the moderator-temperature coefficient of reactivity, to be monitored, and the detection of process anomalies, e.g. in the reactor core, directly from the fluctuation signals.
  • FIGS. 1-3 show the 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.

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

Verfahren zur Zustandsüberwachung dynamischer Rausch¬ prozesseProcess for monitoring the status of dynamic noise processes

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

Eine Vielzahl der stofflichen Umwandlungsprozesse in der Industrie besitzen stochastischen Charakter. Der energieerzeugende Prozeß im Kernreaktor ist stochas- tisch begründet durch den statistischen Charakter der Kernspaltung. Dieser Prozeß wird über ein rückgekoppel¬ tes System der Prozeßgrößen Neutronenfluß, Temperatur und Druck beeinflußt. Die Prozeßgrößen selbst zeigen statistische Fluktuationen und stellen somit Rausch¬ quellen dar.A large number of material conversion processes in industry have a stochastic character. The energy-generating process in the nuclear reactor is stochastically based on the statistical nature of nuclear fission. This process is influenced by a feedback system of the process variables neutron flow, temperature and pressure. The process variables themselves show statistical fluctuations and thus represent sources of noise.

Für die Prozeßüberwachung und Prozeßsteuerung in einem Kernkraftwerk werden die Prozeßgrößen Neutronenfluß, Temperatur und Druck gemessen und bewertet. Für die Diagnose und Zustandsüberwachung der Prozesse im Reak¬ torkern stehen allein diese Signale zur Verfügung.The process variables neutron flux, temperature and pressure are measured and evaluated for process monitoring and process control in a nuclear power plant. Only these signals are available for the diagnosis and status monitoring of the processes in the reactor core.

Während eines Betriebszyklus (Brennelementzyklus) ver¬ ändert sich das Übertragungs erhalten zwischen den Pro¬ zeßgrößen sowie der charakteristische Schwankungsanteil dieser Prozeßgrößen dynamisch. Infolge des Abbrandver- haltens der Brennelemente wird während eines Betriebs-During an operating cycle (fuel element cycle), the transfer obtained between the process variables and the characteristic fluctuation component of these process variables change dynamically. As a result of the combustion behavior of the fuel elements,

ERSÄTZBLÄrT (REGEL 26) zyklus die Borkonzentration im Reaktor und damit der Reaktivitätskoeffizient der Kühlmitteltemperatur verän¬ dert. Die Folge ist eine dynamische Erhöhung des Neu- tronenflußrauschens.ERSÄTZBLÄrT (RULE 26) cycle changes the boron concentration in the reactor and thus the reactivity coefficient of the coolant temperature. The result is a dynamic increase in neutron flux noise.

Die Veränderungen in den dynamischen Prozeßgrößen Neu¬ tronenfluß, Temperatur und Druck werden in der Anlage überwacht und durch Grenzwerte kontrolliert. Dabei kön¬ nen auch Ansprechwerte durch die der Prozeßgröße über¬ lagerten Fluktuationen erreicht werden.The changes in the dynamic process variables neutron flow, temperature and pressure are monitored in the system and controlled by limit values. Response values can also be achieved by the fluctuations superimposed on the process variable.

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 carried out for the monitoring and diagnosis of vibrations of the components and core internals with the aid of FFT procedures.

Das in der Erfindung beschriebene Verfahren der statis¬ tischen Parametermodellierung ermöglicht die Separation des breitbandigen Prozeßrauschens vom gemessenen Ge¬ samtsignal. Dabei werden über rekursive Algorithmen die Parameter des statistischen Modells aus dem Prozeßsi¬ gnal und aus den berechneten statistischen Funktionen ermittelt und als Filterkoeffizienten verwendet.The method of statistical parameter modeling described in the invention enables the broadband process noise to be separated from the measured overall signal. The parameters of the statistical model from the process signal and from the calculated statistical functions are determined using recursive algorithms and used as filter coefficients.

Damit können im Zeitbereich on-line die gemessenen Sig¬ nale gefiltert und der breitbandige Rauschanteil sepa¬ riert werden. Dieses Verfahren ist auch für die Filte¬ rung von Signalen mit überlagertem Rauschen im Bereich außerhalb der Kernenergie einsetzbar.In this way, the measured signals can be filtered on-line in the time domain and the broadband noise component can be separated. This method can also be used for filtering signals with superimposed noise in the area outside of nuclear energy.

Der separierte breitbandige Rauschanteil kann für Diag¬ nosezwecke genutzt werden. Die dynamische Erhöhung desThe separated broadband noise component can be used for diagnostic purposes. The dynamic increase of

EBSÄTZBLAΓΓ(REGEL26) Neutronenflußrauschens über den Brennelement-Zyklus kann mit Hilfe von charakteristischen Kenngrößen auf der Basis der ermittelten Modellparameter überwacht werden.EBSÄTZBLAΓΓ (REGEL26) Neutron flux noise over the fuel element cycle can be monitored using characteristic parameters on the basis of the model parameters determined.

Die Residualfunktion stellt das über das Parametermo¬ dell gefilterte Signal dar. Dieses Signal enthält die deterministischen Schwankungsanteile und wird für die Schwingungsdiagnose genutzt. Dabei können mit Hilfe der multivariaten Analyse die Einflüsse der Prozeßgrößen und ihrer Schwankungen auf das Einzelsignal separiert werden.The residual function represents the signal filtered via the parameter model. This signal contains the deterministic fluctuation components and is used for the vibration diagnosis. With the help of multivariate analysis, the influences of the process variables and their fluctuations on the individual signal can be separated.

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

Zur Erleichterung der Veranschaulichung sind in den Fig. 1 - 3 Autoleistungsdichtespektren des AR-Residuals des Excore-Neutronenflußsignals X10 in linearer Dar¬ stellung und in den Fig. 4 - 6 Autoleistungssdichte- spektren des Excore-Neutronenflußsignals X10 und des AR-Residuals dargestellt.For ease of illustration, FIGS. 1-3 show the 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.

3 3

Claims

Patentanspruch Claim 1. Verfahren zur Überwachung der betriebssicheren Ar¬ beitsweise und der verbesserten Zustandsdiagnose von kontrollierten stochastischen Prozessen, insbesondere des energieerzeugenden Prozesses in Kernreaktoren, un¬ ter Verwendung der mit geeigneten Meßwertaufnehmern (Detektoren) gemessenen Prozeßsignale, wie z.B. Neutro¬ nenfluß, Temperatur und Druck, einschließlich deren Gleichanteile (Prozeßgrößen) und Schwankungsanteile (Prozeßrauschen) ,1.Procedure for monitoring the operationally reliable mode of operation and the improved status diagnosis of controlled stochastic processes, in particular the energy-generating process in nuclear reactors, using the process signals measured with suitable measurement sensors (detectors), e.g. Neutron flow, temperature and pressure, including their constant components (process variables) and fluctuation components (process noise), 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 definierten Datenstruktur abgespeichert werden,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 stored in a defined data structure, b) unter Verwendung statistischer Parameterschätzverfahren der Zeitreihenanalyse, wie der autoregressiven Modellbildung, eine Separation bestimmter Signalanteile mit unterschiedlichen statistischen Eigenschaften durchgeführt wird (autoregressive Filterfunktion) ,b) using statistical parameter estimation methods of time series analysis, such as autoregressive modeling, a separation of certain signal components with different statistical properties is carried out (autoregressive filter function), c) die Änderungen in den berechneten Modellparametern (Merkmalsvektoren) für die Überwachung und Diagnose der im Frequenzbereic breitbandigen Schwankungsanteile mit Hilfe von Kennwerten, wie der Impulsantwort-Funktion, bewertet werden,c) the changes in the calculated model parameters (feature vectors) for 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) die berechnete Residualfunktion im Zeitbereich bzw. das Leistungsdichtespektrum des Signalresiduais für die Verbesserung der Diagnose der im Frequenzbereich schmalbandigen Schwankungsanteile, wie deterministische Schwingungen, verwendet wird,d) the calculated residual function in the time domain or the power density spectrum of the signal residual relay is used to improve the diagnosis of the narrowband fluctuation components in the frequency domain, such as deterministic vibrations, e) mit Hilfe der Anwendung multivariater Parametermodelle, wie der multivariaten autoregressiven Modellbildung, der Einfluß der Schwankungen der Prozeßgrößen eines bestimmten Prozeßvariablenvektors auf das gemessene Einzelsignal für Überwachungszwecke separiert wird,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) auf der Basis multivariater Parametermodelle Signal- sowie Systemübertragungsfunktionen 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,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) eine für die Prozeßüberwachung und Prozeßführung relevante Information zur Unterstützung erforderlicher Maßnahmen zur Verfügung gestellt wird. * g) information relevant to process monitoring and process control to support necessary measures is made available. *
PCT/EP1996/001998 1995-05-10 1996-05-10 Method of monitoring the state of dynamic noise processes Ceased WO1996035981A1 (en)

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Application Number Priority Date Filing Date Title
DE19517104.7 1995-05-10
DE19517104A DE19517104A1 (en) 1995-05-10 1995-05-10 Procedure for monitoring the status of dynamic noise processes

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