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EP2394031B1 - Procédé de commande d'installation dans une centrale électrique - Google Patents

Procédé de commande d'installation dans une centrale électrique Download PDF

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Publication number
EP2394031B1
EP2394031B1 EP09779575.1A EP09779575A EP2394031B1 EP 2394031 B1 EP2394031 B1 EP 2394031B1 EP 09779575 A EP09779575 A EP 09779575A EP 2394031 B1 EP2394031 B1 EP 2394031B1
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EP
European Patent Office
Prior art keywords
power plant
variables
control
values
gradient
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EP09779575.1A
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German (de)
English (en)
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EP2394031A2 (fr
Inventor
Andreas Christidis
Klaus Wendelberger
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Siemens AG
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Siemens AG
Siemens Corp
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
    • F01K—STEAM ENGINE PLANTS; STEAM ACCUMULATORS; ENGINE PLANTS NOT OTHERWISE PROVIDED FOR; ENGINES USING SPECIAL WORKING FLUIDS OR CYCLES
    • F01K13/00—General layout or general methods of operation of complete plants
    • F01K13/02—Controlling, e.g. stopping or starting

Definitions

  • the invention relates to a method for system control in a power plant, in which for a plurality of sets of control values each of a set of environmental values on the one hand and the respective set of control values, on the other hand, a functional value of a target function based on a physical model is assigned to the respective sets, wherein the set of control values for forwarding to a control device of the power plant is selected, the associated function value meets a predetermined optimization criterion.
  • non-electrical energy for example in the form of fossil fuels is converted into electrical energy and made available to a power grid.
  • such a method comprises a target function, which generates a scalar or vector-valued function value based on a physical model of the corresponding power plant from a set of process values.
  • the process values include those that are determined by external influences (environmental values), such as ambient and cooling water temperatures, and that change during operation.
  • the environmental values represent current boundary conditions that you have no influence on but that have an influence on the process.
  • the process values also include the control values, such as, for example, the position of an actuator or valve or the amount of fuel supplied, which can be influenced by operating personnel or an automated control device during ongoing operation of the power station, i. H. within certain limits freely selectable process or state variables.
  • Each set of control values in conjunction with the ambient values yields a value of the target function that can be used to evaluate the respective set and usually the set of control values for forwarding to a control device of the power plant is selected, the associated function value meets a predetermined optimization criterion. For example, in the case of a scalar function value, this may be the largest or smallest function value.
  • gradient methods are commonly used to find a minimum or maximum of the objective function.
  • various methods such as the steepest descent method, the (quasi) Newton method, the sequential quadratic programming or the simplex algorithm are known.
  • Common is the gradient method in that starting from a starting value, a local maximum or minimum of the target function is found.
  • the physical models of power plants that yield the objective function of optimization are mostly non-linear and generally non-convex.
  • the gradient method may under certain circumstances be a local maximum or minimum, ie. H. find a locally optimized operating state of the power plant, but this does not ensure that it also the globally optimal operating condition is found.
  • the invention is therefore an object of the invention to provide a method for plant control in a power plant and a control device for a power plant, which at the lowest possible tax expenditure improved operation of the power plant with respect to a given optimization criterion such. B. allow an improvement in the efficiency or a reduction of emissions.
  • this object is achieved according to the invention in that the number of sets of control values in addition to a starting set and a set determined by the gradient method from the starting set and its assigned function value further comprises a set selected by means of a random generator.
  • the invention is based on the consideration that an improved operation of the power plant would be possible if
  • an optimized set of control values could also be found globally with regard to the given optimization criterion, such as increasing the efficiency and / or reducing emissions. This could be done, for example, with a Monte Carlo method, which randomly selects control values and compares their functional values and optionally in another Step in the range of the best manipulated variable set another number of randomly selected control values checked.
  • a Monte Carlo method which randomly selects control values and compares their functional values and optionally in another Step in the range of the best manipulated variable set another number of randomly selected control values checked.
  • such a method is relatively time-consuming and computationally intensive and therefore also computationally comparatively expensive.
  • the comparatively faster gradient method should basically be retained, but extended in the manner of a hybrid structure by a random-based system, so that the finding of a global optimum of control values is also made possible.
  • a gradient-based method with a random model ensures the finding of a global optimum for the control values for the system control, on the other hand ensures comparatively fast convergence of the optimization algorithm to suitable control values. Therefore, such a designed algorithm is also suitable for online optimization in the power plant process, d. H. for adapting the control values to the respectively optimum operating state during the ongoing operation of the power plant.
  • the method is advantageously carried out cyclically repeating in the manner of a loop, wherein the selected set of control values of one cycle is the starting set of the cycle following this.
  • An online optimization in the plant control of a power plant system makes it possible to determine an optimum set of control values at each operating time, which ensures particularly efficient operation of the power plant.
  • the selected set of control values is advantageously transferred in the control device to the individual control values respectively assigned control devices of the power plant.
  • the objective function advantageously comprises a penalty function.
  • a penalty function is designed to provide a value of zero, provided that the restrictions are not violated, and contains a monotonically increasing relationship between the error from the restriction violation and its function value.
  • the method provides a set of manipulated variables in which the restrictions are not violated.
  • the method is thereby able to start even from an illegal starting value, the gradient method and thus the optimization, which is not always the case with other methods for the integration of restrictions. This allows a further simplification of the method.
  • the gradient serves as an indicator for the direction in which the respective manipulated variables must be changed in order to arrive at an optimum manipulated variable set. It is questionable, however, how far the control values have to be changed, ie which step size should be used in the application of the gradient method. This can be done, for example, by performing a one-dimensional optimization along the search direction in each iteration and thus finding a seemingly optimal step size becomes. However, this results in the search direction being orthogonal to the previous one, since the partial derivative at the current location after the previous search direction was minimized to zero by the one-dimensional optimization in the previous iteration.
  • a step size is advantageously predetermined by the gradient method before the respective determination of the set.
  • a given step size allows the gradient method to be carried out quickly and should be kept constant until one iteration (when minimized) provides a greater function value than the previous one. Then the step size is reduced and the process proceeds from the best value. As a result, a particularly fast execution of the method and a particularly efficient online optimization of the power plant operation is possible.
  • control device for a power plant with a random generator module and a gradient module, which data output side are connected to a comparison module, wherein the control device is designed for carrying out said method.
  • control device is used in a power plant with a control device and such with the control device data input side connected control device used.
  • the advantages achieved by the invention are, in particular, that the possibility of finding a global solution by means of the random number generator with the rapidity of the control by the additional consideration of a set of control values selected by means of a random generator Gradient method is connected.
  • the random number generator generates potential starting values for the gradient method, which are adopted if they are better in the sense of the physical model of the objective function than the local optimum previously found by the gradient method. Due to the cyclical application of the process and the use of current environmental values, which can be taken directly from the process control system, the process is online-enabled. If the operating status of the system changes, this information flows into the physical process model online and the optimization algorithm quickly finds the new optimum.
  • the process in the process control technology of a power plant can initially serve as an aid to the operating personnel, but are also switched directly to corresponding actuators for automatic forwarding for a quick reaction of the power plant control technology. As a result, a particularly efficient operation of a power plant plant is made possible with low technical effort.
  • FIG shows a schematic representation of the method for plant control of a power plant.
  • the method illustrated in the FIGURE optimizes cyclically repeating the control values for the power plant to achieve a particularly efficient operation of the power plant.
  • the process is online-capable, ie it can be integrated directly into the process control technology and determine the currently optimum control values during operation.
  • One possible area of application is, for example, the optimization of the interval between the sootblowing operations in the boiler of the power plant and its duration and the cleaning intervals of the filters in the flue gas cleaning, where a consideration is drawn between short-term malfunction and longer-term increase in efficiency.
  • Two further optimization problems from the power plant area are the determination of the optimum cooling water mass flow, provided that this is regulated, and the litigation in the case of incineration in compliance with emission limits and plant-related restrictions.
  • the FIG shows the structure of the method as a block diagram.
  • the gradient module 1 is given starting values 3 by a memory module 5, from which the closest optimum is found in a number of steps or iterations with the aid of numerical differentiation.
  • the basis for this optimization is the functional values determined for each set of control values and environmental values on the basis of an objective function 7 based on a physical model.
  • Restrictions on the manipulated variables are incorporated additively into the objective function 7 by a penalty function.
  • the penalty function returns the value zero, so that no modification of the objective function 7 takes place. If the restrictions are violated, the penalty function returns a value greater (less) zero if it is a minimization problem (maximization problem). Due to a steadily increasing (falling) relationship between the error resulting from the violation of the restrictions and the function value of the penalty function, the optimization method working with the target function 7 modified by the penalty function is automatically guided in the direction of the valid range, provided the penalty function has a magnitude greater slope than the objective function. In order to ensure this, a strongly increasing penalty function is used, whereby an optimum of the unmodified objective function becomes the optimum of the objective function 7 only under active consideration of the restrictions within the required accuracy.
  • the gradient method makes it possible to find a local optimum of control values for the operation of the power plant. However, in particular when there is a change in the ambient values which can not be influenced by the operating personnel, there may possibly be another global optimum which can not be found by the gradient method.
  • a random generator module 13 is provided which generates in each cycle for each control value 15 approximately equally distributed random values within their respective definition range.
  • the randomly generated set of control values 15 is evaluated via the target function 7 and supplied together with the function value of the target function 7 as a first input sentence to the comparison module 17, which receives as a second input set the determined by the gradient method sentence from the comparison memory module 11.
  • the comparison module 17 compares the function values of the two input sets and, in each calculation cycle, switches the input set to the output having the smaller (larger) function value if minimization (maximization) is provided.
  • the addition of a second or further random number generator modules 13 may also be considered.
  • the optimization space exponentially growing with the number of control values 15 can be searched stochastically more intensively, thus accelerating the finding of the global optimum.
  • the output of the comparison module 17 is connected to a comparison memory module 19 which stores in the time window in which the gradient method is running the smallest or largest function value with the associated control values from the comparison module 17. If the gradient method converges, the stored set is transferred to the storage module 5 and from there to the control device 21 of the power plant, the storage module 5 being connected upstream of the gradient module 1 and delivering its starting values 3. At the same time, the newly found optimum, which is present in the comparison memory modules 9 connected downstream in the gradient module 1, is forwarded to the memory module 11 before the comparison module 17, and in the next cycle the comparison memory modules 9, 19 are reset.
  • the random number generator of each variable is based on the linear congruence generator and is a pseudo-random generator, since the same random number sequence occurs at each start is issued. Like many random number generators, the linear congruence generator works with the modulo function, which outputs the remainder of a division.
  • the recursive prescription of random numbers y i t ⁇ 0 1 and the random variables x i t ⁇ ULx i LLx i describe Equations 1 and 2.
  • the comparison module 17 has analog input sets f ( x 1 , x 1 , and f ( x 2 , x 2 (and optionally f ( x 3 , x 3 )) as well as a set of analogue outputs f ( x ) x ,
  • the third input is normally hidden and not connected, which means that the value is zero. So that this does not lead to a malfunction of the comparison module 17, internally at all inputs, if the value zero is present, this is replaced by the smallest (maximization) or largest (minimization) representable value, so that the desired functionality of the filtration is maintained. This must be taken into account in particular if the optimum sought is zero, since this is consequently not taken into account.
  • the memory module 5, 11 has an analog input set f ( x ) and x , a binary input SET and an analog output set f ( x ) and x , Setting SET to 1 switches the value set at the input to the output, stores it at 0 when SET is reset, and stops at the output until the SET input is set to 1 again.
  • the gradient method forms, as the name already suggests, the partial derivatives of the objective function after the manipulated variables in order to determine the direction of optimization. For this purpose, starting from the location vector x x t , which in the first iteration of the seed vector x s is formed, supporting points, each in the direction of a control value x i order ULx i - LLx i 1 / dx are shifted. By evaluating the objective function at the support points and forming the discretized partial derivatives, the search direction results.
  • the normalized search direction is achieved by dividing the search direction vector (gradient) by the amount of the largest partial derivative so that the normalized main search component has the magnitude one.
  • the initial step size is formed from the domain of definition (U Lx i - LLx i ) of the control values with the largest partial derivative by using steps 1 . 5 min ⁇ step is multiplied. The new vector x t + 1 results from the previous totalized search direction stretched over the step size. This procedure is repeated until the value of the objective function does not change steadily, but oscillates. If the numerically formed gradients have changed their sign three times in series, the value "steps" is internally reduced by one and the method continues with reduced step size. The convergence criterion is met when the step size reaches zero, or the value of the objective function does not change within four iterations. In that case, the binary output "conv" becomes true and the gradient method can be restarted by actuating the "RS" input and new start values.
  • the optimization problem can be scaled. In this way, consideration is primarily given to the requirement of a specific accuracy of the solution, based on the definition range of the manipulated variables 15.
  • the size of the smallest increment in the main search direction can be set via "min-step” just before convergence. This is 1 min step the definition range of the control values 15, which has the largest partial derivative in the immediate vicinity of the optimum. This allows the required accuracy of the solution to be set.
  • the "steps" parameter is used to define the initial step size, which is greater by steps 1.5 than the final step range, based on the definition range of the manipulated variables 15 with the currently largest partial derivative. With the help of this simple, heuristic step size control, the convergence speed can be significantly accelerated.
  • a method for system control in a power plant in the above-mentioned embodiment satisfies the requirements for integrated use in process control technology and makes it possible to quickly find a globally optimal set of control values 15. Thus, a particularly efficient operation of the power plant is made possible with high efficiency and / or very low pollutant emissions.

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  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Feedback Control In General (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Claims (7)

  1. Procédé de commande d'installation dans une centrale électrique, dans lequel pour une pluralité de jeux de valeurs (15) de réglage, on produit respectivement à partir d'un jeu de valeurs d'environnement, d'une part, et du jeu respectif de valeurs (15) de réglage, d'autre part, une valeur de fonction affectée au jeu respectif d'une fonction (7) cible reposant sur un modèle physique, le jeu respectif de valeurs (15) de réglage étant sélectionné pour être acheminé à un dispositif (21) de commande de la centrale électrique, dont la valeur de fonction associée satisfait un critère d'optimisation donné à l'avance, le nombre de jeux de valeurs (15) de réglage, en plus d'un jeu de début et d'un jeu, déterminé au moyen d'un procédé à gradient à partir du jeu de début et de sa valeur de fonction associée, comprenant en outre un jeu sélectionné au moyen d'un générateur de nombres aléatoires.
  2. Procédé de commande d'installation dans une centrale électrique, dans lequel le procédé suivant la revendication 1 est réalisé de manière répétée cycliquement à la façon d'une boucle, le jeu sélectionné de valeurs (15) de réglage d'un cycle étant le jeu de départ du cycle le suivant.
  3. Procédé de commande d'installation suivant la revendication 1 ou 2, dans lequel on remet le jeu sélectionné de valeurs (15) de réglage dans le dispositif (21) de commande aux dispositifs de réglage de la centrale électrique associés respectivement aux diverses valeurs de réglage.
  4. Procédé de commande d'installation suivant l'une des revendications 1 à 3, dans lequel la fonction (7) cible comprend une fonction de pénalisation.
  5. Procédé de commande d'installation suivant l'une des revendications 1 à 4, dans lequel on donne à l'avance, avant la détermination respective du jeu, un pas de progression au moyen du procédé à gradient.
  6. Dispositif de commande d'une centrale électrique, comprenant un module (13) générateur de nombres aléatoires, conçu pour la détermination d'un jeu aléatoire de valeurs de réglage, et un module (1) de gradient, conçu pour la détermination d'un jeu optimum de valeurs de réglage, lesquels sont reliés, du côté de la sortie de données, en ayant un module (17) de comparaison conçu pour la comparaison de jeux de valeurs de réglage, le dispositif de commande étant conçu pour la réalisation du procédé suivant l'une des revendications 1 à 5.
  7. Centrale électrique ayant un dispositif de commande suivant la revendication 6, relié, du côté de l'entrée de données, au dispositif (12) de commande.
EP09779575.1A 2008-06-16 2009-05-28 Procédé de commande d'installation dans une centrale électrique Active EP2394031B1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102008028527 2008-06-16
PCT/EP2009/056529 WO2010003735A2 (fr) 2008-06-16 2009-05-28 Procédé de commande d'une installation dans une centrale électrique

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EP2394031A2 EP2394031A2 (fr) 2011-12-14
EP2394031B1 true EP2394031B1 (fr) 2016-01-13

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EP (1) EP2394031B1 (fr)
CN (1) CN102177476B (fr)
WO (1) WO2010003735A2 (fr)

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Publication number Priority date Publication date Assignee Title
DE102015218472A1 (de) * 2015-09-25 2017-03-30 Siemens Aktiengesellschaft Verfahren und Vorrichtung zum Betreiben eines technischen Systems
BE1027173B1 (nl) * 2019-04-05 2020-11-03 Atlas Copco Airpower Nv Werkwijze voor het regelen van een systeem voor vermogensopwekking, dergelijk systeem voor vermogensopwekking en compressorinstallatie omvattend dergelijk systeem voor vermogensopwekking
US12444943B1 (en) * 2024-10-25 2025-10-14 BrightNight Power LLC Co-optimization of dispatch of multiple types of renewable energy assets to simulate a renewable energy system

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DE3710990A1 (de) 1986-04-02 1987-10-22 Hitachi Ltd Betriebssystem und verfahren zum anfahren eines waermekraftwerkes
EP0770232B1 (fr) * 1994-07-08 1998-03-25 Siemens Aktiengesellschaft Systeme de guidage pour usine electrique
US7146231B2 (en) * 2002-10-22 2006-12-05 Fisher-Rosemount Systems, Inc.. Smart process modules and objects in process plants
DE10309615A1 (de) 2003-03-05 2004-09-23 Siemens Ag Dynamische Verarbeitung von Datenverarbeitungsaufträgen
JP4575176B2 (ja) * 2005-01-17 2010-11-04 株式会社日立製作所 排熱回収ボイラの発生蒸気推定方法及び発電設備の保全計画支援方法
GB2446343B (en) * 2005-12-05 2011-06-08 Fisher Rosemount Systems Inc Multi-objective predictive process optimization with concurrent process simulation
US7389151B2 (en) * 2006-03-06 2008-06-17 General Electric Company Systems and methods for multi-level optimizing control systems for boilers
US7848829B2 (en) * 2006-09-29 2010-12-07 Fisher-Rosemount Systems, Inc. Methods and module class objects to configure absent equipment in process plants

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Publication number Publication date
CN102177476B (zh) 2016-09-21
EP2394031A2 (fr) 2011-12-14
US20110160926A1 (en) 2011-06-30
WO2010003735A3 (fr) 2012-01-26
CN102177476A (zh) 2011-09-07
WO2010003735A2 (fr) 2010-01-14
US9206709B2 (en) 2015-12-08

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