WO2000073861A1 - Procede et dispositif pour concevoir ou optimiser un systeme technique - Google Patents

Procede et dispositif pour concevoir ou optimiser un systeme technique Download PDF

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Publication number
WO2000073861A1
WO2000073861A1 PCT/DE2000/001565 DE0001565W WO0073861A1 WO 2000073861 A1 WO2000073861 A1 WO 2000073861A1 DE 0001565 W DE0001565 W DE 0001565W WO 0073861 A1 WO0073861 A1 WO 0073861A1
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WO
WIPO (PCT)
Prior art keywords
discrete
points
point
pareto
pair
Prior art date
Application number
PCT/DE2000/001565
Other languages
German (de)
English (en)
Inventor
Regina Hellwig
Original Assignee
Siemens Aktiengesellschaft
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 Siemens Aktiengesellschaft filed Critical Siemens Aktiengesellschaft
Priority to EP00940197A priority Critical patent/EP1183574A1/fr
Priority to JP2001500917A priority patent/JP2003501714A/ja
Publication of WO2000073861A1 publication Critical patent/WO2000073861A1/fr

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Classifications

    • 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
    • G05B13/0205—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric not using a model or a simulator of the controlled system
    • G05B13/024—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric not using a model or a simulator of the controlled system in which a parameter or coefficient is automatically adjusted to optimise the performance

Definitions

  • the invention relates to a method and a device for designing or optimizing a technical system.
  • the target functions are usually formulated so that the smaller the scalar value of the target function, the more positive the desired properties.
  • a point under consideration is defined as pareto-optimal if there is no further point with the property that the further point is equally good in all goals (the target function one has a value less than or equal to) and is better in at least one target (the objective function has a lower value) than the point under consideration.
  • multi-goal minimization problems deal with either the solution of continuous or discrete multi-goal problems.
  • a continuous parameter is understood to be a parameter that can assume values from a range of values that is continuous at least in sections, while a discrete parameter is understood to mean a parameter that can only assume values from a discrete range of values.
  • the technical system can be the preheating section for the feed water of a steam power plant.
  • the preheating section is supplied with hot steam, which is taken from the turbine or turbines in areas of low, medium or high pressure.
  • the preheating section comprises several heat exchangers of different types, which serve to supply the thermal energy contained in the steam supplied to the cold feed water and to preheat it.
  • the discrete design or optimization parameters to be taken into account are, for example, the existence of a heat exchanger at a certain point, the type of heat exchanger (desuperheater, condensation preheater, condensate cooler or the combination of the heat exchanger comprising a desuperheater, condensation preheater, condensate cooler), the position of the heat exchanger (relative to several possible steam extraction positions on the turbines) etc.
  • the pressure and temperature of the one matched steam supplied and the temperature and pressure of the steam leaving the heat exchanger or the temperature of the condensate leaving the heat exchanger are taken into account.
  • the invention is based on the object of creating a method and a device for designing or optimizing a technical system, all possible parameter assignments being found which are optimal with respect to the target functions.
  • the discrete-continuous multi-target optimization problem is broken down into a plurality of continuous multi-target optimization problems, each with a fixed parameter assignment for the discrete parameters.
  • the number of continuous optimization problems is determined by the number of possible different parameter assignments for the discrete parameters.
  • the continuous-pareto-optimal value assignments for the continuous parameters found in this way (with a specific value assignment for the discrete parameters in each case) are then searched with a discrete search strategy for points which are pareto-opto al with respect to the target functions (value assignments for all parameters) .
  • the discrete search strategy comprises a series connection of several discrete sub-searches, each with a required number of steps, each step of the sub-search method
  • a pairwise comparison of a reference point (one current reference point per sub-search) taken from the continuously continuous pareto-optimal points is carried out with another continuous-pareto-optimal point.
  • the sub-search procedure operates stochastically on pairs of points, consisting of the reference point of the sub-search (first partner) and the comparison point currently under consideration (second partner), a stochastically selected continuously-pareto-optimal point.
  • the reference point serving as the starting point for the search strategy can be selected at random, with the continuously pareto-optimal points being assigned a uniform probability distribution. Since the search strategy is aimed at finding a Pareto optimum for each start reference point that dominates it, it can be guaranteed that all Pareto-Optima have the best possible chance of being found.
  • the stochastic method which processes a sub-search problem, is such that, starting from the pair of standpoints, a stochastically generated pair of neighboring points is jumped to when the comparison ratio of the pair of neighboring points is at least as high "Good” is like that of the standpoint pair, in other words:
  • the neighboring point pair is accepted with a predetermined probability less than 1 and greater than 0 as a new standing point pair. As a consequence, a proposed neighboring point pair is surely always started if the comparison point dominates the reference point.
  • the current reference point of a sub-search is recognized as the Pareto optimum if the stochastic method is based on a agreed number of examined pairs of points, which can be determined by a given termination criterion, does not provide a pair of points at which the comparison point dominates the current reference point. If this is not the case, the comparison point of the pair of result points becomes the current reference point of a further sub-search.
  • sub-searches are appended to one another until a sub-search ends with the information that their reference point is pareto-optimal.
  • the sequence of sub-searches after the detection of a reference point as Pareto-Optimum with the comparison point of the pair of results as the reference point of the first subsequent sub-search can be used to find several or if possible all Pareto-Optima continue, which is more likely than not to be dominated by the Pareto optimum (by definition this point cannot of course dominate the Pareto optimum), ie with a point that is more likely to be "as good” as the pareto optimum as worse. This point will usually not itself represent a pareto optimum (but can!), so that the search strategy proceeds from this point to another pareto -Optimum can get.
  • the Metropolis algorithm is used as the stochastic method for the sub-searches.
  • the information that continuously-pareto-optimal points each have a fixed value assignment of the discrete parameters is mutually exclusive not dominate, can be used to reduce the search space for the discrete sub-search.
  • all of the continuous-pareto-optimal points belonging to the solution set of the continuous multi-target optimization problem for a first parameter assignment for the discrete parameters can be excluded from the search space if it was found that a solution to the solution set of the continuous multi-target Optimization problem for a second parameter assignment for the continuous-pareto-optimal point belonging to the discrete parameters dominates at least one of the points belonging to the solution set mentioned and is the reference point of the sub-search.
  • the single figure shows a block diagram of a device for carrying out the method according to the invention.
  • the technical system 1 shown schematically in the single figure is described by m c continuous parameters and m D discrete parameters.
  • the pareto-optimal parameter assignments are output by the device 3 for design via an output unit 5.
  • the parameter assignment can be output by an output unit 3 designed for this purpose as a screen. This will generally be required by the expert entrusted with the design of the technical system.
  • a specific one of the possibly several found pareto-optimal parameter assignments are output directly to the technical system 1 in the form of signals.
  • the technical system 1 can also be optimized during its operation.
  • the device 3 further comprises a processor unit 7 and a plurality of memories or memory areas assigned to it, namely the program memory or memory area 9, the memory or memory area 11 for the target functions and the memory or memory area 13 for the value ranges of the individual continuous or discrete parameters.
  • variable constants of the target functions can be entered into the memory area 11 for the target functions via a receiving unit 15.
  • These can be parameters which, according to the procedure for the design or optimization of the technical system, are not freely selectable within certain value ranges; In particular, parameters y s of the system 1 that cannot be directly influenced by the device 3 or variables y ⁇ describing environmental influences on the technical system can be taken into account in this way.
  • a system 1 which is in operation can be adapted to changed environmental conditions or changed system-inherent conditions and can be optimized or tracked accordingly.
  • the system parameters y s which are not freely selectable or are not available for the design or optimization, can be detected, for example, by means of measuring devices (for example, the technical system integrated), which are not shown in detail, and can be supplied to the device 3 of the device 3.
  • the environmental sizes ⁇ ⁇ or sizes of other can be input via the emitting unit 15.
  • the emitting unit 15 also serves at the same time for the manual input of the value ranges required for the system description for the target functions into the memory 13.
  • the input can also be made by data transmission from another computer or the like.
  • the program memory 9 contains a program which is used to determine the one or more pareto-optimal parameter assignments for the m c continuous parameters and m D discrete parameters.
  • the parameters are combined to form a continuously discrete parameter vector (X C , X D ).
  • n target functions f ⁇ (x c , XD) to f n (x c , XD) the multi-target optimization problem can be written as:
  • Parameter assignments for the parameter vector (X C , XD) are therefore sought which are as good as possible with regard to the target functions, ie in which the target functions simultaneously assume the lowest possible value.
  • This discrete-continuous multi-target optimization problem I is broken down into a plurality of purely continuous optimization problems
  • the continuous methods applied to the majority of problems II provide one or more points that are pareto-optimal for each problem II, which are also referred to below as continuous-pareto-optimal points. These form a search space to which a discrete search strategy is applied, which is based on a pairwise comparison of the continuously pareto-optimal points and is subdivided into several discrete sub-searches.
  • a point is randomly selected from the search space as a reference point.
  • the reference points each result from the comparison point of the result point pair of the previous sub-search.
  • the reference point is then compared with other points using a stochastic method.
  • a point is rated "better” if it dominates another point, a point being considered dominant if it is at least equally good in all objective functions (ie has an at least equally low value) and better than that in at least one objective function Reference point.
  • the Metropolis algorithm for example, is suitable for solving the individual discrete sub-problems.
  • a neighborhood structure is determined, which is determined by the probabilities with which each pair of points is generated from each pair of standing points.
  • the stochastic method applied to the individual discrete sub-optimization problem in each step either delivers a new reference point after a certain number of comparisons starting from the starting point or current reference point, or it is terminated with the statement “es” after a defined termination criterion has been reached is no better point than the current reference point ". In this case the current reference point is considered pareto-optimal.
  • the stochastic method which is preferably to be used should also be designed in such a way that not only better or equally good point pairs are jumped to in any case (ie with the probability “1”), but also worse point pairs with a certain probability. The latter is necessary in order to use the method not to get caught in "local minima”.
  • this comparison point is defined as the reference point of the new sub-search and the stochastic, discrete search method is started again. If a current reference point is identified as pareto-optimal according to the aforementioned criteria, the process can be terminated after the method has been trained. However, starting from a starting reference point, only one Pareto optimum is reached. In order to record all or several Pareto-Optima, it is therefore necessary to start the search strategy described above from several different start reference points. This can be done either one after the other or simultaneously.
  • the starting reference points should be chosen so that they do not dominate each other. This can be done using a suitable stochastic selection.
  • Another possibility is not to abort the search strategy described above after recognizing a Pareto optimum, but to continue with an "equally good" point.

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  • Engineering & Computer Science (AREA)
  • Software Systems (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Computation (AREA)
  • Medical Informatics (AREA)
  • Health & Medical Sciences (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Complex Calculations (AREA)
  • Feedback Control In General (AREA)

Abstract

L'invention concerne un procédé pour concevoir ou optimiser un système technique, selon lequel le système technique présente au moins deux fonctions cibles définies. Chaque fonction cible est soumise à l'influence d'un nombre défini de paramètres continus dont les valeurs sont prélevées dans une plage de valeurs continue définie, et d'un nombre défini de paramètres discrets dont les valeurs sont prélevées dans une plage de valeurs discrète. Les valeurs affectées aux paramètres continus et discrets sont combinées en un vecteur de paramètres discrets-continus (xC, xD) où xC désigne les valeurs des paramètres continus et xD désigne les valeurs des paramètres discrets.
PCT/DE2000/001565 1999-05-26 2000-05-17 Procede et dispositif pour concevoir ou optimiser un systeme technique WO2000073861A1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
EP00940197A EP1183574A1 (fr) 1999-05-26 2000-05-17 Procede et dispositif pour concevoir ou optimiser un systeme technique
JP2001500917A JP2003501714A (ja) 1999-05-26 2000-05-17 技術システムを設計または最適化する方法および装置

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE19924011.6 1999-05-26
DE19924011 1999-05-26

Publications (1)

Publication Number Publication Date
WO2000073861A1 true WO2000073861A1 (fr) 2000-12-07

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PCT/DE2000/001565 WO2000073861A1 (fr) 1999-05-26 2000-05-17 Procede et dispositif pour concevoir ou optimiser un systeme technique

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EP (1) EP1183574A1 (fr)
JP (1) JP2003501714A (fr)
WO (1) WO2000073861A1 (fr)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7137809B2 (en) 2001-01-30 2006-11-21 Alstom Technology Ltd. Method for the production of a burner unit
US7198483B2 (en) 2001-01-30 2007-04-03 Alstom Technology Ltd. Burner unit and method for operation thereof
CN106845725A (zh) * 2017-02-13 2017-06-13 广东工业大学 一种工程参数寻优方法及系统

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
C.COELLO ET AL: "AN APPROACH TO MULTIOBJECTIVE OPTIMIZATION USING GENETIC ALGORITHMS", INTELLIGENT ENGINEERING SYSTEMS THROUGH ARTIFICIAL NEURAL NETWORKS, 12 November 1995 (1995-11-12), USA, pages 411 - 416, XP000900855 *
D.JAQUES ET AL: "A MATLAB TOOLBOX FOR FIXED-ORDER, MIXED-NORM CONTROL SYNTHESIS", IEEE CONTROL SYSTEMS MAGAZINE, vol. 16, no. 5, October 1996 (1996-10-01), USA, pages 36 - 44, XP000937481 *
M.SALAPAKA ET AL: "CONTROLLER DESIGN TO OPTIMIZE A COMPOSITE PERFORMANCE MEASURE", PROCEEDINGS OF THE 34TH IEEE CONFERENCE ON DECISION AND CONTROL, vol. 1, 13 December 1995 (1995-12-13), USA, pages 817 - 822, XP000937465 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7137809B2 (en) 2001-01-30 2006-11-21 Alstom Technology Ltd. Method for the production of a burner unit
US7198483B2 (en) 2001-01-30 2007-04-03 Alstom Technology Ltd. Burner unit and method for operation thereof
CN106845725A (zh) * 2017-02-13 2017-06-13 广东工业大学 一种工程参数寻优方法及系统
CN106845725B (zh) * 2017-02-13 2020-11-13 广东工业大学 一种工程参数寻优方法及系统

Also Published As

Publication number Publication date
EP1183574A1 (fr) 2002-03-06
JP2003501714A (ja) 2003-01-14

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