EP3360015A1 - Verfahren und softsensor zum ermitteln einer leistung eines energieerzeugers - Google Patents
Verfahren und softsensor zum ermitteln einer leistung eines energieerzeugersInfo
- Publication number
- EP3360015A1 EP3360015A1 EP16816600.7A EP16816600A EP3360015A1 EP 3360015 A1 EP3360015 A1 EP 3360015A1 EP 16816600 A EP16816600 A EP 16816600A EP 3360015 A1 EP3360015 A1 EP 3360015A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- power
- power value
- generator
- energy
- soft sensor
- 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- Withdrawn
Links
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
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
-
- 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
- G05B17/00—Systems involving the use of models or simulators of said systems
- G05B17/02—Systems involving the use of models or simulators of said systems electric
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
Definitions
- the invention relates to a method and a soft sensor for determining a power of a first power generator, which is coupled to a second power generator.
- Contemporary power generation plants often include a plurality of mechanically and / or electrically coupled to each other energy generators that generate or convert energy from other types of energy.
- Examples of such hybrid systems are gas turbines, which are operated to increase their efficiency in combination with steam turbines, which use the waste heat of the gas turbine via a heat exchanger.
- Other examples are motor vehicles with hybrid drive.
- a power generated by a gas turbine can no longer be measured separately as soon as a steam turbine acting on the same shaft itself provides a power contribution.
- the power specifically generated by the gas turbine, or generally by a first energy generator is an important operating parameter, the knowledge of which can contribute significantly to optimum operation of the gas turbine or of the first energy generator.
- soft sensors are often used, which determine the specific power of the gas turbine by means of a data-driven model from other operating data.
- Such a soft sensor may be implemented using a neural network, for example, the learning based on ge ⁇ accumulated operating data an image of measurable operating and environmental parameters on the power output from the gas turbine performance in a training phase.
- a training phase is generally based on operating data recorded on a single-shaft turbine in pure gas turbine operation, that is to say with an idling steam turbine, or on a multi-shaft turbine.
- Such a pure gas turbine operation is often referred to as a "simple cycle”.
- This object is achieved by a method having the Merkma ⁇ len of claim 1, by a soft sensor with the features of claim 11, by a computer program product with the features of claim 12 and by a computer-readable storage medium having the features of patent claim 13th
- a first soft sensor For determining a power output from a first power generator, wherein the first power generator with a second power generator is coupled, a first soft sensor is queried, which is trained to determine a single-operation power value of the first power generator.
- Energy generators for generating mechanical energy, electrical energy, magnetic energy and / or thermal energy such as, for example, turbines, generators, motors, solar modules, etc., or mixed forms thereof may be provided as the first and second energy generator.
- the first energy generator can preferably be coupled mechanically, electrically, magnetically and / or by mixed forms with the second energy generator. In an operation combining the first and second power generators, a single-operation power value determined by the first soft-sensor for the first power generator is read.
- a first power value for the first energy generator and a second power value for the second energy generator are determined by a second soft sensor.
- a total output of the energy producers is determined.
- the first or second power value can be determined in particular as absolute or relative to the second or first power value or relative to the overall power.
- the second soft sensor is trained in such a way that a one ⁇ zelabweichung between the individual operating-power value and the first power value and a total deviation between the total power and a combination of the first and second power value is reduced, preferably minimized.
- the first power value is output.
- traction may mean, in particular, that a mapping of input parameters of the soft sensors to one or more target variables is optimized according to predefinable criteria during a training phase.
- a soft sensor For performing the method according to the invention a soft sensor, a computer program product and a computer-readable storage medium ⁇ are provided.
- the inventive method or the soft sensor according to the invention for example, by one or more processors, application-specific integrated circuits
- ASIC application specific integrated circuit
- DSP digital signal processors
- FPGA Field Programmable Gate Arrays
- the output from the first energy producer in the combined operation power can be determined much more accurately than by a Single mode only trai ⁇ ned soft sensor.
- the individual deviation and a first part of the total deviation can be assigned to the first energy generator and a remaining part of the total deviation to the second energy generator.
- the second soft sensor may then be trained such that the associated deviations, i. the individual deviation and the first and second part of the total deviation energy-specific reduced, preferably minimized.
- the total deviation in a predetermined ratio can be divided into the first part and the remaining part.
- the predetermined ratio can essentially correspond to a power ratio between the first power generator and the second power generator.
- a gas turbine with a steam turbine can be given as a ratio, for example, a value of about 2: 1, which typically corresponds to the power ratio.
- Such a ratio is often previously known and often has only a slight variation.
- the single-operation power value and / or the first power value can be determined on the basis of operating data of the first energy generator and / or the second power value on the basis of operating data of the second energy generator.
- the operating data may include, among others, default values, control data and / or measurement data.
- the first and / or the second soft sensor can be implemented by means of a data-driven trainable regressor and / or by means of a neural network.
- the first power value can be determined by means of a first neural subnet assigned to the first power generator, the second power value by means of a second neural subnet assigned to the second power generator, and the total deviation and the individual deviation by means of a further neural training layer.
- the first or second neural subnetwork can comprise or form an energy generator-specific neural model of the first or second energy generator in the course of the training. In this way, energy producer-specific data can generally be better recorded and / or determined.
- neural parameters and / or neural weights of the trained first neural subnetwork may be specified. extracted and transferred to a third soft-sensor.
- a third soft-sensor In this way, an accuracy of a third, external soft sensor in determining the output from the first power generator performance can be significantly improved.
- a third soft sensor modified in this way can often be used in conventional systems without any further changes.
- the second soft sensor can be retrained regularly and / or continuously trained in the combined operation. This can preferably be done by autonomous training ⁇ ningshabilit.
- a first power of the first power generator can be measured and compared with the first power value and / or a second power of the second power generator can be measured and compared with the second power value.
- a deviation signal can then be output.
- a monitoring function can be implemented by the second soft sensor is trained on an intact plant and in case of deviations of a ermit ⁇ telten power value from a measured power, for example, it is concluded that a malfunction, and an error signal is given off.
- FIG. 1 shows a first soft sensor and a power generator operated in a single operating mode
- FIG. 2 shows a power generation plant with a fiction, modern ⁇ , second soft sensor and combined powered energy producers and Figure 3 shows the second soft sensor in more detail.
- the first power generator GT may be, for example, a power generator for generating mechanical energy, electrical energy, magnetic energy and / or heat energy, such When ⁇ play a turbine, a generator, a motor, a solar module or a mixed form thereof.
- a gas turbine GT is be ⁇ seeks first energy producer.
- the first energy generator GT is operated in the operating phase described in FIG. 1 for training the first soft sensor S1 in a single operating mode.
- the first Energy generator GT may in this case a gas turbine, the anläge in a single-shaft in the pure gas turbine operation (simple cycle), that is, uncoupled or no load conditions Revolving steam turbine is operated, or a gas turbine in a Mehrwellenanla ⁇ ge be.
- the first soft sensor Sl is implemented in the presentskysbei ⁇ game by an adaptive neural network having an input layer Sil for reading in the single operation ⁇ data EBDG, one or more hidden layers S1H and an output layer SIO for outputting an individual farm power value EL.
- the input layer Sil and the output layer SlO are respectively coupled to the hidden layers S1H.
- the first soft sensor Sl is trained in a particular mode of operation of the f ⁇ th power generator GT on the most accurate determination of the individual operating power value EL for the first Energyer ⁇ producers GT.
- the operating data are Einzelbe- EBDG of the first power generator GT is captured and Sl ⁇ fed into the input layer Sil the first soft sensor. From the individual operating data EBDG, a single-operation power value EL of the first energy generator GT is determined by their propagation through the hidden layers S1H to the output layer S10.
- a single power ELM of the first power generator GT is measured in the individual operating mode, transmitted to the first soft ⁇ sensor Sl and compared there with the determined by the first soft sensor Sl single-operation power value EL. Since ⁇ up based the first soft sensor Sl is trained in such a way that a deviation between the single operating power value EL, and the measured single power ELM, for example, an amount of a difference EL ELM, in single operation mode with regard to a minimizing objective MIN is minimized. This training can be done for example by back propagation training.
- FIG. 2 shows a schematic diagram of a power generation plant A with a plurality of energy generators operated in combination.
- the power generation plant A may be, for example, a power plant or a hybrid drive of a motor vehicle.
- the energy generation system A comprises a single-shaft system with a gas turbine GT as the first energy generator and a steam turbine DT as the second energy generator.
- the gas turbine GT and the steam turbine DT mechanically coupled ⁇ coupled, insofar as they act on a common wave W.
- the gas turbine GT and the steam turbine DT are operated in combination, that is, both energy generators DT and GT deliver power to the common shaft W.
- Such combined or combined operation is often referred to as “combined cycle” or combined cycle operation (CCGT: gas and steam). This is usually a productive or regular operation of such a power plant A.
- CCGT combined cycle operation
- the gas turbine GT may be a gas turbine described in connection with FIG. 1 in the single mode of operation, or a gas turbine of the same or a similar type.
- the steam turbine DT uses for operation waste heat of the gas turbine GT via a heat exchanger, so as to increase efficiency or efficiency of the power generation plant A.
- gas turbine GT gas turbine GT
- steam turbine DT other energy generators for generating mechanical energy, electrical energy, magnetic energy, thermal energy, etc. may be provided, such as turbines, generators, motors, solar modules, etc., or mixed forms thereof.
- the gas turbine GT and the steam turbine DT give about the same shaft ge ⁇ my W power to a current generator G on, the mechanical energy of the shaft W receives and converts them into electrical ⁇ specific energy.
- these can also be coupled electrically and / or magnetically. Due to this coupling, the individual outputs of the energy generators GT and DT are not directly determinable.
- the power generation plant A has for its control via a plant controller AS, the one or more processors PROC for performing all the steps of the Anlagensteue ⁇ tion AS has.
- the system controller AS is coupled to the power generators GT and DT and to the power generator G.
- Control system SD transmits control data SDG to gas turbine GT as well as for controlling steam turbine DT control data SDD to steam turbine DT for controlling gas turbine GT.
- This control is performed depending on operational data BDG of the gas turbine GT as well as a function of operating data BDD of the steam turbine DT so that their operational management is optimized in terms of efficiency for example, wear and / or harmful ⁇ output material.
- the operating data BDG and BDD can be, for example physi ⁇ cal, control engineering and / or design-related operation ⁇ sizes or characteristics of the power generator GT or DT, and for example, a burning current mass flow guide vane, an operating temperature, an exhaust temperature, vibration, pressure, ambient conditions or other default values, control parameters, control data and / or measured values.
- the system control unit AS at least partially reads the operating data BDG from the gas turbine GT and the operating data BDD at least partially from the steam turbine DT.
- the control data and the SDG Be ⁇ operating data BDD may include the control data SDD at least partially the OPERATING ⁇ th BDG.
- a total power GL of the combined power generators GT and DT is measured at the power generator G and transmitted to the system controller AS.
- the system controller AS has the first soft sensor S1 described in connection with FIG. This was previously - as also described in connection with Figure 1 - for determining a single-operation power value EL based on operating data of the gas turbine GT in a single operating mode or training based on operating data of a gas turbine of the same or similar type.
- the current operating data BDG of the gas turbine GT are transmitted to the first soft-sensor S1.
- the first soft-sensor Sl determines from the transmitted operating data BDG of the combined operating mode a current single-operation power value EL.
- the single-operation power value EL is then transmitted from the first soft-sensor Sl to a second soft-sensor S2 coupled thereto.
- the second soft sensor S2 is implemented in the present embodiment by an adaptive neural network, which is trained in the combined operation of the power generator GT and DT to determine power values LI and L2 for Einzelleis ⁇ tions of the power generators GT and DT.
- LI is a first power value for the individual power of the gas turbine GT in the combined operating mode
- L2 a second power value for the individual power of the steam turbine DT in the combined operating mode.
- the second soft sensor S2 For determining the power values LI and L2 are current through the second sensor S2 Soft operating data BDG and BDD so-as a current single-mode power value, and a EL ak ⁇ TULLE measured total power GL read.
- the second soft sensor S2 determines the first power value LI on the basis of the operating data BDG and the second power value L2 on the basis of the operating data BDD.
- the second soft sensor S2 is trained with regard to a minimization target MIN in such a way that a single deviation L1-EL and a total deviation L1 + L2-GL are minimized.
- the second soft sensor S2 can be continuously trained in combined operation and / or be nachtrainiert regularly.
- the determined power values LI and L2 are transmitted from the second soft sensor S2 to a turbine controller CTL of the system controller AS coupled to the second soft sensor S2.
- the turbine controller CTL is used to control the gas turbine GT and the steam turbine DT.
- the door ⁇ termen facedung CTL determined based on the performance levels LI and L2 and using operational data BDG and BDD a current operating state of the turbine DT and GT.
- the turbine control CTL determines the opti mized ⁇ control data SDG and SDD for the optimized management of the turbine GT and DT, for example, in terms of efficiency, wear and / or emissions.
- the control data SDG and SDD are accordingly transmitted to the turbines GT and DT for the above purposes.
- the second soft sensor S2 transmits the power values LI and L2 to a monitoring module MON of the system controller AS.
- the monitoring module MON is coupled to the second soft sensor S2 and to the turbine control CTL and serves to monitor the gas turbine GT and the steam turbine DT.
- the monitoring module compares the transmitted power values LI and / or L2 with predetermined and / or measured individual powers of the gas turbine GT or steam turbine DT. In ei ⁇ ner deviation of the compared sizes can be concluded that a malfunction of the power generation plant A with the proviso that the first and / or second soft sensor has been trained on an intact plant.
- FIG. 3 illustrates the second soft sensor S2 in more detail.
- the second soft sensor S2 comprises a first neural sub ⁇ network S2G for modeling the gas turbine GT and for determining the first power value LI based on Operating data BDG.
- the first neural sub-network S2G has an input layer S2GI for reading the operating data BDG, one or more hidden layers S2GH and an output layer S2GO for outputting the first power value LI.
- the input layer S2GI and the output layer S2GO are each coupled to the one or more hidden layers S2GH.
- the operating data BDG be ⁇ and fed to the input layer S2GI over the one or more hidden layers to the output layer S2GH S2GO propagated, which outputs the first Leis ⁇ tung value LI.
- the second soft sensor S2 also includes a second neural network portion S2D to the modeling of the steam turbine DT and for determining the second power value L2 based on the operation data ⁇ BDD.
- the second neural sub-network comprises an input layer S2D S2DI for reading the operating data of BDD, one or more hidden layers and an output S2DH ⁇ layer S2DO for outputting the second power value L2.
- the input layer and the output layer S2DI S2DO are sorted ⁇ wells coupled to the one or more hidden layers S2DH.
- the operating data BDD be fed into the input layer S2DI and propagating one or more ver ⁇ covered S2DH layers to the output layer S2DO that outputs the second power value L2.
- the second soft sensor S2 furthermore has an energy-generating neural training layer S2GD, which is coupled both to the first neural sub-network S2G and to the second neural sub-network S2D.
- the first neural network S2G part, the second neural network portion S2D and / or the training S2GD layer may be implemented by a plurality of respectively to ⁇ drop weights initialized neural networks or neural layers.
- the output signals of the sub-networks S2G and S2D and / or of the training layer S2GD ⁇ the then each formed by averaging the outputs of the respective plurality of neural networks or neural layers.
- the training layer S2GD be ⁇ value supplied to the individual operating-in EL from the first soft sensor Sl and the measured total power ⁇ GL of the current generator G. Furthermore, the determined first power value LI from the first neural subsystem S2G and the second determined power value L2 from the second neural subsystem S2D are transmitted to the training layer S2GD. From the transmitted data GL, EL, LI and L2, the training layer S2GD determines a total deviation DELG between the total power GL and a sum of the first
- the total deviation DELG is divided by the training layer S2GD in a predetermined ratio into a first part DELG1 and a remaining part DELG2.
- For splitting the total deviation DELG is given with
- the first part DELG1 is allocated together with the individual deviation DEL1 specifically, the gas turbine GT, while the remaining part of the steam turbine DELG2 DT is supplied ⁇ assigns specific.
- the ratio WG: WD of the weight WG assigned to the gas turbine GT to the weight WD assigned to the steam turbine DT is preferably specified according to a power ratio of the gas turbine GT to the steam turbine DT.
- the default one the Leis ⁇ mance ratio corresponding weights ratio to a certain extent reflects a respective influence of turbine models and the modeling errors on the performance determination resist and thus has a stabilizing effect on the training.
- the ⁇ ses ratio usually is already known and has in operation often only a slight variation on.
- the second part DELG2 of the total deviation DELG is called
- This training can preferably be done by backpropagation training.
- the first part of the total deviation DELGl DELG is defined before combination was ⁇ ner back propagation with the individual deviation DEL1 and the combination, for example, the mean
- the trained neural subnetworks S2G and S2D then each comprise an energy generator-specific neuronal model of the gas turbines GT or of the steam turbine DT for determining the respective power value LI or L2.
- the prognosis of the first neural subnetwork S2G is to some extent "pulled" in the direction of a single-operation prognosis.
- undesired propagation of model errors of the second neural subsystem S2D into the neural model of the gas turbine GT in the first neural subsystem S2G can be effectively damped or reduced.
- the weighting of the deviation measures DELG, DEL1, DELG1 and DELG2 can to some extent be used to determine to what extent the neural submodel of the gas turbine GT in the first neural subsystem S2G may deviate from the first softness sensor S1.
- this weighting it is possible on the one hand for the first neural subnetwork S2G to deviate from the previously trained first softstool S1 and, nevertheless, for an undesired propagation of model errors of the second neural subnetwork S2D into the first neural subnetwork S2G to be effectively attenuated.
- a soft sensor which has been trained according to the invention can determine a considerably more accurate power value for the individual power of the gas turbine GT from the operating data BDG in combined operation than a soft sensor trained in single operation, here S1. Because typically it ⁇ considerably more operating data or measured data are available for the combined gas and steam power available and the power range of a combined operation GroE SSSR and is more practical than a single operation can be in this way gas turbine models or generally Energyer ⁇ zeugermodelle with higher accuracy and create wider range ⁇ than with known soft sensors. This reduces the previous difficulty of acquiring a sufficient number of operating data in individual operation or finding energy generators with a comparable operating pattern in multi-shaft systems. Due to the additional training phase in combined operation, ie also in productive operation, the single-run trai ⁇ nated gas turbine model can usually be refined significantly.
- neural weights of the first neurona ⁇ len subnetwork S2G can be specifically extracted and transferred to a best ⁇ Henden, third soft sensor. In this way, the accuracy of existing soft sensors can be significantly improved.
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- Automation & Control Theory (AREA)
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- Engine Equipment That Uses Special Cycles (AREA)
- Control Of Eletrric Generators (AREA)
- Control Of Turbines (AREA)
Abstract
Description
Claims
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
DE102015226656.7A DE102015226656B4 (de) | 2015-12-23 | 2015-12-23 | Verfahren und Softsensor zum Ermitteln einer Leistung eines Energieerzeugers |
PCT/EP2016/080072 WO2017108405A1 (de) | 2015-12-23 | 2016-12-07 | Verfahren und softsensor zum ermitteln einer leistung eines energieerzeugers |
Publications (1)
Publication Number | Publication Date |
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EP3360015A1 true EP3360015A1 (de) | 2018-08-15 |
Family
ID=57609845
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
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EP16816600.7A Withdrawn EP3360015A1 (de) | 2015-12-23 | 2016-12-07 | Verfahren und softsensor zum ermitteln einer leistung eines energieerzeugers |
Country Status (5)
Country | Link |
---|---|
US (1) | US20180364653A1 (de) |
EP (1) | EP3360015A1 (de) |
KR (1) | KR102183563B1 (de) |
DE (1) | DE102015226656B4 (de) |
WO (1) | WO2017108405A1 (de) |
Families Citing this family (1)
Publication number | Priority date | Publication date | Assignee | Title |
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DE102017117370A1 (de) | 2017-08-01 | 2019-02-07 | Vaillant Gmbh | Softsensor zur Identifikation und Regelung oder Steuerung eines Wärmepumpensystems |
Family Cites Families (12)
Publication number | Priority date | Publication date | Assignee | Title |
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JP2533942B2 (ja) * | 1989-03-13 | 1996-09-11 | 株式会社日立製作所 | 知識抽出方法およびプロセス運転支援システム |
US6678640B2 (en) * | 1998-06-10 | 2004-01-13 | Matsushita Electric Industrial Co., Ltd. | Method and apparatus for parameter estimation, parameter estimation control and learning control |
JP2002520719A (ja) * | 1998-07-08 | 2002-07-09 | シーメンス アクチエンゲゼルシヤフト | ニューラルネット及びニューラルネットのトレーニング方法及び装置 |
US6853920B2 (en) * | 2000-03-10 | 2005-02-08 | Smiths Detection-Pasadena, Inc. | Control for an industrial process using one or more multidimensional variables |
US20080061045A1 (en) * | 2006-09-11 | 2008-03-13 | The Esab Group, Inc. | Systems And Methods For Providing Paralleling Power Sources For Arc Cutting And Welding |
DE102007001025B4 (de) * | 2007-01-02 | 2008-11-20 | Siemens Ag | Verfahren zur rechnergestützten Steuerung und/oder Regelung eines technischen Systems |
US20120083933A1 (en) * | 2010-09-30 | 2012-04-05 | General Electric Company | Method and system to predict power plant performance |
DE102011003149A1 (de) * | 2011-01-26 | 2012-07-26 | Robert Bosch Gmbh | Verfahren zum Einspeisen von Energie in ein Energienetz |
EP2706422B1 (de) * | 2012-09-11 | 2016-07-27 | Siemens Aktiengesellschaft | Verfahren zur rechnergestützten Überwachung des Betriebs eines technischen Systems, insbesondere einer elektrischen Energieerzeugungsanlage |
US9141915B2 (en) * | 2013-01-30 | 2015-09-22 | Siemens Aktiengesellschaft | Method and apparatus for deriving diagnostic data about a technical system |
JP6816949B2 (ja) * | 2014-11-26 | 2021-01-20 | ゼネラル・エレクトリック・カンパニイ | 発電プラント発電ユニットの制御を強化するための方法 |
EP3360084A1 (de) * | 2015-11-12 | 2018-08-15 | Google LLC | Erzeugung grösserer neuronaler netzwerke |
-
2015
- 2015-12-23 DE DE102015226656.7A patent/DE102015226656B4/de not_active Expired - Fee Related
-
2016
- 2016-12-07 EP EP16816600.7A patent/EP3360015A1/de not_active Withdrawn
- 2016-12-07 KR KR1020187020015A patent/KR102183563B1/ko active IP Right Grant
- 2016-12-07 WO PCT/EP2016/080072 patent/WO2017108405A1/de active Application Filing
- 2016-12-07 US US16/063,330 patent/US20180364653A1/en not_active Abandoned
Also Published As
Publication number | Publication date |
---|---|
WO2017108405A1 (de) | 2017-06-29 |
US20180364653A1 (en) | 2018-12-20 |
DE102015226656B4 (de) | 2019-10-10 |
KR20180094065A (ko) | 2018-08-22 |
DE102015226656A1 (de) | 2017-06-29 |
KR102183563B1 (ko) | 2020-11-26 |
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