EP3877924A1 - Prédiction de valeur de paramètre énergétique d'un parc éolien - Google Patents

Prédiction de valeur de paramètre énergétique d'un parc éolien

Info

Publication number
EP3877924A1
EP3877924A1 EP19795147.8A EP19795147A EP3877924A1 EP 3877924 A1 EP3877924 A1 EP 3877924A1 EP 19795147 A EP19795147 A EP 19795147A EP 3877924 A1 EP3877924 A1 EP 3877924A1
Authority
EP
European Patent Office
Prior art keywords
energy
wind
parameter value
wind farm
network
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.)
Pending
Application number
EP19795147.8A
Other languages
German (de)
English (en)
Inventor
Hennig Harden
Ali HADJIHOSSEINI
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.)
Siemens Gamesa Renewable Energy Service GmbH
Original Assignee
Siemens Gamesa Renewable Energy Service GmbH
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Siemens Gamesa Renewable Energy Service GmbH filed Critical Siemens Gamesa Renewable Energy Service GmbH
Publication of EP3877924A1 publication Critical patent/EP3877924A1/fr
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/028—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor controlling wind motor output power
    • F03D7/0284—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor controlling wind motor output power in relation to the state of the electric grid
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/04—Automatic control; Regulation
    • F03D7/042—Automatic control; Regulation by means of an electrical or electronic controller
    • F03D7/048—Automatic control; Regulation by means of an electrical or electronic controller controlling wind farms
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01W—METEOROLOGY
    • G01W1/00—Meteorology
    • G01W1/10—Devices for predicting weather conditions
    • 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
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2260/00—Function
    • F05B2260/82—Forecasts
    • F05B2260/821—Parameter estimation or prediction
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00—Control
    • F05B2270/70—Type of control algorithm
    • F05B2270/709—Type of control algorithm with neural networks
    • 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
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/26—Pc applications
    • G05B2219/2619—Wind turbines
    • 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
    • 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
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/72—Wind turbines with rotation axis in wind direction

Definitions

  • the present invention relates to a method and system for forecasting a
  • Network connection point is connected to an energy network and at least one
  • Has wind turbine and a computer program product for performing the method.
  • a forecast of energy parameter values of the wind farms is particularly important, for example, for the network management of energy networks with integrated wind farms
  • the object of the present invention is to improve the forecast of an energy parameter value of one or more wind farms.
  • Claims 8, 9 provide protection for a system or computer program product for carrying out a method described here.
  • the subclaims relate to advantageous further developments.
  • one or more wind farms are temporarily or stationary connected to an energy network via a network connection point
  • the one or more of the wind farm (s) have / have in one embodiment (each) one or more wind energy plant (s), which in turn in one embodiment (each) have a rotor, which in one embodiment has at least one and / or at most has six rotor blades and / or one, at least essentially, horizontal axis of rotation or rotor (longitudinal), and / or one, in particular coupled thereto and / or with the (respective) network connection point, in particular via at least one transformer, temporarily or stationary connected, generator has / have.
  • values of input parameters are recorded, the state parameters (values), control parameters (values) and / or service parameters (values) of the wind farm (s), in particular the wind turbine (s) and / or the (respective) network connection point, and / or one or more wind farm external and / or independent of the wind farm and / or spaced, include devices, in particular can consist of them.
  • this detection can include, in particular, determining, in particular measuring, processing, for example filtering, integrating, classifying or the like, and / or receiving.
  • (the or at least part of) the input parameters (values) are recorded continuously. In this way, a forecast precision and / or timeliness can be improved in one embodiment.
  • the or at least some of the input parameters are recorded discontinuously, in particular cyclically or periodically.
  • a data volume and / or a measurement effort can advantageously be reduced in one embodiment.
  • energy parameter value Multi-dimensional energy parameters based on these recorded input parameter values and a machine-learned assignment between the
  • the generation of the forecast in particular the time required for this, and / or the forecast quality can be improved in one embodiment.
  • the (forecast) input parameter (value) depends on an electrical energy, in particular power, of the (respective) wind farm, which it (probably) makes available at the (respective) grid connection point or
  • network management or control technology of the energy network can advantageously be implemented, in particular individual components of the energy network, in one embodiment the one or more of the wind farm (s), in particular theirs
  • Wind energy plant (s) and / or grid connection point (s) are controlled, in particular regulated, on the basis of the predicted energy parameter value (s).
  • a method, system or computer program product for controlling (a network management) of the energy network is placed under protection on the basis of the predicted energy parameter value or the method comprises the step: controlling, in particular rules (network management) of the energy network on the basis of the predicted energy parameter value, or the system means for
  • Control in particular rules, of the energy network on the basis of the predicted energy parameter value.
  • At least one input parameter value is or is determined on the basis of measured electrical, mechanical, thermal and / or meteorological data, in particular therefore with the help of and / or on, in particular in, the (respective)
  • Wind farm in particular its wind power plant (s) and / or network connection point, and / or with the help of and / or on, in particular in, the (respective) wind farm external
  • Device in particular a (wind park-external) component of the energy network and / or a meteorological station, measured electrical, mechanical, thermal and / or meteorological data, in particular such data
  • At least one is in one embodiment
  • Input parameter value determined on the basis of predicted electrical, mechanical, thermal and / or meteorological data, in particular with the help of and / or on, in particular in, the (respective) wind farm (s), in particular its wind energy plant (s) and / or network connection point, and / or with the help of and / or to, in particular in, the (respective) facility outside the wind farm, in particular one (outside the wind farm)
  • Component of the energy network a meteorological station and / or one
  • Weather forecast (facility), predicted electrical, mechanical, thermal and / or meteorological data, in particular such data
  • An input parameter (value) can in particular be a mechanical, thermal and / or an electrical status, in particular status, and / or control,
  • Weather forecast (facility) (s) include, in particular be. At least one
  • input parameters are or are recorded with the aid of a condition monitoring system of the corresponding wind farm, in particular the corresponding wind energy installation.
  • At least one is in one embodiment
  • the or at least one input parameter value determined on the basis of a planned maintenance is updated one or more times, in one version event-based and / or cyclically, in particular continuously, in one version permanently, in one version based on a currently planned maintenance or an updated planned maintenance.
  • Forecast quality can be (further) improved.
  • An update can postpone scheduled maintenance due to unforeseen events in an execution
  • the energy parameter value is predicted for at least two different time horizons.
  • the energy parameter value is predicted for at least a time horizon of at most 5 minutes, in particular for a time and / or space that is at most 5 minutes in the future.
  • the energy parameter value is forecast in an embodiment for at least a time horizon of at least 5 minutes, in particular at least 10 minutes, and at most 30 minutes, in particular at most 20 minutes, in particular for a time and / or space that is at least 5 or 10 minutes and a maximum of 20 or 30 minutes in the future. Additionally or alternatively, the energy parameter value in one embodiment is forecast, in particular, for at least a time horizon of at least 15 minutes, in particular at least 60 minutes, and / or at most 72 hours, in particular at most 48 hours, in an embodiment at most 24 hours, in particular at most 12 hours for a time and / or space that is at least 15 or 60 minutes and / or at most 12, 24, 48 or 96 hours in the future.
  • Energy parameter values, in particular a control based thereon, in particular rules, of the wind farm (s) and / or the energy network can be improved.
  • the or one or more of the input parameter value (s) and / or the energy parameter value are sent via a VPN gateway, in particular a web-based VPN, and / or to a cloud or data or computer cloud, in particular virtual private Cloud, and / or transmitted from a cloud or data or computer cloud, in particular virtual private cloud, in one embodiment to the or one or more of the wind farm (s) and / or from the or one or more of the wind farm (s) and / or to the or one or more of the wind farm external device (s) and / or from the or one or more of the wind farm external device (s) and / or to a or the network management of the energy network and / or to an artificial neural network and / or from or the artificial neural network that implements the assignment.
  • the assignment between the input parameters and the energy parameter is also learned mechanically even during the operation, in particular normal operation, of the at least one wind farm. Additionally or alternatively, the assignment is implemented in an embodiment using an artificial neural network. Additionally or alternatively, in one embodiment, the assignment is learned by machine on the basis of a comparison of recorded and predicted values of the energy parameter.
  • Input parameters and the energy parameter and thereby in particular the quality of the forecast of the energy parameter value can be improved.
  • Energy parameter values of the at least one wind farm, in particular hardware and / or software, in particular program technology, are set up to carry out a method described here and / or have:
  • Means for acquiring values of input parameters which include status, control and / or service parameters of the wind farm, in particular the wind power installation and / or the network connection point, and / or at least one device external to the wind farm;
  • system or its means have:
  • Means for determining at least one input parameter value is determined on the basis of a planned maintenance of the wind farm, in particular the wind energy installation;
  • a VPN gateway in particular a web-based VPN, and / or to and / or from a cloud, in particular virtual private cloud, in particular to and / or from the at least one wind farm, to and / or from the at least one a device external to the wind farm, to and / or from an artificial neural network and / or to a network management of the energy network;
  • an artificial neural network that implements the assignment or is set up or used for this purpose.
  • a means in the sense of the present invention can be designed in terms of hardware and / or software, in particular one that is preferably data-connected or signal-linked, in particular digital, processing, in particular digitally connected to a memory and / or bus system
  • Microprocessor unit CPU
  • graphics card GPU
  • the processing unit can be designed to process commands that are implemented as a program stored in a memory system, to acquire input signals from a data bus and / or to output signals to a data bus.
  • a storage system can have one or more, in particular different, storage media, in particular optical, magnetic, solid-state and / or other non-volatile media.
  • the program can be designed in such a way that it embodies or is capable of executing the methods described here, so that the processing unit can carry out the steps of such methods and thus in particular can forecast the energy parameter value or control the network management of the energy network based thereon.
  • the computer program product can have, in particular a non-volatile, storage medium for storing a program or with a program stored thereon, an execution of this program prompting a system or a controller, in particular a computer, to do one here perform the described method or one or more of its steps.
  • one or more, in particular all, steps of the method are carried out completely or partially automatically, in particular by the system or its means.
  • the system has at least one wind farm, the energy network and / or its network management. Further advantages and features result from the subclaims and the exemplary embodiments. Here shows, partly schematically:
  • FIG. 1 a system for forecasting an energy parameter value of at least one wind farm according to an embodiment of the present invention
  • FIG. 2 a method for forecasting the energy parameter value after a
  • FIG. 1 shows an example of two wind farms, each of which has a plurality of wind energy plants 10 and 20 and are connected to an energy network 100 via a network connection point 11 and 21.
  • State parameter values of the wind energy plants are transmitted to a controller 12 or 22 and an interface 13 or 23 of the respective wind farm, to which the controller 12 or 22 also transmits control parameters.
  • Meteorological stations 14 and 24, condition monitoring systems and transformers 15 and 25 of the wind farms, if present, can also transmit state parameter values to the interface 13 or 23, as indicated in FIG. 1 by dash-dotted data arrows.
  • the interfaces 13, 23 transmit these, optionally processed, for example filtered, integrated and / or classified, input parameter values via VPN gateways of a web-based VPN to a cloud 30, as indicated in FIG. 1 by dash-and-dot-dash data arrows.
  • Other facilities outside the wind farm such as a meteorological station 40 outside the wind farm or a weather forecast (facility) 41, can also transmit input parameter values to the cloud 30 in a corresponding manner via VPN connections.
  • a service company 42 transmits service parameters for the wind farms in a corresponding manner via a VPN connection to the cloud 30, for example times and times of planned maintenance or the like.
  • An artificial neural network 50 automatically learns an assignment on the basis of these input parameter values transmitted from the cloud 30 in a step S10 (cf. FIG. 2) between these input parameters and an energy parameter, for example an electrical power, which is fed into the energy network at its network connection point at a later point in time or offset by a certain time horizon against a measurement time of the input parameter values, or
  • an energy parameter for example an electrical power
  • the artificial neural network 50 predicts input parameter values and the machine-learned assignment in operation in a step S20 (cf. FIG. 2)
  • Energy parameter value for one or more time horizons for example the electrical power that is likely to be available in 15 minutes or the like.
  • the artificial neural network 50 transmits this energy parameter value to the cloud 30, from which a network management 110 of the energy network 100 receives or retrieves the corresponding predicted energy parameter values. Based on this, the latter can control, in particular regulate, the energy grid 100, for example, call up more or less power at one of the grid connection points 11, 21 or the like. In this way, in particular the network stability of the energy network 100 can be improved.

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  • Engineering & Computer Science (AREA)
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  • Health & Medical Sciences (AREA)
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  • Economics (AREA)
  • Chemical & Material Sciences (AREA)
  • General Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Combustion & Propulsion (AREA)
  • Physics & Mathematics (AREA)
  • Sustainable Energy (AREA)
  • General Physics & Mathematics (AREA)
  • Sustainable Development (AREA)
  • Environmental & Geological Engineering (AREA)
  • Evolutionary Computation (AREA)
  • Human Resources & Organizations (AREA)
  • Strategic Management (AREA)
  • General Business, Economics & Management (AREA)
  • Theoretical Computer Science (AREA)
  • Tourism & Hospitality (AREA)
  • Marketing (AREA)
  • Water Supply & Treatment (AREA)
  • Ecology (AREA)
  • Public Health (AREA)
  • Atmospheric Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Environmental Sciences (AREA)
  • Computer Vision & Pattern Recognition (AREA)
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  • Software Systems (AREA)
  • Automation & Control Theory (AREA)
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  • Development Economics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Operations Research (AREA)
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  • Wind Motors (AREA)

Abstract

L'invention concerne un procédé de prédiction d'une valeur de paramètre énergétique d'au moins un parc éolien (10-15; 20-25) qui est connecté à un réseau d'énergie (100) par l'intermédiaire d'un point de liaison au réseau (11 ; 21) et qui comprend au moins une éolienne (10 ; 20), ledit procédé comprenant les étapes suivantes : détection (S10) de valeurs de paramètres d'entrée qui comprennent des paramètres d'état, de commande et/ou de maintenance du parc éolien, notamment de l'éolienne et/ou du point de liaison au réseau, et/ou d'au moins une installation (40-42) externe au parc éolien ; prédiction (S20) de la valeur de paramètre énergétique sur la base des valeurs de paramètres d'entrée détectées et d'une affectation, apprise par machine, entre les paramètres d'entrée et le paramètre énergétique.
EP19795147.8A 2018-11-06 2019-10-23 Prédiction de valeur de paramètre énergétique d'un parc éolien Pending EP3877924A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102018008700.0A DE102018008700A1 (de) 2018-11-06 2018-11-06 Windpark-Energieparameterwert-Prognose
PCT/EP2019/078798 WO2020094393A1 (fr) 2018-11-06 2019-10-23 Prédiction de valeur de paramètre énergétique d'un parc éolien

Publications (1)

Publication Number Publication Date
EP3877924A1 true EP3877924A1 (fr) 2021-09-15

Family

ID=68387302

Family Applications (1)

Application Number Title Priority Date Filing Date
EP19795147.8A Pending EP3877924A1 (fr) 2018-11-06 2019-10-23 Prédiction de valeur de paramètre énergétique d'un parc éolien

Country Status (5)

Country Link
US (1) US20220012821A1 (fr)
EP (1) EP3877924A1 (fr)
CN (1) CN112997201A (fr)
DE (1) DE102018008700A1 (fr)
WO (1) WO2020094393A1 (fr)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102021210569B4 (de) 2021-09-23 2023-08-24 Zf Friedrichshafen Ag Verfahren zum Betreiben einer Windenergieanlage in einem Windpark und Windparkmanager

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Publication number Priority date Publication date Assignee Title
EP2192456B1 (fr) * 2008-11-26 2017-11-01 Siemens Aktiengesellschaft Évaluation d'une production d'énergie électrique réalisable d'une éolienne au moyen d'un réseau neuronal
US20120083933A1 (en) * 2010-09-30 2012-04-05 General Electric Company Method and system to predict power plant performance
US10132295B2 (en) * 2015-05-15 2018-11-20 General Electric Company Digital system and method for managing a wind farm having plurality of wind turbines coupled to power grid
US10443577B2 (en) * 2015-07-17 2019-10-15 General Electric Company Systems and methods for improved wind power generation
US20170091791A1 (en) * 2015-09-25 2017-03-30 General Electric Company Digital power plant system and method
US11242842B2 (en) * 2016-05-23 2022-02-08 General Electric Company System and method for forecasting power output of a wind farm
DE102016125953A1 (de) * 2016-12-30 2018-07-05 Wobben Properties Gmbh Verfahren zum Betreiben eines Windparks
US10330081B2 (en) * 2017-02-07 2019-06-25 International Business Machines Corporation Reducing curtailment of wind power generation
US10598157B2 (en) * 2017-02-07 2020-03-24 International Business Machines Corporation Reducing curtailment of wind power generation
DE102017205713A1 (de) * 2017-04-04 2018-10-04 Siemens Aktiengesellschaft Verfahren und Steuereinrichtung zum Steuern eines technischen Systems
US10309372B2 (en) * 2017-05-25 2019-06-04 Hitachi, Ltd. Adaptive power generation management
US11047362B2 (en) * 2017-12-05 2021-06-29 VayuAI Corp. Cloud-based turbine control feedback loop
EP3506026A1 (fr) * 2017-12-29 2019-07-03 Siemens Aktiengesellschaft Procédé de prédiction assistée par ordinateur d'au moins une grandeur opérationnelle globale d'un système technique

Also Published As

Publication number Publication date
US20220012821A1 (en) 2022-01-13
DE102018008700A1 (de) 2020-05-07
WO2020094393A1 (fr) 2020-05-14
CN112997201A (zh) 2021-06-18

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