EP4004365A1 - Procédé pour commander un parc éolien, module de commande pour un parc éolien et parc éolien - Google Patents

Procédé pour commander un parc éolien, module de commande pour un parc éolien et parc éolien

Info

Publication number
EP4004365A1
EP4004365A1 EP20737396.0A EP20737396A EP4004365A1 EP 4004365 A1 EP4004365 A1 EP 4004365A1 EP 20737396 A EP20737396 A EP 20737396A EP 4004365 A1 EP4004365 A1 EP 4004365A1
Authority
EP
European Patent Office
Prior art keywords
wind
energy installation
data
wind energy
control
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
EP20737396.0A
Other languages
German (de)
English (en)
Inventor
Luis VERA-TUDELA
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.)
VC VIII Polytech Holding ApS
Original Assignee
Polytech Wind Power Technology Germany 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 Polytech Wind Power Technology Germany GmbH filed Critical Polytech Wind Power Technology Germany GmbH
Publication of EP4004365A1 publication Critical patent/EP4004365A1/fr
Pending legal-status Critical Current

Links

Classifications

    • 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
    • F03D7/043—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic
    • F03D7/046—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic with learning or adaptive control, e.g. self-tuning, fuzzy logic or neural network
    • 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
    • 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
    • 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
    • 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
    • F05B2270/30—Control parameters, e.g. input parameters
    • F05B2270/32—Wind speeds
    • 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
    • F05B2270/30—Control parameters, e.g. input parameters
    • F05B2270/321—Wind directions
    • 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
    • F05B2270/30—Control parameters, e.g. input parameters
    • F05B2270/331—Mechanical loads
    • 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
    • F05B2270/30—Control parameters, e.g. input parameters
    • F05B2270/335—Output power or torque
    • 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
    • F05B2270/40—Type of control system
    • F05B2270/404—Type of control system active, predictive, or anticipative
    • 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

  • Embodiments of the present disclosure relate to a method for controlling a wind farm, a control module and a wind farm.
  • Embodiments of the present disclosure relate in particular to a method for controlling a wind park, a control module and a wind park, which can control at least one second wind energy installation on the basis of data from a first wind energy installation with the inclusion of a statistical forecast model.
  • the forecast of wind power can be relevant in order to ensure the right balance between energy supply possibility and energy demand.
  • the development of the forecast has been limited to a macroscopic level with a focus on regions, portfolios and wind farms.
  • Physical (weather) models were mainly used in order to provide a forecast of the wind power.
  • Physical models estimate an “actual” wind speed in front of the rotor of a wind turbine on the basis of measurements obtained using sodar, lidar, radar etc. or corrected data from anemometers arranged on the nacelle. Every measurement approach aims to look in front of the wind turbine (s) of interest.
  • Statistical corrections for the forecast are typically based on data sets on the weather situation created at the park level.
  • embodiments of the present disclosure provide a control module for a wind farm. Furthermore, embodiments of the present disclosure provide a wind farm.
  • Wind farms provided.
  • the method comprises: reading in data from at least one first wind energy installation of the wind farm; Supplying the read-in data from the at least one first wind energy installation to a statistical forecast model for controlling at least one second wind energy installation in the wind park on the basis of the read-in data from the at least one first wind energy installation; and using the statistical prediction model to control the at least one second wind energy installation.
  • a control module for a wind farm is provided.
  • the control module is configured to carry out a method for controlling a wind farm.
  • the method comprises: reading in data from at least one first wind energy installation of the wind farm; Supplying the read-in data from the at least one first wind energy installation to a statistical forecast model for controlling at least one second wind energy installation in the wind park on the basis of the read-in data from the at least one first wind energy installation; and using the statistical prediction model to control the at least one second wind energy installation.
  • a wind farm comprising: at least one first wind energy installation; at least one second wind energy installation; and a control module for controlling the at least one first and / or second wind energy installation.
  • the control module is configured to carry out a method for controlling a wind farm. The method comprises: reading in data from at least one first wind energy installation of the wind farm; Feeding the read-in data from the at least one first wind energy installation to a statistical forecast model for controlling at least one second wind energy installation Wind farms based on the data read in from the at least one first wind energy installation; and using the statistical prediction model to control the at least one second wind energy installation.
  • FIG. 1 shows schematically, by way of example, a wind park with three wind turbines according to embodiments described herein;
  • Fig. 2 shows schematically a wind turbine according to that described herein
  • FIG. 3 shows a flow chart of a method according to embodiments described herein.
  • FIG. 4 schematically shows, by way of example, a wind park with four wind turbines according to the embodiments described herein;
  • Wind power prediction models with a 5 to 10 minute forecast window would be desirable.
  • high frequency signals that describe a wind-to-power interaction that will dominate the range of predictive variables.
  • loads have not been taken into account as a prediction parameter, especially since there has been a lack of systems for continuous monitoring.
  • the present disclosure can provide a cross-turbine prediction of wind energy and possibly also of mechanical loads.
  • the present disclosure can provide a statistical prediction model that can be used to control a wind park and that, based on the data of a wind energy installation, enables optimized control of at least one further wind energy installation, including the entire wind farm.
  • the energy demand can also be used as an optimization variable in order to ensure efficient utilization of the wind farm.
  • individual wind turbines can be taken out of the wind when the energy requirement is low, or they can be operated with overload when the energy requirement is high.
  • a measuring system can be provided that records mechanical loads and / or electrical power from at least one wind energy installation in a wind park at a high sampling rate in order to be able to make a turbine-to-turbine prediction.
  • the mechanical loads in the leaves can be recorded with sensors (fiber optic or otherwise) and the electrical power with a SCADA system.
  • the mechanical loads and / or the electrical power can be estimated using data recorded with sensors in the rotor blades and statistical models.
  • high-frequency data from neighboring turbines can be used to make predictions about the electrical power and mechanical load approximately 1-3 minutes in advance.
  • the forecast time can depend on the average wind speed and the distance between two turbines (Taylor hypothesis).
  • FIG. 1 shows a wind farm 10 with three wind energy installations 200 by way of example.
  • the wind energy installations 200 as shown in FIG. 1 by dashed lines, are networked with one another.
  • the networking enables communication, for example real-time communication, between the individual wind turbines.
  • the networking also enables joint monitoring, control and / or regulation of the wind energy installations.
  • the wind energy installations can also be monitored, controlled and / or regulated individually.
  • a wind park can contain two or more wind energy installations, in particular five or more wind energy installations, such as ten or more wind energy installations.
  • the wind turbines 200 for example the wind turbines from FIG. 1, form the wind park 10 in their entirety.
  • the wind park comprises at least two wind turbines which are spatially arranged at a distance from one another.
  • FIG. 2 shows, by way of example, a wind energy installation 200 of a wind park on which the method described herein can be used.
  • the wind energy installation 200 includes a tower 40 and a nacelle 42.
  • the rotor is attached to the nacelle 42.
  • the rotor includes a hub 44 to which the rotor blades 100 are attached.
  • the rotor has at least two rotor blades, in particular three rotor blades.
  • the rotor i.e. the hub with the rotor blades, rotates around an axis.
  • a generator is driven to generate electricity.
  • at least one sensor 110 is provided on a rotor blade 100.
  • the sensor 110 can be connected to an interface 50 via a signal line.
  • the interface 50 can deliver a signal to a control and evaluation unit 52 of the wind energy installation 200.
  • the interface 50 can in particular be a SCADA (Supervisory Control and Data Acquisition) interface.
  • SCADA Supervisory Control and Data Acquisition
  • the wind energy installation 200 can include a control module 52.
  • the control module 52 is used in particular to control or regulate and / or to read out the interface 50 or the sensor 110 and the wind turbine.
  • the control module 52 can control or regulate the SCADA interface and / or transmit data between the SCADA interface and the control module 52.
  • the control module 52 can communicate with the interface 50.
  • the control module 52 can be permanently connected to the interface 50 or connected wirelessly.
  • the control module 52 can contain a computer program product that can be loaded into a memory of a digital computing device and includes software code sections with which steps according to one or more of the remaining aspects can be carried out when the computer program product is running on the computing device. Furthermore, a computer program product is proposed which can be loaded directly into a memory, for example a digital memory of a digital computing device.
  • a computing device can contain a CPU, signal inputs and signal outputs, and other elements typical of a computing device.
  • a computing device can be part of an evaluation unit, or the evaluation unit can be part of a computing device.
  • a computer program product can contain software code sections with which the steps of the methods of the embodiments described here are at least partially carried out when the computer program product is running on the computing device. Any embodiments of the method can be carried out by a computer program product.
  • the sensor 110 can in particular be a mechanical load sensor.
  • each rotor blade of the wind energy installation can comprise a sensor.
  • the sensor can in particular be an acceleration sensor, a vibration sensor and / or a strain sensor.
  • the sensor can be designed as an electrical or as a fiber optic sensor.
  • the sensor can also be provided on other components of the wind energy installation 200, such as the tower 40, the nacelle 42, the generator, etc., for example.
  • the sensor 110 can also measure a fatigue load.
  • a wind energy installation 200 can also be equipped with several sensors in order to measure data from several components and / or other types of data from the same component in parallel.
  • FIG. 3 shows a flow chart of a method 300 for controlling a wind farm 10 according to the embodiments described herein.
  • a block 310 data from at least a first
  • Wind energy installation 200 of wind park 10 can be read.
  • the read-in data of the at least one first wind turbine 200 can be fed to a statistical forecast model for controlling at least one second wind turbine 200 of the wind farm 10 on the basis of the read-in data of the at least one first wind turbine.
  • the statistical prediction model can be used
  • Control of the at least one second wind energy installation 200 can be used.
  • a short-term wind power and load forecast can be created based on a static model. Additional information can be obtained in particular through a high sampling rate.
  • a performance optimization can be provided, with which, for example, the operation of wind turbines based on an energy demand can be improved.
  • Embodiments Wind turbines are no longer operated purely as passive systems, but are used as active measuring systems that deliver validated information.
  • Hybrid models i.e. models that include a static forecast model as well as a physical forecast model, can further improve the accuracy of weather forecast models on the level of wind turbines and in very short time units.
  • this can be a Bayesian system of continuous learning that further develops and improves the system over time.
  • the method disclosed here can be used to create an optimization based on the energy requirement, the mechanical load and a performance prediction.
  • the read-in data can have at least electrical performance data and / or mechanical load data.
  • the electrical performance data can be read in via a SCADA system.
  • the mechanical loads and / or the electrical power can be estimated using data recorded with sensors in the rotor blades and statistical models.
  • the mechanical load data can for example be read in via the sensor 110 or a plurality of sensors 110. Additionally or alternatively, the mechanical loads can also be estimated from models that were created in another wind energy installation of the same type, which in particular connect the SCADA system and / or the sensor 110.
  • the wind farm 10 shows a wind farm 10 according to the embodiments described herein.
  • the wind farm 10 is shown as an example with a first wind energy installation 200i, a second wind energy installation 200 2 , a third wind energy installation 2OO 3 , and a wind energy installation 2OO 4 .
  • the wind farm 10 can also have any other number of wind energy installations.
  • the wind energy installations 200i to 2OO 4 are shown with a respective mechanical load value h to L and a respective electrical power value pi to p 4 .
  • These values, in particular the respective mechanical load values h to L, can also represent a set of values made up of several values.
  • a respective mechanical load value pi , i to p 4, i can be provided for each sensor 110i.
  • the electrical power values pi and the mechanical load values h can form the electrical power data or mechanical load data.
  • the electrical power value p j and the mechanical load value l j of the jth wind energy installation 200 j can result as a first function f from the weather model and the electrical power value p j and the mechanical load value l j .
  • the electrical power value pi > j and the mechanical load value h > j of the i> j-th wind turbines 200i > j can result as a second function g from the weather model and the electrical power value pi > j and the mechanical load value h > j .
  • the statistical prediction model can make predictions for an expected electrical power and / or mechanical load on the at least one second wind energy installation.
  • a machine learning model can be dominated by statistics for a short time
  • a weather model can be dominated by weather fronts in the medium to long term.
  • Weather models are already used for longer time horizons.
  • the present disclosure can close the gap for short-term forecasts, in particular by a combination of weather models with statistics or simply by statistics (machine learning models).
  • the present disclosure can provide the possibility of combining known physical relationships and direct measurements in a hybrid model in order to continuously improve predictions over time.
  • a distance d between the first wind energy installation 2001 and the second wind energy installation 2002 is shown as an example in FIG. Of course, there is a corresponding distance between each wind turbine pair.
  • the statistical prediction model can take into account a distance d between the at least one first wind energy installation 2001 and the at least one second wind energy installation 2002, a wind direction and / or a wind speed.
  • the system measures, in addition to other variables such as weather, SCADA, energy demand, etc., setpoint values of e.g. power and mechanical loads.
  • a physical model is created a priori to roughly estimate the target variables.
  • the system or method according to embodiments can, however, continuously select the most relevant variables in order to predict a target value for each of the wind turbines (pl, 11, p2, p2, 12, p3, 13, etc.) and can change its selection and its model over time correct (especially via a Bayesian approach to continuous learning).
  • Wind direction and speed can be good parameters, but it usually requires one (corrective calibration), which can lead to increased expenditure, statistically speaking, signals with information.
  • Predictive model have a machine learning method. As a result, the model can adapt itself to the special environment and the structure of the wind farm 10.
  • Prediction model uses data from at least two first wind turbines to control the at least one second wind turbine. This allows the forecast to be further increased. For example, the prediction for the second wind energy installation can also be made using data from all other wind energy installations.
  • the data can be read in at a high sampling rate of 1 Hz or more.
  • the data can also be read in at different sampling rates.
  • the electrical power can be read in with at least 1 Hz and / or the mechanical load can be read in with at least 10 Hz.
  • control of the at least one second wind energy installation 200 2 can be carried out according to the statistical
  • Prediction model a control of an angle of attack of a blade of the at least one second wind energy installation 2OO2, a control of a torque of the at least one second wind energy installation 2OO2, a damping system in a tower structure of the at least one second wind energy installation 2OO2 and / or active mechanisms in one
  • Blade control system active tip, twist, flap, etc.
  • external data can be read in from meteorological sensors and these can be fed to the statistical forecast model. This can further increase the accuracy.
  • data can be fed to a physical prediction model to at least a second Control wind turbine (200).
  • a hybrid model can result from the statistical prediction model and a physical prediction model. This can further increase the accuracy.
  • control module 52 may be configured to perform some, some, or all of the operations of the method 300 described herein.
  • a wind farm 10 can have a control module 52 configured in this way in order to control at least one first and / or second wind energy installation 200.
  • the control of the second wind energy installation 200 can take place in particular according to the method 300.

Landscapes

  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Sustainable Development (AREA)
  • Sustainable Energy (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Fuzzy Systems (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Wind Motors (AREA)

Abstract

L'invention concerne un procédé (300) pour commander un parc éolien (10). Le procédé (300) comprend les étapes : de lecture de données d'au moins une première éolienne (200) du parc éolien ; d'amenée des données lues de la ou des premières éoliennes à un modèle de prédiction statistique pour la commande d'au moins une deuxième éolienne (200) du parc éolien sur la base des données lues de la ou des premières éoliennes ; et d'utilisation du modèle de prédiction statistique pour la commande de la ou des deuxièmes éoliennes (200).
EP20737396.0A 2019-07-22 2020-07-01 Procédé pour commander un parc éolien, module de commande pour un parc éolien et parc éolien Pending EP4004365A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102019119774.0A DE102019119774A1 (de) 2019-07-22 2019-07-22 Verfahren zur Steuerung eines Windparks, Steuerungsmodul für einen Windpark und Windpark
PCT/EP2020/068507 WO2021013487A1 (fr) 2019-07-22 2020-07-01 Procédé pour commander un parc éolien, module de commande pour un parc éolien et parc éolien

Publications (1)

Publication Number Publication Date
EP4004365A1 true EP4004365A1 (fr) 2022-06-01

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EP20737396.0A Pending EP4004365A1 (fr) 2019-07-22 2020-07-01 Procédé pour commander un parc éolien, module de commande pour un parc éolien et parc éolien

Country Status (6)

Country Link
US (1) US20220260054A1 (fr)
EP (1) EP4004365A1 (fr)
CN (1) CN114127412A (fr)
CA (1) CA3148354A1 (fr)
DE (1) DE102019119774A1 (fr)
WO (1) WO2021013487A1 (fr)

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CN116316612B (zh) * 2023-05-16 2023-09-15 南方电网数字电网研究院有限公司 自动机器学习的新能源功率云边协同预测方法及系统

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DE102019119774A1 (de) 2021-01-28
CN114127412A (zh) 2022-03-01
US20220260054A1 (en) 2022-08-18
CA3148354A1 (fr) 2021-01-28
WO2021013487A1 (fr) 2021-01-28

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