WO2018076236A1 - Parc éolien, procédé et dispositif de commande de l'alimentation de ce dernier - Google Patents

Parc éolien, procédé et dispositif de commande de l'alimentation de ce dernier Download PDF

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Publication number
WO2018076236A1
WO2018076236A1 PCT/CN2016/103578 CN2016103578W WO2018076236A1 WO 2018076236 A1 WO2018076236 A1 WO 2018076236A1 CN 2016103578 W CN2016103578 W CN 2016103578W WO 2018076236 A1 WO2018076236 A1 WO 2018076236A1
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Prior art keywords
wind
wind turbines
defect
fatigue
real
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PCT/CN2016/103578
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English (en)
Inventor
Rongrong Yu
Dawei YAO
Chun Li
Niya CHEN
Yan Zhang
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Abb Schweiz Ag
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Priority to PCT/CN2016/103578 priority Critical patent/WO2018076236A1/fr
Publication of WO2018076236A1 publication Critical patent/WO2018076236A1/fr

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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00004—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the power network being locally controlled
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/10—The network having a local or delimited stationary reach
    • H02J2310/12—The local stationary network supplying a household or a building
    • 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
    • Y02E10/76—Power conversion electric or electronic aspects

Definitions

  • the invention relates to control of wind turbines in a wind farm, and more particularly to an appropriate control of the wind turbines.
  • wind turbines may be arranged according to various configurations depending on the operating needs of the wind farm.
  • Patent WO 2016/077997 A discloses a monitoring method for condition of wind turbine involves using supervisory control and data acquisition (SCADA) data and training model to monitor condition of wind turbines.
  • SCADA supervisory control and data acquisition
  • the monitoring method involves performing overall diagnosis /prognosis by acquiring real time SCADA data, inputting real time SCADA data to trained overall model, obtaining health condition of wind turbine from trained overall model and performing individual diagnosing step if trained overall model determines wind turbine as defective status.
  • the individual diagnosis is executed by inputting real time SCADA data to trained individual model corresponding to defective component and obtaining fault details of defective component from trained individual model corresponding to defective component.
  • the fault detail of each wind turbine can be thus considered to allocate its work load.
  • a method for feeding a grid by a group of wind turbines in a wind farm including steps of: (a) obtaining first real-time SCADA data and first real-time constant variables concerning each of the wind turbines in the group for a first time point, and first calculating fatigue degree of the wind turbine using the obtained first real-time SCADA data and the first real-time constant variables thereof; and (b) first distributing the grid load among the wind turbines in the group using the calculated fatigue degrees thereof.
  • it provides a controller for executing the method.
  • a wind farm including the controller, the grid and the group of wind turbines.
  • the first real-time SCADA data concerns with at least wind speed, output power, generator RPM, and pitch angle; and the first real-time constant variables concern with mass of blade, diameter of blade, blade numbers, tower height, thickness of tower on top and bottom, face width of gears, and gearbox ratio.
  • the first real-time SCADA data may be retrieved from an external source (e.g. from a SCADA system) , and the first real-time constant variables may be found at the data sheets for the wind turbine, both of which are re-used for assessment of fatigue degree of wind turbine. This allows for more simplicity of the system design and increase of operation efficiency.
  • the first distribution is based on a first optimization fatigue function indicative of objective performance of the group of wind turbines, the first optimization fatigue function being set on the base of the calculated fatigue degrees of the wind turbines and power demand of the grid.
  • defect may occur in some of the wind turbines which enter into the operation period of “crack development” (from defect or failure) , and some remain in the period of “crack generation” .
  • Defect degree would be a more appropriate indicating the operating condition of a wind turbine during the period of “crack development” , based on which the wind farm life consumption is optimized.
  • the method for feeding a grid by a group of wind turbines in a wind farm further includes: (c) obtaining second real-time SCADA data concerning each of the wind turbines in the group for a second time point after the first time point, and determining if the wind turbine has defect occurring at the second time point using the obtained second real-time SCADA data thereof; (d) from the determination result, identifying a first subgroup of the wind turbines determined not having defect and a second subgroup of the wind turbines determined having the defect, and second calculating fatigue degree of the wind turbine in the first subgroup and defect degree of the wind turbine in the second subgroup; and (e) second distributing the grid load between the first wind turbine subgroup and the second wind turbine subgroup using the fatigue degree of the first subgroup wind turbine and the defect degree of the second subgroup wind turbine.
  • the second real-time SCADA data concerns with at least wind speed, output power, gearbox temperature, generator temperature, pitch angle, and yaw angle.
  • the second distribution is based on a second optimization fatigue & defect function indicative of objective performance of the first subgroup of the wind turbines and the second subgroup of the wind turbines, the second optimization fatigue &defect function being set on the base of the fatigue degree of the first subgroup wind turbine and the defect degree of the second subgroup wind turbine and power demand of the grid.
  • Figure 1 illustrates a wind farm according to an embodiment of present invention.
  • crack generation Period from normal to defect is called “crack generation” and period from defect to failure is called “crack development” .
  • crack generation is usually caused by fatigue accumulation.
  • Fatigue is the weakening of a material caused by repeatedly applied loads, e.g. time-varying aerodynamic thrust, rotating tangential force, centrifugal force, etc. If load is constant, there will be no fatigue. If load variation is not zero, fatigue will be non-zero. Due to time-varying wind speed and rotating blades, fatigue is inevitable for wind turbine, and the damage caused by fatigue is accumulated as time goes on. When the fatigue damage is accumulated to a threshold, defect (e.g.
  • crack on the gear will occur, where “crack generation” ends and “crack development” starts.
  • the fatigue of the wind turbine makes less contribution to the crack development, because it fails to consider other factors to cause defect development for example inappropriate mount, inherent defects when manufacturing, or mis-operation of staff, etc.
  • the degree of defect may be detected by sensors of parameters, such as real-time SCADA data, and/or real-time vibration signals, etc. If the defect is further accumulated to a certain level, failure (e.g. gear is broken) will occur.
  • Figure 1 illustrates a wind farm according to an embodiment of present invention.
  • the wind farm 1 includes a controller 10 and a group of wind turbines WT1, WT2, WT3, WT4, WT5.
  • the operation of the wind turbines WT1, WT2, WT3, WT4, WT5 are advantageously controlled by the controller 10, which is preferably arranged according to a cascade control architecture.
  • the controller 10 comprises a wind farm controller 100 (e.g. a PID controller) and the wind turbine controllers 101 (e.g. valve regulators, capacity controllers, and the like) , each of which is operatively associated with a corresponding wind turbine 101.
  • a wind farm controller 100 e.g. a PID controller
  • the wind turbine controllers 101 e.g. valve regulators, capacity controllers, and the like
  • the wind farm controller 100 obtains, from an external source (e.g. from a SCADA system) , first real-time SCADA data and first constant variables concerning each of the wind turbines WT1, WT2, WT3, WT4, WT5 in the group for a first time point.
  • the first real-time SCADA data concerns with at least wind speed, output power, generator RPM, and pitch angle.
  • the first real-time constant variables concern with mass of blade, diameter of blade, blade numbers, tower height, thickness of tower on top and bottom, face width of gears, and gearbox ratio, which may be found at the data sheets for the wind turbine.
  • wind farm controller 10 generally controls wind turbines WT1, WT2, WT3, WT4, WT5 proportionally (i.e.
  • the wind farm controller 100 may calculate fatigue degree of the wind turbine WT1, WT2, WT3, WT4, WT5 using the obtained first real-time SCADA data and the constant variables.
  • both real-time SCADA data e.g. at least wind speed, output power, generator RPM, and pitch angle
  • constant variables e.g. at least mass of blade, diameter of blade, blade numbers, tower height, thickness of tower on top and bottom, face width of gears, and gearbox ratio
  • the detailed formula to calculate the real-time force/moment and stress of different components of wind turbines is referred to by standard “IEC 61400-2, International Electrotechnical Committee, 2013” .
  • Fatigue degree is a value ranging between 0 and 1. The higher the value is, the higher the fatigue degree is.
  • the wind farm controller 100 distributes (first distribution) the grid load among the wind turbines WT1, WT2, WT3, WT4, WT5 in the group using the calculated fatigue degrees.
  • the wind farm controller 10 may be programed to provide an optimization fatigue function indicative of the objective performance for wind turbines WT1, WT2, WT3, WT4, WT5 in the wind farm 1.
  • the optimization fatigue function is determined based on the information contained in the first real-time SCADA data and the first constant variables and based on the grid power demand.
  • the optimization fatigue function captures one or more objective quantities describing an objective performance for the wind turbines WT1, WT2, WT3, WT4, WT5, at the first time point.
  • the objective quantity may be the minimum fatigue degree for all of the wind turbines WT1, WT2, WT3, WT4, WT5. It should be known that the optimization fatigue function may be applied at each execution cycle following the first time point until defect occurs with one or more of the wind turbines WT1, WT2, WT3, WT4, WT5, since the objective only concerns with the wind turbines’ fatigue during the operation period of “crack generation” .
  • the optimization fatigue function is thus preferably re-calculated based on the first real-time SCADA data and the first constant variables as obtained or determined at said execution cycle.
  • the optimization problem may be any type falling under the class of MINLP (Mixed-Integer Non-Linear Problems) but it can belong to a MILP (Mixed-Integer Linear Problems) , NLP (Non-Linear Problems) , LP (Linear Problems) , QP (Quadratic Problems) class depending on the application to which the program of the wind farm controller 10 is directed.
  • the optimization problem may be expressed as, or similarly to, the problem of finding the minimum or maximum values for the optimization fatigue function at the first time point, subject to the constraints deriving from the grid power demand indicative of the operating limits established for the wind turbines WT1, WT2, WT3, WT4, WT5 at the first time point.
  • Pi indicates the output power load distributed to the ith wind turbine
  • Fatigue_WTi indicates the fatigue degree of the ith wind turbine
  • i is natural number from 1 to 3.
  • the first distribution is based on a first optimization fatigue function indicative of objective performance of the group of wind turbines, the first optimization fatigue function being set on the base of the calculated fatigue degrees of the wind turbines and power demand of the grid
  • each wind turbine controller 101 receives signal indicative of output power load reference from the wind farm controller 100, which further controls output power load in consideration of the reference, for example by means of PID.
  • defect may occur in some of the wind turbines WT1, WT2, WT3, WT4, WT5 which enter into the operation period of “crack development” (from defect or failure) , and some remain in the period of “crack generation” .
  • Defect degree would be a more appropriate indicating the operating condition of a wind turbine during the period of “crack development” , based on which the wind farm life consumption is optimized.
  • the shift between the two control modes for “crack generation” and “crack development” is triggered by on-line condition monitoring and health evaluation system.
  • the method thereof can be purely SCADA data based approach, or analysing sensor data with domain knowledge.
  • the output of the method is whether every single wind turbine is free of defect or defective, if defective, calculate the defect degree.
  • the wind farm controller 100 may obtain second real-time SCADA data concerning each of the wind turbines WT1, WT2, WT3, WT4, WT5 in the group for a second time point after the first time point, and determine if the wind turbine has defect occurring at the second time point using the obtained second real-time SCADA data thereof.
  • the second real-time SCADA data may concern with at least wind speed, output power, gearbox temperature, generator temperature, pitch angle, and yaw angle (e.g. from the SCADA system) .
  • defect symptoms can be detected.
  • data mining technology or domain knowledge based analysis
  • defect degree defect severity
  • the wind farm controller 100 may identify the first subgroup of the wind turbines WT1, WT2, WT3 free of defect, while the second subgroup of the wind turbines WT4, WT5 having the defect.
  • the wind farm controller 100 may further distribute (second distribution) the grid load between the first wind turbine subgroup WT1, WT2, WT3 and the second wind turbine subgroup WT4, WT5 using the fatigue degree of the first subgroup wind turbine and the defect degree of the second subgroup wind turbine.
  • defect degree level is further calculated (the higher defect degree is, the severe the defects are) , after which each wind turbine is allocated with both condition stamp and defect degree; for those wind turbines WT1, WT2, WT3 without defect, its defect degree is assigned as zero.
  • the wind farm controller 100 may allocate (second distribution) the overall output power demand to the two groups of wind turbines (free of defect and defective) , namely the first subgroup of the wind turbines WT1, WT2, WT3 and the second subgroup of the wind turbines WT4, WT5.
  • Health condition Defect degree Max power WT1 free of defect 0% 3MW WT2 free of defect 0% 2MW WT3 free of defect 0% 1MW WT4 defective 10% 2MW WT5 defective 40% 1.5MW
  • the first group is a group consisting of:
  • the second group is a first group:
  • the second distribution is based on a second optimization fatigue &defect function indicative of objective performance of the first subgroup of the wind turbines WT1, WT2, WT3 and the second subgroup of the wind turbines WT4, WT5, the second optimization fatigue & defect function being set on the base of the fatigue degree of the first subgroup wind turbine WT1, WT2, WT3 and the defect degree of the second subgroup wind turbine WT4, WT5 and power demand of the grid.
  • the wind farm controller 110 may be programed to provide an optimization defect function indicative of the objective performance for wind turbines WT4, WT5 in the wind farm 1.
  • the optimization defect function is determined based on the information contained in the second real-time SCADA data and based on the grid power demand to the second subgroup.
  • the optimization defect function captures one or more objective quantities describing an objective performance for the wind turbines WT4, WT5, at the second time point after the first time point.
  • the objective quantity may be the minimum defect degree for all of the wind turbines WT4, WT5. It should be known that the optimization defect function may be applied at each execution cycle following the defect occurs with one or more of the wind turbinesWT4, WT5, since the objective only concerns with the wind turbines’ defect during the operation period of “crack development” .
  • the optimization defect function is thus preferably re-calculated based on the second real-time SCADA data as obtained determined at said execution cycle.
  • the defect balance is adopted as control objective, at the same time power output demand of grid is satisfied as shown in equation (2) to (3) (two wind turbines) , lifetime consumption of the entire wind farm can be thereby minimized.
  • SCADA can allocate the power command of each wind turbine WT4, WT5, as shown in equation (2) and (3) (two defective wind turbines WT4, WT5 as example) .
  • turbine WT4 with defect degree 10% is given higher output power command than turbine WT5 with defect degree 40%, so that turbines WT4 and WT5 can reach similar defect degree level at similar time so that both can be repaired together to save maintenance cost.
  • P 4 and P 5 are power output of WT4 and WT5
  • P demand is overall demand from grid allocated to the second subgroup of the wind turbines WT4 and WT5
  • P 4_max andP 5_max are maximum power output of WT4 and WT5 due to facing different wind speeds
  • S 4 and S 5 are defect degree of WT4 and WT5.
  • the wind farm 1 having both of wind turbines WT1, WT2, WT3 of “crack generation” and those WT4, WT5 of “crack development” , it provides output data indicative of a load distribution among the wind turbines WT1 to WT5 in relation to the fatigue degrees of the first subgroup WT1, WT2, WT3 and defect degrees of the second subgroup WT4, WT5.
  • This allows achieving an optimal load distribution among the wind turbines from time to time (i.e. at each execution cycle thereof) always taking into account the fatigue degrees and defect degrees of the wind turbines.
  • “crack development” operation period of wind turbines this is helpful for controlling in a mode designed based on defect degree balance so as to make remaining useful lifetime of defective wind turbines similar to each other which is able to save maintenance cost for farm operator.
  • the hybrid control mode for the wind turbines some free of defects and some with defects
  • defect may occur in all of the wind turbines WT1, WT2, WT3, WT4, WT5 which entered into the operation period of “crack development” (from defect or failure) .
  • Defect degree balance based control as in equations (2) and (3) is used so as to balance the remaining useful lifetime of wind turbines WT1 to WT5 whose operating conditions are under “crack development” till failure.

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Human Computer Interaction (AREA)
  • Wind Motors (AREA)

Abstract

La présente invention concerne un procédé d'alimentation d'un réseau par un groupe d'éoliennes (WT1, WT2, WT3, WT4, WT5) dans un parc éolien, un dispositif de commande (10) et un parc éolien l'utilisant. Le procédé consiste : (a) à obtenir des premières données SCADA en temps réel et des premières variables constantes en temps réel concernant chacune des éoliennes (WT1, WT2, WT3, WT4, WT5) dans le groupe, à un premier instant, et à calculer un premier degré de fatigue de l'éolienne à l'aide des premières données SCADA obtenues en temps réel et des premières variables constantes de celle-ci en temps réel ; (b) à répartir une première charge de réseau entre les éoliennes (WT1, WT2, WT3, WT4, WT5) dans le groupe à l'aide des degrés de fatigue calculés de celles-ci. Grâce aux solutions susmentionnées, pendant la période de fonctionnement "de génération de fissures", des données de sortie indiquant une répartition de charge entre les éoliennes (WT1, WT2, WT3, WT4, WT5) du parc éolien par rapport aux degrés de fatigue des éoliennes (WT1, WT2, WT3, WT4, WT5) sont fournies. Ceci permet d'obtenir périodiquement une répartition de charge optimale entre les éoliennes (c'est-à-dire à chaque cycle d'exécution de ces dernières) en tenant toujours compte des degrés de fatigue des éoliennes (WT1, WT2, WT3, WT4, WT5), qui représentent les conditions de fonctionnement de l'éolienne avant l'apparition d'un défaut. Pendant la période de fonctionnement "de génération de fissures" des éoliennes, ceci est utile pour la gestion dans un mode conçu en fonction d'une réduction à un minimum de la fatigue afin de retarder l'apparition de défauts autant que possible, ce qui permet d'étendre la durée de vie d'un parc éolien ayant un coût de capital fixe.
PCT/CN2016/103578 2016-10-27 2016-10-27 Parc éolien, procédé et dispositif de commande de l'alimentation de ce dernier WO2018076236A1 (fr)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109681381A (zh) * 2018-12-24 2019-04-26 浙江大学 一种利用率可变的风电场负荷分摊控制方法
CN113394813A (zh) * 2021-05-31 2021-09-14 南方电网海上风电联合开发有限公司 海上风电场的机组功率指令值计算方法和分布式调度方法
EP4280412A1 (fr) * 2022-05-06 2023-11-22 General Electric Renovables España S.L. Système et procédé de commande d'une pluralité de générateurs électriques

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CN104521090A (zh) * 2012-07-20 2015-04-15 乌本产权有限公司 用于控制风电场的方法
CN104603455A (zh) * 2012-08-15 2015-05-06 维斯塔斯风力系统集团公司 风力发电站控制系统、包括风力发电站控制系统的风力发电站以及控制风力发电站的方法
CN104810860A (zh) * 2015-02-06 2015-07-29 华北水利水电大学 一种风电场内功率分配方法及分配装置
WO2016077997A1 (fr) * 2014-11-18 2016-05-26 Abb Technology Ltd Procédé et système de contrôle d'état de turbine éolienne
WO2016086360A1 (fr) * 2014-12-02 2016-06-09 Abb Technology Ltd Procédé et système de surveillance d'état de parc éolien

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104521090A (zh) * 2012-07-20 2015-04-15 乌本产权有限公司 用于控制风电场的方法
CN104603455A (zh) * 2012-08-15 2015-05-06 维斯塔斯风力系统集团公司 风力发电站控制系统、包括风力发电站控制系统的风力发电站以及控制风力发电站的方法
WO2016077997A1 (fr) * 2014-11-18 2016-05-26 Abb Technology Ltd Procédé et système de contrôle d'état de turbine éolienne
WO2016086360A1 (fr) * 2014-12-02 2016-06-09 Abb Technology Ltd Procédé et système de surveillance d'état de parc éolien
CN104810860A (zh) * 2015-02-06 2015-07-29 华北水利水电大学 一种风电场内功率分配方法及分配装置

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109681381A (zh) * 2018-12-24 2019-04-26 浙江大学 一种利用率可变的风电场负荷分摊控制方法
CN113394813A (zh) * 2021-05-31 2021-09-14 南方电网海上风电联合开发有限公司 海上风电场的机组功率指令值计算方法和分布式调度方法
EP4280412A1 (fr) * 2022-05-06 2023-11-22 General Electric Renovables España S.L. Système et procédé de commande d'une pluralité de générateurs électriques

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