WO2023050930A1 - Procédé et système de commande auxiliaire pour ensemble générateur éolien, et ensemble générateur éolien - Google Patents

Procédé et système de commande auxiliaire pour ensemble générateur éolien, et ensemble générateur éolien Download PDF

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Publication number
WO2023050930A1
WO2023050930A1 PCT/CN2022/101438 CN2022101438W WO2023050930A1 WO 2023050930 A1 WO2023050930 A1 WO 2023050930A1 CN 2022101438 W CN2022101438 W CN 2022101438W WO 2023050930 A1 WO2023050930 A1 WO 2023050930A1
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Prior art keywords
control system
data
auxiliary control
wind turbine
algorithm model
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PCT/CN2022/101438
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English (en)
Chinese (zh)
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文辉
黄晓芳
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新疆金风科技股份有限公司
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Priority to BR112024000658A priority Critical patent/BR112024000658A2/pt
Priority to AU2022356758A priority patent/AU2022356758A1/en
Publication of WO2023050930A1 publication Critical patent/WO2023050930A1/fr

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    • 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 
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computing arrangements based on specific mathematical models
    • 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 application relates to the technical field of artificial intelligence, and in particular to an auxiliary control method and system for a wind power generating set and a wind generating set.
  • the wind farm includes multiple wind power generators, the wind power generators include wind rotors, generators, etc., and the wind rotors are composed of blades, hubs, reinforcements, etc.
  • wind farms control wind turbines mainly through multiple independent subsystems with different functions, including online monitoring system, unit health assessment system, blade video monitoring system, etc.
  • the field-level controller platform conducts network communication to realize the monitoring and control of the unit.
  • the wind turbine main control system manages multiple subsystems, the functional coupling is high and the deployment is complicated.
  • the hardware computing power of the wind turbine main control system is limited, and resources such as network load limit the application of machine learning and predictive control to the wind turbine main control system.
  • Embodiments of the present application provide an auxiliary control method and system for a wind power generating set, and the wind generating set, so as to realize local control of the wind generating set.
  • an embodiment of the present application provides an auxiliary control method for a wind power generating set, the method is applied to an auxiliary control system installed at the wind turbine end, and the method includes:
  • the control instruction is sent to the main control system of the wind generating set, so that the main control system completes the control of the wind generating set according to the control instruction.
  • the embodiment of the present application provides an auxiliary control system
  • the auxiliary control system is set at the wind turbine end of the wind power generating set, and includes: an acquisition unit, a memory, and a processor;
  • the acquiring unit is configured to acquire data collected by sensors connected to the auxiliary control system, and send the data to the memory and/or the processor;
  • the memory is used to store the data and related program codes
  • the processor is configured to invoke the program code to execute the auxiliary control method for a wind power generating set according to any one of the implementation manners of the first aspect above.
  • an embodiment of the present application further provides a wind power generating set, the wind generating set includes the auxiliary control system described in the implementation manner of the second aspect above.
  • the embodiment of the present application also provides a computer-readable storage medium, the computer-readable storage medium is used to store a computer program, and the computer program is used to execute the method described in any one of the implementation manners of the above-mentioned first aspect.
  • Auxiliary control method for wind turbines is also provided.
  • the auxiliary control method of the wind power generating set is applied to the auxiliary control system installed at the wind turbine end.
  • various The data collection and processing are communicated by the auxiliary control system and the main control system to realize the on-site diagnosis and control of the wind power generation unit, without occupying the computing power of the main control system, reducing the amount of data transmitted by the network, and realizing wind power generation. Local control of the unit.
  • FIG. 1 is a flow chart of an auxiliary control method for a wind power generating set in an embodiment of the present application
  • Fig. 2 is a flow chart of another auxiliary control method for a wind power generating set in the embodiment of the present application;
  • FIG. 3 is a structural distribution diagram of a wind power generating set in the embodiment of the present application.
  • Fig. 4 is a schematic structural diagram of a wind power generating set in an embodiment of the present application.
  • FIG. 5 is a schematic diagram of a software architecture of an auxiliary controller in an embodiment of the present application.
  • FIG. 6 is a schematic structural diagram of an auxiliary control system in an embodiment of the present application.
  • the control of wind farms on wind turbines is mainly realized through multiple independent systems with different functions, including online monitoring system, unit health assessment system, blade video monitoring system, etc.
  • the main control system of wind turbines communicates with field-level controllers
  • the network communication of the platform realizes the monitoring and control of the unit.
  • the wind turbine main control system manages multiple subsystems, the functional coupling is high and the deployment is complicated.
  • the hardware computing power of the wind turbine main control system is limited, and resources such as network load limit the application of machine learning and predictive control to the wind turbine main control system.
  • an embodiment of the present application provides an auxiliary control method for a wind power generating set.
  • the method is applied to the auxiliary control system installed at the wind turbine end to obtain the data collected by the sensor connected to the auxiliary control system; call the algorithm model to analyze the data, and generate Control instruction: send the control instruction to the main control system of the wind power generating set, so that the main control system completes the control of the wind generating set according to the control instruction.
  • the algorithm model is integrated into the auxiliary control system to realize the collection and processing of various data, and the auxiliary control system communicates with the main control system to realize the on-site diagnosis and control of the wind turbine. It does not need to occupy the computing power of the main control system, reduces the amount of data transmitted by the network, and realizes local control of the wind turbine.
  • FIG. 1 this figure is a flow chart of an auxiliary control method for a wind power generating set provided in an embodiment of the present application.
  • This method is applied to the auxiliary control system set at the fan end, and specifically includes the following steps:
  • the auxiliary control system has a variety of external interfaces, supports a variety of communication protocols, and can be connected to various types of sensors to collect the required data. For example, various types of sensors such as pickups, cameras, radars, etc. can be connected.
  • the external sensors that can be connected to the auxiliary control system are scalable sensors, including but not limited to the above sensors.
  • S102 call the algorithm model to analyze data, and generate control instructions
  • the relevant algorithm model is called, and the algorithm model analyzes the data collected by the sensor, and generates corresponding control instructions according to the analysis result.
  • S103 Send the control command to the main control system of the wind power generating set, so that the main control system completes the control of the wind power generating set according to the control command.
  • the auxiliary control system After the auxiliary control system generates control instructions according to the called algorithm model, it sends the control instructions to the main control system of the wind turbine, and the main control system completes the control of the wind turbine according to the control instructions.
  • the auxiliary control system collects the data of the external sensor, calls the relevant algorithm model to analyze the data, generates the control command and forwards it to the main control system, and realizes the on-site diagnosis of the wind generating set , reducing the amount of data transmitted by the network, and realizing local control of the wind turbine.
  • the auxiliary control system at the wind turbine side not only needs to collect data from its own sensors, but also needs to interact with the main control system to integrate all the data, so that all the working condition data and monitoring data of the wind turbine can be obtained to realize Local control of wind turbines.
  • the auxiliary control system provided by the embodiment of this application adopts a dual-core system, that is, a real-time kernel and a non-real-time kernel. Real-time control of generator sets.
  • the non-real-time kernel is used to run algorithm models that do not require high timeliness, such as analysis and diagnosis models, and does not participate in real-time control.
  • different algorithm models can be called for different application scenarios to generate corresponding control instructions.
  • the algorithm model analyzes the relevant working condition data of the wind turbine and/or the data collected by the sensor, and generates a wind turbine operation control instruction.
  • FIG. 2 is a flow chart of an auxiliary controlling method for a wind generating set provided in this embodiment.
  • the method specifically includes the following steps:
  • S203 In response to triggering the algorithm model corresponding to the data of the sensor, call the service interface related to the algorithm model to obtain the working condition data related to the wind turbine;
  • S205 Send the control command to the main control system of the wind power generating set, so that the main control system completes the control of the wind power generating set according to the control command.
  • step S201 when the characteristic conditions of the data collected by the sensor are judged, and the corresponding algorithm model is called, the embodiment of the present application provides a perceptual scheduling algorithm.
  • the perceptual scheduling algorithm defines a set of scheduling rules about the algorithm model, and the calling algorithm The model needs to obey the scheduling rules of the perceptual scheduling algorithm.
  • the perceptual scheduling algorithm is configured with judgment thresholds, judgment conditions, and calculation formulas for different units. It can analyze large-scale flow data in real time in the process of continuous change through the processing method of flow computing, capture potentially useful information, and Send the result to the next computing node.
  • the perceptual scheduling algorithm automatically adapts the list of algorithm models that can meet the data requirements according to the list of sensors connected to the wind turbine, and sets the algorithm models that are not connected to the required sensors to the disabled state.
  • Configure the scheduling mode including: conditional trigger, timing trigger, cyclic scheduling, and delayed execution
  • the job type is real-time control, or You can set the priority of the job
  • Resource management define memory, central processing unit CPU, running time, hard disk space, etc.
  • Set the trigger condition take the setting of active power as an example,
  • the main control system monitors and controls the blades of the wind turbine, when the ambient temperature is lower than 5°C, the blades cannot work normally, and blade protection needs to be activated. Therefore, when the temperature sensor acquires that the current temperature is lower than 5°C, the perceptual scheduling algorithm will make a judgment based on the data collected by the sensor. After calling the icing protection model, the icing protection model will call the data service function to obtain more data of the wind turbine for analysis, and generate corresponding control instructions according to the analysis results. In this application scenario, the control command generated by the icing protection model is shutdown, and the control command is sent to the main control system, and the main control system realizes the control of the wind turbine blades according to the control command.
  • the auxiliary control system can be connected with various types of sensors, in order to facilitate access to various scalable sensors and intelligently collect the data of the main control system, this embodiment can classify and manage the sensors and formulate collection strategies.
  • the sensors are classified and managed according to type (analog sensor/digital sensor), signal, interface, communication protocol, etc., wherein the communication protocol represents the standard protocol for signal transmission.
  • the serial port sensor adopts RS-485 communication interface, which can be The programmable logic controller (Programmable Logic Controller, PLC) is transmitted through the network port and adopts the OPCUA protocol. Then configure the protocol of the sensor according to the type of the sensor, that is, the communication datagram format specification. Since the types of sensors are different, the types of data collected are also different. It is necessary to configure the protocol according to the type of sensor, and then parse the collected data according to the configured protocol to obtain the data.
  • PLC Programmable Logic Controller
  • a preferred implementation mode can set the attributes of the measuring point corresponding to the sensor.
  • the measuring point is also called the collection variable.
  • Each measuring point corresponds to a standard entry, and the entry includes: entry identity (Identity document, ID) , name, collection frequency, data type, limit value and applicable models, etc.
  • the auxiliary control system establishes a connection with the main control system, first obtain the protocol version of the main control system, determine the data of the port to be collected according to the protocol version and the collection rules, and automatically adapt the collection channel after the sensor is connected to the port, that is Input/output channels.
  • the input/output channel corresponding to the sensor can be automatically verified, making the sensor configuration more convenient.
  • the senor is automatically connected to the IEPE channel, and the sampling strategy of the sensor can be defined, including the length of the data involved in the calculation, the sampling rate, the length of the original data, and the interval time of the feature value calculation.
  • the embodiment of the present application provides a structural distribution diagram of the wind power generators. Referring to FIG.
  • the tower bottom area 302 and the hub area 303 form an organic whole.
  • the auxiliary control system mainly includes an auxiliary controller, a first auxiliary control sub-station and a second auxiliary control sub-station.
  • the first auxiliary control sub-station can collect data connected to the bottom area of the tower and send it to the auxiliary
  • the controller the second auxiliary control sub-station can collect the data connected to the hub area and send it to the auxiliary controller.
  • the auxiliary controller can collect the data of the sensors connected to the auxiliary control system, call the algorithm model to analyze and calculate the data, etc. Generate control instructions according to the calculation results, and send the control instructions to the main control system.
  • FIG. 4 is a schematic structural diagram of a wind power generating set provided by an embodiment of the present application.
  • the wind power generating set can be divided into the nacelle area 301, the tower bottom area 302 and the hub area 303.
  • One possible implementation is to install the auxiliary control system 401 in the The sensors of the auxiliary control system 401 in the The sensors of the auxiliary control system 401 include sensor 1, sensor 2, until sensor N; the first auxiliary control substation 402 tower bottom area 302, the first auxiliary control substation 402 is connected to the sensors distributed in the tower bottom area 302, including tower Sensor 1 at the bottom, sensor 2 at the bottom of the tower, until sensor N at the bottom of the tower, and the first auxiliary control substation 402 is connected with the auxiliary control system 401, and the data of the sensor at the bottom of the tower collected is sent to the auxiliary control system 401;
  • the auxiliary control sub-station 403 is distributed in the hub area 303, and the second auxiliary control sub-station 403 accesses the hub sensor 1, the hub sensor 2, and the hub sensor N distributed in the hub area 303, and the second auxiliary control sub-station 403 communicate
  • the auxiliary control system 401 includes: an acquisition unit 4011, a memory 4012 and a processor 4013;
  • the collection unit 4011 can obtain the data collected by the sensors connected to the auxiliary control system, and can also obtain the data collected by the sensors of the first auxiliary control substation and the second auxiliary control substation, and send the obtained data to the memory 4012 and/or or processor 4013;
  • the processor 4013 can process the acquired data according to the algorithm model, and generate a control command; send the control command to the main control system of the wind power generating set, so that the main control system completes the control of the wind power generating set according to the control command;
  • the first auxiliary control substation 402 is configured to obtain data collected by sensors connected to the first auxiliary control substation, and send the data to the acquisition unit 4011 of the auxiliary control system;
  • the second auxiliary control sub-station 403 is used to obtain data collected by sensors connected to the second auxiliary control sub-station, and send the data to the acquisition unit 4011 of the auxiliary control system;
  • the auxiliary control system has a variety of external ports, supports a variety of communication protocols, and can be connected to sensors such as pickups, cameras, radars, and vibrations.
  • the auxiliary controller can integrate heterogeneous chips to realize Efficient processing of data from multiple sensors.
  • the main control function of the auxiliary control system can be realized by the auxiliary controller.
  • the auxiliary controller can monitor video, audio and other working condition data, monitor sensors online, and use machine learning and other algorithms to analyze and process the data.
  • One possible implementation method can use a Graphics Processing Unit (GPU) for image processing, such as monitoring the trajectory of the blade through blade video, preventing blade sweeping, and judging whether the blade is icing or broken through image recognition. , based on the real-time working condition data of the unit for simulation, predicting the future working condition data, intervening in advance and optimizing the control unit.
  • GPU Graphics Processing Unit
  • the auxiliary controller obtains the data of the sensors connected to the auxiliary control system, and then processes the collected data.
  • a specific scenario will be combined to introduce an auxiliary control method for a wind power generating set.
  • auxiliary controller includes a digital signal processor (Digital Signal Processor, DSP) and a graphics processor GPU.
  • DSP Digital Signal Processor
  • the digital signal processor DSP is used for image preprocessing.
  • the digital signal processor DSP has strong computing power and can complete the Fast Fourier Transform (FFT) of the signal, that is, to quickly calculate the discrete method of Fourier transform.
  • the digital signal processor DSP outputs the preprocessed feature value result frame, and the graphics processor GPU performs target recognition.
  • FFT Fast Fourier Transform
  • each computing task is subdivided and disassembled, and assigned to heterogeneous computing units for execution.
  • the calculation unit can be dispatched in real time to complete the calculation task of the data.
  • the algorithm model generates control instructions based on the calculation results of the calculation unit, and the auxiliary controller sends the control instructions Send it to the main control system, and the main control system realizes the control of the wind power generating set.
  • a field programmable gate array (Field Programmable Gate Array, FPGA) chip can be integrated, which is mainly used for image recognition and video monitoring, such as monitoring whether there are cracks or icing on the fan blades through fan video wait.
  • FPGA Field Programmable Gate Array
  • the FPGA chip has cache logic, through which the FPGA chip logic can be quickly modified globally or locally, and the dynamic reconfiguration of the system can be accelerated by controlling the resource allocation of re-layout and wiring.
  • the system can have the advantages of both software and hardware implementations while only adding a small amount of hardware resources.
  • this figure is a schematic diagram of a software architecture of an auxiliary controller provided in an embodiment of the present application.
  • the software architecture of the auxiliary control system is mainly divided into a base layer 501 and an application layer 502.
  • the base layer 501 mainly includes: a data acquisition module 5011, a perception scheduling module 5012, and a data service module 5013.
  • the application layer 502 mainly includes Algorithm model, a possible implementation method is that the algorithm model includes: machine learning model 5021, predictive control model 5022, and stand-alone health assessment model 5023, etc., and the algorithm model is used to assist the main control system to realize local control of the wind turbine.
  • the above-mentioned modules included in the base layer and the algorithm model of the application layer are only exemplary introductions, not all implementation methods, and do not make any formal limitations on this application. Other possible implementation methods are also included in this application. within the scope of protection.
  • the data acquisition module implements data acquisition and access according to different acquisition strategies. Since the auxiliary control system is connected with a variety of sensors, the sensors can be classified and managed according to type, signal, interface, etc., and different protocols can be configured. For example, the serial port sensor adopts RS-485 communication interface, and the programmable logic controller PLC is transmitted through the network port, using the OPCUA protocol. Set the properties of the measuring point corresponding to the sensor. The measuring point is also called the collection variable. Each measuring point corresponds to a standard entry, which includes: entry ID, name, collection frequency, data type, limit value, and applicable models, etc. .
  • a communication protocol with the main control system can also be formulated, and different protocol versions correspond to different acquisition variables.
  • the auxiliary control system establishes a connection with the main control system, first obtain the protocol version of the main control system.
  • the data acquisition model can automatically adapt the input/output channel of the data to obtain the sensor The data.
  • the perception scheduling module belongs to the core module of the auxiliary control system. According to the data collected by the sensor, the module judges the characteristic conditions, identifies the current event status of the wind turbine, and determines whether to trigger the algorithm model corresponding to the sensor data. After the characteristic conditions are met, the perception scheduling module can trigger and call the algorithm model related to the application layer.
  • the algorithm model calls the data service module to obtain more working condition data of the wind turbine, analyze the working condition data, and output the analysis results
  • the perceptual scheduling module sends control instructions to the main control system, and the main control system controls the operation of the wind turbine according to the control instructions.
  • the data acquisition module is responsible for collecting the data of the acceleration sensor of the main bearing, and pushes the collected vibration data to the perception scheduling module in real time.
  • the perception scheduling module judges the vibration limit of the main bearing. If the vibration exceeds the upper limit Value, the perception scheduling module calls the vibration algorithm model of the application layer, the vibration algorithm model calls the data service module, obtains more working condition data of the wind turbine for analysis, and generates control instructions according to the analysis results, and the perception scheduling module forwards the control instructions to The main control system, the main control system controls the operation of the wind turbine, that is, controls the main bearing to reduce the number of vibrations.
  • the auxiliary control system adopts a dual-core system, that is, a real-time kernel and a non-real-time kernel.
  • the real-time kernel is used to run a real-time algorithm model, so that the auxiliary control system has real-time and stable communication capabilities with the main control system, and realizes wind power generation. Local control of the unit.
  • the non-real-time kernel is used to run algorithm models that do not require high timeliness, such as analysis and diagnosis models, and does not participate in real-time control.
  • the auxiliary control system can use a non-real-time communication protocol with the main control system, and the auxiliary control system uses a switch and the main control system
  • the auxiliary control system communicates with the main control system to realize local control of the wind turbine and reduce communication costs.
  • the embodiment of the present application further provides an auxiliary control system, the auxiliary control system is arranged at the wind turbine end, refer to FIG. 6 , which is a schematic diagram of an auxiliary control system provided in the embodiment of the present application.
  • the auxiliary control system 600 includes an acquisition unit 601, a memory 602 and a processor 603;
  • An acquisition unit 601 configured to acquire data collected by sensors connected to the auxiliary control system, and send the data to a memory and/or a processor;
  • Memory 602 configured to store the above data and related program codes
  • the processor 603 is configured to call the above program code to execute the auxiliary control method for the wind power generating set described in the above method embodiment.
  • the embodiments of the present application further provide a wind power generating set, which includes the auxiliary control system described in the above embodiments.
  • an embodiment of the present application further provides a computer-readable storage medium, which is used to store a computer program, and the computer program is used to execute the auxiliary control method for a wind power generating set described in the method embodiment above.
  • each embodiment in this specification is described in a progressive manner, and the similar parts of each embodiment can be referred to each other, and each embodiment focuses on the differences from other embodiments.
  • the description is relatively simple, and the relevant parts can be referred to the part description of the method embodiment.
  • the device embodiments described above are only illustrative, where the units or modules described as separate components may or may not be physically separated, and the components shown as units or modules may or may not be physical modules, that is It may be located in one place, or may be distributed to multiple network units, and some or all of the units or modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. It can be understood and implemented by those skilled in the art without creative effort.

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Abstract

L'invention concerne un procédé et un système de commande auxiliaire destiné à un ensemble générateur éolien, et un ensemble générateur éolien. Le procédé est appliqué à un système de commande auxiliaire disposé au niveau d'une extrémité d'hélice, et comprend les étapes consistant à : acquérir des données collectées par un capteur qui accède au système de commande auxiliaire ; faire appel à un modèle d'algorithme pour analyser les données, et générer une instruction de commande ; et envoyer l'instruction de commande à un système de commande principal d'un ensemble générateur éolien, de telle sorte que le système de commande principal achève la commande de l'ensemble générateur éolien selon l'instruction de commande. En disposant le système de commande auxiliaire au niveau de l'extrémité de pale, et en intégrant le modèle d'algorithme dans le système de commande auxiliaire, la collecte et le traitement de diverses données sont obtenus ; puisque le système de commande auxiliaire communique avec le système de commande principal, l'ensemble générateur éolien est diagnostiqué localement, et la quantité de données pour la transmission de réseau est réduite, ce qui permet d'obtenir une commande locale dudit ensemble générateur éolien.
PCT/CN2022/101438 2021-09-29 2022-06-27 Procédé et système de commande auxiliaire pour ensemble générateur éolien, et ensemble générateur éolien WO2023050930A1 (fr)

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BR112024000658A BR112024000658A2 (pt) 2021-09-29 2022-06-27 Método e sistema de controle auxiliar para conjunto de gerador de turbina eólica e conjunto de gerador de turbina eólica
AU2022356758A AU2022356758A1 (en) 2021-09-29 2022-06-27 Auxiliary control method and system for wind turbine generator set, and wind turbine generator set

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CN202111152513.0A CN114278496A (zh) 2021-09-29 2021-09-29 风力发电机组的辅助控制方法、系统及风力发电机组

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