WO2024077753A1 - Procédé de prédiction de défaillance d'éolienne basé sur des données hétérogènes multi-sources - Google Patents

Procédé de prédiction de défaillance d'éolienne basé sur des données hétérogènes multi-sources Download PDF

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WO2024077753A1
WO2024077753A1 PCT/CN2022/137737 CN2022137737W WO2024077753A1 WO 2024077753 A1 WO2024077753 A1 WO 2024077753A1 CN 2022137737 W CN2022137737 W CN 2022137737W WO 2024077753 A1 WO2024077753 A1 WO 2024077753A1
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data
wind turbine
target
sample
historical data
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PCT/CN2022/137737
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English (en)
Chinese (zh)
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杨之乐
安钊
郭媛君
胡天宇
吴承科
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深圳先进技术研究院
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Publication of WO2024077753A1 publication Critical patent/WO2024077753A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • 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 the field of new energy technology, and in particular to a wind turbine fault prediction method based on multi-source heterogeneous data.
  • the present invention provides a wind turbine fault prediction method based on multi-source heterogeneous data, aiming to solve the problem of low efficiency of fault detection of wind turbines requiring manual inspection in the prior art.
  • a first aspect of the present invention provides a wind turbine fault prediction method based on multi-source heterogeneous data, the method comprising:
  • Acquire historical data of a target wind turbine wherein the historical data of the target wind turbine at least includes deformation data of each blade of the target wind turbine within a preset time period and vibration data of the target wind turbine, wherein the preset time period is a time period of a preset duration before a current moment;
  • the neural network is trained based on a training data set, and the training data set includes sample historical data and sample expansion data, the sample historical data is historical data of wind turbines actually collected, and the sample expansion data is data generated by performing sample expansion processing on the sample historical data.
  • the wind turbine fault prediction method based on multi-source heterogeneous data wherein the acquiring of historical data of the target wind turbine comprises:
  • the nose position of the target wind turbine is acquired, and deformation data of each blade of the target wind turbine is acquired according to the nose position and a reflection signal of a light signal sent to the target wind turbine.
  • the wind turbine fault prediction method based on multi-source heterogeneous data wherein the step of acquiring deformation data of each blade of the target wind turbine according to the position of the wind turbine head and the reflection signal of the light signal sent to the target wind turbine, comprises:
  • the deformation data of each blade of the target wind turbine is acquired according to the difference between the nose position and the nose reference position, and the reflected signal of the light signal sent to the target wind turbine and the reference reflected signal data.
  • the wind turbine fault prediction method based on multi-source heterogeneous data, wherein, after obtaining the deformation data of each blade of the target wind turbine according to the position of the wind turbine head and the reflection signal of the light signal sent to the target wind turbine, comprises:
  • the method further comprises:
  • the fault prediction result of the target wind turbine is updated into the digital twin model of the target wind turbine.
  • the wind turbine fault prediction method based on multi-source heterogeneous data wherein the neural network includes a feature extraction module and a prediction module, and the training process of the neural network is:
  • the feature extraction module extracts the features of the target sample historical data and the target sample expanded data respectively to obtain a first feature and a second feature;
  • the parameters of the sample expansion module, the feature extraction module and the prediction module are updated according to the batch training loss, and the step of selecting part of the sample historical data from each of the sample historical data to form a target training batch is re-executed until the parameters converge.
  • the wind turbine fault prediction method based on multi-source heterogeneous data, wherein the batch training loss is obtained according to the first feature, the second feature, the first prediction result, the second prediction result and the fault label corresponding to each sample historical data in the target training batch, including:
  • sample losses corresponding to each sample historical data in the training batch are summed to obtain the second batch loss
  • the updating of the parameters of the sample expansion module, the feature extraction module and the prediction module according to the batch training loss includes:
  • the parameters of the sample expansion module, the feature extraction module, the prediction module and the discriminator are updated according to the batch training loss.
  • the wind turbine fault prediction method based on multi-source heterogeneous data, wherein the features of the target sample historical data and the target sample expanded data are respectively extracted by the feature extraction module to obtain the first feature and the second feature, including:
  • the first dimension reduction data and the second dimension reduction data are respectively input into the feature extraction module to obtain the first feature and the second feature output by the feature extraction module.
  • a second aspect of the present invention provides a wind turbine fault prediction device based on multi-source heterogeneous data, comprising:
  • a data acquisition module is used to acquire historical data of a target wind turbine, the historical data of the target wind turbine at least including deformation data of each blade of the target wind turbine within a preset time period and vibration data of the target wind turbine, the preset time period being a period of preset duration before a current moment;
  • a prediction module the prediction module is used to input the historical data of the target wind turbine into a trained neural network, and obtain a fault prediction result of the target wind turbine output by the neural network;
  • the neural network is trained based on a training data set, and the training data set includes sample historical data and sample expansion data, the sample historical data is historical data of wind turbines actually collected, and the sample expansion data is data generated by performing sample expansion processing on the sample historical data.
  • a terminal comprising a processor and a computer-readable storage medium communicatively connected to the processor, wherein the computer-readable storage medium is suitable for storing a plurality of instructions, and the processor is suitable for calling the instructions in the computer-readable storage medium to execute the steps of implementing any one of the above-mentioned methods for predicting wind turbine faults based on multi-source heterogeneous data.
  • a fourth aspect of the present invention provides a computer-readable storage medium storing one or more programs, which can be executed by one or more processors to implement the steps of any of the above-mentioned methods for predicting wind turbine faults based on multi-source heterogeneous data.
  • the present invention provides a wind turbine fault prediction method based on multi-source heterogeneous data, which predicts wind turbine faults through a neural network.
  • the real data is expanded and processed, so that the amount of data in the training data set of the neural network is larger, thereby improving the accuracy of fault prediction of the trained neural network.
  • FIG1 is a flow chart of an embodiment of a method for predicting wind turbine faults based on multi-source heterogeneous data provided by the present invention
  • FIG2 is a structural principle diagram of an embodiment of a wind turbine fault prediction device based on multi-source heterogeneous data provided by the present invention
  • FIG3 is a schematic diagram showing the principles of an embodiment of a terminal provided by the present invention.
  • the wind turbine fault prediction method based on multi-source heterogeneous data provided by the present invention can be applied to a terminal with computing capabilities.
  • the terminal can execute the wind turbine fault prediction method based on multi-source heterogeneous data provided by the present invention to perform power grid peak-shaving scheduling.
  • the terminal can be but is not limited to various computers, mobile terminals, smart home appliances, wearable devices, etc.
  • the steps include:
  • the preset time length may be one week, one month, etc., and the preset time length may be determined through experiments, that is, different time lengths are used to conduct experiments using the method provided in this embodiment to obtain a more accurate fault prediction result as the preset time length.
  • the vibration data in the historical data of the target wind turbine may be obtained by a sensor installed on the target wind turbine.
  • the blade deformation data in the historical data of the target wind turbine may be obtained by optical signal detection.
  • the historical data of the target wind turbine may be obtained by:
  • the nose position of the target wind turbine is acquired, and deformation data of each blade of the target wind turbine is acquired according to the nose position and a reflection signal of a light signal sent to the target wind turbine.
  • an optical signal generator and an optical signal detector may be arranged around the target wind turbine.
  • the number of the optical signal generator and the optical signal detector may be multiple so that the detection range covers the entire blade of the target wind turbine.
  • the optical signal generator sends an optical signal to the target wind turbine and is reflected and received by the optical signal detector.
  • the optical signal detector receives the reflected signal and analyzes it to obtain the deformation data of the blade. Specifically, the reflected signal pre-set when the blade of the wind turbine is not deformed can be used as a reference, and the deformation data is determined according to the difference between the actually received reflected signal and the reflected signal when it is not deformed.
  • the head of the wind turbine will rotate accordingly to keep the blade in the right wind. Therefore, in this embodiment, when obtaining the deformation data of the blade, it is also necessary to combine the real-time head position of the wind turbine.
  • the step of acquiring deformation data of each blade of the target wind turbine according to the position of the wind turbine head and the reflected signal of the light signal sent to the target wind turbine includes:
  • the deformation data of each blade of the target wind turbine is acquired according to the difference between the nose position and the nose reference position, and the reflection signal of the light signal sent to the target wind turbine and the standard reflection signal data.
  • the nose reference position refers to the pre-set position of the nose of the target wind turbine relative to the optical signal generator and the optical signal detector.
  • the positions of the optical signal generator and the optical signal detector remain unchanged, while the nose position of the target wind turbine will change with different wind directions.
  • the reflected signal data received by the optical signal detector after the optical signal emitted by the optical signal generator is reflected by the blades of the target wind turbine when the nose of the target wind turbine is at the nose reference position is obtained in advance as the reference reflected signal data.
  • the reflected signal of the optical signal sent to the blades of the target wind turbine when the blades of the target wind turbine are not deformed can be obtained.
  • the deformation data of the blades of the target wind turbine can be determined.
  • the method After acquiring deformation data of each blade of the target wind turbine according to the position of the wind turbine head and the reflected signal of the light signal sent to the target wind turbine, the method includes:
  • the three-dimensional model in the digital twin model of the target wind turbine is updated based on the current blade deformation data of the target wind turbine.
  • a digital twin model of the target wind turbine is set up, and the digital twin model includes a three-dimensional model of the target wind turbine. After obtaining the current blade deformation data of the target wind turbine, the three-dimensional model of the target wind turbine is updated according to the blade deformation data, so that maintenance personnel can intuitively obtain the deformation condition of the blades through the three-dimensional model.
  • the method provided in this embodiment further includes the steps of:
  • S200 Inputting historical data of the target wind turbine into a trained neural network, and obtaining a fault prediction result of the target wind turbine output by the neural network.
  • the method provided in this embodiment uses sample expansion to expand the data set.
  • the neural network is trained based on the training data set, and the training data set includes sample historical data and sample expansion data.
  • the sample historical data is the historical data of the wind turbine that is actually collected, and the sample expansion data is the data generated by performing sample expansion processing on the sample historical data.
  • the neural network includes a feature extraction module and a prediction module, and the training process of the neural network is:
  • a label is a fault category label
  • a label is a fault category label
  • the training process of the adversarial network is not ideal, and the fault prediction network trained with the expanded data generated by the adversarial network after training is not high in the final prediction accuracy.
  • a new training method for a data expansion network is proposed, and the data expansion network and the fault prediction network are jointly trained.
  • the label of a single data is not restricted, but a part of all the real labeled data is selected.
  • the sample historical data is the real historical data of the wind turbine, and the sample historical data has corresponding labels, that is, the actual fault type of the wind turbine and the corresponding real historical data when the fault type occurs are collected to obtain the sample historical data and the corresponding fault label label.
  • the selected part of the sample historical data is used as the target training batch, and each of the sample historical data in the target training batch is input into the sample expansion module respectively.
  • the batch training loss of the target training batch is obtained, and then the parameters of each network module are updated based on the batch training loss, and then part of the sample historical data is randomly removed as the new target training batch, and this is iterated multiple times until the parameters converge.
  • the expanded data is not labeled. Instead, the outputs of multiple network modules are used to calculate multiple losses to update the parameters of the sample expansion module, so that the sample expansion module can learn the essence of the feature space where data of different fault types are located.
  • obtaining the batch training loss according to the first feature, the second feature, the first prediction result, the second prediction result and the fault label corresponding to each sample historical data in the target training batch includes:
  • sample losses corresponding to each sample historical data in the training batch are summed to obtain the second batch loss
  • the batch training loss is obtained according to the first batch loss and the second batch loss.
  • multiple losses are set to constrain the optimization direction of the network module parameters.
  • the first loss can enable the feature extraction module and the prediction module to learn the intrinsic relationship between the historical data and faults of the wind turbine
  • the second loss can enable the feature extraction module and the sample expansion module to learn the feature space of the real data of the wind turbine for predicting faults, so that the generated expanded data can be consistent with the real data in terms of the features used to predict faults.
  • the module parameters are also updated by constraining the distance between the probability distribution of the fault prediction results of the real data and the probability distribution of the fault prediction results of the expanded data.
  • the first probability distribution is obtained according to the corresponding first prediction results
  • the corresponding second features are input into the prediction module
  • the second probability distribution is obtained based on the multiple second prediction results output by the prediction module. If the expanded data generated by the sample expansion module has the same feature space as the real data, and the prediction module has fully learned the intrinsic connection between the feature space and the fault category, then the first probability distribution and the second probability distribution should be consistent. Therefore, calculating the first batch distribution loss based on the difference between the first probability distribution and the second probability distribution for updating the module parameters can effectively improve the accuracy of the model.
  • the corresponding first loss and second loss can be obtained, the first loss and the second loss are summed to obtain a sample loss corresponding to the sample historical data, the sample losses corresponding to each sample historical data in the target training batch are summed to obtain the second batch loss, the first batch loss and the second batch loss are summed to obtain the batch training loss corresponding to the target training batch, and the parameters of the sample expansion module, the feature extraction module, the prediction module and the discriminator are updated based on the batch training loss.
  • each time the sample historical data is selected to form the target training batch it is selected randomly, that is, the data combination in the target training batch used to update the module parameters each time is different, so that the probability distribution is also different each time, which can have a similar effect as expanding the training data.
  • the historical data of the target wind turbine is also subjected to dimensionality reduction processing.
  • the features of the target sample historical data and the target sample expanded data are respectively extracted by the feature extraction module to obtain the first feature and the second feature, including:
  • the first dimension reduction data and the second dimension reduction data are respectively input into the feature extraction module to obtain the first feature and the second feature output by the feature extraction module.
  • the fault prediction result of the target wind turbine generator can be input into the trained neural network, and the neural network outputs the fault prediction result of the target wind turbine generator.
  • the method includes:
  • the fault prediction result of the target wind turbine is updated into the digital twin model of the target wind turbine.
  • Displaying the fault prediction result in the digital twin model of the target wind turbine may be based on the components corresponding to the fault prediction result, and displaying the fault prediction result on the three-dimensional model of the corresponding components, which is more intuitive.
  • this embodiment provides a wind turbine fault prediction method based on multi-source heterogeneous data, which predicts wind turbine faults through a neural network, and in order to address the problem of insufficient real label data of existing wind turbines, expands the real data, so that the amount of data in the training data set of the neural network is larger, thereby improving the accuracy of fault prediction of the trained neural network, eliminating the need for manual inspection of wind turbines to troubleshoot faults, and improving the fault detection efficiency of wind turbines.
  • steps in the flowchart may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.
  • Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory.
  • Volatile memory can include random access memory (RAM) or external cache memory.
  • RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
  • the present invention further provides a wind turbine fault prediction device based on multi-source heterogeneous data.
  • the wind turbine fault prediction device based on multi-source heterogeneous data includes:
  • a data acquisition module the data acquisition module is used to acquire historical data of a target wind turbine, the historical data of the target wind turbine at least includes deformation data of each blade of the target wind turbine within a preset time period and vibration data of the target wind turbine, the preset time period is a period of preset duration before the current moment, as described in the first embodiment;
  • a prediction module the prediction module is used to input the historical data of the target wind turbine into a trained neural network, and obtain a fault prediction result of the target wind turbine output by the neural network, as specifically described in the first embodiment;
  • the neural network is trained based on a training data set, and the training data set includes sample historical data and sample expansion data, the sample historical data is historical data of wind turbines actually collected, and the sample expansion data is data generated by performing sample expansion processing on the sample historical data, as specifically described in Example 1.
  • the present invention also provides a terminal, as shown in Figure 3, the terminal includes a processor 10 and a memory 20.
  • Figure 3 only shows some components of the terminal, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead.
  • the memory 20 may be an internal storage unit of the terminal, such as a hard disk or memory of the terminal. In other embodiments, the memory 20 may also be an external storage device of the terminal, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the terminal. Further, the memory 20 may also include both an internal storage unit of the terminal and an external storage device. The memory 20 is used to store application software and various types of data installed on the terminal. The memory 20 may also be used to temporarily store data that has been output or is to be output.
  • a plug-in hard disk such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the terminal.
  • the memory 20 may also include both an internal storage unit of the terminal and an external storage device.
  • the memory 20 is used to store
  • a wind turbine fault prediction program 30 based on multi-source heterogeneous data is stored on the memory 20, and the wind turbine fault prediction program 30 based on multi-source heterogeneous data can be executed by the processor 10, thereby realizing the wind turbine fault prediction method based on multi-source heterogeneous data in the present application.
  • the processor 10 may be a central processing unit (CPU), a microprocessor or other chip, used to run the program code or process data stored in the memory 20, such as executing the wind turbine fault prediction method based on multi-source heterogeneous data.
  • CPU central processing unit
  • microprocessor or other chip
  • the processor 10 executes the wind turbine fault prediction program 30 based on multi-source heterogeneous data in the memory 20, the following steps are implemented:
  • Acquire historical data of a target wind turbine wherein the historical data of the target wind turbine at least includes deformation data of each blade of the target wind turbine within a preset time period and vibration data of the target wind turbine, wherein the preset time period is a time period of a preset duration before a current moment;
  • the neural network is trained based on a training data set, and the training data set includes sample historical data and sample expansion data, the sample historical data is historical data of wind turbines actually collected, and the sample expansion data is data generated by performing sample expansion processing on the sample historical data.
  • the present invention also provides a computer-readable storage medium, which stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the wind turbine fault prediction method based on multi-source heterogeneous data as described above.

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Abstract

La présente invention divulgue un procédé de prédiction de défaillance d'éolienne basé sur des données hétérogènes multi-sources. Le procédé consiste à : acquérir des données historiques d'une éolienne cible, les données historiques de l'éolienne cible comprenant au moins des données de déformation de chaque pale de l'éolienne cible dans une période prédéfinie et des données de vibration de l'éolienne cible, et la période prédéfinie étant une durée prédéfinie avant le moment actuel ; et entrer les données historiques de l'éolienne cible dans un réseau neuronal entraîné, et acquérir un résultat de prédiction de défaillance de l'éolienne cible délivrée par le réseau neuronal, le réseau neuronal étant entraîné sur la base d'un ensemble de données d'apprentissage, l'ensemble de données d'apprentissage comprenant des données historiques d'échantillon et des données d'expansion d'échantillon, les données historiques d'échantillon étant des données historiques réellement collectées de l'éolienne, et les données d'expansion d'échantillon étant des données générées par réalisation d'un traitement d'expansion d'échantillon sur les données historiques d'échantillon. La présente invention peut améliorer l'efficacité de détection de défaillance de l'éolienne.
PCT/CN2022/137737 2022-10-10 2022-12-08 Procédé de prédiction de défaillance d'éolienne basé sur des données hétérogènes multi-sources WO2024077753A1 (fr)

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WO2021107866A1 (fr) * 2019-11-25 2021-06-03 Envision Digital International Pte. Ltd. Procédé et appareil de détection de panne, procédé et appareil d'entraînement de modèle, et dispositif et support de stockage
CN112733283A (zh) * 2020-12-21 2021-04-30 北京华能新锐控制技术有限公司 一种风电机组部件故障预测方法
CN114118251A (zh) * 2021-11-22 2022-03-01 国网辽宁省电力有限公司沈阳供电公司 基于多源数据融合与卷积孪生神经网络的故障诊断与预警方法
CN115293057A (zh) * 2022-10-10 2022-11-04 深圳先进技术研究院 一种基于多源异构数据的风力发电机故障预测方法

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