Wind turbine generator operation and maintenance management system based on artificial intelligence and operation and maintenance method thereof

Abstract

The invention discloses a wind turbine generator operation and maintenance management system based on artificial intelligence and an operation and maintenance method and system thereof. A data processing layer; the intelligent analysis layer integrates artificial intelligence technologies of machine learning and deep learning, performs intelligent analysis on the processed data, and realizes predictive maintenance, intelligent fault diagnosis and optimal scheduling functions; the decision support layer is used for generating operation and maintenance decision suggestions according to a result of the intelligent analysis layer and providing a visual report; and the user interaction layer provides a Web or mobile terminal interface, displays real-time operation data, historical data and fault alarm information of the wind turbine generator, and supports operation and maintenance instruction issuing, task distribution and progress tracking. The invention relates to the technical field of wind power generation operation and maintenance. According to the wind turbine generator operation and maintenance management system based on artificial intelligence and the operation and maintenance method thereof, potential faults are found and processed in time, major safety accidents are avoided, meanwhile, non-planned downtime is shortened, and the overall operation efficiency of a wind power plant is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation operation and maintenance, and specifically to an artificial intelligence-based wind turbine operation and maintenance management system and an operation and maintenance method thereof. Background Art

[0002] With the rapid development of the wind power industry, the scale of wind turbines has continued to expand, and the operation and maintenance management of wind turbines has become a key link in ensuring the efficient operation of wind farms. Traditional operation and maintenance methods rely on manual inspections and troubleshooting, and there are problems such as untimely fault warnings, high operation and maintenance costs, and low maintenance efficiency. Existing wind turbine operation and maintenance systems mostly rely on manual inspections and regular maintenance, which makes it difficult to achieve accurate predictions and rapid responses. In recent years, the rise of artificial intelligence technology has provided new solutions for the operation and maintenance management of wind turbines, but the existing systems on the market mostly focus on the intelligence of a single function and lack a comprehensive and systematic intelligent operation and maintenance management system. Therefore, it is particularly important to develop a system that can perform intelligent analysis, prediction, and optimization of operation and maintenance management based on artificial intelligence technology. Summary of the invention

[0003] 1. Technical issues to be resolved

[0004] In view of the shortcomings of the prior art, the present invention provides an artificial intelligence-based wind turbine operation and maintenance management system and an operation and maintenance method thereof, which can timely detect and handle potential faults, avoid the occurrence of major safety accidents, reduce unplanned downtime, and improve the overall operating efficiency of wind farms.

[0005] (II) Technical solution

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an artificial intelligence-based wind turbine operation and maintenance management system, comprising:

[0007] The data collection layer is used to collect the operating data of wind turbines in real time through the sensor network deployed at each wind turbine site, and perform preliminary data processing and analysis at the edge computing node;

[0008] The data processing layer is used to receive data from the data collection layer, perform deep cleaning, denoising, and compression processing, and store it in a distributed big data processing platform and real-time database;

[0009] The intelligent analysis layer integrates machine learning and deep learning artificial intelligence technologies to perform intelligent analysis on processed data to achieve predictive maintenance, intelligent fault diagnosis, and optimized scheduling functions;

[0010] The decision support layer generates operation and maintenance decision suggestions based on the results of the intelligent analysis layer and provides visual reports. This layer also introduces a federated learning mechanism to achieve cross-domain collaborative learning while protecting data privacy.

[0011] The user interaction layer provides a web or mobile interface to display the real-time operating data, historical data, and fault alarm information of the wind turbine, and supports the issuance of operation and maintenance instructions, task allocation, and progress tracking; at the same time, this layer also includes a collaboration platform to support multi-user online collaborative work and improve the collaboration efficiency of the operation and maintenance team.

[0012] Preferably, the data collection layer has at least one edge computing node, and each node is configured with a data processing module and a communication module for performing preliminary data processing and analysis at the data source, and transmitting the processed data to the data processing layer through the communication module.

[0013] Preferably, the data processing layer further includes:

[0014] Distributed big data processing platform for parallel processing of massive data;

[0015] Real-time database, used to store processed real-time data and support fast retrieval;

[0016] Data warehouses and data lakes are used to build multi-dimensional data analysis models and provide comprehensive data support for the intelligent analysis layer.

[0017] Preferably, the intelligent analysis layer further includes:

[0018] Predictive maintenance models to predict the remaining life and potential failures of wind turbine components based on operating conditions, environmental factors, historical maintenance records and real-time data;

[0019] Intelligent fault diagnosis system, which uses convolutional neural network or recurrent neural network learning model to extract and classify fault signals, and combines expert knowledge base and fault case library to realize accurate identification and rapid location of faults;

[0020] Optimize the scheduling algorithm, use genetic algorithm or particle swarm optimization algorithm, combine the actual situation of the wind farm and the needs of the power grid, formulate the optimal operation strategy of the wind turbine, and achieve multi-objective optimization.

[0021] Preferably, the user interaction layer further includes:

[0022] A natural language processing module is used to parse the user's natural language instructions and generate a corresponding operation interface;

[0023] An augmented reality display module is used to display the operating data and fault information of the wind turbine to the user in an augmented reality manner;

[0024] The collaboration platform supports multi-user online collaborative work, including task allocation, progress tracking, communication and resource sharing.

[0025] An operation and maintenance method of a wind turbine operation and maintenance management system based on artificial intelligence comprises the following steps:

[0026] Step 1: Collect the operating data of wind turbines in real time;

[0027] Step 2: Preprocess and store the collected data;

[0028] Step 3: Use artificial intelligence technology to analyze data, predict the remaining life and potential failures of wind turbine components, and diagnose failures;

[0029] Step 4: Generate operation and maintenance decision suggestions based on the analysis results and generate a visual report;

[0030] Step 5: Display data, reports, and decision-making recommendations to operation and maintenance personnel through the user interaction layer, and receive operation and maintenance instructions.

[0031] Preferably, the fifth step also includes executing corresponding operation and maintenance tasks according to the received operation and maintenance instructions, and the operation and maintenance tasks include starting maintenance tasks, sending maintenance notifications, adjusting operating parameters and remote monitoring.

[0032] Preferably, the operating data of the wind turbine generator set collected in the first step includes wind farm environmental parameters and operating status parameters of the wind turbine generator set.

[0033] (III) Beneficial effects

[0034] The present invention provides an artificial intelligence-based wind turbine operation and maintenance management system and an operation and maintenance method thereof. It has the following beneficial effects:

[0035] Using advanced machine learning algorithms, combined with wind turbine operating data and historical maintenance records, a predictive maintenance model is constructed to predict the failure risk of wind turbine components in advance, provide maintenance suggestions to operation and maintenance personnel, and avoid unplanned downtime;

[0036] Use convolutional neural network or recurrent neural network learning models to extract and classify fault signals, and combine expert knowledge base and fault case library to achieve accurate fault identification and rapid fault location, thereby improving the efficiency and accuracy of fault handling;

[0037] Based on the actual situation of the wind farm and the needs of the power grid, formulate an optimized operation strategy for wind turbines, and maximize the economic and environmental benefits of the wind farm by dynamically adjusting the output power and operating status of the wind turbines;

[0038] Use natural language processing, augmented reality and other technologies to build a user-friendly interactive interface; introduce a collaborative platform to support multi-user online collaboration and improve the collaboration efficiency of the operation and maintenance team. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is the overall architecture diagram of the present invention;

[0040] Figure 2 This is a functional architecture diagram of the system of the present invention;

[0041] Figure 3 The figure is a flow chart of the operation and maintenance method of the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] See also Figure 1-3 The present invention provides a technical solution: an artificial intelligence-based wind turbine operation and maintenance management system.

[0044] The data acquisition layer is used to collect the operating data of the wind turbines in real time through the sensor network deployed at each wind turbine site, and perform preliminary data processing and analysis at the edge computing node; the data acquisition layer has at least one edge computing node, each of which is equipped with a data processing module and a communication module, which are used to perform preliminary data processing and analysis at the data source, and transmit the processed data to the data processing layer through the communication module, so as to reduce the amount of data transmission and improve the real-time performance of the system;

[0045] The data processing layer is used to receive data from the data collection layer, perform deep cleaning, denoising, and compression processing, and store it in a distributed big data processing platform and real-time database to provide fast retrieval and data mining capabilities; the data processing layer also includes:

[0046] Distributed big data processing platform, used to process massive data in parallel, improving data processing efficiency and storage capacity;

[0047] Real-time database, used to store processed real-time data and support fast retrieval;

[0048] Data warehouses and data lakes are used to build multi-dimensional data analysis models and provide comprehensive data support for the intelligent analysis layer.

[0049] The intelligent analysis layer integrates machine learning and deep learning artificial intelligence technologies to perform intelligent analysis on processed data to achieve predictive maintenance, intelligent fault diagnosis, and optimized scheduling functions;

[0050] The decision support layer generates operation and maintenance decision suggestions based on the results of the intelligent analysis layer and provides visual reports. It provides intuitive visual reports to help operation and maintenance personnel quickly understand the operating status and operation and maintenance needs of wind turbines. This layer also introduces a federated learning mechanism to achieve cross-domain collaborative learning while protecting data privacy.

[0051] The intelligent analysis layer also includes:

[0052] Predictive maintenance model, which is used to predict the remaining life and potential failure of wind turbine components based on operating conditions, environmental factors, historical maintenance records and real-time data. It can predict the failure risk of wind turbine components in advance, provide maintenance suggestions to operation and maintenance personnel, and avoid unplanned downtime;

[0053] Intelligent fault diagnosis system uses convolutional neural network or recurrent neural network learning model to extract and classify fault signals, and combines expert knowledge base and fault case library to realize accurate fault identification and rapid fault location, thus improving the efficiency and accuracy of fault handling;

[0054] Optimize the scheduling algorithm, use genetic algorithm or particle swarm optimization algorithm, combine the actual situation of the wind farm and the needs of the power grid, formulate the optimal operation strategy of the wind turbine, and achieve multi-objective optimization.

[0055] The user interaction layer provides a web or mobile interface to display the real-time operating data, historical data, and fault alarm information of the wind turbine, and supports the issuance of operation and maintenance instructions, task allocation, and progress tracking; at the same time, this layer also includes a collaboration platform to support multi-user online collaborative work and improve the collaboration efficiency of the operation and maintenance team.

[0056] The user interaction layer also includes:

[0057] A natural language processing module is used to parse the user's natural language instructions and generate a corresponding operation interface;

[0058] An augmented reality display module is used to display the operating data and fault information of the wind turbine to the user in an augmented reality manner;

[0059] The collaboration platform supports multi-user online collaborative work, including task allocation, progress tracking, communication and resource sharing, to improve the collaboration efficiency of the operation and maintenance team.

[0060] An operation and maintenance method of a wind turbine operation and maintenance management system based on artificial intelligence comprises the following steps:

[0061] Step 1: Collect the operation data of wind turbines in real time;

[0062] Step 2: Preprocess and store the collected data;

[0063] Step 3: Use artificial intelligence technology to analyze data, predict the remaining life and potential failures of wind turbine components, and diagnose failures;

[0064] Step 4: Generate operation and maintenance decision suggestions based on the analysis results and generate a visual report;

[0065] Step 5: Display data, reports, and decision-making recommendations to operation and maintenance personnel through the user interaction layer, and receive operation and maintenance instructions.

[0066] The fifth step also includes executing corresponding operation and maintenance tasks according to the received operation and maintenance instructions. The operation and maintenance tasks include starting maintenance tasks, sending maintenance notifications, adjusting operating parameters and remote monitoring.

[0067] In the first step, the operating data of the wind turbines are collected, including the environmental parameters of the wind farm and the operating status parameters of the wind turbines. The environmental parameters include but are not limited to wind speed, wind direction, temperature, humidity and other environmental parameters. The operating status parameters of the wind turbines include but are not limited to speed, power, vibration, temperature and the like.

[0068] Data collection methods include but are not limited to direct sensor measurement, SCADA system data interface acquisition, etc.

[0069] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0070] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An artificial intelligence-based wind turbine operation and maintenance management system, characterized by: include: The data collection layer is used to collect the operating data of wind turbines in real time through the sensor network deployed at each wind turbine site, and perform preliminary data processing and analysis at the edge computing node; The data processing layer is used to receive data from the data collection layer, perform deep cleaning, denoising, and compression processing, and store it in a distributed big data processing platform and real-time database; The intelligent analysis layer integrates machine learning and deep learning artificial intelligence technologies to perform intelligent analysis on processed data to achieve predictive maintenance, intelligent fault diagnosis, and optimized scheduling functions; The decision support layer generates operation and maintenance decision suggestions based on the results of the intelligent analysis layer and provides visual reports. This layer also introduces a federated learning mechanism to achieve cross-domain collaborative learning while protecting data privacy. The user interaction layer provides a web or mobile interface to display the real-time operating data, historical data, and fault alarm information of the wind turbine, and supports the issuance of operation and maintenance instructions, task allocation, and progress tracking; at the same time, this layer also includes a collaboration platform to support multi-user online collaborative work and improve the collaboration efficiency of the operation and maintenance team.

2. The wind turbine operation and maintenance management system based on artificial intelligence according to claim 1, characterized in that: The data collection layer has at least one edge computing node, and each node is configured with a data processing module and a communication module for performing preliminary data processing and analysis at the data source, and transmitting the processed data to the data processing layer through the communication module.

3. The wind turbine operation and maintenance management system based on artificial intelligence according to claim 1 is characterized in that: The data processing layer also includes: Distributed big data processing platform for parallel processing of massive data; Real-time database, used to store processed real-time data and support fast retrieval; Data warehouses and data lakes are used to build multi-dimensional data analysis models and provide comprehensive data support for the intelligent analysis layer.

4. The wind turbine operation and maintenance management system based on artificial intelligence according to claim 1, characterized in that: The intelligent analysis layer also includes: Predictive maintenance models to predict the remaining life and potential failures of wind turbine components based on operating conditions, environmental factors, historical maintenance records and real-time data; Intelligent fault diagnosis system, which uses convolutional neural network or recurrent neural network learning model to extract and classify fault signals, and combines expert knowledge base and fault case library to realize accurate identification and rapid location of faults; Optimize the scheduling algorithm, use genetic algorithm or particle swarm optimization algorithm, combine the actual situation of the wind farm and the needs of the power grid, formulate the optimal operation strategy of the wind turbine, and achieve multi-objective optimization.

5. The wind turbine operation and maintenance management system based on artificial intelligence according to claim 1, characterized in that: The user interaction layer also includes: A natural language processing module is used to parse the user's natural language instructions and generate a corresponding operation interface; An augmented reality display module is used to display the operating data and fault information of the wind turbine to the user in an augmented reality manner; The collaboration platform supports multi-user online collaborative work, including task allocation, progress tracking, communication and resource sharing.

6. An operation and maintenance method for a wind turbine operation and maintenance management system based on artificial intelligence according to any one of claims 1 to 5, characterized in that: The following steps are involved: Step 1: Collect the operating data of wind turbines in real time; Step 2: Preprocess and store the collected data; Step 3: Use artificial intelligence technology to analyze data, predict the remaining life and potential failures of wind turbine components, and diagnose failures; Step 4: Generate operation and maintenance decision suggestions based on the analysis results and generate a visual report; Step 5: Display data, reports, and decision-making recommendations to operation and maintenance personnel through the user interaction layer, and receive operation and maintenance instructions.

7. The operation and maintenance method of a wind turbine operation and maintenance management system based on artificial intelligence according to claim 1, characterized in that: The fifth step also includes executing corresponding operation and maintenance tasks according to the received operation and maintenance instructions. The operation and maintenance tasks include starting maintenance tasks, sending maintenance notifications, adjusting operating parameters and remote monitoring.

8. The operation and maintenance method of a wind turbine operation and maintenance management system based on artificial intelligence according to claim 1, characterized in that: In the first step, the operating data of the wind turbines are collected, including wind farm environmental parameters and operating status parameters of the wind turbines.

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