Offshore wind turbine generator operation and maintenance method and device based on operation and maintenance time window calculation

Abstract

The embodiment of the invention discloses an offshore wind turbine generator operation and maintenance method and device based on operation and maintenance time window calculation, and the method comprises the steps: responding to fault warning information of any one wind turbine generator, and predicting the remaining life of the other wind turbine generators, so as to judge the life states of the other wind turbine generators; collecting historical operation and maintenance data and calculating an operation and maintenance time window; and constructing an operation and maintenance optimization model, and generating an operation and maintenance scheduling mode of the wind turbine generator based on the operation and maintenance optimization model. According to the embodiment, potential problems can be found in time, prevention measures can be taken, the operation and maintenance time window is calculated with the minimum operation and maintenance economic cost in the life cycle as the target, the operation and maintenance optimization model considering operation and maintenance emergencies is solved through an intelligent optimization algorithm, the corresponding wind turbine generator maintenance mode, personnel and equipment distribution and ship dispatching and operation and maintenance routes are obtained, and the maintenance efficiency is improved. Finally, single-time operation and maintenance work scheduling arrangement is completed, maintenance tasks and resources can be arranged more reasonably based on the scheduling mode of the operation and maintenance time window, and unnecessary waiting time and resource waste are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of offshore wind turbine operation and maintenance optimization, and in particular to an offshore wind turbine operation and maintenance method and device based on operation and maintenance time window calculation. Background Art

[0002] At present, apart from wind turbines, the cost of offshore wind power is mainly due to the cost of supporting structures, operation and maintenance, and grid connection, of which the operation and maintenance cost is as high as 18%-23%. Due to its advantages such as abundant wind resources, low turbulence, and stable power generation, offshore wind power is bound to develop towards large-scale installed capacity and deep sea areas in the future, which will further increase the difficulty and cost of operation and maintenance. Therefore, the optimization of offshore wind power operation and maintenance has become an important part of reducing the cost of offshore wind power development and promoting the development of offshore wind power.

[0003] In current practical applications, the mainstream operation and maintenance strategy is fault repair. After the wind turbine reports a fault, it waits for the weather time window to carry out the corresponding operation and maintenance work. However, the fault repair strategy easily ignores the hidden faults that have not been exposed, such as the goodness of the lubrication system, the tightness of the bolts and device connections of the pitch system, the cable connection status of the electrical system and the blade status, etc. These hidden faults will appear within a period of time after the end of a single operation and maintenance, causing the wind turbine to shut down due to faults. At this time, it is necessary to wait for the time window again to carry out operation and maintenance, which will cause unnecessary loss of wind turbine power generation. Summary of the invention

[0004] In view of the above-mentioned defects, an embodiment of the present invention discloses an offshore wind turbine operation and maintenance method and device based on operation and maintenance time window calculation, which aims at maximizing economic benefits and comprehensively reducing offshore wind power operation and maintenance costs.

[0005] A first aspect of an embodiment of the present invention discloses an offshore wind turbine operation and maintenance method based on operation and maintenance time window calculation, comprising:

[0006] In response to a fault warning message of any wind turbine generator set, the remaining life of the remaining wind turbine generator sets is predicted to determine the life status of the remaining wind turbine generator sets;

[0007] Collect historical operation and maintenance data to calculate the operation and maintenance time window;

[0008] An operation and maintenance optimization model is constructed based on the life state and the operation and maintenance time window, and an operation and maintenance scheduling method of the wind turbine generator set is generated based on the operation and maintenance optimization model.

[0009] As an optional implementation manner, in the first aspect of the embodiment of the present invention, it also includes:

[0010] External operation and maintenance conditions are obtained to determine a weather time window, and the operation and maintenance optimization model is constrained based on the weather time window.

[0011] As an optional implementation, in the first aspect of the embodiment of the present invention, the external operation and maintenance conditions include weather conditions and resource conditions, the weather conditions include wind speed data and wave height data, and the resource conditions include operation and maintenance personnel, operation and maintenance data, and operation and maintenance vessels.

[0012] As an optional implementation manner, in the first aspect of the embodiment of the present invention, the collecting of historical operation and maintenance data to calculate the operation and maintenance time window includes:

[0013] Collect historical operation and maintenance data, including historical wind farm warning data, fault causes, fault frequency statistics, and historical operation and maintenance work records;

[0014] Establishing a reliability distribution model corresponding to each component based on the historical operation and maintenance data;

[0015] The operation and maintenance time window is calculated based on the reliability distribution model.

[0016] As an optional implementation manner, in the first aspect of the embodiment of the present invention, it also includes:

[0017] Calculate the reliability of preventive maintenance and the reliability of opportunistic maintenance respectively;

[0018] The operation and maintenance time window is calculated based on the reliability distribution model, preventive maintenance reliability and opportunistic maintenance reliability.

[0019] As an optional implementation manner, in the first aspect of the embodiment of the present invention, calculating the preventive maintenance reliability includes:

[0020] Set the optimization range of preventive maintenance reliability and obtain the current preventive maintenance reliability value;

[0021] Calculating the preventive maintenance interval corresponding to each component of the wind turbine generator set according to the current preventive maintenance reliability value;

[0022] Start the preventive maintenance process, perform preventive maintenance on parts, and update the maintenance frequency, preventive maintenance interval and life of parts;

[0023] Based on the life status of the components, the life cycle of the components is traversed until the updated life is greater than the original life, and the preventive maintenance reliability is obtained by combining the calculation of the updated maintenance times, preventive maintenance interval and life.

[0024] As an optional implementation manner, in the first aspect of the embodiment of the present invention, the computer will maintain reliability, including:

[0025] Set the optimization range of opportunistic maintenance reliability and obtain preventive maintenance reliability;

[0026] Calculate the opportunity maintenance interval corresponding to each component of the wind turbine generator set according to the preventive maintenance reliability;

[0027] Initiate the opportunity maintenance process, perform opportunity maintenance on parts, and update the maintenance number, opportunity maintenance interval and life of parts;

[0028] The entire life cycle of the component is traversed based on the life state of the component until the updated life is greater than the original life, and the opportunity maintenance reliability is calculated by combining the updated maintenance times, opportunity maintenance interval and life.

[0029] A second aspect of an embodiment of the present invention discloses an offshore wind turbine operation and maintenance device based on operation and maintenance time window calculation, comprising:

[0030] A fault response module is used to respond to the fault warning information of any wind turbine set and predict the remaining life of the remaining wind turbine sets to determine the life status of the remaining wind turbine sets;

[0031] Data collection module, used to collect historical operation and maintenance data and calculate the operation and maintenance time window;

[0032] The scheduling generation module is used to build an operation and maintenance optimization model based on the life state and the operation and maintenance time window, and generate an operation and maintenance scheduling method for the wind turbine generator set based on the operation and maintenance optimization model.

[0033] As an optional implementation manner, in the second aspect of the embodiment of the present invention, it also includes:

[0034] External operation and maintenance conditions are obtained to determine a weather time window, and the operation and maintenance optimization model is constrained based on the weather time window.

[0035] As an optional implementation, in the second aspect of the embodiment of the present invention, the external operation and maintenance conditions include weather conditions and resource conditions, the weather conditions include wind speed data and wave height data, and the resource conditions include operation and maintenance personnel, operation and maintenance data, and operation and maintenance vessels.

[0036] As an optional implementation manner, in the second aspect of the embodiment of the present invention, the collecting of historical operation and maintenance data to calculate the operation and maintenance time window includes:

[0037] Collect historical operation and maintenance data, including historical wind farm warning data, fault causes, fault frequency statistics, and historical operation and maintenance work records;

[0038] Establishing a reliability distribution model corresponding to each component based on the historical operation and maintenance data;

[0039] The operation and maintenance time window is calculated based on the reliability distribution model.

[0040] As an optional implementation manner, in the second aspect of the embodiment of the present invention, it also includes:

[0041] Calculate the reliability of preventive maintenance and the reliability of opportunistic maintenance respectively;

[0042] The operation and maintenance time window is calculated based on the reliability distribution model, preventive maintenance reliability and opportunistic maintenance reliability.

[0043] As an optional implementation manner, in the second aspect of the embodiment of the present invention, calculating the preventive maintenance reliability includes:

[0044] Set the optimization range of preventive maintenance reliability and obtain the current preventive maintenance reliability value;

[0045] Calculating the preventive maintenance interval corresponding to each component of the wind turbine generator set according to the current preventive maintenance reliability value;

[0046] Start the preventive maintenance process, perform preventive maintenance on parts, and update the maintenance frequency, preventive maintenance interval and life of parts;

[0047] Based on the life status of the components, the life cycle of the components is traversed until the updated life is greater than the original life, and the preventive maintenance reliability is obtained by combining the calculation of the updated maintenance times, preventive maintenance interval and life.

[0048] As an optional implementation manner, in the second aspect of the embodiment of the present invention, the computer will maintain reliability, including:

[0049] Set the optimization range of opportunistic maintenance reliability and obtain preventive maintenance reliability;

[0050] Calculate the opportunity maintenance interval corresponding to each component of the wind turbine generator set according to the preventive maintenance reliability;

[0051] Initiate the opportunity maintenance process, perform opportunity maintenance on parts, and update the maintenance number, opportunity maintenance interval and life of parts;

[0052] The entire life cycle of the component is traversed based on the life state of the component until the updated life is greater than the original life, and the opportunity maintenance reliability is calculated by combining the updated maintenance times, opportunity maintenance interval and life.

[0053] A third aspect of an embodiment of the present invention discloses an electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the processor calls the executable program code stored in the memory to execute the offshore wind turbine operation and maintenance method based on operation and maintenance time window calculation disclosed in the first aspect of the embodiment of the present invention.

[0054] A fourth aspect of an embodiment of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the offshore wind turbine operation and maintenance method based on operation and maintenance time window calculation disclosed in the first aspect of an embodiment of the present invention.

[0055] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0056] In the embodiment of the present invention, when a fault warning message of any wind turbine is received, life prediction is performed on the components of the remaining wind turbines respectively, the life status of the wind turbine is obtained, and the historical operation and maintenance data of the glue-filled components are collected, including maintenance equipment for different maintenance methods, the number of maintenance personnel, the cause of the failure, etc., thereby calculating the operation and maintenance time window, and jointly constructing an operation and maintenance optimization model in combination with the current life status of the wind turbine and the operation and maintenance time window, and then generating an operation and maintenance scheduling method, including personnel scheduling, operation and maintenance scheduling, ship scheduling, etc. In the embodiment, the life status of the wind turbines that are not currently faulty is predicted. Taking their life cycle into consideration, potential problems can be discovered in time and preventive measures can be taken to avoid sudden failures. At the same time, the operation and maintenance time window is calculated with the minimum operation and maintenance economic cost within the life cycle as the goal. The intelligent optimization algorithm solves the operation and maintenance optimization model that considers the urgency of operation and maintenance, and obtains the corresponding wind turbine maintenance method and personnel and equipment allocation, as well as ship scheduling and operation and maintenance routes, and finally completes the scheduling arrangement of a single operation and maintenance work. The scheduling method based on the operation and maintenance time window can arrange maintenance tasks and resources more reasonably, reduce unnecessary waiting time and waste of resources, and optimize the operation and maintenance plan. It can effectively reduce the wind power operation and maintenance costs while ensuring the healthy operation of the wind turbines, improve the overall economic benefits of the wind farm, and use big data and artificial intelligence technologies to realize intelligent management of wind farm operation and maintenance, and improve operation and maintenance efficiency and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0058] Figure 1 It is a flow chart of an offshore wind turbine operation and maintenance method based on operation and maintenance time window calculation disclosed in an embodiment of the present invention;

[0059] Figure 2 It is a flow chart of another offshore wind turbine operation and maintenance method based on operation and maintenance time window calculation disclosed in an embodiment of the present invention;

[0060] Figure 3It is a flow chart of another offshore wind turbine operation and maintenance method based on operation and maintenance time window calculation disclosed in an embodiment of the present invention;

[0061] Figure 4 It is a structural schematic diagram of an offshore wind turbine operation and maintenance device based on operation and maintenance time window calculation provided by an embodiment of the present invention;

[0062] Figure 5 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0063] 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.

[0064] It should be noted that the terms "first", "second", "third", "fourth", etc. in the specification and claims of the present invention are used to distinguish different objects rather than to describe a specific order. The terms "including" and "having" in the embodiments of the present invention and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device including a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0065] An embodiment of the present invention discloses an offshore wind turbine operation and maintenance method, device, electronic device and storage medium based on operation and maintenance time window calculation. In the embodiment, when a fault warning message of any wind turbine is received, life prediction is performed on the components of the remaining wind turbines respectively, the life status of the wind turbines is obtained, and historical operation and maintenance data of the glue-filled components are collected, including maintenance equipment for different maintenance methods, the number of maintenance personnel, causes of failures, etc., thereby calculating the operation and maintenance time window, and jointly constructing an operation and maintenance optimization model in combination with the current life status of the wind turbine and the operation and maintenance time window, and then generating an operation and maintenance scheduling method, including personnel scheduling, operation and maintenance scheduling, ship scheduling, etc. In the embodiment, the life status of the wind turbines that are not currently faulty is predicted. Taking their life cycle into consideration, potential problems can be discovered in time and preventive measures can be taken to avoid sudden failures. At the same time, the operation and maintenance time window is calculated with the minimum operation and maintenance economic cost within the life cycle as the goal. The intelligent optimization algorithm solves the operation and maintenance optimization model that considers the urgency of operation and maintenance, and obtains the corresponding wind turbine maintenance method and personnel and equipment allocation, as well as ship scheduling and operation and maintenance routes, and finally completes the scheduling arrangement of a single operation and maintenance work. The scheduling method based on the operation and maintenance time window can arrange maintenance tasks and resources more reasonably, reduce unnecessary waiting time and waste of resources, and optimize the operation and maintenance plan. It can effectively reduce the wind power operation and maintenance costs while ensuring the healthy operation of the wind turbines, improve the overall economic benefits of the wind farm, and use big data and artificial intelligence technologies to realize intelligent management of wind farm operation and maintenance, and improve operation and maintenance efficiency and accuracy.

[0066] Embodiment 1

[0067] See also Figure 1 , Figure 1 It is a flow chart of an offshore wind turbine operation and maintenance method based on operation and maintenance time window calculation disclosed in an embodiment of the present invention. Among them, the execution subject of the method described in the embodiment of the present invention is an execution subject composed of software and / or hardware, and the execution subject can receive relevant information by wired or / and wireless means, and can send certain instructions. Of course, it can also have certain processing functions and storage functions. The execution subject can control multiple devices, such as a remote physical server or cloud server and related software, or it can be a local host or server and related software that performs related operations on a device placed somewhere. In some scenarios, multiple storage devices can also be controlled, and the storage devices can be placed in the same place or different places as the devices. For example Figure 1 As shown, the offshore wind turbine operation and maintenance method based on operation and maintenance time window calculation includes the following steps:

[0068] 101. In response to a fault warning message of any wind turbine generator set, a remaining life span of the remaining wind turbine generator sets is predicted to determine the life status of the remaining wind turbine generator sets.

[0069] When any wind turbine in the wind farm issues a fault warning message, the system immediately triggers a response mechanism. At this point, the embodiment not only focuses on the current faulty unit, but also predicts the remaining life of all other units. This step uses machine learning, big data analysis and other technologies, comprehensively considers various factors such as unit operation history data, current operating status, environmental conditions, etc., and predicts the remaining life of each unit or the health status in the future through an algorithm model.

[0070] In practical applications, wind farms install responsive monitoring systems and sensor networks to monitor the operating status of wind turbines in real time. When the system detects that abnormal parameters (such as temperature, vibration, current, etc.) exceed preset thresholds, fault warning information is immediately triggered. A special monitoring center is set up to receive and process fault warning information from wind farms, and immediately confirm the authenticity and accuracy of the information after receiving the warning to avoid false alarms or missed reports. After that, a preliminary evaluation is conducted on the fault warning information to determine the severity and urgency of the fault. According to the evaluation results, preliminary response measures are quickly formulated, such as dispatching maintenance personnel to the site, adjusting the operating parameters of the unit, etc. In terms of remaining life prediction, the embodiment collects historical operating data, fault records, maintenance records and other related information of wind turbines, organizes and analyzes the collected data, and extracts key parameters and features. According to the operating characteristics and failure modes of wind turbines, a suitable prediction model is selected. Common models include physics-based models, statistics-based models (such as Weibull distribution, gamma process, etc.) and data-driven models (such as machine learning, deep learning, etc.), and historical data models are used for training and verification to ensure the accuracy and reliability of the model. Input the current wind turbine operating status data into the prediction model to predict the remaining life. The prediction results should include the specific value of the remaining life, the prediction confidence, and the possible failure mode and impact range. At the same time, the prediction results are evaluated to determine their rationality and feasibility. According to the evaluation results, corresponding operation and maintenance decisions are made, such as arranging maintenance plans in advance and adjusting the unit operation strategy.

[0071] The embodiment integrates the fault alarm application and remaining life prediction function into the wind farm's automated management system to achieve real-time monitoring, automatic alarm and intelligent prediction. It improves operation and maintenance efficiency through automated means and reduces human intervention and misjudgment. Based on the continuous feedback of actual operation conditions and prediction results, the prediction model is continuously optimized and improved, and new technologies and methods are introduced to improve prediction accuracy and reliability, reduce operation and maintenance costs and improve the overall benefits of the wind farm.

[0072] 102. Collect historical operation and maintenance data to calculate the operation and maintenance time window.

[0073] In order to formulate a reasonable operation and maintenance plan, it is necessary to collect and analyze the historical operation and maintenance data of the wind farm. Historical operation and maintenance data includes but is not limited to fault records, maintenance time, maintenance cost, and frequency of component replacement. By analyzing these data, the time window required for different types of operation and maintenance tasks can be calculated, that is, the optimal maintenance or inspection time period. The determination of this time window takes into account the operation and maintenance efficiency, cost-effectiveness, and the impact on the operation of other units, aiming to find a balance point to minimize the impact of operation and maintenance activities on overall power generation.

[0074] 103. Construct an operation and maintenance optimization model based on the life state and the operation and maintenance time window, and generate an operation and maintenance scheduling method for the wind turbine generator system based on the operation and maintenance optimization model.

[0075] Based on the life status and operation and maintenance time window of the wind turbines obtained in the previous two steps, an operation and maintenance optimization model is constructed. This model comprehensively considers multiple factors, such as the health status of the unit, operation and maintenance costs, power generation losses, resource scheduling (such as personnel, equipment, spare parts), etc., and generates the optimal operation and maintenance scheduling method through algorithm optimization calculation. The scheduling method may include specific maintenance time, maintenance content, maintenance sequence, required resources, etc., aiming to maximize the overall operation and maintenance benefits of the wind farm.

[0076] In this embodiment, by analyzing historical operation and maintenance data and applying machine learning algorithms, the remaining life and potential failures of wind turbines can be predicted more accurately, so that the operation and maintenance team can intervene in advance to avoid failures or reduce the impact of failures on power generation. The operation and maintenance optimization model based on the time window can generate the optimal operation and maintenance scheduling plan, including maintenance time, maintenance content, maintenance sequence, etc., so as to ensure the efficient operation and maintenance activities and reduce waiting time and resource waste. In terms of reducing costs, the embodiment can significantly reduce the unplanned downtime caused by sudden failures through early prediction and prevention, thereby reducing the power generation loss and maintenance costs caused by downtime. The operation and maintenance optimization model can reasonably arrange operation and maintenance resources, such as personnel, equipment, spare parts, etc., to ensure that they can be in place in time when needed, avoiding cost waste caused by insufficient or excessive resources. In terms of enhancing reliability, the embodiment emphasizes the continuous monitoring and evaluation of the operating status of wind turbines, which can timely discover and solve potential problems, thereby maintaining the good operating status of the unit and improving overall reliability. Based on the concept of predictive maintenance, through early intervention, failures caused by equipment aging, wear and tear, etc. can be reduced and the service life of the unit can be extended. At the same time, the embodiment also promotes intelligent management, guides operation and maintenance decisions through data analysis, and realizes the transformation from experience-driven to data-driven. With the continuous development of technology, the operation and maintenance method based on time windows can be integrated with the automation system and information management system of the wind farm to realize the automation and intelligent management of the operation and maintenance process, and improve management efficiency and accuracy. In addition, by reducing unplanned downtime and improving power generation efficiency, the embodiment can ensure that the wind farm provides a stable and reliable power supply to the power grid, meet customer needs, and improve customer satisfaction. The operation and maintenance plan based on the time window can provide wind farm operators and customers with a clearer operation and maintenance schedule and expected results, and enhance trust and communication between the two parties.

[0077] Figure 2 A flowchart of another offshore wind turbine operation and maintenance method based on operation and maintenance time window calculation according to an embodiment of the present invention is shown. Figure 2 As shown, the offshore wind turbine operation and maintenance method based on operation and maintenance time window calculation includes:

[0078] 201. In response to fault warning information of any wind turbine generator set, predict the remaining lifespan of the remaining wind turbine generator sets to determine the life status of the remaining wind turbine generator sets.

[0079] The embodiment aims to minimize the operation and maintenance cost within the life cycle, so the life cycle of the wind turbine unit is taken into consideration. In the existing operation and maintenance management of wind turbine units, when a fault alarm message is received, the wind turbine unit with a fault alarm is usually maintained, but the life status of the remaining wind turbine units is not considered. The present invention can more accurately predict the remaining life and potential failures of wind turbine units through the analysis of historical operation and maintenance data and the application of machine learning algorithms, so that the operation and maintenance team can intervene in advance to avoid failures or reduce the impact of failures on power generation.

[0080] 202. Collect historical operation and maintenance data to calculate the operation and maintenance time window.

[0081] In this step, collecting historical operation and maintenance data includes data collection, data processing, data analysis, etc. In terms of data collection, for example, it can be collected through server logs, monitoring tool data, databases, network traffic data, etc. Server logs often record the server's operating status, errors, warnings and other information. Monitoring and consolidation data can collect server performance indicators in real time or regularly. The database stores the operating data of the application, including user access volume, business processing volume, etc. Network traffic data reflects the network usage of the server, which is helpful for judging the server's load status. When collecting data, determine the time range for collection, and continue to determine how much historical data needs to be collected, set the frequency of data collection, such as collecting once a minute, hourly or daily, and determine the collection indicators. In the embodiment, historical wind farm warning data, fault causes, fault frequency statistics, and historical operation and maintenance work records are mainly collected.

[0082] After collecting the data, the data is cleaned, including removing duplicate data, processing missing values, removing shifted data, converting the data into a suitable format, normalizing or standardizing the data, and then aggregating the data. The data can be aggregated according to the required time period and grouped according to different dimensions.

[0083] Specifically, this step may include collecting historical operation and maintenance data, wherein the historical operation and maintenance data includes historical wind farm warning data, fault causes, fault frequency statistics, and historical operation and maintenance work records; establishing a reliability distribution model corresponding to each component based on the historical operation and maintenance data; and calculating the operation and maintenance time window according to the reliability distribution model.

[0084] Historical wind farm warning data includes all warning information generated during the operation of the wind farm, such as equipment abnormalities, performance degradation, etc. These data are important bases for judging the status of equipment and predicting potential failures. The cause of the failure is to record the cause of each failure in detail, which can help understand the mechanism of the failure and provide guidance for subsequent equipment maintenance and improvement. Statistics on the number of occurrences of various types of failures can reveal the failure mode and vulnerable parts of the equipment, and record detailed information on each operation and maintenance work, including time, content, results, etc. This helps to analyze the efficiency and quality of operation and maintenance work, and identify potential problems in the operation and maintenance process.

[0085] Clean and organize the collected data, remove outliers and noise data, ensure the accuracy and reliability of the data, extract features related to component reliability from the data, such as operating time, working environment, maintenance history, etc., select a suitable reliability model for fitting according to the characteristics of the components and the distribution of historical data, common reliability models include exponential distribution, Weibull distribution, gamma distribution, etc., use historical data to estimate the parameters of the model, obtain the reliability distribution function of the components, use verification data to verify the accuracy and reliability of the model, and ensure that the model can accurately reflect the actual reliability of the components. How to perform reliability modeling is not the focus of this invention, and a commonly used reliability model is used. Use the reliability distribution model to predict the failure probability of components in the future. According to the operation and maintenance requirements and resource conditions of the wind farm, determine the appropriate operation and maintenance time window length and frequency, consider the impact of operation and maintenance activities on the wind farm business, select the time period with the least impact on the business for operation and maintenance, and optimize the scheduling and arrangement of operation and maintenance tasks in combination with the actual situation and operation and maintenance requirements of the wind farm to ensure the efficient conduct of operation and maintenance activities.

[0086] 203. Construct an operation and maintenance optimization model based on the life state and the operation and maintenance time window.

[0087] Based on the life status and operation and maintenance time window of the wind turbines obtained in the previous two steps, an operation and maintenance optimization model is constructed. This model comprehensively considers multiple factors, such as the health status of the unit, operation and maintenance costs, power generation losses, resource scheduling (such as personnel, equipment, spare parts), etc., and generates the optimal operation and maintenance scheduling method through algorithm optimization calculation. The scheduling method may include specific maintenance time, maintenance content, maintenance sequence, required resources, etc., aiming to maximize the overall operation and maintenance benefits of the wind farm.

[0088] 204. Obtain external operation and maintenance conditions to determine a weather time window, and constrain the operation and maintenance optimization model based on the weather time window.

[0089] In this embodiment, external operation and maintenance conditions are further considered as constraints. Among them, the weather data is collected by using multiple channels such as meteorological stations, satellite remote sensing, and meteorological models to obtain weather data in the area where the wind farm is located, which may include key meteorological elements such as temperature, humidity, wind speed, wind direction, and precipitation. The collected weather data is cleaned and sorted, outliers and noise data are removed, the accuracy and reliability of the data are ensured, and the weather conditions in the future are predicted using meteorological prediction models or machine learning algorithms. These models can generate prediction results for future weather based on historical weather data and current meteorological conditions. According to the predicted weather conditions, the safety of wind farm operation and maintenance activities is evaluated. For example, severe weather conditions such as strong winds, heavy rains, and lightning may increase the risk of operation and maintenance work, and it is necessary to avoid operation and maintenance during these time periods, considering the impact of weather conditions on operation and maintenance efficiency. For example, high temperature weather may cause equipment to overheat and increase the risk of failure; while low temperature weather may affect the normal operation and performance of the equipment. Therefore, it is necessary to select a time period with suitable weather conditions for operation and maintenance. According to the evaluation results of the operation and maintenance conditions, a weather time window suitable for operation and maintenance is set. This time window should comprehensively consider factors such as safety, efficiency, and resource utilization. The weather time window is not fixed, but needs to be adjusted dynamically according to the actual situation. For example, when bad weather is predicted, the operation and maintenance plan needs to be adjusted in advance to avoid operation and maintenance under adverse weather conditions.

[0090] Specifically, the external operation and maintenance conditions include weather conditions and resource conditions. The weather conditions include wind speed data and wave height data. The resource conditions include operation and maintenance personnel, operation and maintenance data, and operation and maintenance vessels.

[0091] Wind speed has a direct impact on the operation and maintenance activities of wind farms. Too high wind speed may increase the risk of personnel operations and affect the navigation and operation stability of operation and maintenance vessels. Although too low wind speed reduces the operation risk, it may also affect the normal operation and performance monitoring of wind power equipment. Based on historical wind speed data and meteorological forecasts, a wind speed range suitable for operation and maintenance activities is set. Within this range, the wind speed will neither be too high to increase the risk nor too low to affect the operation and maintenance effect. For offshore wind farms, wave height is an important operation and maintenance consideration. High waves will increase the navigation difficulty and operation risk of operation and maintenance vessels, and may also cause impact on the basic structure of wind power equipment. Combined with the wave height characteristics of the sea area and forecast data, a wave height threshold suitable for operation and maintenance is set. Operation and maintenance activities are carried out during the time period when the wave height is below the threshold to ensure safety and efficiency.

[0092] In terms of the allocation of operation and maintenance personnel, according to the needs and difficulty of the operation and maintenance tasks, the operation and maintenance personnel should be reasonably allocated to ensure that there are sufficient number of personnel with corresponding skills to participate in the operation and maintenance activities within the operation and maintenance time window. Consider the scheduling and rest time of the operation and maintenance personnel to avoid excessive fatigue leading to reduced work efficiency or safety accidents. Before the operation and maintenance time window, collect and organize the required operation and maintenance data in advance, including historical fault records, equipment performance monitoring data, etc., and use data analysis tools and methods to conduct in-depth analysis of the operation and maintenance data to identify potential problems and optimize the operation and maintenance strategy. In terms of operation and maintenance ship scheduling, according to the operation and maintenance time window and sea conditions, reasonably arrange the scheduling of operation and maintenance ships to ensure that they can quickly reach the operation site when needed, and have sufficient operating capacity, and regularly maintain and maintain the operation and maintenance ships to ensure their performance and safety. Carry out special inspections and maintenance before the operation and maintenance time window to reduce operation and maintenance delays caused by ship failures.

[0093] The embodiment adds external operation and maintenance conditions as constraints, and constructs an operation and maintenance optimization model together with the life state and the operation and maintenance time window, and comprehensively evaluates weather conditions and resource conditions to determine the best operation and maintenance time window. Considering multiple factors such as wind speed, wave height, operation and maintenance personnel, operation and maintenance data, and operation and maintenance ships, the optimal operation and maintenance time and plan are found through mathematical modeling or simulation methods. Due to the uncertainty and dynamic nature of external operation and maintenance conditions, the operation and maintenance optimization model needs to have the ability to adjust dynamically. According to real-time monitoring data and prediction results, the operation and maintenance plans and strategies are adjusted in time to cope with emergencies and changes. Through the accumulation and feedback of operation and maintenance practical experience, the operation and maintenance optimization model is continuously improved. Continuously optimize the evaluation method, adjust the parameter settings, and introduce new technologies and methods to improve the accuracy and effectiveness of the model. The operation and maintenance optimization model based on weather conditions and resource conditions is an important tool in the operation and maintenance management of wind farms and offshore wind power. By comprehensively considering multiple factors and dynamically adjusting the operation and maintenance plan, the safety, efficiency and resource utilization of operation and maintenance activities can be ensured.

[0094] 205. Generate the operation and maintenance scheduling method of wind turbines based on the operation and maintenance optimization model.

[0095] The constructed operation and maintenance optimization model comprehensively considers multiple factors, such as unit health, operation and maintenance costs, power generation losses, resource scheduling (such as personnel, equipment, spare parts), etc., and generates the optimal operation and maintenance scheduling method through algorithm optimization calculation. The scheduling method may include specific maintenance time, maintenance content, maintenance sequence, required resources, etc., aiming to maximize the overall operation and maintenance benefits of the wind farm.

[0096] Figure 3 A flowchart of another offshore wind turbine operation and maintenance method based on operation and maintenance time window calculation according to an embodiment of the present invention is shown. Figure 3 As shown, the wind turbine operation and maintenance method based on time window calculation includes:

[0097] 301. In response to fault warning information of any wind turbine generator set, predict the remaining lifespan of the remaining wind turbine generator sets to determine the life status of the remaining wind turbine generator sets.

[0098] When a unit in a wind farm issues a fault warning, the system responds immediately, not only focusing on the faulty unit itself, but also expanding its attention to other units in the entire wind farm. By predicting the remaining life of the remaining wind turbines, we can understand their current life status, that is, the time until the next possible failure, in order to identify potential risks in advance and avoid chain reactions or multiple failures at the same time, which would increase the cost and difficulty of operation and maintenance.

[0099] 302. Obtain external operation and maintenance conditions to determine the weather time window. Similarly, the external operation and maintenance conditions include weather conditions and resource conditions. The weather conditions include wind speed data and wave height data, and the resource conditions include operation and maintenance personnel, operation and maintenance data, and operation and maintenance vessels. Determining the weather time window, that is, selecting the time period that is most suitable for operation and maintenance work, can ensure the safety of operation and maintenance personnel and equipment, while improving operation and maintenance efficiency. For example, performing operation and maintenance work during a period of low wind speed and fine weather can reduce the impact on the power generation of the wind farm and reduce the difficulty of operation and maintenance.

[0100] 303. Collect historical operation and maintenance data, wherein the historical operation and maintenance data includes historical wind farm warning data, fault causes, fault frequency statistics, and historical operation and maintenance work records.

[0101] Historical operation and maintenance data is an important basis for establishing reliability models and formulating operation and maintenance strategies. By collecting and analyzing these data, we can understand the historical fault conditions, fault cause distribution, fault frequency and other information of the wind farm, which helps to identify common fault modes and potential fault risks, and provide support for subsequent predictions and decisions. The collected data is cleaned and sorted, and outliers and noise data are removed to ensure the accuracy and reliability of the data. Features related to the reliability of components are extracted from the data, such as operating time, working environment, maintenance history, etc. According to the characteristics of the components and the distribution of historical data, a suitable reliability model is selected for fitting. Common reliability models include exponential distribution, Weibull distribution, gamma distribution, etc. The parameters of the model are estimated using historical data to obtain the reliability distribution function of the components. The accuracy and reliability of the model are verified using verification data to ensure that the model can accurately reflect the actual reliability of the components. How to perform reliability modeling is not the focus of this invention, and a commonly used reliability model is used. The reliability distribution model is used to predict the failure probability of components in the future. According to the operation and maintenance needs and resource conditions of the wind farm, the appropriate operation and maintenance time window length and frequency are determined. Considering the impact of operation and maintenance activities on the wind farm business, the time period with the least impact on the business is selected for operation and maintenance. Combined with the actual situation of the wind farm and the operation and maintenance needs, the scheduling and arrangement of operation and maintenance tasks are optimized to ensure the efficient implementation of operation and maintenance activities.

[0102] 304. Establish a reliability distribution model corresponding to each component based on the historical operation and maintenance data.

[0103] The reliability distribution model is used to describe the reliability level of components at different time points, that is, the probability of their normal operation. By establishing a reliability distribution model for each component, the probability of failure at a certain point in the future can be predicted, so that targeted preventive maintenance plans can be formulated, making operation and maintenance work more scientific, accurate and efficient.

[0104] 305. Calculate the preventive maintenance reliability and opportunistic maintenance reliability respectively.

[0105] Preventive maintenance reliability refers to the reliability level of parts within a specific period of time under the preventive maintenance plan, while opportunity maintenance reliability refers to the reliability level of the entire system after taking advantage of maintenance opportunities to perform preventive maintenance on other parts during unplanned maintenance (such as breakdown maintenance). By calculating these two reliabilities, the effects of different maintenance strategies can be evaluated and the optimal maintenance plan can be selected. For example, if the preventive maintenance reliability is significantly higher than the opportunity maintenance reliability, it means that the preventive maintenance plan is more effective and should be implemented first.

[0106] Among them, calculating the preventive maintenance reliability includes: setting an optimization range for the preventive maintenance reliability and obtaining a current preventive maintenance reliability value; calculating the preventive maintenance interval corresponding to each component of the wind turbine according to the current preventive maintenance reliability value; starting a preventive maintenance process, performing preventive maintenance on the components, and updating the number of maintenances, preventive maintenance intervals and lifespan of the components; traversing the life cycle of the components based on the life state of the components until the updated lifespan is greater than the original lifespan, and obtaining the preventive maintenance reliability in combination with the calculation of the updated number of maintenances, preventive maintenance intervals and lifespan.

[0107] Calculating the opportunity maintenance reliability includes: setting an optimization range for the opportunity maintenance reliability and obtaining the preventive maintenance reliability; calculating the opportunity maintenance interval corresponding to each component of the wind turbine according to the preventive maintenance reliability; starting the opportunity maintenance process, performing opportunity maintenance on the components, and updating the number of maintenance, opportunity maintenance interval and life of the components; traversing the entire life cycle of the components based on the life state of the components until the updated life is greater than the original life, and calculating the opportunity maintenance reliability in combination with the updated number of maintenance, opportunity maintenance interval and life.

[0108] In this example, the reliability of preventive maintenance and the reliability of opportunistic maintenance are increased, which can further effectively avoid hidden faults and reduce the operation and maintenance costs throughout the life cycle. This example adopts an opportunistic maintenance strategy, looking at the entire life cycle of the wind turbine, with the lowest operation and maintenance cost as the goal, and calculating the operation and maintenance time window of the wind turbine. In the subsequent wind turbine operation and maintenance optimization, the penalty cost of violating the operation and maintenance time window can be increased, the urgency of operation and maintenance is fully considered, and the single operation and maintenance work is viewed horizontally. With the goal of maximizing economic benefits, the operation and maintenance scheduling of offshore wind turbines is optimized to comprehensively reduce the operation and maintenance costs of offshore wind power.

[0109] Specifically, first set the preventive maintenance time array and the opportunity maintenance time array T fix , preventive maintenance cost array and opportunity maintenance cost array C fix , Fixed maintenance cost C f , accessibility maintenance cost C r , wind turbine life L. Among them, T fix and C fix They are all Mx2 arrays, where M represents M parts, and the first and second columns represent preventive maintenance information and opportunity maintenance information respectively.

[0110] Secondly, set the preventive reliability threshold [R p_0 ,R max 〕and the opportunity maintenance reliability threshold 〔R o_0 ,R max〕, where the optimization step length of the preventive reliability threshold and the opportunistic maintenance reliability threshold is set to ΔR.

[0111] Then initialize the optimization number n = 0 and set the wind turbine operating time T U =0, downtime is T D =0, the current service life of the component is T L =0, the number of maintenance activities is f, the number of parts maintenance times array F fix Based on the preventive maintenance reliability R p Calculate the preventive maintenance time interval T1. Then find the component m corresponding to min(T1) for preventive maintenance and update the corresponding component maintenance frequency array F fix , now let T U =min(T1) to update T1, T L =T L +T U .

[0112] Carry out this preventive maintenance activity, update the number of maintenance activities f = f + 1, the number of parts maintenance F fix (m, 1) = F fix (m, 1)+1, downtime T D =T D +F fix (m, 1), update the life of the fan unit T L =T L +T D .

[0113] Repeat the above steps to traverse the entire life cycle of the wind turbine until T L >L, calculate the preventive maintenance cost C pre (n) = sum(C fix (:,1)×F fix '(:,1))+f×(C f +C r ).

[0114] Let n = n + 1, R p =R p_0 +n·ΔR, and repeat the above steps until R p >R max , take the minimum C pre (n) Corresponding R p As R p_0pt If opportunistic maintenance, n = 0, based on R p_0pt Calculate the preventive maintenance time interval T1, execute again to find the component m corresponding to min(T1) for preventive maintenance, and update the corresponding component maintenance frequency array F fix , now let TU =min(T1) to update T1, T L =T L +T U In this step, based on the opportunity maintenance reliability R o The computer will maintain the time interval T2. Find the number of times T2 is less than T U The parts p and K corresponding to the value of are opportunistically repaired and T2 is updated.

[0115] Carry out this preventive maintenance and opportunity maintenance activities, update the number of maintenance activities f = f + 1, the number of parts maintenance F fix (p, 2) = F fix (p, 1)+1K, downtime T D =T D +max(T fix (m, 1), T fix (p, 2), K), update the wind turbine life T L =T L +T D Repeat the above steps to traverse the entire life cycle of the wind turbine until TL>L, and calculate the maintenance cost C opp (n) = sum(C fix ×F fix ')+f×(C f +C r ).

[0116] Let n = n + 1, R o =R o_0 +n·ΔR, and repeat the above steps until R o >R max , take the minimum C opp (n) Corresponding R o As R o_0pt .

[0117] In the above, a specific calculation method is used to explain how to calculate the preventive maintenance reliability and opportunistic maintenance reliability with the goal of minimizing the operation and maintenance cost during the entire life cycle of a wind turbine. By setting the optimization range of the preventive maintenance reliability and the opportunistic maintenance reliability, the preventive reliability threshold is optimized, and the preventive maintenance strategy is adopted without considering random failures. The maintenance interval of wind turbine components is calculated based on the current preventive maintenance reliability, and the operation and maintenance work is arranged in combination with the current wind turbine operating status and component reliability. The operation and maintenance cost is calculated through the entire life cycle, and the preventive maintenance reliability threshold that minimizes the operation and maintenance cost is sought. The calculation formula is: Among them C pre The total maintenance cost of the wind turbine during the whole life cycle under the preventive maintenance strategy; is the preventive maintenance cost of component i, is a logical value. If component i is maintained in the kth maintenance activity, then otherwise With R p The value is related to q, which is the number of maintenance activities in the whole life cycle of the wind turbine, and M, which is the number of parts of the wind turbine. In terms of the calculation of the reliability of opportunity maintenance, the threshold of the reliability of opportunity maintenance is also optimized, and the opportunity maintenance strategy is adopted, including preventive maintenance and opportunity maintenance. The optimal preventive reliability threshold is used as the benchmark, based on the formula Calculate the operation and maintenance cost of the entire life cycle under different opportunity maintenance reliability, and select the opportunity maintenance reliability that minimizes the operation and maintenance cost. opp is the total maintenance cost of the wind turbine in the whole life cycle under the opportunistic maintenance strategy; is the opportunity maintenance cost of component i; is a logical value. If component i is repaired opportunistically in the kth maintenance activity, then otherwise With R o Value related.

[0118] 306. Calculate the operation and maintenance time window based on the reliability distribution model, the preventive maintenance reliability and the opportunistic maintenance reliability.

[0119] The calculation of the operation and maintenance time window is the key technology of this example. First, it is necessary to obtain the operating data and maintenance data of key components based on historical data, including maintenance equipment, number of maintenance personnel and maintenance time for different maintenance methods, for the calculation of the operation and maintenance time window. Secondly, it is necessary to obtain meteorological data to predict wind speed and wave height to determine the weather time window. A variety of methods can be used, which are not the key content of the present invention and will not be described in detail. If practical application is required, it is also necessary to obtain the address location of the wind farm and the arrangement of wind turbines, the power curve of the wind turbine unit and the on-grid electricity price to determine the sailing distance between wind turbines and the loss of power generation due to shutdown. Finally, it is necessary to obtain the rental cost, navigation cost, navigation speed, etc. of the operation and maintenance ship to establish an optimization model for the operation and maintenance of offshore wind turbines. In addition, it is necessary to obtain the number of operation and maintenance ships, the maximum equipment carrying capacity, the personnel carrying capacity and the longest working time, etc. as constraints.

[0120] 307. Construct an operation and maintenance optimization model based on the life state, weather time window and operation and maintenance time window, and generate an operation and maintenance scheduling method for the wind turbine generator system based on the operation and maintenance optimization model.

[0121] Embodiment 2

[0122] See also Figure 4 , Figure 4 Schematic diagram of the structure of an offshore wind turbine operation and maintenance device based on operation and maintenance time window calculation disclosed in an embodiment of the present invention. Figure 4As shown, the wind turbine operation and maintenance device based on time window calculation may include: a fault response module 401, a data collection module 402 and a scheduling generation module 403, wherein the fault response module 402 is used to respond to the fault warning information of any wind turbine and predict the remaining life of the remaining wind turbines to determine the life status of the remaining wind turbines; the data collection module 402 is used to collect historical operation and maintenance data to calculate the operation and maintenance time window; the scheduling generation module 403 is used to construct an operation and maintenance optimization model based on the life status and the operation and maintenance time window, and generate an operation and maintenance scheduling method for the wind turbine based on the operation and maintenance optimization model.

[0123] The embodiment may further include a condition acquisition module for acquiring external operation and maintenance conditions to determine a weather time window, and constraining the operation and maintenance optimization model based on the weather time window. The external operation and maintenance conditions include weather conditions and resource conditions, the weather conditions include wind speed data and wave height data, and the resource conditions include operation and maintenance personnel, operation and maintenance data, and operation and maintenance vessels.

[0124] In the data collection module 402, specifically, historical operation and maintenance data are collected, and the historical operation and maintenance data include historical wind farm warning data, fault causes, fault frequency statistics, and historical operation and maintenance work records; a reliability distribution model corresponding to each component is established based on the historical operation and maintenance data; and the operation and maintenance time window is calculated according to the reliability distribution model.

[0125] Furthermore, the embodiment also considers preventive maintenance and opportunistic maintenance, and calculates the preventive maintenance reliability and the opportunistic maintenance reliability respectively; and jointly calculates the operation and maintenance time window according to the reliability distribution model, the preventive maintenance reliability and the opportunistic maintenance reliability.

[0126] Specifically, calculating the reliability of preventive maintenance includes: setting the optimization range of the reliability of preventive maintenance, obtaining the current value of the reliability of preventive maintenance; calculating the preventive maintenance interval corresponding to each component of the wind turbine according to the current value of the reliability of preventive maintenance; starting the preventive maintenance process, performing preventive maintenance on the components, and updating the number of maintenance, preventive maintenance interval and life of the components; traversing the life cycle of the components based on the life state of the components until the updated life is greater than the original life, and combining the calculation of the number of maintenance, preventive maintenance interval and life after the update to obtain the preventive maintenance reliability. Calculating the reliability of opportunity maintenance includes: setting the optimization range of the reliability of opportunity maintenance, and obtaining the reliability of preventive maintenance; calculating the opportunity maintenance interval corresponding to each component of the wind turbine according to the reliability of preventive maintenance; starting the opportunity maintenance process, performing opportunity maintenance on the components, and updating the number of maintenance, opportunity maintenance interval and life of the components; traversing the entire life cycle of the components based on the life state of the components until the updated life is greater than the original life, and combining the updated number of maintenance, opportunity maintenance interval and life to calculate the reliability of opportunity maintenance.

[0127] Embodiment 3

[0128] See also Figure 5 , Figure 5 Schematic diagram of the structure of an electronic device disclosed in an embodiment of the present invention. The electronic device may be a computer, a server, etc. Of course, in certain circumstances, it may also be a collection, a tablet computer, a monitoring terminal, and other intelligent devices, as well as an image acquisition device with processing functions. Figure 5 As shown, the electronic device may include:

[0129] A memory 501 storing executable program codes;

[0130] a processor 502 coupled to the memory 501;

[0131] The processor 502 calls the executable program code stored in the memory 501 to execute part or all of the steps in the offshore wind turbine operation and maintenance method based on operation and maintenance time window calculation in the first embodiment.

[0132] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute part or all of the steps in the offshore wind turbine operation and maintenance method based on operation and maintenance time window calculation in embodiment one.

[0133] An embodiment of the present invention further discloses a computer program product, wherein when the computer program product runs on a computer, the computer executes part or all of the steps in the offshore wind turbine operation and maintenance method based on operation and maintenance time window calculation in embodiment one.

[0134] An embodiment of the present invention also discloses an application publishing platform, wherein the application publishing platform is used to publish a computer program product, wherein when the computer program product runs on a computer, the computer executes part or all of the steps in the offshore wind turbine operation and maintenance method based on operation and maintenance time window calculation in embodiment one.

[0135] In various embodiments of the present invention, it should be understood that the size of the serial numbers of the processes does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0136] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, i.e., they may be located in one place or distributed over multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0137] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0138] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-accessible memory. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a memory and includes several requests for a computer device (which can be a personal computer, a server or a network device, etc., specifically a processor in a computer device) to perform some or all of the steps of the method described in each embodiment of the present invention.

[0139] In the embodiments provided by the present invention, it should be understood that "B corresponding to A" means that B is associated with A, and B can be determined according to A. However, it should also be understood that determining B according to A does not mean determining B only according to A, and B can also be determined according to A and / or other information.

[0140] A person of ordinary skill in the art can understand that some or all of the steps in the various methods of the embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0141] The above is a detailed introduction to the offshore wind turbine operation and maintenance method, device, electronic device and storage medium based on operation and maintenance time window calculation disclosed in the embodiments of the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. An offshore wind turbine operation and maintenance method based on operation and maintenance time window calculation, characterized in that: include: In response to a fault warning message of any wind turbine generator set, the remaining life of the remaining wind turbine generator sets is predicted to determine the life status of the remaining wind turbine generator sets; Collect historical operation and maintenance data to calculate the operation and maintenance time window; An operation and maintenance optimization model is constructed based on the life state and the operation and maintenance time window, and an operation and maintenance scheduling method of the wind turbine is generated based on the operation and maintenance optimization model.

2. The offshore wind turbine operation and maintenance method according to claim 1, characterized in that: Also includes: External operation and maintenance conditions are obtained to determine a weather time window, and the operation and maintenance optimization model is constrained based on the weather time window.

3. The offshore wind turbine operation and maintenance method according to claim 2, characterized in that: The external operation and maintenance conditions include weather conditions and resource conditions. The weather conditions include wind speed data and wave height data. The resource conditions include operation and maintenance personnel, operation and maintenance data, and operation and maintenance vessels.

4. The offshore wind turbine operation and maintenance method according to claim 1, characterized in that: The collecting of historical operation and maintenance data and calculating the operation and maintenance time window includes: Collect historical operation and maintenance data, including historical wind farm warning data, fault causes, fault frequency statistics, and historical operation and maintenance work records; Establishing a reliability distribution model corresponding to each component based on the historical operation and maintenance data; The operation and maintenance time window is calculated based on the reliability distribution model.

5. The offshore wind turbine operation and maintenance method according to claim 4, characterized in that: Also includes: Calculate the reliability of preventive maintenance and the reliability of opportunistic maintenance respectively; The operation and maintenance time window is calculated based on the reliability distribution model, preventive maintenance reliability and opportunistic maintenance reliability.

6. The offshore wind turbine operation and maintenance method according to claim 5, characterized in that: Calculate preventive maintenance reliability, including: Set the optimization range of preventive maintenance reliability and obtain the current preventive maintenance reliability value; Calculating the preventive maintenance interval corresponding to each component of the wind turbine generator set according to the current preventive maintenance reliability value; Start the preventive maintenance process, perform preventive maintenance on parts, and update the maintenance frequency, preventive maintenance interval and life of parts; Based on the life status of the components, the life cycle of the components is traversed until the updated life is greater than the original life, and the preventive maintenance reliability is obtained by combining the calculation of the updated maintenance times, preventive maintenance interval and life.

7. The offshore wind turbine operation and maintenance method according to claim 6, characterized in that: Computers will maintain reliability, including: Set the optimization range of opportunistic maintenance reliability and obtain preventive maintenance reliability; Calculate the opportunity maintenance interval corresponding to each component of the wind turbine generator set according to the preventive maintenance reliability; Initiate the opportunity maintenance process, perform opportunity maintenance on parts, and update the maintenance number, opportunity maintenance interval and life of parts; The entire life cycle of the component is traversed based on the life state of the component until the updated life is greater than the original life, and the opportunity maintenance reliability is calculated by combining the updated maintenance times, opportunity maintenance interval and life.

8. An offshore wind turbine operation and maintenance device based on operation and maintenance time window calculation, characterized in that: include: A fault response module is used to respond to the fault warning information of any wind turbine set and predict the remaining life of the remaining wind turbine sets to determine the life status of the remaining wind turbine sets; Data collection module, used to collect historical operation and maintenance data and calculate the operation and maintenance time window; The scheduling generation module is used to build an operation and maintenance optimization model based on the life state and the operation and maintenance time window, and generate an operation and maintenance scheduling method for the wind turbine generator set based on the operation and maintenance optimization model.

9. An electronic device, characterized in that: include: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the offshore wind turbine operation and maintenance method based on operation and maintenance time window calculation as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program enables a computer to execute the offshore wind turbine operation and maintenance method based on operation and maintenance time window calculation as described in any one of claims 1 to 7.

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