Wind turbine generator health state monitoring method and device, storage medium and processor

Abstract

The embodiment of the invention provides a wind turbine generator health state monitoring method and device, a storage medium and a processor, and belongs to the technical field of wind power station fault early warning. According to the wind turbine generator health state monitoring method, the current operation condition parameters and the actual power generation power of a to-be-monitored wind turbine generator are obtained firstly, and then the expected power generation power under the operation condition parameters is obtained by querying a power curve obtained based on operation historical data of the wind turbine generator; finally, the health state of the wind turbine generator is determined according to the deviation between the actual generated power and the expected generated power or the deviation trend of the actual generated power relative to the expected generated power, and the health condition of the wind turbine generator under the influence of different operation condition parameters can be monitored in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power station fault early warning, and in particular to a method, device, storage medium and processor for monitoring the health status of a wind turbine set. Background Art

[0002] The reliable operation of wind turbines is crucial to ensure the stability and economy of wind power station power supply. However, wind turbines are exposed to the outside for a long time and are susceptible to mechanical wear, electrical system aging and environmental factors, which may lead to reduced power generation efficiency or even equipment failure, affecting the overall operational efficiency of the wind farm.

[0003] Traditional wind turbine health monitoring mostly relies on maintenance after shutdown due to faulty functions or time-based regular maintenance strategies. However, these traditional maintenance methods are not only costly, but also unable to effectively prevent sudden failures. Summary of the invention

[0004] The purpose of the embodiment of the present invention is to provide a method for real-time monitoring of the health status of a wind turbine generator set under the influence of different operating condition parameters.

[0005] In order to achieve the above object, an embodiment of the present invention provides a method for monitoring the health status of a wind turbine generator set, comprising:

[0006] Obtain the current operating condition parameters and actual power generation of the wind turbine to be monitored;

[0007] Query the power curve obtained based on the historical operation data of the wind turbine to obtain the expected power generation under the operating condition parameters; and

[0008] The health status of the wind turbine is determined according to a deviation between the actual power generation and the expected power generation, and / or according to a deviation trend of the actual power generation compared to the expected power generation.

[0009] Optionally, the power curve is a correspondence between operating condition parameters and expected power generation; the correspondence is obtained by a power generation prediction model trained with historical operating data, or by statistically analyzing historical operating data.

[0010] Optionally, the operating condition parameters include one or more of the following: wind speed, blade angle of the wind turbine, degree of mechanical wear of the wind turbine, efficiency loss of the electrical system of the wind turbine, ambient temperature, ambient humidity, degree of environmental pollution, and the geographical location of the wind turbine.

[0011] Optionally, before determining the health status of the wind turbine based on the deviation between the actual power generation and the expected power generation, or based on the changing trend of the actual power generation compared with the expected power generation, the expected power generation obtained based on the power curve is corrected according to the influencing parameters affecting the power generation of the wind turbine.

[0012] Furthermore, the operating condition parameters are one or more of the following, and the influencing parameters are one or more of the following other than the operating condition parameters: wind speed, blade angle of the wind turbine, degree of mechanical wear of the wind turbine, efficiency loss of the electrical system of the wind turbine, turbulence intensity, air density, ambient temperature, ambient humidity, degree of environmental pollution, and the geographical location of the wind turbine.

[0013] Furthermore, the expected power generation obtained based on the power curve is corrected according to the following formula: Where p1, p1, … p m is the operating condition parameter of item 1-m, Pexpected(p1,p1,…p m ) is the corrected expected power generation, Pbase(p1,p1,…p m ) is the expected power generation before correction, k i is the correction coefficient corresponding to the i-th influencing parameter, and n is the total number of influencing parameters.

[0014] Optionally, the wind turbine health status monitoring method further includes one or more of the following:

[0015] When the deviation does not reach a specified first threshold value, and the deviation trend does not reach a specified second threshold value, determining that the wind turbine generator set is currently in a normal state;

[0016] When the deviation reaches a specified first threshold, or the deviation trend reaches a specified second threshold, determining that the wind turbine is currently in a warning state;

[0017] When the deviation reaches a specified third threshold, or the deviation trend reaches a specified fourth threshold, it is determined that the wind turbine generator set is currently in a serious fault state.

[0018] Optionally, the wind turbine health status monitoring method also includes: when the wind turbine is determined to be in a warning state or a serious fault state, sending a maintenance reminder to a designated person according to a preset method, the maintenance reminder includes the current time and the current operating condition parameters and actual power generation of the wind turbine.

[0019] On the other hand, an embodiment of the present invention provides a wind turbine health status monitoring device, including: a collection module, a query module and a processing module.

[0020] The acquisition module is configured to: obtain the current operating condition parameters and actual power generation of the wind turbine to be monitored;

[0021] The query module is configured to query a power curve obtained based on the historical operation data of the wind turbine generator set to obtain the expected power generation under the operating condition parameters.

[0022] The processing module is configured to determine the health status of the wind turbine according to a deviation between the actual power generation and the expected power generation, and / or according to a deviation trend of the actual power generation compared to the expected power generation.

[0023] Optionally, the operating condition parameters collected by the collection module include one or more of the following: wind speed, blade angle of the wind turbine, degree of mechanical wear of the wind turbine, efficiency loss of the electrical system of the wind turbine, ambient temperature, ambient humidity, degree of environmental pollution, and the geographical location of the wind turbine.

[0024] Optionally, the processing module is further configured to: correct the expected power generation obtained based on the power curve according to the influencing parameters affecting the power generation of the wind turbine generator set.

[0025] Furthermore, the acquisition module is also configured to: collect influencing parameters, wherein the operating condition parameters are one or more of the following, and the influencing parameters are one or more of the following other than the operating condition parameters: wind speed, blade angle of the wind turbine, degree of mechanical wear of the wind turbine, efficiency loss of the electrical system of the wind turbine, turbulence intensity, air density, ambient temperature, ambient humidity, degree of environmental pollution, and the geographical location of the wind turbine.

[0026] On the other hand, an embodiment of the present invention provides a machine-readable storage medium, on which instructions are stored, and the instructions are used to enable a machine to execute: the wind turbine health status monitoring method of the present application.

[0027] On the other hand, an embodiment of the present invention provides a processor for running a program, wherein the program, when being run, is used to execute: the wind turbine health status monitoring method of the present application.

[0028] On the other hand, an embodiment of the present invention provides a computer program product, including a computer program, which implements the wind turbine health status monitoring method of the present application when executed by a processor.

[0029] Through the above technical solution, the current operating condition parameters and actual power generation of the wind turbine to be monitored are first obtained, and then the power curve obtained based on the historical operating data of the wind turbine is queried to obtain the expected power generation under the operating condition parameters. Finally, the health status of the wind turbine is determined based on the deviation between the actual power generation and the expected power generation, or based on the deviation trend of the actual power generation compared to the expected power generation. The health status of the wind turbine under the influence of different operating condition parameters can be monitored in real time.

[0030] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0032] Figure 1 is a flow chart of an embodiment of a method for monitoring the health status of a wind turbine generator system according to the present invention;

[0033] Figure 2 is a flow chart of another embodiment of the method for monitoring the health status of a wind turbine generator system of the present invention;

[0034] Figure 3 It is a structural diagram of an embodiment of a wind turbine health status monitoring device of the present invention. DETAILED DESCRIPTION

[0035] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0036] If there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0037] The acquisition, transmission, storage, use, and processing of data in the technical solution of this application are in compliance with the relevant provisions of national laws and regulations. In addition, it should be noted that in the embodiments of this application, some existing solutions in the industry such as certain software, components, and models may be mentioned, which should be considered as exemplary, and their purpose is only to illustrate the feasibility of the implementation of the technical solution of this application, but it does not mean that the applicant has or will necessarily use the solution.

[0038] The embodiment of the present invention provides a method for monitoring the health status of a wind turbine. The embodiment is intended to be applied to a wind power station or multiple wind power stations distributed in different geographical locations. The embodiment runs as a program entity in a cloud information processing center and can be centrally deployed or distributedly deployed, which is not limited by the present invention. Figure 1 As shown, the wind turbine health status monitoring method includes the following steps:

[0039] Step S101, obtaining the current operating condition parameters and actual power generation of the wind turbine to be monitored;

[0040] Step S102, querying a power curve obtained based on historical operation data of the wind turbine generator set to obtain expected power generation under operating condition parameters;

[0041] Step S103: determining the health status of the wind turbine generator system according to the deviation between the actual power generation and the expected power generation, or according to the deviation trend of the actual power generation compared to the expected power generation.

[0042] During the operation of a wind turbine, the degree of mechanical wear of the wind turbine, the efficiency loss of the electrical system, and environmental changes such as wind speed, temperature, and humidity may affect its power generation capacity. That is, a variety of different factors may affect the performance of the wind turbine. Therefore, in step S101, it is necessary to first clarify which factors need to be considered, that is, the operating condition parameters to be obtained should at least include the influencing factors to be considered, such as the wind turbine angle and wind speed.

[0043] It should be noted that the operating condition parameters and actual power generation obtained in step S101 may be a data set consisting of operating condition parameters and actual power generation data for all individual wind turbines in the current generator set, or operating condition parameters and actual power generation for a certain individual wind turbine, or statistical data such as the average, median, mode, etc. of operating condition parameters and actual power generation for all individual wind turbines in the current generator set. For the above different situations of the operating condition parameters and actual power generation obtained in step S101, the data objects in steps S102 and S103 should also be adjusted accordingly.

[0044] In step S102, the power curve is the correspondence between the operating condition parameters and the expected power generation, for example, the correspondence between the wind speed, the blade angle of the wind turbine, the generator speed and the expected power generation. The power curve obtained based on the historical operating data of the wind turbine can be obtained by the following steps:

[0045] Collect the actual operation data of each wind turbine in the wind power station over a period of time, record the wind speed, blade angle, generator speed and output power to obtain an operation history data set;

[0046] Remove invalid data or outliers from historical data sets to ensure data quality;

[0047] For a portion of the data in the operation history data set, a power curve is fitted based on three variables: wind speed, blade angle of the wind turbine, and generator speed, according to statistical methods or machine learning algorithms. For example, a linear regression, polynomial regression or other suitable models can be used to establish a relationship model between the output power of the wind turbine and the wind speed, blade angle of the wind turbine, and generator speed;

[0048] Another part of the historical data set is used as a validation data set to verify the accuracy of the above relationship model and adjust the parameters to optimize the model.

[0049] It should be noted that in step S102, it is necessary to consider the important factors that actually affect the output power, determine the operating condition parameters that should be included in the operating history data set, so as to improve the accuracy of the expected power generation obtained according to the power curve. Among them, considering the mechanical wear parameters can accurately estimate the impact of the mechanical wear degree on the power generation output power, for example, by obtaining the mechanical wear parameters through vibration analysis, sound signal processing and other methods, and adjusting the influence coefficient of the mechanical wear parameters accordingly; considering the electrical system parameters can accurately estimate the health of the electrical system, such as the changes in parameters such as current, voltage, temperature, and the efficiency loss of the generator, so as to accurately estimate the impact of the electrical system on the power generation output power, and adjust the influence coefficient of the electrical system parameters accordingly; environmental factors and other influencing factors can also be considered. The factors that can be considered to affect the output power, that is, the operating condition parameters, can be one or more of the following: wind speed, the blade angle of the fan, the mechanical wear degree of the fan, the efficiency loss of the fan's electrical system, the ambient temperature, the ambient humidity, the degree of environmental pollution, and the geographical location of the wind turbine.

[0050] It should also be noted that the wear and damage status of the fan will change over time. Therefore, in step S102, it is necessary to consider that the important factors that actually affect the output power may change dynamically. Therefore, the power curve in step S102 should be updated regularly to correct the influence coefficients of different factors on the power generation, so as to improve the accuracy of the expected power generation obtained according to the power curve.

[0051] Specifically, in step S103, the health status of the wind turbine is determined based on the deviation between the actual power generation and the expected power generation, and / or based on the deviation trend of the actual power generation compared to the expected power generation, which can be: the actual power output data of the wind turbine is collected in real time through the SCADA system or other sensors to obtain the actual power generation, and the actual power generation is compared with the expected power generation. At the same time, a reasonable error range is set. When the actual power generation deviates from the expected power generation beyond this error range, it is considered that the wind turbine may have health problems, that is, the health status of the wind turbine is determined by deviation analysis. Among them, the deviation trend of the actual power generation compared to the expected power generation can be used as a basis for auxiliary determination of the health status of the wind turbine.

[0052] In some embodiments, different thresholds are set to evaluate the degree of the above deviations, and determine the health status of the wind turbine as "normal", "warning", "serious fault", etc. For example: when the deviation does not reach the specified first threshold, and the deviation trend does not reach the specified second threshold, it is determined that the wind turbine is currently in a normal state; when the deviation reaches the specified first threshold, or the deviation trend reaches the specified second threshold, it is determined that the wind turbine is currently in a warning state; when the deviation reaches the specified third threshold, or the deviation trend reaches the specified fourth threshold, it is determined that the wind turbine is currently in a serious fault state.

[0053] Compared with the prior art, the technical advantages of this embodiment are:

[0054] 1. Only based on the operating parameters of the fan, without stopping the fan, the fan's health status can be monitored in real time, and the fan can be maintained in time to prevent sudden failures;

[0055] 2. The health status of wind turbines is not only analyzed by a single operating parameter, but also by comprehensively considering multiple influencing factors such as wind speed, blade angle, mechanical wear, electrical system efficiency and environmental factors. It can accurately predict the health status of wind turbines and avoid over-maintenance or under-maintenance.

[0056] 3. By comparing the expected power generation with the actual power generation in real time, potential health problems can be discovered in time and early warning can be achieved, which helps to reduce maintenance costs and downtime. Maintenance reminder information is automatically generated according to the health status of the wind turbine, helping wind power operators to achieve more effective preventive maintenance, reduce downtime caused by sudden failures, and improve overall energy production and economic benefits.

[0057] The present application also proposes a preferred embodiment of a method for monitoring the health status of a wind turbine. On the one hand, the amount of data that needs to be processed simultaneously is reduced, and the calculation pressure is reduced without affecting the accuracy of the expected power generation obtained according to the power curve. On the other hand, when it is determined that the health status of the wind turbine is not good, relevant personnel are notified to handle it in time. The process is as follows Figure 2 As shown, the following steps are included:

[0058] Step S101, obtaining the current operating condition parameters and actual power generation of the wind turbine to be monitored;

[0059] Step S102, querying a power curve obtained based on historical operation data of the wind turbine generator set to obtain expected power generation under operating condition parameters;

[0060] Step S102', correcting the expected power generation obtained based on the power curve according to the influencing parameters affecting the power generation of the wind turbine generator set;

[0061] Step S103, determining the health status of the wind turbine according to the deviation between the actual power generation and the expected power generation, or according to the deviation trend of the actual power generation compared to the expected power generation;

[0062] Step S103': when the wind turbine generator set is determined to be in a warning state or a serious fault state, a maintenance reminder is sent to a designated person according to a preset method.

[0063] and Figure 1Compared with the corresponding embodiments, this embodiment adds step S102' and step S103'. Specifically, in order to reduce the amount of data that needs to be processed simultaneously when the power curve is obtained based on the operating history data of the wind turbine in step S102, and to reduce the calculation pressure, the number of operating condition parameters included in the operating history data can be reduced, and the basic power curve is obtained based on only the most important part of the influencing factors, and then the influence of other influencing factors on the output power is considered to correct the expected power generation obtained by the basic power curve. For example, the operating condition parameters obtained in step S101, which are also the operating history data included in the operating history data in step S102, are the most important part of the influencing factors affecting the output power, and the influencing parameters in S102' are another part of the influencing factors other than the above-mentioned most important part of the influencing factors. That is, the most important part of the influencing factors is used to obtain the expected power generation under the current operating condition parameters, and the other part is used to correct the expected power generation. The influencing parameters in step S102' may be one or more of the following except the operating condition parameters in step S101: wind speed, blade angle of the wind turbine, degree of mechanical wear of the wind turbine, efficiency loss of the electrical system of the wind turbine, turbulence intensity, air density, ambient temperature, ambient humidity, degree of environmental pollution, and the geographical location of the wind turbine.

[0064] It should be noted that the technicians can use the most important part of the influencing factors to obtain the basic power curve, or according to the correlation between the influencing factors, obtain the basic power curve based on the associated part, divide the other influencing factors into one or several groups according to the correlation, and consider the impact of each group of influencing factors on the output power respectively. Alternatively, the basic power curve can be obtained based on a part of the associated operating condition parameters, and then the impact of each influencing factor on the output power can be considered respectively.

[0065] The following example illustrates the specific implementation steps of obtaining a basic power curve based on a portion of the associated operating condition parameters and then considering the impact of each influencing factor on the output power:

[0066] 1. Considering the influence of m main influencing factors on the output power, the power curve is obtained based on the operation history data of the corresponding m operating condition parameters, and the expected power generation under the operating condition parameters is obtained;

[0067] For example, if wind speed and the angle of wind turbine blades are taken as the main influencing factors, the actual operating data of each wind turbine in the wind power station over the past period of time should be collected, including wind speed, blade angle and output power, and invalid data or outliers should be removed. Then, according to statistical methods or machine learning algorithms, the power curve of the wind turbine should be fitted based on the two variables of wind speed and blade angle.

[0068] 2. Query the power curve to obtain the expected power generation under the operating conditions;

[0069] Specifically, the power curve may be queried according to the current operating condition parameters of the wind turbine generator set acquired in step S101 to obtain the expected power generation under the operating condition parameters.

[0070] 3. Calculate the correction coefficient corresponding to each influencing parameter in step S102 ′, and correct the expected generated power according to each correction coefficient.

[0071] Specifically, the expected power generation can be corrected by the following formula: Where p1, p1, …p m is the operating condition parameter of item 1-m, Pexpected(p1, p1, ... p m ) is the corrected expected power generation, Pbase(p1, p1, ...P m ) is the expected power generation before correction, k i is the correction coefficient corresponding to the i-th influencing parameter, and n is the total number of influencing parameters. i It can be to collect the actual operation data of each wind turbine in the wind power station under different i-th influencing parameters over a period of time in the past, remove invalid data or outliers, and then fit the power curve of the wind turbine based on the i-th influencing parameter according to statistical methods or machine learning algorithms.

[0072] For example: when the wind speed v and the blade angle θ of the wind turbine are taken as the most important influencing factors, a power curve is generated, and then three other influencing factors are considered: mechanical wear, electrical system efficiency and environmental conditions. The expected power generation power can be: Pexpected(v, θ) = Pbase(v, θ)×k1×k2×k3, where k1, k2, and k3 are the corresponding influence coefficients of mechanical wear, electrical system efficiency, and environmental conditions, respectively.

[0073] Specifically, step S103' may be that when the wind turbine is determined to be in a warning state or a serious fault state, a maintenance reminder is sent to a designated person according to a preset method, and the maintenance reminder includes the current time and the current operating condition parameters and actual power generation of the wind turbine.

[0074] More specifically, when it is determined that there is a problem with the health status of the wind turbine, a prompt message containing the wind turbine number, fault type, etc. is generated. Among them, the fault type can identify different fault modes that may cause this deviation according to the specific circumstances of the deviation, such as blade damage, bearing wear, poor electrical connection, etc. The prompt message is then sent to the maintenance team via email, SMS or other communication means. After the maintenance personnel complete the inspection and repair, they need to record the maintenance results and provide feedback, so as to facilitate subsequent analysis and optimization of the correction factor based on the maintenance results.

[0075] It should be noted that the above-mentioned fitted wind turbine power curve and correction coefficient should be updated regularly to fit the actual situation of the wind turbines in the wind farm. The specific update cycle can be customized or set in combination with the actual situation of the wind farm.

[0076] The above steps can help wind power operators achieve more effective preventive maintenance and reduce downtime caused by sudden failures, thereby improving overall energy production efficiency and economic benefits.

[0077] In some implementations, in order to improve the accuracy of the judgment, the data of other wind turbines in the same wind farm that are working normally or in the same area as the wind farm can be used as a reference to verify the accuracy of the health status judgment of a single wind turbine. Specifically, before generating the prompt information, the data of all wind turbines in the same area that are determined to be reference prototypes can be used to verify the accuracy of the health status of other wind turbines.

[0078] In some embodiments, a group of wind turbines that are considered to be operating normally are selected from other wind turbines in the same wind power station or in the same area of ​​the wind power station as a benchmark. The benchmark wind turbine may refer to a wind turbine that has undergone regular maintenance and does not show any abnormal behavior. The average actual power generation of the benchmark wind turbine at the same time is obtained. The accuracy of the judgment of the health status of the current wind turbine can be verified by analyzing the deviation at the same time point or comparing the similarity of trends in the same time period. If there is a significant deviation or deviation from the trend, it means that the current wind turbine has a health problem. Finally, combined with the above analysis results, the health status of a single wind turbine is comprehensively judged, and corresponding prompt information is generated accordingly.

[0079] Compared with the prior art, this embodiment also has the following technical advantages:

[0080] 1. Based on some influencing factors, the power curve is obtained, and the basic expected power generation is obtained based on the basic power curve. Then, the expected power generation is corrected based on the correction coefficient obtained based on another part of the influencing factors. On the one hand, the amount of data to be processed simultaneously is reduced, the calculation pressure is reduced, and on the other hand, the actual operating status of the wind turbine can be more accurately evaluated;

[0081] 2. Using some fans under the same conditions as reference benchmarks to verify the judgment of the health status of other fans can improve the accuracy of the judgment. When the accuracy of the judgment decreases, the power curve and correction factor should be updated in a timely manner.

[0082] The embodiment of the present invention provides a wind turbine health status monitoring device, such as Figure 3 As shown, it includes: an acquisition module, a query module and a processing module, wherein the acquisition module is configured to: obtain the current operating condition parameters and actual power generation of the wind turbine to be monitored; the query module is configured to: query the power curve obtained based on the operating history data of the wind turbine to obtain the expected power generation under the operating condition parameters; the processing module is configured to: determine the health status of the wind turbine according to the deviation between the actual power generation and the expected power generation, or according to the deviation trend of the actual power generation compared to the expected power generation.

[0083] In some embodiments, the operating condition parameters collected by the collection module include one or more of the following: wind speed, blade angle of the wind turbine, degree of mechanical wear of the wind turbine, efficiency loss of the electrical system of the wind turbine, ambient temperature, ambient humidity, degree of environmental pollution, and the geographical location of the wind turbine.

[0084] In some embodiments, the processing module is further configured to: correct the expected power generation obtained based on the power curve according to the influencing parameters affecting the power generation of the wind turbine generator set.

[0085] In some embodiments, the acquisition module is further configured to: collect influencing parameters, wherein the operating condition parameters are one or more of the following, and the influencing parameters are one or more of the following other than the operating condition parameters: wind speed, blade angle of the wind turbine, degree of mechanical wear of the wind turbine, efficiency loss of the electrical system of the wind turbine, ambient temperature, ambient humidity, degree of environmental pollution, and the geographical location of the wind turbine.

[0086] An embodiment of the present invention provides a wind turbine health status monitoring device, which includes a processor and a memory. The above-mentioned acquisition module, query module and processing module are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize corresponding functions.

[0087] The processor includes a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be set, and the health status of the wind turbine under the influence of different operating condition parameters can be monitored in real time by adjusting the kernel parameters.

[0088] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0089] An embodiment of the present invention provides a storage medium on which a program is stored. When the program is executed by a processor, the method for monitoring the health status of a wind turbine generator system of the present application is implemented.

[0090] An embodiment of the present invention provides a processor, which is used to run a program, wherein the program executes the wind turbine health status monitoring method of the present application when it is run.

[0091] The embodiment of the present invention provides a device, which includes a processor, a memory, and a program stored in the memory and executable on the processor, and the steps of the wind turbine health status monitoring method of the present application are implemented when the processor executes the program. The device in this article can be a server, a PC, a PAD, a mobile phone, etc.

[0092] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program that initializes the steps of the wind turbine health status monitoring method of the present application.

[0093] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0094] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0095] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0096] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0097] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0098] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0099] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0100] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity 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, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0101] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for monitoring the health status of a wind turbine generator system, comprising: Obtain the current operating condition parameters and actual power generation of the wind turbine to be monitored; Querying a power curve obtained based on the historical operation data of the wind turbine generator set to obtain the expected power generation under the operating condition parameters; and The health status of the wind turbine generator set is determined according to a deviation between the actual power generation and the expected power generation, and / or according to a deviation trend of the actual power generation compared to the expected power generation.

2. The wind turbine health status monitoring method according to claim 1, characterized in that: The power curve is the corresponding relationship between the operating condition parameter and the expected power generation; The corresponding relationship is obtained through a power generation prediction model trained by the operation history data, or obtained by statistics of the operation history data.

3. The method for monitoring the health status of a wind turbine generator system according to claim 1, characterized in that: The operating condition parameters include one or more of the following: wind speed, blade angle of the wind turbine, degree of mechanical wear of the wind turbine, efficiency loss of the electrical system of the wind turbine, ambient temperature, ambient humidity, degree of environmental pollution, and geographical location of the wind turbine.

4. The method for monitoring the health status of a wind turbine generator set according to claim 1, characterized in that: Before determining the health status of the wind turbine according to the deviation between the actual power generation and the expected power generation, or according to the changing trend of the actual power generation compared with the expected power generation, the expected power generation obtained based on the power curve is corrected according to the influencing parameters affecting the power generation of the wind turbine.

5. The method for monitoring the health status of a wind turbine generator set according to claim 4, characterized in that: The operating condition parameters are one or more of the following, and the influencing parameters are one or more of the following other than the operating condition parameters: Wind speed, blade angle of the wind turbine, degree of mechanical wear of the wind turbine, efficiency loss of the wind turbine's electrical system, turbulence intensity, air density, ambient temperature, ambient humidity, degree of environmental pollution, and the geographical location of the wind turbine.

6. The method for monitoring the health status of a wind turbine generator set according to claim 5, characterized in that: The expected power generation obtained based on the power curve is corrected according to the following formula: Where p1, p1, … p m is the operating condition parameter of item 1-m, Pexpected(p1,p1,…p m ) is the corrected expected power generation, Pbase(p1,p1,…p m ) is the expected power generation before correction, k i is the correction coefficient corresponding to the i-th influencing parameter, and n is the total number of influencing parameters.

7. The method for monitoring the health status of a wind turbine according to any one of claims 1 to 6, characterized in that: Also includes one or more of the following: When the deviation does not reach a specified first threshold value, and the deviation trend does not reach a specified second threshold value, determining that the wind turbine generator set is currently in a normal state; When the deviation reaches a specified first threshold, or the deviation trend reaches a specified second threshold, determining that the wind turbine generator set is currently in a warning state; When the deviation reaches a specified third threshold, or the deviation trend reaches a specified fourth threshold, it is determined that the wind turbine generator set is currently in a serious fault state.

8. The method for monitoring the health status of a wind turbine generator set according to claim 7, characterized in that: Also includes: When the wind turbine generator set is determined to be in a warning state or a serious fault state, a maintenance reminder is sent to a designated person according to a preset method, and the maintenance reminder includes the current time and the current operating condition parameters and actual power generation of the wind turbine generator set.

9. A wind turbine health status monitoring device, characterized in that: include: Collection module, query module and processing module, Wherein, the acquisition module is configured to: obtain the current operating condition parameters and actual power generation of the wind turbine to be monitored; The query module is configured to: query a power curve obtained based on the operation history data of the wind turbine generator set to obtain the expected power generation under the operation condition parameters; The processing module is configured to determine the health status of the wind turbine according to a deviation between the actual power generation and the expected power generation, and / or according to a deviation trend of the actual power generation compared to the expected power generation.

10. The wind turbine health status monitoring device according to claim 9, characterized in that: The operating condition parameters collected by the collection module include one or more of the following: wind speed, blade angle of the wind turbine, degree of mechanical wear of the wind turbine, efficiency loss of the electrical system of the wind turbine, ambient temperature, ambient humidity, degree of environmental pollution, and geographical location of the wind turbine.

11. The wind turbine health status monitoring device according to claim 9, characterized in that: The processing module is further configured to correct the expected power generation obtained based on the power curve according to an influencing parameter affecting the power generation of the wind turbine generator set.

12. The wind turbine health status monitoring device according to claim 11, characterized in that: The acquisition module is further configured to: acquire the influencing parameters, Among them, the operating condition parameters are one or more of the following, and the influencing parameters are one or more of the following other than the operating condition parameters: wind speed, blade angle of the wind turbine, degree of mechanical wear of the wind turbine, efficiency loss of the electrical system of the wind turbine, turbulence intensity, air density, ambient temperature, ambient humidity, degree of environmental pollution, and the geographical location of the wind turbine.

13. A machine-readable storage medium, characterized in that: The machine-readable storage medium stores instructions, which are used to enable the machine to execute: a method for monitoring the health status of a wind turbine according to any one of claims 1-8.

14. A processor, characterized in that: Used to run a program, wherein the program, when run, is used to execute: a method for monitoring the health status of a wind turbine set according to any one of claims 1-8.

15. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the computer program implements the wind turbine health status monitoring method according to any one of claims 1 to 8.

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