Fault detection method and device, electronic equipment and storage medium

Abstract

The invention discloses a fault detection method and device, electronic equipment and a storage medium. The method comprises the steps that the real-time amplitude of a gearbox and the real-time operation power and the real-time voltage of a generator are obtained; determining a power correlation characteristic quantity corresponding to the real-time operation power according to the equipment attribute of the wind turbine generator and the real-time operation power; the power correlation characteristic quantity comprises a voltage characteristic quantity and an amplitude characteristic quantity; calculating a voltage deviation coefficient based on the real-time voltage and the voltage characteristic quantity, and calculating an amplitude deviation coefficient based on the real-time amplitude and the amplitude characteristic quantity; calculating a detection coefficient of the gearbox according to the voltage deviation coefficient and the amplitude deviation coefficient; and determining a detection result of the gearbox based on the detection coefficient. According to the technical scheme of the embodiment of the invention, the detection accuracy of the wind turbine generator gearbox is improved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the technical field of fault detection, and in particular to a fault detection method, device, electronic device, and storage medium. Background Art

[0002] Wind turbines are key equipment for clean energy generation, and their stable operation is crucial to the reliability of power supply. Gearboxes are the core component for energy transmission in wind turbines. Failure will cause the turbine to shut down, resulting in significant economic losses. Therefore, testing wind turbine gearboxes is extremely important.

[0003] Currently, fault detection methods are based solely on single-dimensional parameters (such as vibration signals), ignoring the complex correlations and multi-dimensional characteristics between different data, resulting in inaccurate detection results.

[0004] Therefore, it is urgent to propose a new method to solve the above problems. Summary of the Invention

[0005] The present invention provides a fault detection method, device, electronic equipment and storage medium to improve the detection accuracy of a wind turbine gearbox.

[0006] In a first aspect, an embodiment of the present invention provides a fault detection method applied to a wind turbine generator set, wherein the wind turbine generator set includes a generator and a gearbox, and the method includes:

[0007] Obtaining the real-time amplitude of the gearbox, the real-time operating power and the real-time voltage of the generator;

[0008] Determine a power-related characteristic quantity corresponding to the real-time operating power according to the device attributes of the wind turbine generator set and the real-time operating power; the power-related characteristic quantity includes a voltage characteristic quantity and an amplitude characteristic quantity;

[0009] Calculating a voltage deviation coefficient based on the real-time voltage and the voltage characteristic quantity, and calculating an amplitude deviation coefficient based on the real-time amplitude and the amplitude characteristic quantity;

[0010] Calculating a detection coefficient of the gearbox according to the voltage deviation coefficient and the amplitude deviation coefficient;

[0011] A detection result of the gearbox is determined based on the detection coefficient.

[0012] The technical solution of the embodiment of the present invention first obtains the real-time amplitude of the gearbox, the real-time operating power and the real-time voltage of the generator, providing a data basis for the subsequent calculation of the voltage deviation coefficient and the amplitude deviation coefficient. Then, the power-related characteristic quantity corresponding to the real-time operating power is determined based on the equipment attributes and real-time operating power of the wind turbine. Taking into account the differences in equipment attributes between different units, the characteristic parameters of the gearbox and generator at different powers can be accurately defined for the specific characteristics of each wind turbine. Therefore, during the detection process, abnormal conditions of voltage and amplitude at specific power can be captured more keenly, greatly improving the accuracy of subsequent detection. At the same time, the personalized detection and analysis mechanism based on individual differences in equipment can avoid the problem of misjudgment caused by "unified standards", making the detection results of the gearbox more in line with the actual operating status, and providing a reliable basis for the efficient operation and maintenance and safe operation of wind turbines. Next, the voltage deviation coefficient is calculated based on the real-time voltage and voltage characteristics, and the amplitude deviation coefficient is calculated based on the real-time amplitude and amplitude characteristics. This quantifies the degree of parameter deviation, making the detection of gearbox operating status more objective and accurate. This avoids the difficulty of accurately grasping parameter changes based on subjective judgment or simple comparison, thus helping to improve detection accuracy. Next, the gearbox detection coefficient is calculated based on the voltage deviation coefficient and the amplitude deviation coefficient. This comprehensively considers the impact of both voltage and amplitude on the gearbox status, enabling a more comprehensive and accurate assessment of the overall gearbox operating condition. This avoids the bias and inaccuracy caused by judging based on a single parameter, effectively improving detection accuracy. Finally, the gearbox detection result is determined based on the detection coefficient, achieving intelligent gearbox status detection. This reduces the subjectivity and uncertainty of manual judgment, improves detection accuracy and reliability, and provides strong support for remote monitoring and automated operation and maintenance of wind turbines, effectively reducing operation and maintenance costs. Furthermore, the gearbox detection results are clearly output, providing personnel with a clear decision-making basis, allowing them to quickly implement targeted measures such as maintenance arrangements and component replacement, thereby ensuring the safe and stable operation of wind turbines. Therefore, the technical solution of the present invention solves the problem in the prior art of insufficient detection accuracy due to failure to consider the correlation and multi-dimensional characteristics between data.

[0013] In a second aspect, an embodiment of the present invention further provides a fault detection device, which is applied to a wind turbine generator set, wherein the wind turbine generator set includes a generator and a gearbox, and the device includes:

[0014] an acquisition module, configured to acquire the real-time amplitude of the gearbox, the real-time operating power and the real-time voltage of the generator;

[0015] a determination module, configured to determine a power-related characteristic quantity corresponding to the real-time operating power according to the device attributes of the wind turbine generator set and the real-time operating power; the power-related characteristic quantity includes a voltage characteristic quantity and an amplitude characteristic quantity;

[0016] a first calculation module, configured to calculate a voltage deviation coefficient based on the real-time voltage and the voltage characteristic quantity, and to calculate an amplitude deviation coefficient based on the real-time amplitude and the amplitude characteristic quantity;

[0017] a second calculation module, configured to calculate a detection coefficient of the gear box according to the voltage deviation coefficient and the amplitude deviation coefficient;

[0018] A detection module is used to determine a detection result of the gearbox based on the detection coefficient.

[0019] In a third aspect, an embodiment of the present invention further provides an electronic device, the electronic device comprising:

[0020] at least one processor; and a memory communicatively coupled to the at least one processor;

[0021] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the fault detection method described in any embodiment of the present invention.

[0022] In a fourth aspect, an embodiment of the present invention further provides a storage medium comprising computer-executable instructions, wherein the computer-executable instructions, when executed by a computer processor, implement the fault detection method described in any embodiment of the present invention.

[0023] It should be noted that the above-mentioned computer instructions may be stored in whole or in part on a computer-readable storage medium. The computer-readable storage medium may be packaged together with the processor of the fault detection device or may be packaged separately from the processor of the fault detection device, and this application does not limit this.

[0024] The description of the second, third and fourth aspects in this application can refer to the detailed description of the first aspect; and the beneficial effects of the description of the second, third and fourth aspects can refer to the analysis of the beneficial effects of the first aspect, which will not be repeated here.

[0025] In this application, the names of the above-mentioned fault detection devices do not limit the devices or functional modules themselves. In actual implementation, these devices or functional modules may appear with other names. As long as the functions of each device or functional module are similar to those of this application, they are within the scope of the claims of this application and their equivalents.

[0026] These and other aspects of the present application will become more readily apparent from the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. 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 creative work.

[0028] Figure 1 A flowchart of a fault detection method provided by an embodiment of the present invention;

[0029] Figure 2 A flowchart of another fault detection method provided by an embodiment of the present invention;

[0030] Figure 3 A schematic structural diagram of a fault detection device provided by an embodiment of the present invention;

[0031] Figure 4 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The present invention will be further described in detail below with reference to the accompanying drawings and examples. It will be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions relevant to the present invention, not all structures.

[0033] The term "and / or" in this article is merely a description of the association relationship between associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone.

[0034] The terms "first" and "second" and the like in the specification and drawings of this application are used to distinguish different objects, or to distinguish different processing of the same object, rather than to describe a specific order of objects.

[0035] Furthermore, the terms "including," "having," and any variations thereof, as used in the description of this application are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or units is not limited to the listed steps or units but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to the process, method, product, or apparatus.

[0036] It should be mentioned before discussing exemplary embodiments in more detail that some exemplary embodiments are described as processes or methods depicted as flow charts. Although flow charts describe various operations (or steps) as sequential processes, many operations therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of various operations can be rearranged. When its operation is completed, the process can be terminated, but can also have additional steps not included in the accompanying drawings. The process can correspond to methods, functions, procedures, subroutines, subprograms, etc. In addition, the features in the embodiments of the present invention and the embodiments can be combined with each other without conflict.

[0037] It should be noted that in the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be interpreted as being more preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0038] In the description of the present application, unless otherwise specified, “plurality” means two or more.

[0039] Figure 1 This is a flow chart of a fault detection method provided by an embodiment of the present invention. This embodiment is applicable to situations where the gearbox of a wind turbine needs to be detected. The method is applied to a wind turbine, which includes a generator and a gearbox. The method can be performed by a fault detection device, which can be implemented in software and / or hardware. For example, the device can be an electronic device in the wind turbine. Figure 1 The fault detection method of this embodiment specifically includes the following steps:

[0040] Step 110: Acquire the real-time amplitude of the gearbox, and the real-time operating power and voltage of the generator.

[0041] Specifically, the gearbox refers to the component in a wind turbine that connects the rotor and generator. A wind turbine is a complete system that converts wind kinetic energy into electrical energy. Real-time amplitude refers to the vibration amplitude of the gearbox at the current moment. The generator refers to the device in the wind turbine that converts mechanical energy into electrical energy. Real-time operating power refers to the electrical power output of the generator at the current moment. Real-time voltage refers to the voltage value of the generator at the current moment.

[0042] In a specific implementation, the real-time operating power can be obtained through a power sensor installed on the generator, the real-time voltage can be obtained through a voltage sensor installed on the generator, and the real-time amplitude can be obtained through an amplitude sensor installed on the gearbox.

[0043] In this embodiment, the above steps provide a data basis for the subsequent calculation of the voltage deviation coefficient and the amplitude deviation coefficient.

[0044] Step 120: Determine a power-related characteristic quantity corresponding to the real-time operating power according to the device attributes and the real-time operating power of the wind turbine generator set.

[0045] Specifically, the equipment attributes of a wind turbine refer to the inherent characteristics and parameters of each device in the wind turbine (such as a gearbox, generator, etc.). For example, the equipment attributes of a wind turbine can be design parameters, ratings, model specifications, technical indicators, etc. Power-related characteristic quantities are characteristic parameters associated with operating power, which are used to characterize the characteristics of the equipment at different powers. Power-related characteristic quantities include voltage characteristic quantities and amplitude characteristic quantities. Voltage characteristic quantities refer to parameters that describe the normal range of generator voltage. Amplitude characteristic quantities refer to parameters that describe the normal range of gearbox vibration.

[0046] In a specific implementation, a query is performed in the equipment power-characteristic quantity mapping table according to the equipment attributes and real-time operating power of the wind turbine generator set to obtain the power-related characteristic quantity corresponding to the real-time operating power.

[0047] It should be noted that the device power-characteristic quantity mapping table is established in advance based on actual conditions or needs.

[0048] In this embodiment, by combining the equipment attributes and real-time operating power of the wind turbine to determine the power-related characteristic quantity, the differences in equipment attributes between different units are fully considered. The characteristic parameters of the gearbox and generator at different powers can be accurately defined based on the specific characteristics of each wind turbine. As a result, during the detection process, abnormalities in voltage and amplitude at specific powers can be more keenly captured, greatly improving the accuracy of subsequent detection. At the same time, the personalized detection and analysis mechanism based on individual differences in equipment can avoid misjudgments caused by "unified standards", making the gearbox detection results more consistent with the actual operating status and providing a reliable basis for the efficient operation and maintenance and safe operation of wind turbines.

[0049] Step 130 : Calculate a voltage deviation coefficient based on the real-time voltage and the voltage characteristic quantity, and calculate an amplitude deviation coefficient based on the real-time amplitude and the amplitude characteristic quantity.

[0050] Specifically, the voltage deviation coefficient is an indicator calculated based on the real-time voltage and voltage characteristic quantity, and is used to quantify the voltage deviation. The amplitude deviation coefficient is an indicator calculated based on the real-time amplitude and amplitude characteristic quantity, and is used to quantify the amplitude deviation.

[0051] Exemplarily, when the voltage characteristic value is the voltage reference value and the amplitude characteristic value is the amplitude reference value, the voltage deviation coefficient = |real-time voltage - voltage characteristic value| / voltage characteristic value, and the amplitude deviation coefficient = (real-time amplitude - amplitude characteristic value) / amplitude characteristic value.

[0052] In this embodiment, through the above steps, the degree of parameter deviation is quantified, making the detection of the gearbox operating status more objective and accurate, avoiding the problem of being unable to accurately grasp the parameter changes based on subjective judgment or simple comparison, and helping to improve detection accuracy.

[0053] Step 140: Calculate the detection coefficient of the gearbox according to the voltage deviation coefficient and the amplitude deviation coefficient.

[0054] Specifically, the detection coefficient refers to a comprehensive indicator calculated based on the voltage deviation coefficient and the amplitude deviation coefficient, which is used to comprehensively evaluate the operating status of the gearbox.

[0055] In a specific implementation, the voltage deviation coefficient and the amplitude deviation coefficient can be weighted based on the preset voltage weight and amplitude weight to obtain the gearbox detection coefficient. Alternatively, the product of the voltage deviation coefficient and the amplitude deviation coefficient can be calculated to obtain the gearbox detection coefficient.

[0056] In this embodiment, through the above steps, the influence of the two parameters of voltage and amplitude on the gearbox status is comprehensively considered, which can more comprehensively and accurately evaluate the overall operating condition of the gearbox, avoid the one-sidedness and inaccuracy caused by judging based on only a single parameter, and effectively improve the accuracy of detection.

[0057] Step 150: Determine a detection result of the gearbox based on the detection coefficient.

[0058] Specifically, the detection result refers to the judgment conclusion on the operating status of the gearbox based on the detection coefficient.

[0059] In a specific implementation, the detection result of the gearbox can be determined based on a pre-set detection threshold and detection coefficient. Specifically, if the detection coefficient is greater than the detection threshold, the detection result of the gearbox is determined to be abnormal. At this time, an alarm message can be sent to the terminal device of the staff (such as a mobile phone, computer, etc.) to remind the staff to conduct inspections and repairs. At the same time, the detected key data (including real-time voltage, real-time operating power, real-time amplitude, etc.), the corresponding timestamp, and information such as the voltage deviation coefficient, amplitude deviation coefficient and detection coefficient can be sent synchronously to help the staff quickly locate the cause of the abnormality. If the detection coefficient is not greater than the detection threshold, the detection result of the gearbox is determined to be normal. At this time, the real-time amplitude of the gearbox, the real-time operating power and real-time voltage of the generator can be continued to be obtained, and a new round of detection of the gearbox can be carried out to ensure operational safety.

[0060] In addition, detection threshold intervals of different risk levels may be divided according to actual conditions or requirements, and the detection result of the gearbox may be determined based on the divided detection threshold intervals and the detection coefficient. For example, if the detection coefficient ranges from [0, 1], the detection threshold includes a warning threshold (such as 0.3) and a fault threshold (such as 0.75), and the warning threshold is less than the fault threshold, the detection threshold interval can be divided into three levels: a normal interval [0, warning threshold], a warning interval (warning threshold, fault threshold], and a fault interval (fault threshold, 1]. When 0 ≤ detection coefficient ≤ warning threshold, the gearbox detection result is determined to be normal. At this time, the real-time amplitude of the gearbox, the real-time operating power and real-time voltage of the generator can be continuously obtained to conduct a new round of gearbox detection. When the warning threshold is less than the detection coefficient ≤ the fault threshold, the gearbox detection result is determined to be a warning. At this time, a warning message can be sent to the staff's terminal to remind the staff to check as soon as possible and take maintenance measures in advance to avoid the occurrence of faults and ensure the safe and stable operation of the equipment. When the fault threshold is less than the detection coefficient ≤ 1, the gearbox detection result is determined to be abnormal. At this time, an alarm message can be sent to the staff's terminal device to remind the staff to check and repair. At the same time, a safe shutdown procedure can be executed to gradually cut off the generator load and shut down the system according to the preset process to prevent the fault from expanding.

[0061] In this embodiment, the above steps enable intelligent detection of gearbox status, reducing the subjectivity and uncertainty of manual judgment, improving detection accuracy and reliability, and providing strong support for remote monitoring and automated operation and maintenance of wind turbines, effectively reducing operation and maintenance costs. Furthermore, the gearbox detection results can be clearly output, providing personnel with a clear basis for decision-making, allowing them to quickly implement targeted measures such as maintenance arrangements and component replacement, thereby ensuring the safe and stable operation of the wind turbine.

[0062] The fault detection method provided by the embodiment of the present invention first obtains the real-time amplitude of the gearbox, the real-time operating power of the generator, and the real-time voltage, providing a data basis for the subsequent calculation of the voltage deviation coefficient and the amplitude deviation coefficient. Next, the power-related characteristic quantity corresponding to the real-time operating power is determined based on the equipment attributes and real-time operating power of the wind turbine. Taking into account the differences in equipment attributes between different units, the characteristic parameters of the gearbox and generator at different powers can be accurately defined based on the specific characteristics of each wind turbine. As a result, during the detection process, abnormalities in voltage and amplitude at specific powers can be more keenly captured, greatly improving the accuracy of detection. At the same time, the personalized detection and analysis mechanism based on individual differences in equipment can avoid the problem of misjudgment caused by "unified standards", making the gearbox detection results more consistent with the actual operating status, and providing a reliable basis for the efficient operation and maintenance and safe operation of wind turbines. Next, the voltage deviation coefficient is calculated based on the real-time voltage and voltage characteristics, and the amplitude deviation coefficient is calculated based on the real-time amplitude and amplitude characteristics. This quantifies the degree of parameter deviation, making the detection of gearbox operating status more objective and accurate. This avoids the difficulty of accurately grasping parameter changes based on subjective judgment or simple comparison, thus helping to improve detection accuracy. Next, the gearbox detection coefficient is calculated based on the voltage deviation coefficient and the amplitude deviation coefficient. This comprehensively considers the impact of both voltage and amplitude on the gearbox status, enabling a more comprehensive and accurate assessment of the overall gearbox operating condition. This avoids the bias and inaccuracy caused by judging based on a single parameter, effectively improving detection accuracy. Finally, the gearbox detection result is determined based on the detection coefficient, achieving intelligent gearbox status detection. This reduces the subjectivity and uncertainty of manual judgment, improves detection accuracy and reliability, and provides strong support for remote monitoring and automated operation and maintenance of wind turbines, effectively reducing operation and maintenance costs. Furthermore, the gearbox detection results are clearly output, providing personnel with a clear decision-making basis, allowing them to quickly implement targeted measures such as maintenance arrangements and component replacement, thereby ensuring the safe and stable operation of wind turbines. Therefore, the technical solution of the present invention solves the problem in the prior art of insufficient detection accuracy due to failure to consider the correlation and multi-dimensional characteristics between data.

[0063] Figure 2 This is a flowchart of another fault detection method provided by an embodiment of the present invention. This embodiment is specific based on the above embodiment. In this embodiment, the method may also include:

[0064] Step 210: Acquire the real-time amplitude of the gearbox, and the real-time operating power and voltage of the generator.

[0065] Step 211: Determine a power-related characteristic quantity corresponding to the real-time operating power according to the equipment attributes and the real-time operating power of the wind turbine generator set.

[0066] Furthermore, step 211 may specifically include: querying the correspondence table between equipment attributes and power characteristic quantities according to the equipment attributes of the wind turbine generator set to obtain the correspondence table between the power and characteristic quantities of the wind turbine generator set; querying the correspondence table between the power and characteristic quantities of the wind turbine generator set according to the real-time operating power to obtain the power-related characteristic quantities corresponding to the real-time operating power.

[0067] Specifically, the device attribute and power characteristic quantity correspondence table refers to a general directory table that stores the indexes of the corresponding power and characteristic quantity correspondence table for different wind turbine device attributes (such as generator model, generator specifications, gearbox model, gearbox specifications, etc.). The wind turbine power and characteristic quantity correspondence table refers to a personalized data table for a single device (such as a wind turbine) that is used to store the power-related characteristic quantities of the device at different power levels.

[0068] In the specific implementation, first, based on the equipment attributes of the wind turbine, the correspondence table between the equipment attributes and the power characteristic quantities is queried to obtain the correspondence table between the power and the characteristic quantities of the wind turbine. Then, based on the obtained real-time operating power, the correspondence table between the power and the characteristic quantities of the wind turbine is queried to obtain the power-related characteristic quantities corresponding to the real-time operating power.

[0069] In addition, when there is no power point that completely matches the real-time operating power in the power-feature quantity correspondence table, the corresponding power-related feature quantity can be calculated using an interpolation algorithm (such as linear interpolation, spline interpolation, etc.).

[0070] It should be noted that the table of correspondence between device attributes and power characteristic quantities and the table of correspondence between power and characteristic quantities are both established in advance based on actual conditions or needs.

[0071] In this embodiment, through the above steps, data management efficiency and query efficiency are improved, and more complex scenarios can be adapted, thereby enhancing flexibility.

[0072] Optionally, the voltage characteristic quantity includes a voltage reference value and a voltage fluctuation value.

[0073] Specifically, the voltage reference value refers to the rated voltage or theoretical standard voltage of the generator at a specific power, which serves as a reference for calculating the voltage deviation coefficient. The voltage fluctuation value is a statistical indicator used to characterize voltage fluctuations at a specific operating power. This indicator is calculated using the historical normal operating data of the wind turbine (e.g., historical normal generator voltage values ​​corresponding to the same specific operating power at different historical moments). For example, the voltage fluctuation value can be the standard deviation or variance of the normal voltage at a specific operating power.

[0074] Step 212: Calculate the difference between the real-time voltage and the voltage reference value to obtain the voltage reference deviation.

[0075] Specifically, the voltage reference deviation refers to the difference between the real-time voltage and the voltage reference value, reflecting the degree to which the voltage deviates from the standard state.

[0076] In a specific implementation, after obtaining the voltage reference value, the difference between the real-time voltage and the voltage reference value can be calculated to obtain the voltage reference deviation. The specific calculation formula is as follows:

[0077] U o =UU b

[0078] Among them, U o is the voltage reference deviation, U is the real-time voltage, U b is the voltage reference value.

[0079] In this embodiment, through the above steps, the degree of deviation between the real-time voltage and the standard value can be intuitively quantified, providing a data basis for the subsequent calculation of the voltage deviation coefficient.

[0080] Step 213: Calculate the square of the ratio of the voltage reference deviation to the voltage fluctuation value to obtain a voltage deviation factor.

[0081] Specifically, the voltage deviation factor refers to an indicator that quantifies the degree of influence of the voltage deviation.

[0082] In a specific implementation, after obtaining the voltage reference deviation, the square of the ratio of the voltage reference deviation to the voltage fluctuation value can be calculated to obtain the voltage deviation factor. The specific calculation formula is as follows:

[0083]

[0084] Among them, U s is the voltage fluctuation value, U x is the voltage deviation factor.

[0085] In this embodiment, through the above steps, even a relatively small voltage deviation can have a significant impact on subsequent calculations, thereby increasing the sensitivity to voltage anomalies and further improving the accuracy of subsequent detection.

[0086] Step 214: Calculate the voltage deviation coefficient based on the voltage deviation factor.

[0087] In a specific implementation, after obtaining the voltage deviation factor, the voltage deviation coefficient can be calculated based on the voltage deviation factor. The specific calculation formula is as follows:

[0088]

[0089] Among them, K U is the voltage deviation coefficient.

[0090] In this embodiment, through the above steps, the obtained voltage deviation coefficient can have a more reasonable numerical range, which is convenient for subsequent comprehensive calculation and analysis with other coefficients, avoiding calculation errors or data insensitivity problems caused by excessively large or small values, and thus improving the accuracy of subsequent detection.

[0091] Optionally, the amplitude feature value includes an amplitude reference value and an amplitude fluctuation value.

[0092] Specifically, the amplitude reference value refers to the expected value or standard value of the gearbox amplitude corresponding to the generator at a specific power, which serves as a reference for calculating the amplitude deviation coefficient. For example, the amplitude reference value includes the planetary gear amplitude reference value, the high-speed shaft amplitude reference value, and the low-speed shaft amplitude reference value. The amplitude fluctuation value refers to a statistical indicator used to characterize the vibration condition of the gearbox at a specific operating power. The indicator is calculated based on the historical normal operating data of the wind turbine (such as the historical normal amplitude value of the gearbox corresponding to the same specific operating power at different historical moments). For example, the amplitude fluctuation value can be the standard deviation or variance of the normal amplitude of the gearbox at a specific operating power, including the planetary gear amplitude fluctuation value, the high-speed shaft amplitude fluctuation value, and the low-speed shaft amplitude fluctuation value.

[0093] Step 215: Calculate the difference between the real-time amplitude and the amplitude reference value to obtain the amplitude reference deviation.

[0094] Specifically, the amplitude reference deviation refers to the difference between the real-time amplitude and the amplitude reference value, reflecting the degree to which the gearbox vibration deviates from the normal state. For example, the amplitude reference deviation may include the planetary gear amplitude reference deviation, the high-speed shaft amplitude reference deviation, and the low-speed shaft amplitude reference deviation. For example, the real-time amplitude may include the planetary gear real-time amplitude, the high-speed shaft real-time amplitude, and the low-speed shaft real-time amplitude.

[0095] In specific implementation, after obtaining the amplitude reference value, the difference between the real-time amplitude and the amplitude reference value can be calculated to obtain the amplitude reference deviation. The specific calculation formula is as follows:

[0096] A o =AA b

[0097] Among them, A o is the amplitude reference deviation, A is the real-time amplitude, A b is the amplitude reference value.

[0098] In this embodiment, through the above steps, the degree of deviation between the real-time amplitude and the amplitude reference value can be intuitively quantified, providing a data basis for the subsequent calculation of the amplitude deviation coefficient.

[0099] Step 216: Calculate the amplitude deviation factor based on the amplitude reference deviation and the amplitude fluctuation value.

[0100] Specifically, the amplitude deviation factor refers to a parameter calculated based on the amplitude reference deviation and the amplitude fluctuation value, and is used to quantify the impact of vibration deviation. For example, the amplitude deviation factor can include the planetary gear amplitude deviation factor, the high-speed shaft amplitude deviation factor, and the low-speed shaft amplitude deviation factor.

[0101] In a specific implementation, after obtaining the amplitude reference deviation, the amplitude deviation factor can be calculated based on the amplitude reference deviation and the amplitude fluctuation value. The specific calculation formula is as follows:

[0102]

[0103] Among them, A s is the amplitude fluctuation value, A x is the amplitude deviation factor, and a is a preset multiple, for example, a=2.

[0104] In this embodiment, by introducing a preset multiple as a safety buffer, the tolerance range of the normal amplitude is expanded, avoiding false fault alarms in subsequent detections due to slight amplitude fluctuations, thereby improving the accuracy of detection.

[0105] Step 217: Perform exponential transformation on the amplitude deviation factor to obtain an amplitude deviation coefficient.

[0106] In the specific implementation, after obtaining the amplitude deviation factor, the amplitude deviation factor can be exponentially transformed to obtain the amplitude deviation coefficient. The specific calculation formula is as follows: K A =e -A , where K A is the amplitude deviation coefficient.

[0107] In this embodiment, exponential transformation of the amplitude deviation factor can change the data distribution characteristics, making the numerical range of the amplitude deviation coefficient more reasonable and facilitating subsequent comprehensive calculations with other coefficients. Furthermore, exponential transformation can further highlight large amplitude deviations, improving the ability to detect severe amplitude anomalies, thereby enhancing the accuracy of subsequent detection.

[0108] Step 218: Calculate the detection coefficient of the gearbox according to the voltage deviation coefficient and the amplitude deviation coefficient.

[0109] Optionally, the amplitude deviation coefficient includes a planetary gear amplitude deviation coefficient, a high-speed shaft amplitude deviation coefficient, and a low-speed shaft amplitude deviation coefficient.

[0110] Specifically, the planetary gear amplitude deviation coefficient refers to the vibration deviation coefficient of the planetary gear part of the gearbox. The high-speed shaft amplitude deviation coefficient refers to the vibration deviation coefficient of the high-speed shaft part of the gearbox. The low-speed shaft amplitude deviation coefficient refers to the vibration deviation coefficient of the low-speed shaft part of the gearbox.

[0111] Furthermore, the detection coefficient of the gearbox is calculated based on the voltage deviation coefficient and the amplitude deviation coefficient, including: calculating the product of the planetary gear amplitude deviation coefficient and the voltage deviation coefficient to obtain the planetary gear detection coefficient; calculating the product of the high-speed shaft amplitude deviation coefficient and the voltage deviation coefficient to obtain the high-speed shaft detection coefficient; calculating the product of the low-speed shaft amplitude deviation coefficient and the voltage deviation coefficient to obtain the low-speed shaft detection coefficient; and calculating the detection coefficient of the gearbox based on the planetary gear detection coefficient, the high-speed shaft detection coefficient and the low-speed shaft detection coefficient.

[0112] Specifically, the planetary gear detection coefficient refers to a quantitative indicator used to evaluate the operating status of the planetary gear of the gearbox. The low-speed shaft amplitude deviation coefficient refers to a quantitative indicator used to evaluate the operating status of the low-speed shaft of the gearbox. The high-speed shaft amplitude deviation coefficient refers to a quantitative indicator used to evaluate the operating status of the high-speed shaft of the gearbox. Among them, the calculation method of the planetary gear amplitude deviation coefficient, the high-speed shaft amplitude deviation coefficient and the low-speed shaft amplitude deviation coefficient is consistent with the calculation method of the aforementioned amplitude deviation coefficient: first, the difference between the real-time amplitude of the three and the corresponding amplitude reference value is calculated respectively to obtain the amplitude reference deviation of each component; then, based on the amplitude reference deviation of each component and the corresponding amplitude fluctuation value, the amplitude deviation factor of each component is calculated; finally, the deviation factor of each component is exponentially transformed to obtain the corresponding amplitude deviation coefficient, for example: Among them, K A1 is the planetary gear amplitude deviation coefficient, A1 is the planetary gear real-time amplitude, A b1 A is the planetary gear amplitude reference value, s1 is the planet gear amplitude fluctuation value, K A2 is the high-speed shaft amplitude deviation coefficient, A2 is the high-speed shaft real-time amplitude, A b2 A is the high-speed shaft amplitude reference value, s2 is the high-speed shaft amplitude fluctuation value, K A3 is the low-speed shaft amplitude deviation coefficient, A3 is the low-speed shaft real-time amplitude, A b3 A is the low-speed shaft amplitude reference value, s3 is the low-speed shaft amplitude fluctuation value. Furthermore, the real-time amplitude of each component is obtained via amplitude sensors installed at corresponding locations on the gearbox (planetary gears, high-speed shaft, and low-speed shaft). The corresponding amplitude reference value and amplitude fluctuation value are obtained by querying the corresponding relationship table between the wind turbine's power and characteristic quantities.

[0113] In the specific implementation, the product of the planetary gear amplitude deviation coefficient and the voltage deviation coefficient is first calculated to obtain the planetary gear detection coefficient. The specific calculation formula is as follows:

[0114] HI1=K A1 ×K U

[0115] Among them, HI1 is the planetary gear detection coefficient, KA1 is the planet gear amplitude deviation coefficient.

[0116] Calculate the product of the high-speed shaft amplitude deviation coefficient and the voltage deviation coefficient to obtain the high-speed shaft detection coefficient. The specific calculation formula is as follows:

[0117] HI2=K A2 ×K U

[0118] Among them, HI2 is the high-speed shaft detection coefficient, K A2 is the high-speed shaft amplitude deviation coefficient.

[0119] Calculate the product of the low-speed shaft amplitude deviation coefficient and the voltage deviation coefficient to obtain the low-speed shaft detection coefficient. The specific calculation formula is as follows:

[0120] HI3=K A3 ×K U

[0121] Among them, HI3 is the low-speed shaft detection coefficient, K A3 is the low-speed shaft amplitude deviation coefficient.

[0122] Finally, based on the preset planetary gear weight, high-speed shaft weight, and low-speed shaft weight, the planetary gear detection coefficient, high-speed shaft detection coefficient, and low-speed shaft detection coefficient are weighted to obtain the gearbox detection coefficient. The specific calculation formula is as follows:

[0123] HI=ACC1×HI1+ACC2×HI2+ACC3×HI3

[0124] Among them, ACC1 is the planetary gear weight, for example: ACC1 = 0.5, ACC2 is the high-speed shaft weight, for example: ACC2 = 0.2, ACC3 is the low-speed shaft weight, ACC3 = 0.3.

[0125] In this embodiment, by calculating the detection coefficient of each component separately, the mutual interference of the amplitude abnormalities of different components can be avoided, and the detection sensitivity of single component failure can be effectively improved. Compared with the method of conducting overall detection of the gearbox as a "black box", the detection accuracy is further improved by integrating the multi-dimensional data fusion analysis of the detection coefficients of each component.

[0126] Optionally, the wind turbine generator set further includes a nacelle, and the power-related characteristic quantity further includes a temperature characteristic quantity.

[0127] Specifically, the nacelle refers to the enclosed space within a wind turbine that houses the generator, gearbox, control system, and other key equipment, providing protection and a foundation for their installation. Temperature characteristic quantities are parameters used to describe the normal temperature range of the equipment.

[0128] Furthermore, before calculating the detection coefficient of the gearbox based on the planetary gear detection coefficient, the high-speed shaft detection coefficient and the low-speed shaft detection coefficient, it also includes: obtaining the real-time gearbox temperature of the gearbox and the real-time cabin temperature of the cabin; calculating the temperature deviation coefficient based on the real-time gearbox temperature, the real-time cabin temperature and the temperature characteristic; updating the planetary gear detection coefficient, the high-speed shaft detection coefficient and the low-speed shaft detection coefficient according to the temperature deviation coefficient, and obtaining the updated planetary gear detection coefficient, the high-speed shaft detection coefficient and the low-speed shaft detection coefficient.

[0129] Specifically, the real-time cabin temperature refers to the current temperature inside the cabin. The real-time gearbox temperature refers to the current gearbox temperature. The temperature deviation coefficient is an indicator calculated based on the real-time gearbox temperature, the real-time cabin temperature, and temperature characteristic quantities, used to quantify temperature deviation. For example, the temperature characteristic quantities include the gearbox temperature baseline value and the cabin temperature baseline value.

[0130] In a specific implementation, the real-time gearbox temperature can be obtained by a temperature sensor installed on the gearbox, and the real-time cabin temperature can be obtained by a temperature sensor installed inside the cabin. Then, the difference between the real-time gearbox temperature and the real-time cabin temperature is calculated to obtain the real-time temperature difference. Next, the ratio of the real-time temperature difference to the gearbox temperature reference value is calculated to obtain the gearbox temperature deviation rate, and the ratio of the real-time temperature difference to the cabin temperature reference value is calculated to obtain the cabin temperature deviation rate. The two deviation rates are then weighted and combined to obtain the temperature deviation coefficient. Finally, the product of the temperature deviation coefficient and the planetary gear detection coefficient, the high-speed shaft detection coefficient, and the low-speed shaft detection coefficient are calculated respectively to obtain the updated planetary gear detection coefficient, the high-speed shaft detection coefficient, and the low-speed shaft detection coefficient. For example, the updated planetary gear detection coefficient, the high-speed shaft detection coefficient, and the low-speed shaft detection coefficient can be expressed by the following formula: HI1=K A1 ×K U ×K T , HI2=K A2 ×K U ×K T , HI3=K A3 ×K U ×K T , where K T is the temperature deviation coefficient.

[0131] In this embodiment, updating the planetary gear detection coefficient, high-speed shaft detection coefficient and low-speed shaft detection coefficient according to the temperature deviation coefficient can eliminate the interference of temperature on the detection results, so that the subsequently obtained detection coefficients more accurately reflect the actual operating status of each component of the gearbox, thereby improving the accuracy of the detection.

[0132] Optionally, the temperature characteristic value includes a temperature difference reference value and a temperature fluctuation value.

[0133] Specifically, the temperature difference baseline refers to the expected or standard temperature difference between the gearbox temperature and the nacelle temperature at a specific generator power level. This difference serves as a reference for calculating the temperature deviation coefficient. The temperature fluctuation value is a statistical indicator used to characterize temperature fluctuations at a specific operating power level. This indicator is calculated based on historical normal operating data of the wind turbine. For example, the temperature fluctuation value could be the standard deviation or variance of the difference between the normal gearbox temperature and the nacelle temperature at a specific operating power level.

[0134] Furthermore, a temperature deviation coefficient is calculated based on the real-time gearbox temperature, the real-time cabin temperature and the temperature characteristic quantity, including: calculating the difference between the real-time gearbox temperature and the real-time cabin temperature to obtain a temperature reference deviation; calculating a temperature deviation factor based on the temperature reference deviation and the temperature difference reference value; and performing an exponential transformation on the temperature deviation factor to obtain a temperature deviation coefficient.

[0135] Specifically, the temperature baseline deviation is the difference between the real-time gearbox temperature and the real-time cabin temperature, reflecting the degree to which the gearbox temperature deviates from the ambient temperature. The temperature deviation factor is a parameter calculated based on the temperature baseline deviation and the temperature difference baseline value, used to quantify the impact of temperature deviation.

[0136] In the specific implementation, the difference between the real-time gearbox temperature and the real-time cabin temperature is first calculated to obtain the temperature reference deviation. The specific calculation formula is as follows:

[0137] ΔT=T G -T E

[0138] Where ΔT is the temperature reference deviation, T G is the real-time gearbox temperature, T E The real-time cabin temperature.

[0139] Then, the temperature deviation factor is calculated based on the temperature reference deviation and the temperature difference reference value. The specific calculation formula is as follows:

[0140]

[0141] Among them, T x is the temperature deviation factor, ΔT b is the temperature difference reference value, ΔT s is the temperature fluctuation value, and a is a preset multiple, for example, a=2.

[0142] Finally, perform exponential transformation on the temperature deviation factor to obtain the temperature deviation coefficient. The specific calculation formula is as follows: Among them, K T is the temperature deviation coefficient.

[0143] In this embodiment, exponential transformation of the temperature deviation factor can change the data distribution characteristics, making the numerical range of the temperature deviation coefficient more reasonable and facilitating subsequent comprehensive calculations with other coefficients. Furthermore, exponential transformation can further highlight larger temperature deviations, improving the ability to detect severe temperature anomalies.

[0144] Step 219: Determine the detection result of the gearbox based on the detection coefficient.

[0145] In one embodiment, to improve the detection accuracy of the gearbox, the real-time cabin temperature of the nacelle, the real-time amplitude and temperature of the gearbox, and the real-time operating power and voltage of the generator may be obtained first. Subsequently, the power-related characteristic quantities (including voltage characteristic quantities, amplitude characteristic quantities, and temperature characteristic quantities) corresponding to the real-time operating power may be determined based on the equipment properties and real-time operating power of the wind turbine generator set. A voltage deviation coefficient may be calculated based on the real-time voltage and voltage characteristic quantities, and an amplitude deviation coefficient may be calculated based on the real-time amplitude and amplitude characteristic quantities. A temperature deviation coefficient may be calculated based on the real-time gearbox temperature, the real-time cabin temperature, and the temperature characteristic quantities. Finally, a gearbox detection coefficient may be calculated based on the voltage deviation coefficient, the amplitude deviation coefficient, and the temperature deviation coefficient. The gearbox detection result may be determined based on this detection coefficient.

[0146] The fault detection method provided by the embodiment of the present invention first obtains the real-time amplitude of the gearbox, the real-time operating power of the generator, and the real-time voltage, providing a data basis for the subsequent calculation of the voltage deviation coefficient and the amplitude deviation coefficient. Next, the power-related characteristic quantity corresponding to the real-time operating power is determined based on the equipment attributes and real-time operating power of the wind turbine. Taking into account the differences in equipment attributes between different units, the characteristic parameters of the gearbox and generator at different powers can be accurately defined based on the specific characteristics of each wind turbine. As a result, during the detection process, abnormalities in voltage and amplitude at specific powers can be more keenly captured, greatly improving the accuracy of subsequent detection. At the same time, the personalized detection and analysis mechanism based on individual differences in equipment can avoid the problem of misjudgment caused by "unified standards", making the gearbox detection results more consistent with the actual operating status, and providing a reliable basis for the efficient operation and maintenance and safe operation of wind turbines. Afterwards, the difference between the real-time voltage and the voltage reference value is calculated to obtain the voltage reference deviation. The square of the ratio of the voltage reference deviation to the voltage fluctuation value is calculated to obtain the voltage deviation factor. The voltage deviation coefficient is calculated based on the voltage deviation factor. This allows the obtained voltage deviation coefficient to have a more reasonable numerical range, facilitating subsequent comprehensive calculations and analysis with other coefficients, avoiding calculation errors or data insensitivity caused by values ​​that are too large or too small, thereby improving the accuracy of subsequent detection. Then, the difference between the real-time amplitude and the amplitude reference value is calculated to obtain the amplitude reference deviation. Based on the amplitude reference deviation and the amplitude fluctuation value, the amplitude deviation factor is calculated. The amplitude deviation factor is subjected to an exponential transformation to obtain the amplitude deviation coefficient. This allows the numerical range of the amplitude deviation coefficient to be more reasonable, facilitating subsequent comprehensive calculations with other coefficients. At the same time, the exponential transformation can further highlight larger amplitude deviations, improve the ability to detect severe amplitude anomalies, and thus improve the accuracy of subsequent detection. Then, based on the voltage deviation coefficient and the amplitude deviation coefficient, the detection coefficient of the gearbox is calculated, which comprehensively considers the influence of the two parameters of voltage and amplitude on the state of the gearbox, and can more comprehensively and accurately evaluate the overall operating condition of the gearbox, avoiding the one-sidedness and inaccuracy caused by judging based on a single parameter, and effectively improving the accuracy of the detection. Finally, the detection result of the gearbox is determined based on the detection coefficient, which realizes the intelligent detection of the gearbox state, reduces the subjectivity and uncertainty of manual judgment, improves the accuracy and reliability of the detection, provides strong support for the remote monitoring and automated operation and maintenance of wind turbines, and effectively reduces the operation and maintenance costs. At the same time, the gearbox detection results can be clearly output, providing staff with a clear decision-making basis, so that they can quickly take targeted measures such as maintenance arrangements and component replacement, thereby ensuring the safe and stable operation of wind turbines. Therefore, the technical solution of the present invention solves the problem of insufficient detection accuracy in the prior art due to the failure to consider the correlation and multi-dimensional characteristics between data.

[0147] Figure 3This is a structural diagram of a fault detection device provided in an embodiment of the present invention. The device and the fault detection methods of the above embodiments belong to the same inventive concept. For details not fully described in the embodiments of the fault detection device, reference can be made to the embodiments of the above fault detection methods.

[0148] like Figure 3 As shown, the device includes:

[0149] An acquisition module 310 is configured to acquire the real-time amplitude of the gearbox, the real-time operating power and the real-time voltage of the generator;

[0150] A determination module 320 is configured to determine a power-related characteristic quantity corresponding to the real-time operating power according to the device attributes of the wind turbine generator set and the real-time operating power; the power-related characteristic quantity includes a voltage characteristic quantity and an amplitude characteristic quantity;

[0151] A first calculation module 330 is configured to calculate a voltage deviation coefficient based on the real-time voltage and the voltage characteristic quantity, and to calculate an amplitude deviation coefficient based on the real-time amplitude and the amplitude characteristic quantity;

[0152] A second calculation module 340 is configured to calculate a detection coefficient of the gearbox according to the voltage deviation coefficient and the amplitude deviation coefficient;

[0153] The detection module 350 is configured to determine a detection result of the gearbox based on the detection coefficient.

[0154] Based on the above embodiment, the voltage characteristic value includes a voltage reference value and a voltage fluctuation value. The first calculation module 330 calculates the voltage deviation coefficient based on the real-time voltage and the voltage characteristic value, including:

[0155] Calculating a difference between the real-time voltage and the voltage reference value to obtain a voltage reference deviation;

[0156] Calculating the square of the ratio of the voltage reference deviation to the voltage fluctuation value to obtain a voltage deviation factor;

[0157] The voltage deviation coefficient is calculated based on the voltage deviation factor.

[0158] Based on the above embodiment, the amplitude feature includes an amplitude reference value and an amplitude fluctuation value. The first calculation module 330 calculates the amplitude deviation coefficient based on the real-time amplitude and the amplitude feature, including:

[0159] Calculating the difference between the real-time amplitude and the amplitude reference value to obtain an amplitude reference deviation;

[0160] Calculating an amplitude deviation factor based on the amplitude reference deviation and the amplitude fluctuation value;

[0161] Performing exponential transformation on the amplitude deviation factor to obtain the amplitude deviation coefficient.

[0162] Based on the above embodiment, the amplitude deviation coefficient includes the planet gear amplitude deviation coefficient, the high-speed shaft amplitude deviation coefficient and the low-speed shaft amplitude deviation coefficient. The second calculation module 340 is specifically used to:

[0163] Calculating the product of the planetary gear amplitude deviation coefficient and the voltage deviation coefficient to obtain a planetary gear detection coefficient;

[0164] Calculating the product of the high-speed shaft amplitude deviation coefficient and the voltage deviation coefficient to obtain a high-speed shaft detection coefficient;

[0165] Calculating the product of the low-speed shaft amplitude deviation coefficient and the voltage deviation coefficient to obtain a low-speed shaft detection coefficient;

[0166] A detection coefficient of the gear box is calculated based on the planetary gear detection coefficient, the high-speed shaft detection coefficient, and the low-speed shaft detection coefficient.

[0167] Based on the above embodiment, the wind turbine generator set further includes a nacelle, the power-related characteristic quantity further includes a temperature characteristic quantity, and the device further includes:

[0168] an updating module, configured to obtain a real-time gearbox temperature of the gearbox and a real-time cabin temperature of the nacelle before calculating the detection coefficient of the gearbox based on the planetary gear detection coefficient, the high-speed shaft detection coefficient, and the low-speed shaft detection coefficient; calculate a temperature deviation coefficient based on the real-time gearbox temperature, the real-time cabin temperature, and the temperature characteristic quantity; and update the planetary gear detection coefficient, the high-speed shaft detection coefficient, and the low-speed shaft detection coefficient respectively according to the temperature deviation coefficient to obtain updated planetary gear detection coefficient, high-speed shaft detection coefficient, and low-speed shaft detection coefficient.

[0169] Based on the above embodiment, the temperature characteristic value includes a temperature difference reference value and a temperature fluctuation value. The updating module calculates the temperature deviation coefficient based on the real-time gearbox temperature, the real-time cabin temperature, and the temperature characteristic value, including:

[0170] Calculating a difference between the real-time gearbox temperature and the real-time cabin temperature to obtain a temperature reference deviation;

[0171] Calculating a temperature deviation factor according to the temperature reference deviation and the temperature difference reference value;

[0172] Performing exponential transformation on the temperature deviation factor to obtain the temperature deviation coefficient.

[0173] Based on the above embodiment, the determination module 320 is specifically configured to:

[0174] According to the device attributes of the wind turbine generator set, a table of correspondence between device attributes and power characteristic quantities is searched to obtain a table of correspondence between the power and characteristic quantities of the wind turbine generator set;

[0175] According to the real-time operating power, a query is performed in the corresponding relationship table between the power and characteristic quantities of the wind turbine generator set to obtain the power-related characteristic quantity corresponding to the real-time operating power.

[0176] The data screening device provided in the embodiment of the present invention can execute the fault detection method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0177] It is worth noting that in the embodiment of the above-mentioned fault detection device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0178] Figure 4 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. Figure 4 A block diagram of an exemplary electronic device 4 suitable for implementing embodiments of the present invention is shown. Figure 4 The electronic device 4 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0179] like Figure 4 As shown, electronic device 4 is in the form of a general-purpose computing electronic device. Components of electronic device 4 may include, but are not limited to, one or more processors or processing units 16, system memory 28, and a bus 18 connecting various system components (including system memory 28 and processing unit 16).

[0180] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. Examples of these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0181] The electronic device 4 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 4, including volatile and non-volatile media, removable and non-removable media.

[0182] The system memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The electronic device 4 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be configured to read and write non-removable, non-volatile magnetic media ( Figure 4 Not shown, often called a "hard drive"). Although Figure 4 Not shown, a magnetic disk drive for reading and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of various embodiments of the present invention.

[0183] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may include an implementation of a network environment. Program modules 42 generally perform the functions and / or methods of the embodiments described herein.

[0184] The electronic device 4 may also communicate with one or more external devices 14 (e.g., a keyboard, a pointing device, a display 24, etc.), one or more devices that enable a user to interact with the electronic device 4, and / or any device that enables the electronic device 4 to communicate with one or more other computing devices (e.g., a network card, a modem, etc.). Such communication may be performed via an input / output (I / O) interface 22. Furthermore, the electronic device 4 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 20. Figure 4 As shown, the network adapter 20 communicates with other modules of the electronic device 4 via the bus 18. Figure 4 Not shown, other hardware and / or software modules may be used in conjunction with the electronic device 4, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0185] The processing unit 16 executes various functional applications and page displays by running programs stored in the system memory 28. For example, the fault detection method provided in an embodiment of the present invention is implemented and applied to a wind turbine generator system including a generator and a gearbox. The method includes:

[0186] Obtaining the real-time amplitude of the gearbox, the real-time operating power and the real-time voltage of the generator;

[0187] Determine a power-related characteristic quantity corresponding to the real-time operating power according to the device attributes of the wind turbine generator set and the real-time operating power; the power-related characteristic quantity includes a voltage characteristic quantity and an amplitude characteristic quantity;

[0188] Calculating a voltage deviation coefficient based on the real-time voltage and the voltage characteristic quantity, and calculating an amplitude deviation coefficient based on the real-time amplitude and the amplitude characteristic quantity;

[0189] Calculating a detection coefficient of the gearbox according to the voltage deviation coefficient and the amplitude deviation coefficient;

[0190] A detection result of the gearbox is determined based on the detection coefficient.

[0191] Of course, those skilled in the art will appreciate that the processor may also implement the technical solution of the fault detection method provided in any embodiment of the present invention.

[0192] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the fault detection method provided in the embodiment of the present invention is implemented, for example, in a wind turbine generator system including a generator and a gearbox. The method includes:

[0193] Obtaining the real-time amplitude of the gearbox, the real-time operating power and the real-time voltage of the generator;

[0194] Determine a power-related characteristic quantity corresponding to the real-time operating power according to the device attributes of the wind turbine generator set and the real-time operating power; the power-related characteristic quantity includes a voltage characteristic quantity and an amplitude characteristic quantity;

[0195] Calculating a voltage deviation coefficient based on the real-time voltage and the voltage characteristic quantity, and calculating an amplitude deviation coefficient based on the real-time amplitude and the amplitude characteristic quantity;

[0196] Calculating a detection coefficient of the gearbox according to the voltage deviation coefficient and the amplitude deviation coefficient;

[0197] A detection result of the gearbox is determined based on the detection coefficient.

[0198] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device.

[0199] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0200] Program code embodied on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0201] Computer program code for performing the operations of the present invention may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0202] Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using a general-purpose computing device. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Alternatively, they can be implemented using program code executable by a computer device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.

[0203] In addition, the acquisition, storage, use, and processing of data in the technical solution of the present invention comply with relevant provisions of laws and regulations.

[0204] Note that the above are only preferred embodiments of the present invention and the technical principles employed. Those skilled in the art will appreciate that the present invention is not limited to the specific embodiments herein, and that various obvious changes, readjustments, and substitutions are possible for those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments and may include many other equivalent embodiments without departing from the scope of the present invention. The scope of the present invention is determined by the scope of the appended claims.

Claims

1. A fault detection method, characterized in that: Applied to a wind turbine generator set, the wind turbine generator set includes a generator and a gearbox, and the method includes: Acquiring the real-time amplitude of the gearbox, the real-time operating power and the real-time voltage of the generator; Determine a power-related characteristic quantity corresponding to the real-time operating power according to the device attributes of the wind turbine generator set and the real-time operating power; the power-related characteristic quantity includes a voltage characteristic quantity and an amplitude characteristic quantity; Calculating a voltage deviation coefficient based on the real-time voltage and the voltage characteristic quantity, and calculating an amplitude deviation coefficient based on the real-time amplitude and the amplitude characteristic quantity; Calculating a detection coefficient of the gearbox according to the voltage deviation coefficient and the amplitude deviation coefficient; A detection result of the gearbox is determined based on the detection coefficient.

2. The fault detection method according to claim 1, characterized in that: The voltage characteristic value includes a voltage reference value and a voltage fluctuation value; Calculating a voltage deviation coefficient based on the real-time voltage and the voltage characteristic quantity includes: Calculating a difference between the real-time voltage and the voltage reference value to obtain a voltage reference deviation; Calculating the square of the ratio of the voltage reference deviation to the voltage fluctuation value to obtain a voltage deviation factor; The voltage deviation coefficient is calculated based on the voltage deviation factor.

3. The fault detection method according to claim 1, characterized in that: The amplitude characteristic value includes an amplitude reference value and an amplitude fluctuation value; Calculating an amplitude deviation coefficient based on the real-time amplitude and the amplitude feature quantity includes: Calculating the difference between the real-time amplitude and the amplitude reference value to obtain an amplitude reference deviation; Calculating an amplitude deviation factor based on the amplitude reference deviation and the amplitude fluctuation value; Performing exponential transformation on the amplitude deviation factor to obtain the amplitude deviation coefficient.

4. The fault detection method according to claim 1, characterized in that: The amplitude deviation coefficient includes the planetary gear amplitude deviation coefficient, the high-speed shaft amplitude deviation coefficient, and the low-speed shaft amplitude deviation coefficient. The detection coefficient of the gearbox is calculated according to the voltage deviation coefficient and the amplitude deviation coefficient, including: Calculating the product of the planetary gear amplitude deviation coefficient and the voltage deviation coefficient to obtain a planetary gear detection coefficient; Calculating the product of the high-speed shaft amplitude deviation coefficient and the voltage deviation coefficient to obtain a high-speed shaft detection coefficient; Calculating the product of the low-speed shaft amplitude deviation coefficient and the voltage deviation coefficient to obtain a low-speed shaft detection coefficient; A detection coefficient of the gear box is calculated based on the planetary gear detection coefficient, the high-speed shaft detection coefficient, and the low-speed shaft detection coefficient.

5. The fault detection method according to claim 4, characterized in that: The wind turbine generator set further includes a nacelle, the power-related characteristic quantity further includes a temperature characteristic quantity, and before calculating the detection coefficient of the gearbox based on the planetary gear detection coefficient, the high-speed shaft detection coefficient, and the low-speed shaft detection coefficient, the further comprising: Acquiring a real-time gearbox temperature of the gearbox and a real-time cabin temperature of the cabin; calculating a temperature deviation coefficient based on the real-time gearbox temperature, the real-time cabin temperature, and the temperature characteristic amount; The planetary gear detection coefficient, the high-speed shaft detection coefficient, and the low-speed shaft detection coefficient are updated respectively according to the temperature deviation coefficient to obtain updated planetary gear detection coefficient, high-speed shaft detection coefficient, and low-speed shaft detection coefficient.

6. The fault detection method according to claim 5, characterized in that: The temperature characteristic quantity includes a temperature difference reference value and a temperature fluctuation value; and calculating a temperature deviation coefficient based on the real-time gearbox temperature, the real-time cabin temperature, and the temperature characteristic quantity includes: Calculating a difference between the real-time gearbox temperature and the real-time cabin temperature to obtain a temperature reference deviation; Calculating a temperature deviation factor according to the temperature reference deviation and the temperature difference reference value; Performing exponential transformation on the temperature deviation factor to obtain the temperature deviation coefficient.

7. The fault detection method according to claim 1, characterized in that: Determining a power-related characteristic quantity corresponding to the real-time operating power according to the device attributes of the wind turbine generator set and the real-time operating power includes: According to the device attributes of the wind turbine generator set, a table of correspondence between device attributes and power characteristic quantities is searched to obtain a table of correspondence between the power and characteristic quantities of the wind turbine generator set; According to the real-time operating power, a query is performed in the corresponding relationship table between the power and characteristic quantities of the wind turbine generator set to obtain the power-related characteristic quantity corresponding to the real-time operating power.

8. A fault detection device, characterized in that: Applied to a wind turbine generator set, the wind turbine generator set includes a generator and a gearbox, and the device includes: an acquisition module, configured to acquire the real-time amplitude of the gearbox, the real-time operating power and the real-time voltage of the generator; a determination module, configured to determine a power-related characteristic quantity corresponding to the real-time operating power according to the device attributes of the wind turbine generator set and the real-time operating power; the power-related characteristic quantity includes a voltage characteristic quantity and an amplitude characteristic quantity; a first calculation module, configured to calculate a voltage deviation coefficient based on the real-time voltage and the voltage characteristic quantity, and to calculate an amplitude deviation coefficient based on the real-time amplitude and the amplitude characteristic quantity; a second calculation module, configured to calculate a detection coefficient of the gear box according to the voltage deviation coefficient and the amplitude deviation coefficient; A detection module is used to determine a detection result of the gearbox based on the detection coefficient.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the fault detection method according to any one of claims 1 to 7.

10. A storage medium containing computer-executable instructions, characterized in that: The computer executable instructions are used to perform the fault detection method according to any one of claims 1 to 7 when executed by a computer processor.

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