Wind turbine generator yaw fault diagnosis method based on ternary data abrupt change coupling

Abstract

The invention relates to a wind turbine generator yaw fault diagnosis method based on ternary data abrupt change coupling, and the method comprises the steps: obtaining ternary yaw monitoring parameters in a preset time period, the ternary yaw monitoring parameters comprising a yaw rotation rate, a yaw motor current and yaw pressure pulsation; determining a ternary abnormal value interval according to the change rate standard deviation of each yaw monitoring parameter, and determining an abnormal value of each yaw monitoring parameter based on values of adjacent monitoring moments in the ternary abnormal value interval; judging whether the abnormal values are abrupt change data or not according to a preset abrupt change threshold value, and coupling the ternary abnormal values at the same moment according to a judgment result to obtain a coupled target abnormal state code; and obtaining a mapping relationship among the abnormal state code, the diagnosis basis and the fault type, and determining a target diagnosis basis and a target fault type corresponding to the target abnormal state code from the mapping relationship according to the target abnormal state code.

Description

Technical Field

[0001] The present application relates to the field of yaw fault diagnosis, and in particular to a wind turbine yaw fault diagnosis method based on ternary data mutation coupling. Background Art

[0002] The yaw system is the core subsystem of a wind turbine, responsible for adjusting the orientation of the nacelle to maximize the capture of wind energy. Its reliability directly affects the power generation efficiency and equipment safety. Frequent failures of the yaw system will cause power generation losses, and the maintenance cost accounts for 10%-30% of the wind farm's revenue, and may cause chain structural damage. The types and causes of yaw system failures are complex. Yaw failures are multi-source and sudden. Failure types include (1) mechanical failures: gear and bearing failure; gear tooth breakage (due to insufficient lubrication or assembly stress); bearing raceway peeling (long-term overload or corrosion). Braking system abnormalities: hydraulic caliper oil leakage leads to insufficient braking force, causing nacelle slip and impact load. (2) Electrical and control failures: sensor distortion, wind vane interference by blade wake, power generation drops by 2.1% when the measurement error is greater than 3°. Control logic defects: frequent start and stop of the yaw motor exacerbates vibration, triggering acceleration limit shutdown. (3) Environmental coupling mutation; dynamic load mutation, yaw drive torque fluctuation reaches 30% of the rated value under turbulent wind conditions, inducing gear overload. The lubrication state changes suddenly, low temperature causes the grease to harden, and dry friction of the gears causes abnormal noise and wear.

[0003] The existing technology relies on a single state signal, which has the problem of low fault diagnosis accuracy and inability to accurately determine the specific cause of yaw abnormality. Summary of the Invention

[0004] The embodiment of the present application provides a wind turbine yaw fault diagnosis method based on ternary data mutation coupling, so as to at least solve the problem in the related art that the fault diagnosis accuracy is low and the specific cause of the yaw abnormality cannot be accurately determined.

[0005] In a first aspect, an embodiment of the present application provides a wind turbine yaw fault diagnosis method based on ternary data mutation coupling, comprising: Acquiring three-dimensional yaw monitoring parameters within a preset time period, wherein the three-dimensional yaw monitoring parameters include yaw rotation rate, yaw motor current, and yaw pressure pulsation; determining a ternary abnormal value interval according to a standard deviation of a rate of change of each yaw monitoring parameter, and determining an abnormal value of each yaw monitoring parameter based on values ​​at adjacent monitoring moments in the ternary abnormal value interval; Determine whether the abnormal value is a mutation data according to a preset mutation threshold, and couple the ternary abnormal values ​​at the same time according to the judgment result to obtain a coupled target abnormal status code; A mapping relationship among an abnormal status code, a diagnostic basis, and a fault type is obtained, and a target diagnostic basis and a target fault type corresponding to the target abnormal status code are determined from the mapping relationship according to the target abnormal status code.

[0006] In one embodiment, determining the ternary abnormal value interval according to the standard deviation of the rate of change of each yaw monitoring parameter includes: Calculate the change rate of each yaw monitoring parameter between the current moment and the previous moment; Calculate the standard deviation of the rate of change of each yaw monitoring parameter from the starting time to the current time; In response to an absolute value of the change rate being greater than three times the standard deviation of the corresponding yaw monitoring parameter within a preset time period, the multivariate yaw monitoring parameter at the corresponding moment is added to the ternary abnormal value interval.

[0007] In one embodiment, determining the abnormal value of each yaw monitoring parameter based on the values ​​at adjacent monitoring moments in the ternary abnormal value interval includes: Determine whether, in the three-element abnormal value interval, the average value of the values ​​of each yaw monitoring parameter at two adjacent monitoring moments is greater than a preset mean value, or whether the difference between the values ​​at two adjacent monitoring moments is greater than a preset difference value; If so, the monitoring value at the current monitoring moment is used as the abnormal value of the corresponding yaw monitoring parameter.

[0008] In one embodiment, coupling the ternary abnormal value values ​​at the same time according to the judgment result to obtain the coupled target abnormal status code includes: For the abnormal value, the mutation data is marked with a first identifier, and the non-mutation data is marked with a second identifier, and the target abnormal status code is determined according to the first identifier and the second identifier.

[0009] In one embodiment, the mapping relationship between the abnormal status code and the fault type includes: When the yaw pressure pulsation is a sudden change data, and the yaw rotation rate and the yaw motor current are sudden change data, the fault type is a hydraulic system failure or a motor system failure; When the yaw pressure pulsation is non-mutation data, and the yaw rotation rate and the yaw motor current are mutation data, the fault type is yaw gear tooth breakage or soft starter failure; When the yaw pressure pulsation is sudden change data, the yaw rotation rate is non-sudden change data, and the yaw motor current is sudden change data, the fault type is yaw resonance.

[0010] In one embodiment, in response to the target abnormal status code corresponding to more than one target fault type, the method further includes: Determine the order in which the yaw monitoring parameters suddenly change based on the abnormal values ​​at adjacent monitoring moments; If the yaw pressure pulsation first shows a sudden change, and the yaw rotation rate and the yaw motor current are both sudden change data, the fault type is hydraulic system failure; If the motor current first experiences a sudden change, and both the yaw rotation rate and the yaw motor current are sudden change data, the fault type is a motor system failure; If the yaw rotation rate first shows a sudden change, and the yaw pressure pulsation is non-sudden change data, and the yaw motor current is sudden change data, the fault type is yaw gear tooth breakage.

[0011] In a second aspect, an embodiment of the present application provides a wind turbine yaw fault diagnosis system based on ternary data mutation coupling, comprising: Acquisition module: used to acquire three-dimensional yaw monitoring parameters within a preset time period, wherein the three-dimensional yaw monitoring parameters include yaw rotation rate, yaw motor current and yaw pressure pulsation; Anomaly module: used to determine a ternary anomaly value interval according to the standard deviation of the rate of change of each yaw monitoring parameter, and determine the abnormal value of each yaw monitoring parameter based on the values ​​of adjacent monitoring moments in the ternary anomaly value interval; Status code module: used to determine whether the abnormal value is a mutation data according to a preset mutation threshold, and couple the ternary abnormal values ​​at the same time according to the judgment result to obtain the coupled target abnormal status code; Diagnostic module: used to obtain the mapping relationship between the abnormal status code, diagnostic basis and fault type, and determine the corresponding target diagnostic basis and target fault type from the mapping relationship according to the target abnormal status code.

[0012] In a third aspect, an embodiment of the present application provides a computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, a method for diagnosing wind turbine yaw faults based on ternary data mutation coupling as described in the first aspect above is implemented.

[0013] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a wind turbine yaw fault diagnosis method based on ternary data mutation coupling as described in the first aspect above.

[0014] The embodiment of the present application provides a wind turbine yaw fault diagnosis method based on ternary data mutation coupling, which has at least the following technical effects.

[0015] This application analyzes the change rate and monitoring amount of the yaw rotation rate, yaw motor current and yaw pressure pulsation, and gradually accurately locates abnormal data from the monitoring data. By collaboratively screening and determining the mutation data through the preset mutation threshold, the abnormal data is further screened, which is conducive to accurately determining the mutation data, and then coupling the ternary parameters of yaw motor current, yaw hydraulic pressure, and yaw rate to determine the target abnormal status code, thereby determining the fault type according to the target abnormal status code. This application comprehensively utilizes the characteristics of the ternary monitoring data to improve the accuracy of fault diagnosis. It solves the problem of insufficient fault diagnosis accuracy caused by relying on a single state signal in the current diagnosis process of the yaw system, and can accurately derive the specific cause of the yaw abnormality.

[0016] The details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 This is a flow chart of a wind turbine yaw fault diagnosis method based on ternary data mutation coupling according to an embodiment of the present application; Figure 2 A method for generating a target abnormal status code is shown according to an exemplary embodiment; Figure 3 This is a structural block diagram of a wind turbine yaw fault diagnosis system based on ternary data mutation coupling according to an embodiment of the present application; Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of this application more clearly understood, the present application is described and illustrated below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely used to explain this application and are not intended to limit this application. Based on the embodiments provided in this application, all other embodiments obtained by those of ordinary skill in the art without making any creative efforts are within the scope of protection of this application.

[0019] Obviously, the drawings described below are merely examples or embodiments of the present application. Those skilled in the art can, without inventive effort, apply the present application to other similar scenarios based on these drawings. Furthermore, it is also understood that, although the effort involved in such a development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, changes in design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as an insufficiency of the content disclosed in this application.

[0020] References to "embodiments" in this application mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it refer to independent or alternative embodiments that are mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described in this application may be combined with other embodiments unless there is a conflict.

[0021] Unless otherwise defined, technical or scientific terms used herein shall have the ordinary meaning as understood by persons of ordinary skill in the art to which this application belongs. The terms "a," "an," "an," "the," and similar expressions used herein do not denote quantitative limitations and may refer to either the singular or the plural. The terms "comprise," "include," "have," and any variations thereof, used herein, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or modules (units) is not limited to the listed steps or units but may also include steps or units not listed, or may include other steps or units inherent to the process, method, product, or apparatus. The terms "connected," "connected," "coupled," and similar expressions used herein are not limited to physical or mechanical connections but may include electrical connections, whether direct or indirect. As used herein, "plurality" means two or more. "And / or" describes an association between associated objects, indicating that three possible relationships exist. For example, "A and / or B" may mean: A exists alone; A and B exist simultaneously; or B exists alone. The character " / " generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific order for the objects.

[0022] In a first aspect, the embodiments of the present application provide a wind turbine yaw fault diagnosis method based on ternary data mutation coupling. Figure 1FIG. 1 is a flow chart of a wind turbine yaw fault diagnosis method based on ternary data mutation coupling according to an embodiment of the present application. Figure 1 As shown, the method includes: Step S101 : Acquire three-dimensional yaw monitoring parameters within a preset time period, where the three-dimensional yaw monitoring parameters include yaw rotation rate, yaw motor current, and yaw pressure pulsation.

[0023] Optionally, the preset time interval can be set according to actual needs, for example, it can be set to 0.5min, 1min, etc. For the convenience of observation and calculation, it is usually set to 1 minute. The three-dimensional yaw monitoring parameters include yaw rotation rate (m / s), yaw motor current (A) and yaw pressure pulsation (bar). However, generally speaking, the larger the dimension of the yaw monitoring parameter, the higher the corresponding diagnostic accuracy. Among them, the yaw pressure pulsation data is collected in real time by a pressure sensor and is used to monitor the pressure of the yaw hydraulic system (such as yaw residual pressure). The yaw rotation rate data is collected through an encoder or pulse signal, and the yaw speed (unit: degree / second) is used to detect sudden rate changes during yaw start / braking (such as soft start failure causing the speed to return to zero). The yaw motor current data records the current value through the yaw motor current relay to determine whether the yaw motor current exceeds the protection set value.

[0024] In this way, multiple monitoring parameters such as yaw rotation rate, yaw motor current and yaw pressure pulsation are obtained, and the comprehensive use of multiple monitoring data characteristics is conducive to improving the accuracy of subsequent fault diagnosis.

[0025] Step S102 : determining a ternary abnormal value interval according to the standard deviation of the rate of change of each yaw monitoring parameter, and determining the abnormal value of each yaw monitoring parameter based on the values ​​at adjacent monitoring moments in the ternary abnormal value interval.

[0026] Optionally, abnormal values ​​in the monitored parameters can be gradually screened out by analyzing the change rate, change amount, and monitored quantity of the yaw rotation rate, yaw motor current, and yaw pressure pulsation. This facilitates accurate identification of abnormal data and facilitates subsequent fault diagnosis based on the precise abnormal data, thereby improving the accuracy of fault diagnosis.

[0027] In one example, step S102 includes: Step S1021 , calculating the change rate of each yaw monitoring parameter between the current moment and the previous moment.

[0028] Step S1022 , calculating the standard deviation of the rate of change of each yaw monitoring parameter from the starting time to the current time.

[0029] Step S1023 : within a preset time period, in response to the absolute value of the rate of change being greater than three times the standard deviation of the corresponding yaw monitoring parameter, adding the multivariate yaw monitoring parameter at the corresponding moment to the ternary abnormal value interval.

[0030] Optionally, assuming that the preset time period is 1 minute, the numerical range of any one of the yaw rotation rate, yaw motor current and yaw pressure pulsation within 1 minute can be expressed as {x i} (i=1,2,…,n), you can filter out the ternary abnormal value intervals by following the steps below.

[0031] Calculate the rate of change Δx of the observed quantity between the current observation time and the previous observation time i Calculate the standard deviation s of the rate of change of the observation time series at the current moment i i , determine the rate of change |Δx i |With 3s i The size of |Δx i |>3s i , then x i is an abnormal observation point, and x i The monitoring data corresponding to the time is added to the abnormal value interval; otherwise x i It is a normal observation point. i For normal observation points, continue to judge the observation values ​​at the next moment until all abnormal observation points in the current time period are screened out.

[0032] In one example, step S102 includes determining whether, within the ternary abnormal value interval, the average of the values ​​of each yaw monitoring parameter at two adjacent monitoring moments is greater than a preset average, or whether the difference between the values ​​at two adjacent monitoring moments is greater than a preset difference. If so, the monitored value at the current monitoring moment is considered the abnormal value of the corresponding yaw monitoring parameter.

[0033] Optionally, the preset difference and the preset mean can be set according to the actual application scenario. In the ternary abnormal value interval, for any one of the yaw rotation rate, yaw motor current and yaw pressure pulsation, determine whether the difference between the monitoring data at the current monitoring moment and the previous monitoring moment is greater than the preset difference. If so, the monitoring value at the current moment is used as the abnormal value of the corresponding yaw monitoring parameter. Alternatively, determine whether the mean of the monitoring data at the current monitoring moment and the previous monitoring moment is greater than the preset mean. If so, the monitoring value at the current moment is used as the abnormal value of the corresponding yaw monitoring parameter. In this way, the abnormal data can be accurately located from the monitoring data, which is conducive to fault diagnosis based on precise abnormal data, thereby improving the accuracy of fault diagnosis.

[0034] Step S103 , judging whether the abnormal value is a mutation data according to a preset mutation threshold, and coupling the ternary abnormal values ​​at the same time according to the judgment result to obtain a coupled target abnormal status code.

[0035] Optionally, the preset mutation thresholds include a pressure mutation threshold, a rate mutation threshold, and a motor current threshold. The mutation thresholds can be set based on the actual operating conditions of the unit and are not limited to the examples listed in this application. The current mutation threshold can be a current value or a current variance. Mutation data and non-mutation data are differentially identified, and the identification results of the ternary parameters are coupled to obtain the target abnormal status code.

[0036] The preset mutation thresholds can be set based on actual conditions. For example, the pressure mutation threshold is set outside the operating pressure range of ±10 bar and the stop pressure range of ±15 bar. Yaw pressure pulsation outside the pressure mutation threshold is considered a mutation. The rate mutation threshold is set to a yaw speed deviation of >0.2 degrees / second from the command value. The yaw motor current threshold is set to 9.5A, and the current value is >9.5A in the yaw state. A multivariate mutation interval can also be determined based on the motor current variance. The current variance can be set based on actual conditions. For example, the non-yaw state can be set to a current variance of <0.5, and the yaw state can be set to a variance of >2.0.

[0037] In this way, by collaboratively screening and identifying mutation data using a preset mutation threshold, abnormal data is further screened, facilitating the precise identification of mutation data. This allows for coupling of the ternary parameters to determine the target abnormality status code, thereby determining the fault type based on the mutation data and mutation status. Furthermore, the abnormality status code characterizes parameter abnormality, leveraging the characteristics of multivariate monitoring data to improve the accuracy of subsequent fault diagnosis.

[0038] In one example, step S103 includes: for abnormal numerical values, marking mutation data with a first identifier, marking non-mutation data with a second identifier, and determining a target abnormal status code according to the first identifier and the second identifier.

[0039] Optional, Figure 2 This is a method for generating a target abnormal status code according to an exemplary embodiment. Figure 2As shown, according to the first identifier and the second identifier abnormal status code, the first identifier and the second identifier can be set according to actual needs. In the present application, the first identifier and the second identifier are 0 and 1 respectively. For example, the pressure mutation threshold range is the range outside the operating pressure ±10bar and the stop pressure ±15bar. When it is within the threshold range, it is identified as 1; when it is outside the threshold range, it is identified as 0. The rate mutation threshold is set to the yaw speed deviation instruction value>0.2 degrees / second, then the identifier within the threshold range is 1, and the identifier outside the threshold range is 0. It is identified as 1 within the threshold range and 0 outside the threshold range. The yaw motor current threshold is set to the yaw state variance>2.0. It is identified as 1 within the threshold range and 0 outside the threshold range. Among them, 1 identifies mutation data and 0 represents normal data. In this way, the ternary data state is marked by the identifier to mark whether the data is, which facilitates the coupling of the ternary data, thereby directly determining the fault type based on the coupling result.

[0040] Step S104: Obtain the mapping relationship between the abnormal status code, the diagnostic basis, and the fault type, and determine the corresponding target diagnostic basis and target fault type from the mapping relationship according to the target abnormal status code. Optionally, the abnormal status code can be set according to actual needs, and the mapping relationship between the abnormal status code, the diagnosis basis and the fault type is obtained in advance, and the specific basis is derived from historical experience and relevant field knowledge.

[0041] In an example, the mapping relationship between the abnormal status code and the fault type includes: When the yaw pressure pulsation is a sudden change, and the yaw rotation rate and yaw motor current are sudden changes, the fault type is hydraulic system failure or motor system failure.

[0042] When the yaw pressure pulsation is non-mutational data, and the yaw rotation rate and yaw motor current are mutational data, the fault type is yaw gear broken teeth or soft starter failure.

[0043] When the yaw pressure pulsation is a sudden change, the yaw rotation rate is a non-sudden change, and the yaw motor current is a sudden change, the fault type is yaw resonance.

[0044] Optionally, Table 1 is a mapping relationship table between abnormal status codes, diagnostic basis and fault types.

[0045] Table 1 Fault type Yaw pressure pulsation characteristics Yaw rotation rate characteristics Yaw motor current characteristics Diagnostic basis Hydraulic system failure 1. The pressure is continuously lower than the set value. 1. The rate fluctuates greatly or returns to zero 1. The current mean is normal, but the variance suddenly increases (current mutation) Pressure and velocity mismatch Yaw gear broken teeth 0 pressure normal 1 rate jump (encoder abnormality) 1. Current variance > 3.0 and current surges Abnormal coupling between current and rate Soft starter failure 0 pressure normal 1 rate reset to zero 1. Current average > 1.5 (motor overload) Rate zeroing with current imbalance Yaw resonance 1. The median of residual pressure exceeds the limit 0 rate is normal 1 Variance > 2.5 (tower / brake system excitation) Current and pressure exceed the standard As shown in Table 1, the corresponding fault type and diagnostic basis can be determined based on the status code of the ternary yaw monitoring parameters. For example, the cause of a sudden pressure change + rate return to zero may be a blockage in the hydraulic system; when the pressure and current synchronization exceeds the limit, the cause of the fault may be a stagnation or resonance of the yaw brake system. As an example, when the abnormal status code is 111, the corresponding fault type is hydraulic system failure, and the corresponding diagnostic basis is pressure and rate mismatch; when the abnormal status code is 011, the corresponding fault type is yaw gear tooth breakage or soft starter failure, and the corresponding diagnostic basis is abnormal coupling of current and rate or rate return to zero with current imbalance; when the abnormal status code is 101, the corresponding fault type is yaw resonance, and the corresponding diagnostic basis is the coordinated excess of current and pressure.

[0046] Furthermore, the cause of the fault is related to the abnormal sequence of the yaw motor current, yaw hydraulic pressure, and yaw rate. For example, when the abnormal status code is 111, the corresponding fault type may also be a motor system failure. In cases where the abnormal status code corresponds to two fault types, additional judgment conditions are set when the target abnormal status codes are the same to further refine the fault type and accurately determine the specific cause of the yaw abnormality.

[0047] In this way, the state of the yaw motor current, yaw hydraulic pressure, and yaw rate data is coupled, fully utilizing multi-dimensional feature information and judgment criteria to perform yaw fault diagnosis. This solves the current problem of insufficient fault diagnosis accuracy caused by relying on a single state signal in yaw system diagnosis.

[0048] In one example, in response to the target abnormal status code corresponding to more than one target fault type, the method further includes: The order in which the yaw monitoring parameters suddenly change is determined based on the abnormal values ​​at adjacent monitoring moments.

[0049] If the yaw pressure pulsation first shows a sudden change, and the yaw rotation rate and yaw motor current are both sudden change data, the fault type is hydraulic system failure.

[0050] If the motor current suddenly changes first, and both the yaw rotation rate and the yaw motor current are sudden changes, the fault type is motor system failure.

[0051] If the yaw rotation rate suddenly changes first, and the yaw pressure pulsation is non-mutation data, and the yaw motor current is mutation data, the fault type is yaw gear tooth breakage.

[0052] Optionally, the order in which the yaw monitoring parameters suddenly change is determined based on the abnormal values ​​at adjacent monitoring moments. For example, at the previous moment, only the yaw pressure pulsation suddenly changes, and at the current moment, the pressure, yaw rotation rate, and yaw motor current suddenly change at the same time, then the yaw pressure pulsation suddenly changes first. Further, assuming that the abnormal status code is 111, the corresponding fault type may be motor system failure or hydraulic system failure. If the yaw pressure pulsation suddenly changes first, the fault type is hydraulic system failure, and the corresponding diagnostic basis is pressure and rate mismatch; if the motor current suddenly changes first, the fault type is motor system failure. Assuming that the abnormal status code is 011, the corresponding fault type is yaw gear tooth breakage or soft starter failure. If the yaw rotation rate suddenly changes first, the corresponding fault type is yaw gear tooth breakage, and the corresponding diagnostic basis is abnormal coupling of current and rate.

[0053] In some embodiments, the final fault type and the final diagnosis basis may also be determined from the target fault types according to the mean, variance, yaw rotation rate, and a preset judgment threshold.

[0054] Optionally, continue to refer to Table 1. When the abnormal status code is 011, the corresponding fault type is yaw gear tooth breakage or soft starter fault. At this time, the mean and variance of the yaw motor current at the current moment and the previous moment are required to further accurately determine the final fault type. When the current variance is >3.0, the corresponding fault type is yaw gear tooth breakage, and the corresponding diagnostic basis is abnormal coupling of current and rate; when the current mean is >1.5, the corresponding fault type is soft starter fault, and the corresponding diagnostic basis is rate zeroing with current imbalance. Alternatively, further judgment can be made based on the yaw rotation rate. When the yaw rotation rate returns to 0, the corresponding fault type is soft starter fault, and the corresponding diagnostic basis is rate zeroing with current imbalance. Otherwise, the corresponding fault type is yaw gear tooth breakage, and the corresponding diagnostic basis is abnormal coupling of current and rate.

[0055] In this way, when the abnormal status codes are consistent, additional judgment conditions are set to further accurately judge the fault type, which improves the accuracy of fault diagnosis and can accurately determine the specific cause of the yaw abnormality.

[0056] In summary, the present application analyzes the rate of change, amount of change and monitored quantity of the yaw rotation rate, yaw motor current and yaw pressure pulsation, and gradually accurately locates abnormal data from the monitoring data, which is conducive to fault diagnosis based on accurate abnormal data, thereby improving the accuracy of fault diagnosis. By collaboratively screening and determining the mutation data through the preset mutation threshold, the abnormal data is further screened, which is conducive to accurately determining the mutation data, and then coupling the ternary parameters of yaw motor current, yaw hydraulic pressure and yaw rate to determine the target abnormal state code, thereby determining the fault type based on the mutation data and mutation state. The present application comprehensively utilizes the characteristics of multivariate monitoring data to improve the accuracy of fault diagnosis. It solves the problem of insufficient fault diagnosis accuracy caused by relying on a single state signal in the current yaw system diagnosis process. In addition, when the abnormal state code is consistent, additional judgment conditions are set to further accurately judge the fault type, thereby improving the accuracy of fault diagnosis and accurately deriving the specific cause of the yaw abnormality.

[0057] In a second aspect, the embodiment of the present application provides a wind turbine yaw fault diagnosis system based on ternary data mutation coupling. Figure 3 FIG. 1 is a structural block diagram of a wind turbine yaw fault diagnosis system based on ternary data mutation coupling according to an embodiment of the present application. Figure 3 As shown, the system includes: The acquisition module 100 is used to acquire three-dimensional yaw monitoring parameters within a preset time period. The three-dimensional yaw monitoring parameters include yaw rotation rate, yaw motor current and yaw pressure pulsation.

[0058] The abnormal module 200 is used to determine a ternary abnormal value interval according to the standard deviation of the change rate of each yaw monitoring parameter, and determine the abnormal value of each yaw monitoring parameter based on the values ​​of adjacent monitoring moments in the ternary abnormal value interval.

[0059] The status code module 300 is used to determine whether the abnormal value is a mutation data according to a preset mutation threshold, and couple the ternary abnormal values ​​at the same time according to the judgment result to obtain the coupled target abnormal status code.

[0060] Diagnostic module 400: used to obtain the mapping relationship between the abnormal status code, the diagnostic basis and the fault type, and determine the corresponding target diagnostic basis and target fault type from the mapping relationship according to the target abnormal status code.

[0061] In one example, the anomaly module 200 includes a function for calculating the rate of change of each yaw monitoring parameter between the current moment and the previous moment. It also calculates the standard deviation of the rate of change of each yaw monitoring parameter from the starting moment to the current moment. Within a preset time period, if the absolute value of the rate of change is greater than three times the standard deviation of the corresponding yaw monitoring parameter, the multivariate yaw monitoring parameter at the corresponding moment is added to the ternary anomaly value interval.

[0062] In one example, the anomaly module 200 includes a function for determining whether the average of the values ​​of each yaw monitoring parameter at two adjacent monitoring moments within a ternary anomaly value interval is greater than a preset average, or whether the difference between the values ​​at two adjacent monitoring moments is greater than a preset difference. If so, the monitoring value at the current monitoring moment is considered the anomaly value of the corresponding yaw monitoring parameter.

[0063] In one example, the status code module 300 includes: for marking abnormal values, using a first identifier to mark mutation data, using a second identifier to mark non-mutation data, and determining a target abnormal status code according to the first identifier and the second identifier.

[0064] In one example, the mapping relationship between abnormal status codes and fault types in the diagnosis module 400 includes: When the yaw pressure pulsation is a sudden change, and the yaw rotation rate and yaw motor current are sudden changes, the fault type is hydraulic system failure or motor system failure.

[0065] When the yaw pressure pulsation is non-mutational data, and the yaw rotation rate and yaw motor current are mutational data, the fault type is yaw gear broken teeth or soft starter failure.

[0066] When the yaw pressure pulsation is a sudden change, the yaw rotation rate is a non-sudden change, and the yaw motor current is a sudden change, the fault type is yaw resonance.

[0067] In one example, in response to the target abnormal status code corresponding to more than one target fault type, the method further includes: It is used to determine the order in which sudden changes in yaw monitoring parameters occur based on abnormal values ​​at adjacent monitoring moments.

[0068] If the yaw pressure pulsation first shows a sudden change, and the yaw rotation rate and yaw motor current are both sudden changes, the fault type is hydraulic system failure; If the motor current suddenly changes first, and both the yaw rotation rate and the yaw motor current are sudden changes, the fault type is motor system failure.

[0069] If the yaw rotation rate suddenly changes first, and the yaw pressure pulsation is non-mutation data, and the yaw motor current is mutation data, the fault type is yaw gear tooth breakage.

[0070] In summary, the present application analyzes the rate of change, amount of change and monitored quantity of the yaw rotation rate, yaw motor current and yaw pressure pulsation, and gradually accurately locates abnormal data from the monitoring data, which is conducive to fault diagnosis based on accurate abnormal data, thereby improving the accuracy of fault diagnosis. By collaboratively screening and determining the mutation data through the preset mutation threshold, the abnormal data is further screened, which is conducive to accurately determining the mutation data, and then coupling the ternary parameters of yaw motor current, yaw hydraulic pressure and yaw rate to determine the target abnormal state code, thereby determining the fault type based on the mutation data and mutation state. The present application comprehensively utilizes the characteristics of multivariate monitoring data to improve the accuracy of fault diagnosis. It solves the problem of insufficient fault diagnosis accuracy caused by relying on a single state signal in the current yaw system diagnosis process. In addition, when the abnormal state code is consistent, additional judgment conditions are set to further accurately judge the fault type, thereby improving the accuracy of fault diagnosis and accurately deriving the specific cause of the yaw abnormality.

[0071] In a third aspect, an embodiment of the present application provides an electronic device, Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, a wind turbine yaw fault diagnosis method based on ternary data mutation coupling provided in the first aspect is implemented. Figure 4 The electronic device 60 shown is only an example and should not limit the functions and scope of use of the embodiments of the present application.

[0072] The electronic device 60 may be a general-purpose computing device, such as a server device. Components of the electronic device 60 may include, but are not limited to, the at least one processor 61, the at least one memory 62, and a bus 63 connecting different system components (including the memory 62 and the processor 61).

[0073] The bus 63 includes a data bus, an address bus, and a control bus.

[0074] The memory 62 may include a volatile memory, such as a random access memory (RAM) 621 and / or a cache memory 622 , and may further include a read-only memory (ROM) 623 .

[0075] The memory 62 may also include a program / utility 625 having a set (at least one) of program modules 624, such program modules 624 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0076] The processor 61 executes various functional applications and data processing by running the computer program stored in the memory 62, such as a wind turbine yaw fault diagnosis method based on ternary data mutation coupling provided in the first aspect of the present application.

[0077] The electronic device 60 can also communicate with one or more external devices 64 (e.g., a keyboard, pointing device, etc.). This communication can occur via an input / output (I / O) interface 65. Furthermore, the model-generating electronic device 60 can 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 66. As shown, the network adapter 66 communicates with other modules of the model-generating electronic device 60 via a bus 63. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with the model-generating electronic device 60, including but not limited to microcode, device drivers, redundant processors, external disk drive arrays, RAID (RAID) systems, tape drives, and data backup storage systems.

[0078] It should be noted that although several units / modules or sub-units / modules of the electronic device are mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more units / modules described above may be embodied in a single unit / module. Conversely, the features and functions of a single unit / module described above may be further divided and embodied by multiple units / modules.

[0079] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a program stored thereon. When the program is executed by a processor, the method for diagnosing wind turbine yaw faults based on ternary data mutation coupling provided in the first aspect is implemented.

[0080] The readable storage medium may include, but is not limited to, a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0081] In a possible embodiment, the present invention can also be implemented in the form of a program product, which includes program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps of a wind turbine yaw fault diagnosis method based on ternary data mutation coupling provided in the first aspect.

[0082] The program code for executing the present invention may be written in any combination of one or more programming languages, and may be executed entirely on the user device, partially on the user device, as an independent software package, partially on the user device and partially on a remote device, or entirely on the remote device.

[0083] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0084] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A wind turbine yaw fault diagnosis method based on ternary data mutation coupling, characterized in that: include: Acquiring three-dimensional yaw monitoring parameters within a preset time period, wherein the three-dimensional yaw monitoring parameters include yaw rotation rate, yaw motor current, and yaw pressure pulsation; determining a ternary abnormal value interval according to a standard deviation of a rate of change of each yaw monitoring parameter, and determining an abnormal value of each yaw monitoring parameter based on values ​​at adjacent monitoring moments in the ternary abnormal value interval; Determine whether the abnormal value is a mutation data according to a preset mutation threshold, and couple the ternary abnormal values ​​at the same time according to the judgment result to obtain a coupled target abnormal status code; A mapping relationship among an abnormal status code, a diagnostic basis, and a fault type is obtained, and a target diagnostic basis and a target fault type corresponding to the target abnormal status code are determined from the mapping relationship according to the target abnormal status code.

2. The wind turbine yaw fault diagnosis method based on ternary data mutation coupling according to claim 1 is characterized in that: Determining the ternary abnormal value interval according to the standard deviation of the change rate of each yaw monitoring parameter includes: Calculate the change rate of each yaw monitoring parameter between the current moment and the previous moment; Calculate the standard deviation of the rate of change of each yaw monitoring parameter from the starting time to the current time; In response to an absolute value of the change rate being greater than three times the standard deviation of the corresponding yaw monitoring parameter within a preset time period, the multivariate yaw monitoring parameter at the corresponding moment is added to the ternary abnormal value interval.

3. The wind turbine yaw fault diagnosis method based on ternary data mutation coupling according to claim 2 is characterized in that: The determining of the abnormal value of each yaw monitoring parameter based on the values ​​at adjacent monitoring moments in the ternary abnormal value interval includes: Determine whether, in the three-element abnormal value interval, the average value of the values ​​of each yaw monitoring parameter at two adjacent monitoring moments is greater than a preset mean value, or whether the difference between the values ​​at two adjacent monitoring moments is greater than a preset difference value; If so, the monitoring value at the current monitoring moment is used as the abnormal value of the corresponding yaw monitoring parameter.

4. The wind turbine yaw fault diagnosis method based on ternary data mutation coupling according to claim 3 is characterized in that: The method of coupling the ternary abnormal values ​​at the same time according to the judgment result to obtain the coupled target abnormal status code includes: For the abnormal value, the mutation data is marked with a first identifier, and the non-mutation data is marked with a second identifier, and the target abnormal status code is determined according to the first identifier and the second identifier.

5. The wind turbine yaw fault diagnosis method based on ternary data mutation coupling according to claim 1 is characterized in that: The mapping relationship between abnormal status codes and fault types includes: When the yaw pressure pulsation is a sudden change data, and the yaw rotation rate and the yaw motor current are sudden change data, the fault type is a hydraulic system failure or a motor system failure; When the yaw pressure pulsation is non-mutation data, and the yaw rotation rate and the yaw motor current are mutation data, the fault type is yaw gear tooth breakage or soft starter failure; When the yaw pressure pulsation is sudden change data, the yaw rotation rate is non-sudden change data, and the yaw motor current is sudden change data, the fault type is yaw resonance.

6. The wind turbine yaw fault diagnosis method based on ternary data mutation coupling according to claim 5 is characterized in that: In response to the target abnormal status code corresponding to more than one target fault type, the method further includes: Determine the order in which the yaw monitoring parameters suddenly change based on the abnormal values ​​at adjacent monitoring moments; If the yaw pressure pulsation first shows a sudden change, and the yaw rotation rate and the yaw motor current are both sudden change data, the fault type is hydraulic system failure; If the motor current first experiences a sudden change, and both the yaw rotation rate and the yaw motor current are sudden change data, the fault type is a motor system failure; If the yaw rotation rate first shows a sudden change, and the yaw pressure pulsation is non-sudden change data, and the yaw motor current is sudden change data, the fault type is yaw gear tooth breakage.

7. A wind turbine yaw fault diagnosis system based on ternary data mutation coupling, characterized in that: include: Acquisition module: used to acquire three-dimensional yaw monitoring parameters within a preset time period, wherein the three-dimensional yaw monitoring parameters include yaw rotation rate, yaw motor current and yaw pressure pulsation; Anomaly module: used to determine a ternary anomaly value interval according to the standard deviation of the rate of change of each yaw monitoring parameter, and determine the abnormal value of each yaw monitoring parameter based on the values ​​of adjacent monitoring moments in the ternary anomaly value interval; Status code module: used to determine whether the abnormal value is a mutation data according to a preset mutation threshold, and couple the ternary abnormal values ​​at the same time according to the judgment result to obtain the coupled target abnormal status code; Diagnostic module: used to obtain the mapping relationship between the abnormal status code, diagnostic basis and fault type, and determine the corresponding target diagnostic basis and target fault type from the mapping relationship according to the target abnormal status code.

8. An electronic device, characterized in that: The invention comprises a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a wind turbine yaw fault diagnosis method based on ternary data mutation coupling as described in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, a wind turbine yaw fault diagnosis method based on ternary data mutation coupling is implemented as described in any one of claims 1 to 6.

Images