Wind driven generator fault prediction system based on machine learning

Abstract

The invention relates to the technical field of wind driven generator fault prediction, in particular to a wind driven generator fault prediction system based on machine learning, and the system comprises a deviation data detection module which determines the wind facing accuracy of a wind driven generator and outputs a deviation data detection result; the deviation change trend analysis module is used for identifying a deviation change development trend and predicting a future deviation increase condition; the equipment real-time data acquisition and synchronization module is used for acquiring equipment real-time data related to the yaw system; and the equipment and deviation relation analysis module is used for generating an equipment fault judgment result and judging specific equipment or parts causing small-amplitude deviation. According to the method, multi-level equipment data analysis is carried out, specific equipment causing deviation is deeply analyzed, a fault source is accurately positioned, it is ensured that the working state of a yaw system is rapidly adjusted, deviation is prevented from being further aggravated, it is ensured that the wind driven generator always keeps the optimal matching degree with the wind direction, the power generation efficiency is improved, and energy loss caused by deviation is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of wind turbine fault prediction, and in particular to a wind turbine fault prediction system based on machine learning. Background Art

[0002] The efficiency of a wind turbine is closely related to the accuracy of its wind direction during operation. The wind turbine continuously adjusts the direction of the nacelle through the yaw system to ensure that the wind rotor is always facing the wind direction, maximizing the capture of wind energy and improving power generation efficiency. If the yaw system deviates from the wind, the wind rotor will not be able to face the wind direction, resulting in a decrease in power generation efficiency and may cause additional mechanical stress to the system, accelerate equipment wear and increase maintenance costs.

[0003] At present, most traditional yaw systems rely on fixed rules and preset parameters to control the yaw angle. These methods can only handle large deviations and cannot accurately capture and handle small deviations. In the long-term operation of wind turbines, the accumulation of small deviations may lead to serious energy efficiency losses and mechanical problems. Most existing monitoring systems lack comprehensive analysis of equipment parameters and deviation data in the yaw system, making it difficult to identify and correct potential equipment failures in the system in real time, which not only limits the maintenance efficiency of the system, but may also lead to major failures.

[0004] In addition, the existing yaw system fault detection mainly relies on preset thresholds and rules, and lacks in-depth analysis of complex, multi-dimensional equipment data. Especially when dealing with the synergy between equipment, it is often difficult to make accurate judgments, which makes fault prediction delayed and causes problems not to be discovered and solved in the early stage. Summary of the invention

[0005] The present invention provides a wind turbine fault prediction system based on machine learning.

[0006] Wind turbine fault prediction system based on machine learning, including:

[0007] Deviation data detection module: collects the yaw angle and wind direction sensor data of the yaw system in real time, calculates the deviation between the yaw angle and the wind direction, detects the matching degree between the yaw angle and the wind direction, and determines the wind accuracy of the wind turbine. The data detection module detects small deviations in real time based on the time series regression algorithm and outputs the deviation data detection result;

[0008] Deviation change trend analysis module: analyzes the change trend of small deviations and captures the cumulative effect of small deviations in different periods. The deviation change trend analysis module uses a time series model to identify the development trend of deviation changes and predict the increase of deviations in the future;

[0009] Equipment real-time data acquisition and synchronization module: used to collect real-time data of equipment related to the yaw system, including the current, voltage, torque of the yaw motor, pressure and temperature of the hydraulic system, to ensure that all equipment data is synchronously collected with the yaw angle and wind direction sensor data, and to perform data standardization processing;

[0010] Equipment and deviation relationship analysis module: According to the deviation change development trend, the relationship between the real-time data of each device and the small deviation is analyzed. The equipment and deviation relationship analysis module uses multivariate regression analysis and correlation analysis algorithms to identify which equipment parameter fluctuations show a significant correlation with the yaw angle deviation, generate equipment failure judgment results, and determine the specific equipment or components that cause the small deviation.

[0011] Optionally, the deviation data detection module specifically includes:

[0012] Yaw angle acquisition unit: connected to the yaw sensor to obtain the yaw angle data of the wind turbine nacelle in real time, and the yaw angle data is collected at a set frequency; the collection frequency is set to between 1 Hz and 5 Hz, that is, data is collected 1 to 5 times per second;

[0013] Wind direction data acquisition unit: The wind direction data of the area where the wind turbine is located is acquired in real time through the wind direction sensor. The wind direction data is acquired synchronously with the yaw angle data to ensure that the two collect data under the same time reference, and the instantaneous fluctuation interference of the wind direction data is eliminated through the data smoothing method;

[0014] Deviation calculation unit: The deviation calculation unit receives the yaw angle data and the wind direction data, and determines the wind accuracy of the wind turbine by calculating the angle between the yaw angle and the wind direction. If the angle exceeds a preset threshold (3°), it is considered that there is a deviation, and the deviation data detection result is output;

[0015] Small deviation detection unit: The small deviation detection unit analyzes multiple groups of deviation data in a short period of time based on the time series regression algorithm, identifies persistent small deviations, and outputs the small deviation results to the deviation change trend analysis module in real time to perform deviation accumulation effect and trend prediction.

[0016] Optionally, the deviation change trend analysis module specifically includes:

[0017] Historical deviation data storage unit: used to store historical deviation data generated by the deviation data detection module, the data includes the difference between the yaw angle and the wind direction angle and the corresponding timestamp. The historical data is stored in a set time window to ensure that it contains enough samples for trend analysis;

[0018] Time series data processing unit: receives historical deviation data, arranges it in chronological order, builds a time series model of deviation changes, and analyzes the variation trend of deviations in different periods. The time series model adopts the ARIMA model;

[0019] Small deviation accumulation detection unit: Identify small deviations that persist over multiple time periods and capture the cumulative effect of deviations through time series models. If the deviations do not decrease or show an increasing trend over multiple consecutive time periods, it is considered that the deviations are accumulated, indicating that there may be a need for system fine-tuning.

[0020] Future Deviation Prediction Unit: Based on the prediction capability of the time series model, it uses the current and historical deviation data to predict the deviation changes in future periods.

[0021] The ARIMA model analyzes and predicts time series data. It captures the trend and seasonal effects of time series through three parts: autoregression (AR), difference (I), and moving average (MA). It is suitable for the analysis of accumulated deviations of wind turbines and the prediction of future trends.

[0022] Optionally, the ARIMA model reviews the deviation data of the past period of time, analyzes the data change pattern, and when the deviation of the yaw angle and the wind direction is collected, it identifies whether the current deviation is gradually increasing, decreasing, or remaining stable according to the deviation changes in multiple consecutive time periods. This trend analysis can help determine whether the operation of the yaw system is entering an abnormal state;

[0023] The variation trends of the deviation data include:

[0024] Short-term changes: The ARIMA model determines whether there are short-term fluctuations in the deviation based on the data of the recent period. If the deviation fluctuates greatly in the short term, it may indicate that the system response is unstable.

[0025] Long-term trends: By integrating data over a long period of time, the ARIMA model identifies the long-term trend of the deviation and analyzes whether there is a continuous accumulation or slow change in the deviation.

[0026] The ARIMA model analyzes the deviation data and captures the phenomenon of deviation accumulation. The cumulative effect is that the deviation does not decrease in multiple time periods and shows a trend of gradually increasing. The ARIMA model determines whether the deviation is accumulating by identifying the trend changes in the time period.

[0027] Optionally, the ARIMA model infers the future deviation development trend by analyzing current and historical deviation data. The prediction process includes:

[0028] Impact of current deviation data: The current deviation value affects the performance in the short term. If the current deviation is large, it is predicted that the system will continue to maintain the deviation in the short term.

[0029] Historical deviation trends: By identifying the deviation pattern over a period of time in the past (such as gradual increase, fluctuation, stability, etc.), it is inferred that future deviations will continue the deviation pattern.

[0030] Optionally, the device real-time data acquisition and synchronization module specifically includes:

[0031] Motor data acquisition unit: collects the operating parameters of the yaw motor in real time, including motor current, voltage and torque signals. The motor data acquisition unit collects these data at a set sampling frequency by connecting to the motor sensor in the yaw system;

[0032] Hydraulic system data acquisition unit: collects real-time data of the hydraulic components of the yaw system, including the pressure and temperature signals of the hydraulic system. The hydraulic data is acquired through hydraulic sensors, and the changes in the operation of the hydraulic system are recorded synchronously to ensure that the pressure and temperature data can match the yaw motor data;

[0033] Data synchronization unit: This unit ensures the synchronous collection of the current, voltage, torque data of the yaw motor, the pressure and temperature data of the hydraulic system, and the yaw angle and wind direction sensor data. Through timestamp matching and timing alignment, the time base of the equipment data is unified to ensure that the data of each device can be correlated and analyzed in real time;

[0034] Data standardization processing unit: used to standardize all collected equipment data to ensure that data of different units and magnitudes can be effectively compared and analyzed. The standardization processing is based on the Z-score standardization method to convert the equipment data into a dimensionless form so that the subsequent analysis module can perform correlation analysis between equipment status and deviation.

[0035] Optionally, the device and deviation relationship analysis module specifically includes the following units:

[0036] Correlation analysis unit: receives real-time data from each device, including the current, voltage, torque data of the yaw motor, the pressure and temperature data of the hydraulic system, and the deviation data of the yaw angle and wind direction. Through the correlation analysis algorithm, it preliminarily screens out the device parameters that show a significant linear correlation with the deviation data.

[0037] Multivariate regression analysis unit: Based on the highly correlated equipment parameters screened by the correlation analysis unit, multivariate regression analysis is performed to refine the relationship between equipment parameters and deviations. Multivariate regression analysis quantifies the weight of the influence of each equipment parameter on the deviation change, and establishes a linear regression model to evaluate the comprehensive influence of multiple equipment parameters on small deviations. Through regression analysis, it is determined that one or more equipment parameters have the greatest contribution to the deviation change, thereby accurately identifying potential faulty equipment or components;

[0038] Equipment failure judgment unit: Based on the results of the multivariate regression analysis unit, the final judgment of equipment failure is generated. If the fluctuation of a certain equipment parameter has a large impact on the deviation and is significantly correlated with the cumulative effect of small deviations, the equipment will be marked as a potential fault source and fault warning information will be generated.

[0039] Optionally, the correlation analysis unit uses a Pearson correlation coefficient to measure the linear relationship between each device parameter and the yaw angle deviation. The Pearson correlation coefficient compares the operating parameter of each device with the deviation between the yaw angle and the wind direction, specifically including:

[0040] Calculate differences: The correlation analysis unit calculates the overall trend of equipment operating parameters and deviation data;

[0041] Measuring correlation: Calculate the degree of correlation between the equipment operating parameters and the deviation. The Pearson correlation coefficient outputs whether the two variables change at the same time. If the parameter change of a device is positively correlated with the deviation (that is, when the device parameter increases, the deviation also increases, or when the device parameter decreases, the deviation also decreases), it is considered that the device may have a strong impact on the deviation. If the correlation coefficient is negative, it indicates the opposite relationship (that is, when one increases, the other decreases). If the correlation coefficient is close to zero, it means that there is almost no linear relationship between the two.

[0042] Preliminary screening of equipment: By calculating the correlation between multiple equipment parameters and deviations, the equipment operating parameters that have a great impact on the deviation can be quickly identified.

[0043] Optionally, the linear regression model evaluates the impact of multiple device parameters on the deviation change simultaneously, and the expression is:

[0044] Δθ t =β0+β1x 1,t +β2x 2,t +…+β i x i,t +∈ t ,in;

[0045] Δθ t Indicates the deviation value at time t, which is the difference between the yaw angle and the wind direction angle, x 1,t ,x 2,t ,…,x i,t Indicates the parameter values ​​of each device collected at time t, which respectively represent the real-time data of the device related to the yaw system, including the current x of the yaw motor 1,t , voltage x 2,t , torque x 3,t and the hydraulic system pressure x 4,t and temperature x 5,t, β0 is the constant term of the linear regression model, which represents the baseline value of the deviation when there is no equipment parameter fluctuation, β1,β2,…,β i Represents the regression coefficient, representing each device parameter x i,t The weight of the impact on the change in deviation, each β i Indicates device parameter x i,t Contribution to the deviation change. The larger the weight, the greater the impact of the device on the deviation, ∈ t is a random error term; the linear regression model is used to quantify the impact of each device parameter on the deviation change.

[0046] Optionally, in the equipment fault judgment unit, a fault judgment is generated based on the following conditions according to the result calculated by the regression model:

[0047] Contribution judgment: If a device parameter x i,t The regression coefficient β on the deviation i If it is significantly greater than other equipment parameters, the equipment is judged to be the main source of failure, and it is determined that the equipment parameters cause deviation accumulation and cause failure;

[0048] Comprehensive fault score: Use the normalized regression coefficient to generate the comprehensive fault score S of the device i , expressed as follows: Among them, S i represents the fault score of the ith device, β i is the regression coefficient of the ith device parameter, β j is the regression coefficient of the jth device parameter, k is the total number of devices, the sum of the fault scores of all devices is 1, and the device with a high score is the source of the fault.

[0049] Beneficial effects of the present invention:

[0050] The present invention effectively avoids the problem of wind deviation in the yaw system of the wind turbine by introducing deviation detection and predictive analysis. The deviation data detection module continuously monitors the matching degree of the yaw angle and the wind direction, and can accurately capture small deviations. It can also predict the cumulative trend of the deviation in real time in combination with the time series model. Then, through multi-level equipment data analysis, the specific equipment causing the deviation is deeply analyzed, the source of the fault is accurately located, and the working state of the yaw system is ensured to be quickly adjusted to prevent the deviation from further aggravation, thereby ensuring that the wind turbine always maintains the best matching degree with the wind direction, thereby improving the power generation efficiency and reducing the energy loss caused by the deviation.

[0051] The present invention, by introducing a time series regression algorithm and a time series analysis model, can accurately capture the changing trend of the matching degree between the yaw angle and the wind direction of the wind turbine. By continuously monitoring the matching situation of the yaw angle and the wind direction, it can not only detect small real-time deviations, but also identify the cumulative effect of the deviations, and predict future deviation changes through a time series model. This trend prediction can effectively avoid the neglect of potential problems, and can provide an early warning system that small deviations may develop into major failures, providing a reliable data basis for timely adjustment and maintenance.

[0052] The present invention, through progressive equipment fault identification, first performs preliminary screening through a correlation analysis unit to quickly identify equipment parameters that are significantly correlated with yaw angle deviation, then uses a multivariate regression analysis unit to further quantify the influence weight of each equipment parameter on the deviation change, constructs a regression model of equipment parameters and deviation, and accurately evaluates the comprehensive influence of multiple devices on the deviation. Through this progressive analysis method, the system can efficiently and accurately locate the specific equipment that causes the deviation, significantly improve the accuracy and efficiency of fault identification, and reduce misjudgments and missed judgments.

[0053] The present invention introduces a comprehensive fault scoring mechanism in the equipment fault judgment unit, and calculates the fault score of each device through the normalized regression coefficient. The scoring mechanism enables the system to intuitively compare the fault possibilities of multiple devices, thereby accurately marking potential fault sources. Combining historical data and current analysis results, the system generates an early warning to prompt operation and maintenance personnel to take maintenance or adjustment measures in time to prevent larger problems from occurring in the system. The mechanism ensures the intelligence and automation of fault prediction through real-time analysis and scoring mechanisms, greatly improving the maintenance efficiency and stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0055] Figure 1 A schematic diagram of various functional modules of a system according to an embodiment of the present invention;

[0056] Figure 2 Schematic diagram of a device and deviation relationship analysis module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0058] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0059] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0060] like Figure 1-Figure 2 As shown, the wind turbine fault prediction system based on machine learning includes:

[0061] Deviation data detection module: collects the yaw angle and wind direction sensor data of the yaw system in real time, calculates the deviation between the yaw angle and the wind direction, detects the matching degree between the yaw angle and the wind direction, and determines the wind accuracy of the wind turbine. The data detection module detects small deviations in real time based on the time series regression algorithm and outputs the deviation data detection results;

[0062] Deviation change trend analysis module: Analyze the change trend of small deviations and capture the cumulative effect of small deviations in different periods. The deviation change trend analysis module uses the time series model to identify the development trend of deviation changes and predict the increase of deviations in the future.

[0063] Equipment real-time data acquisition and synchronization module: used to collect real-time data of equipment related to the yaw system, including the current, voltage, torque of the yaw motor, pressure and temperature of the hydraulic system, to ensure that all equipment data is synchronously collected with the yaw angle and wind direction sensor data, and to perform data standardization processing;

[0064] Equipment and deviation relationship analysis module: According to the deviation change development trend, the relationship between the real-time data of each device and the small deviation is analyzed. The equipment and deviation relationship analysis module uses multivariate regression analysis and correlation analysis algorithms to identify which equipment parameter fluctuations show a significant correlation with the yaw angle deviation, generate equipment failure judgment results, and determine the specific equipment or components that cause the small deviation.

[0065] The deviation data detection module specifically includes:

[0066] Yaw angle acquisition unit: connects to the yaw sensor to obtain the yaw angle data of the wind turbine nacelle in real time. The yaw angle data is collected at a set frequency; the collection frequency is set between 1Hz and 5Hz, that is, data is collected 1 to 5 times per second;

[0067] 1Hz: When the wind speed changes slowly, data can be collected once per second, which is suitable for monitoring under normal wind conditions;

[0068] 5Hz: When wind conditions fluctuate greatly or more accurate real-time feedback is needed, data can be collected 5 times per second, which is suitable for high-precision requirements when detecting small deviations;

[0069] Wind direction data acquisition unit: The wind direction data of the area where the wind turbine is located is acquired in real time through the wind direction sensor. The wind direction data is synchronized with the yaw angle data acquisition to ensure that the two collect data under the same time reference, and the instantaneous fluctuation interference of the wind direction data is eliminated through the data smoothing method;

[0070] Deviation calculation unit: The deviation calculation unit receives the yaw angle data and wind direction data, and determines the wind accuracy of the wind turbine by calculating the angle between the yaw angle and the wind direction. If the angle exceeds the preset threshold (3°), it is considered that there is a deviation, and the deviation data detection result is output;

[0071] Small deviation detection unit: Based on the time series regression algorithm, the small deviation detection unit analyzes multiple sets of deviation data in a short period of time, identifies persistent small deviations, and outputs the small deviation results to the deviation change trend analysis module in real time to predict the deviation accumulation effect and trend.

[0072] The matching degree between the yaw angle and the wind direction is achieved by calculating the angle between the two. Assume:

[0073] θ y Indicates the yaw angle of the wind turbine;

[0074] θ w Indicates the current wind direction angle;

[0075] The deviation angle Δθ is calculated as: Δθ = θ y -θ w, where Δθ represents the angle between the yaw angle and the wind direction. If the angle is too large, it means that the wind turbine is not accurately aligned with the wind direction, resulting in a decrease in power generation efficiency. It should be noted that if Δθ exceeds a preset threshold (e.g., 3°), it is considered that there is a problem with the wind turbine's accuracy in facing the wind, and there is a small deviation, which requires further analysis;

[0076] The detection of small deviations not only depends on the current deviation angle, but also needs to combine multiple data points in the time series, use regression algorithms for trend analysis, regress the past n deviation detection data, and use the linear regression formula: Δθ t =β0+β1t+ε, where Δθ t It represents the deviation value at time t, t represents the time variable, θ0 and β1 are the parameters in the regression model, β1 represents the trend of the deviation over time (i.e. the speed of increase or decrease of the deviation), and ε is the error term. Through the time series regression model, it is detected whether there is a continuous small deviation (i.e. the β1 value is large or continuously positive). If the value of β1 is continuously positive in a certain period of time and the deviation angle does not decrease significantly, it can be considered that there is a small deviation that needs to be processed.

[0077] The deviation change trend analysis module specifically includes:

[0078] Historical deviation data storage unit: used to store historical deviation data generated by the deviation data detection module. The data includes the difference between the yaw angle and the wind direction angle and the corresponding timestamp. The historical data is stored in a set time window to ensure that it contains enough samples for trend analysis;

[0079] Time series data processing unit: receives historical deviation data, arranges it in chronological order, builds a time series model of deviation changes, and analyzes the variation trend of deviations in different periods. The time series model adopts the ARIMA model;

[0080] Small deviation accumulation detection unit: Identify small deviations that persist over multiple time periods and capture the cumulative effect of deviations through time series models. If the deviations do not decrease or show an increasing trend over multiple consecutive time periods, it is considered that the deviations are accumulated, indicating that there may be a need for system fine-tuning.

[0081] Future Deviation Prediction Unit: Based on the prediction capability of the time series model, it uses the current and historical deviation data to predict the deviation changes in future periods.

[0082] The ARIMA model analyzes and predicts time series data. It captures the trend and seasonal effects of time series through three parts: autoregression (AR), difference (I), and moving average (MA). It is suitable for the analysis of accumulated deviations of wind turbines and the prediction of future trends.

[0083] The ARIMA model is expressed as:

[0084]

[0085] y t : represents the deviation value Δθ at time t t ;

[0086] α: constant term;

[0087] Autoregressive coefficient, deviation value y at the i-th past moment t-i The weight of the impact on the current moment;

[0088] p: the order of the autoregressive model, which indicates the impact of the data in the previous moments on the current value;

[0089] θ j : Moving average coefficient, representing the past error term ∈ t-j The impact weight on the current error term;

[0090] q: the order of the moving average model, indicating the impact of past errors;

[0091] ∈ t : represents the error term at the current moment;

[0092] d: The number of differences, used to make the time series data stationary.

[0093] In the present invention, the ARIMA model is used to analyze the deviation data Δθ between the yaw angle and the wind direction. t , the deviation data changes over time to form a time series, through the autoregressive part of the model Capture the correlation between deviation and historical deviation, judge how past deviation affects current deviation value, and at the same time, the sliding average part It captures the impact of historical noise on current deviations and reduces the interference of random fluctuations.

[0094] The ARI MA model reviews the deviation data of the past period and analyzes the data change pattern. When the deviation of the yaw angle and wind direction is collected, it identifies whether the current deviation is gradually increasing, decreasing or remaining stable based on the deviation changes in multiple consecutive periods. This trend analysis can help determine whether the operation of the yaw system is entering an abnormal state;

[0095] The changing trends of deviation data include:

[0096] Short-term changes: The ARI MA model determines whether there are short-term fluctuations in the deviation based on the data of the most recent periods. If the deviation fluctuates greatly in the short term, it may indicate that the system response is unstable;

[0097] Long-term trend: By integrating long-term data, the ARI MA model identifies the long-term trend of the deviation and analyzes whether there is a continuous accumulation or slow change in the deviation. If the deviation value is slowly increasing in multiple consecutive periods, this cumulative trend may mean that the system needs adjustment or maintenance.

[0098] The ARI MA model analyzes the deviation data and captures the phenomenon of deviation accumulation. The cumulative effect is that the deviation has not decreased in multiple time periods and shows a trend of gradual increase. The ARI MA model determines whether the deviation is accumulating by identifying the trend changes in the time period.

[0099] Specifically, if the system detects persistent deviations over multiple consecutive periods without significant decreases, the model will assume that the deviations are accumulating. The cumulative effect usually means that the yaw system is not adjusted in time, or there is some equipment problem that prevents the deviation from returning to normal levels. If this accumulation continues, the system's performance may gradually deteriorate, leading to more serious failures.

[0100] The ARIMA model is particularly good at capturing this kind of change in continuous time. If the deviation value fails to decrease effectively in multiple consecutive periods and shows a gradually increasing trend, the model will determine that the deviation is accumulating. This cumulative effect indicates that the wind turbine yaw system may be gradually deviating from the normal working state. If the model finds that the magnitude of the deviation continues to increase at regular intervals, although the magnitude is not large, but it continues to accumulate, the system will output an alarm to indicate that deviation accumulation is occurring.

[0101] The ARIMA model analyzes current and historical deviation data to infer future deviation trends. The prediction process includes:

[0102] Impact of current deviation data: The current deviation value affects the performance in the short term. If the current deviation is large, it is predicted that the system will continue to maintain the deviation in the short term.

[0103] Historical deviation trends: By identifying the deviation pattern over the past period of time (such as gradual increase, fluctuation, stability, etc.), it is inferred that future deviations will continue the deviation pattern. For example, if historical data shows that the deviation continues to increase, the model may predict that the deviation will increase further in the next few periods.

[0104] The ARIMA model also considers the volatility of the deviation. If there are large fluctuations in the data, it may be predicted that the deviation in the future will also show a fluctuating trend.

[0105] The equipment real-time data acquisition and synchronization module specifically includes:

[0106] Motor data acquisition unit: collects the operating parameters of the yaw motor in real time. The operating parameters include motor current, voltage and torque signals. The motor data acquisition unit collects these data at a set sampling frequency by connecting to the motor sensor in the yaw system.

[0107] Hydraulic system data acquisition unit: collects real-time data of the hydraulic components of the yaw system, including the pressure and temperature signals of the hydraulic system. The hydraulic data is obtained through hydraulic sensors, and the changes in the operation of the hydraulic system are recorded synchronously to ensure that the pressure and temperature data can match the yaw motor data;

[0108] Data synchronization unit: This unit ensures the synchronous collection of the current, voltage, torque data of the yaw motor, the pressure and temperature data of the hydraulic system, and the yaw angle and wind direction sensor data. Through timestamp matching and timing alignment, the time base of the equipment data is unified to ensure that the data of each device can be correlated and analyzed in real time;

[0109] Data standardization processing unit: used to standardize all collected equipment data to ensure that data of different units and magnitudes can be effectively compared and analyzed. The standardization process is based on the Z-score standardization method to convert the equipment data into a dimensionless form so that the subsequent analysis module can perform correlation analysis between equipment status and deviation.

[0110] The equipment and deviation relationship analysis module specifically includes the following units, which use a progressive analysis method to identify the relationship between equipment parameter fluctuations and small yaw angle deviations:

[0111] Correlation analysis unit: Receives real-time data from each device, including current, voltage, and torque data of the yaw motor, pressure and temperature data of the hydraulic system, and deviation data of the yaw angle and wind direction. Through the correlation analysis algorithm, it preliminarily screens out device parameters that have a significant linear correlation with the deviation data. The role of the correlation analysis unit is to quickly identify variables in the device that have a strong linear correlation with the deviation data, thereby reducing the amount of calculation for subsequent analysis and focusing on highly correlated devices.

[0112] Multivariate regression analysis unit: Based on the highly correlated equipment parameters screened by the correlation analysis unit, multivariate regression analysis is performed to refine the relationship between equipment parameters and deviations. Multivariate regression analysis quantifies the weight of the influence of each equipment parameter on the deviation change, and establishes a linear regression model to evaluate the comprehensive influence of multiple equipment parameters on small deviations. Through regression analysis, it is determined that one or more equipment parameters have the greatest contribution to the deviation change, thereby accurately identifying potential faulty equipment or components;

[0113] Equipment fault judgment unit: Based on the results of the multivariate regression analysis unit, the final judgment of equipment fault is generated. If the fluctuation of a certain equipment parameter has a large impact on the deviation and is significantly correlated with the cumulative effect of small deviations, the equipment is marked as a potential fault source and fault warning information is generated. The equipment fault judgment unit combines historical fault data to further verify the matching degree between the current equipment status and the typical fault mode to improve the accuracy of fault identification;

[0114] Preliminary screening of the correlation analysis unit: First, through correlation analysis, the correlation coefficient between the operating parameters and deviation data of each device is calculated. If some device parameters show a strong linear correlation with the deviation change (such as a high correlation coefficient), these devices will be marked as possible sources of influence. Correlation analysis narrows the scope of analysis for subsequent more complex multivariate regression analysis, reduces unnecessary calculations, and focuses on highly correlated devices. Detailed analysis of the multivariate regression analysis unit: After preliminarily screening out highly correlated devices, the multivariate regression analysis unit further establishes a multivariate regression model to quantify the specific impact of each device parameter on the deviation change through the regression coefficient. This step can not only identify the combined impact between multiple devices, but also deal with the interdependence between device parameters, providing more accurate judgment results. After the multivariate regression analysis is completed, the system will combine the weight results in the regression analysis to determine which one or more devices have the most significant impact on the deviation change.

[0115] Preliminary screening by the correlation analysis unit can reduce the computational burden of subsequent analysis and improve the real-time response capability of the system. Multivariate regression analysis further quantifies the impact of the device on the deviation, ensuring that the system can accurately determine which device or devices are the root cause of the deviation. The device fault judgment unit combines historical data with current analysis results to provide more reliable fault predictions to avoid misjudgments or missed judgments.

[0116] The correlation analysis unit uses the Pearson correlation coefficient to measure the linear relationship between each device parameter and the yaw angle deviation. The Pearson correlation coefficient compares the operating parameters of each device with the deviation between the yaw angle and the wind direction, including:

[0117] Calculate the difference: The correlation analysis unit calculates the overall trend of the equipment operating parameters and deviation data. If the deviation changes as the current or pressure changes over time, then the parameters of these equipment may show a certain correlation with the deviation;

[0118] Measuring correlation: Calculate the degree of correlation between the equipment operating parameters and the deviation. The Pearson correlation coefficient outputs whether the two variables change at the same time. If the parameter change of a device is positively correlated with the deviation (that is, when the device parameter increases, the deviation also increases, or when the device parameter decreases, the deviation also decreases), it is considered that the device may have a strong impact on the deviation. If the correlation coefficient is negative, it indicates the opposite relationship (that is, when one increases, the other decreases). If the correlation coefficient is close to zero, it means that there is almost no linear relationship between the two.

[0119] Preliminary device screening: By calculating the correlation between multiple device parameters and deviations, quickly identify the device operating parameters that have a large impact on the deviation. For example, if the correlation coefficient between current and deviation is close to 1, this means that the change in current has a strong positive correlation with the deviation, which may mean that the state of the motor has a greater impact on the deviation of the yaw angle. On the other hand, if the correlation coefficient between a device parameter and the deviation is close to 0, the system believes that the device parameter has nothing to do with the deviation change, and it can temporarily exclude the possibility of it as a potential fault source;

[0120] The Pearson correlation coefficient is expressed as:

[0121] The parameters are defined as follows:

[0122] x i : The value of a device parameter at the i-th moment (such as the current, voltage, torque of the yaw motor, or the pressure, temperature of the hydraulic system, etc.);

[0123] y i : The deviation value Δθ between the yaw angle and the wind direction at the i-th moment i ,Right now in is the yaw angle, is the wind direction angle;

[0124] Device Parameters x i The mean of

[0125] Deviation value y i The mean of

[0126] r xy :Device parameter x i and the deviation value y i The Pearson correlation coefficient between xy The value range of is [-1, 1]. A value close to 1 indicates a high positive correlation, a value close to -1 indicates a high negative correlation, and a value close to 0 indicates no significant correlation.

[0127] By calculating the device parameter x i and the deviation value y i The correlation coefficient r xy , the system can preliminarily screen out which equipment parameters have a strong linear relationship with the deviation change. If the correlation coefficient r xy If it is close to 1 or -1, it indicates that the parameter fluctuation of the equipment has a strong influence on the deviation change, and the next step is multivariate regression analysis.

[0128] The linear regression model evaluates the impact of multiple equipment parameters on the deviation change simultaneously, and the expression is:

[0129] Δθ t =β0+β1x 1,t +β2x 2,t +…+β i x i,t +∈ t ,in;

[0130] Δθ t Indicates the deviation value at time t, which is the difference between the yaw angle and the wind direction angle, x 1,t ,x 2,t ,…,x i,t Indicates the parameter values ​​of each device collected at time t, which respectively represent the real-time data of the device related to the yaw system, including the current x of the yaw motor 1,t , voltage x 2,t , torque x 3,t and the hydraulic system pressure x 4,t and temperature x 5,t , β0 is the constant term of the linear regression model, which represents the baseline value of the deviation when there is no equipment parameter fluctuation, β1,β2,…,β i Represents the regression coefficient, representing each device parameter x i,t The weight of the impact on the change in deviation, each β i Indicates device parameter x i,t Contribution to the deviation change. The larger the weight, the greater the impact of the device on the deviation, ∈ t is the random error term, which represents the portion of the deviation that cannot be explained by the equipment parameters.

[0131] Through the linear regression model, the influence of each device parameter on the deviation change is quantified. If a device parameter x 1,t The regression coefficient β1 of the deviation change of (such as current) is large, indicating that the parameter change of the device has a significant impact on the deviation. Multivariate regression analysis can not only identify the combined impact of multiple devices on the deviation at the same time, but also evaluate the interaction between them.

[0132] In the equipment fault judgment unit, the results of the regression model calculation are used to generate fault judgments based on the following conditions:

[0133] Contribution judgment: If a device parameter x i,t The regression coefficient β on the deviation i Significantly larger than other device parameters (such as β i >β j , where i≠j), the device is judged to be the main fault source, and it is determined that the device parameters cause deviation accumulation and cause the fault.

[0134] Example: If β i >β threshold Then determine the device x i Fault;

[0135] Comprehensive fault score: Use the normalized regression coefficient to generate the comprehensive fault score S of the device i , expressed as follows: Among them, S i represents the fault score of the ith device, β i is the regression coefficient of the ith device parameter, β j is the regression coefficient of the jth device parameter, k is the total number of devices, the sum of the fault scores of all devices is 1, and the device with a high score is the source of the fault.

[0136] Based on the analysis results of the regression model, the equipment fault judgment unit can quantify the possibility of fault according to the weight of the equipment's impact on the deviation change. When the fault score Si of a certain device is significantly higher than that of other devices, the system marks the device as a fault source and issues an early warning.

[0137] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0138] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A wind turbine fault prediction system based on machine learning, characterized in that: include: Deviation data detection module: collects the yaw angle and wind direction sensor data of the yaw system in real time, calculates the deviation between the yaw angle and the wind direction, detects the matching degree between the yaw angle and the wind direction, and determines the wind accuracy of the wind turbine. The data detection module detects small deviations in real time based on the time series regression algorithm and outputs the deviation data detection result; Deviation change trend analysis module: analyzes the change trend of small deviations and captures the cumulative effect of small deviations in different periods. The deviation change trend analysis module uses a time series model to identify the development trend of deviation changes and predict the increase of deviations in the future; Equipment real-time data acquisition and synchronization module: used to collect real-time data of equipment related to the yaw system, including the current, voltage, torque of the yaw motor, pressure and temperature of the hydraulic system, to ensure that all equipment data is synchronously collected with the yaw angle and wind direction sensor data, and to perform data standardization processing; Equipment and deviation relationship analysis module: According to the deviation change development trend, the relationship between the real-time data of each device and the small deviation is analyzed. The equipment and deviation relationship analysis module uses multivariate regression analysis and correlation analysis algorithms to identify which equipment parameter fluctuations are correlated with the yaw angle deviation, generate equipment failure judgment results, and determine the specific equipment or components that cause the small deviation.

2. The wind turbine fault prediction system based on machine learning according to claim 1, characterized in that: The deviation data detection module specifically includes: Yaw angle acquisition unit: connected to the yaw sensor to obtain the yaw angle data of the wind turbine nacelle in real time, and the yaw angle data is collected at a set frequency; Wind direction data acquisition unit: The wind direction data of the area where the wind turbine is located is acquired in real time through the wind direction sensor. The wind direction data is acquired synchronously with the yaw angle data; Deviation calculation unit: The deviation calculation unit receives yaw angle data and wind direction data, determines the wind accuracy of the wind turbine by calculating the angle between the yaw angle and the wind direction, and outputs the deviation data detection result; Small deviation detection unit: The small deviation detection unit analyzes multiple groups of deviation data in a short period of time based on the time series regression algorithm, identifies persistent small deviations, and outputs the small deviation results to the deviation change trend analysis module in real time.

3. The wind turbine fault prediction system based on machine learning according to claim 1, characterized in that: The deviation change trend analysis module specifically includes: A historical deviation data storage unit: used to store historical deviation data generated by the deviation data detection module, the data including the difference between the yaw angle and the wind direction angle and the corresponding timestamp; Time series data processing unit: receives historical deviation data, arranges it in chronological order, builds a time series model of deviation changes, and analyzes the variation trend of deviations in different periods. The time series model adopts the ARI MA model; Small deviation accumulation detection unit: Identify small deviations that persist over multiple time periods and capture the cumulative effect of deviations through time series models. If the deviation does not decrease or shows an increasing trend over multiple consecutive time periods, it is judged that the deviation is accumulated; Future Deviation Prediction Unit: Based on the prediction capability of the time series model, it uses the current and historical deviation data to predict the deviation changes in future periods.

4. The wind turbine fault prediction system based on machine learning according to claim 3 is characterized in that: The ARIMA model reviews the deviation data of the past period of time, analyzes the data change pattern, and when the deviation of the yaw angle and the wind direction is collected, it identifies whether the current deviation is gradually increasing, decreasing or remaining stable based on the deviation changes in multiple consecutive time periods, and determines whether the operation of the yaw system is entering an abnormal state; The variation trends of the deviation data include: Short-term changes: The ARI MA model uses the data from the most recent periods to determine whether there are short-term fluctuations in the deviation; Long-term trends: By integrating data over a long period of time, the ARIMA model identifies the long-term trend of the deviation and analyzes whether there is a continuous accumulation or slow change in the deviation.

5. The wind turbine fault prediction system based on machine learning according to claim 4, characterized in that: The ARIMA model analyzes current and historical deviation data to infer future deviation trends. The prediction process includes: Impact of current deviation data: The current deviation value affects the performance in the short term. If the current deviation is large, it is predicted that the system will continue to maintain the deviation in the short term. Historical deviation trends: By identifying deviation patterns over a period of time in the past, it is inferred that future deviations will continue that deviation pattern.

6. The wind turbine fault prediction system based on machine learning according to claim 1, characterized in that: The device real-time data acquisition and synchronization module specifically includes: Motor data acquisition unit: collects the operating parameters of the yaw motor in real time, including motor current, voltage and torque signals. The motor data acquisition unit collects these data at a set sampling frequency by connecting to the motor sensor in the yaw system; Hydraulic system data acquisition unit: collects real-time data of the hydraulic components of the yaw system, including pressure and temperature signals of the hydraulic system. The hydraulic data is acquired through hydraulic sensors, and the changes in the operation of the hydraulic system are synchronously recorded; Data synchronization unit: This unit ensures the synchronous acquisition of the current, voltage, torque data of the yaw motor, the pressure and temperature data of the hydraulic system, and the yaw angle and wind direction sensor data. It unifies the time base of the equipment data through timestamp matching and timing alignment. Data standardization processing unit: used to perform standardization processing on all collected device data to ensure that data of different units and magnitudes can be effectively compared and analyzed. The standardization processing is based on the Z-score standardization method.

7. The wind turbine fault prediction system based on machine learning according to claim 1, characterized in that: The equipment and deviation relationship analysis module specifically includes the following units: Correlation analysis unit: receives real-time data from each device, including the current, voltage, torque data of the yaw motor, the pressure and temperature data of the hydraulic system, and the deviation data of the yaw angle and wind direction. Through the correlation analysis algorithm, it preliminarily screens out the device parameters that are linearly correlated with the deviation data. Multivariate regression analysis unit: Based on the highly correlated equipment parameters screened by the correlation analysis unit, multivariate regression analysis is performed to refine the relationship between equipment parameters and deviations. Multivariate regression analysis quantifies the weight of each equipment parameter's influence on deviation changes, and establishes a linear regression model to evaluate the combined influence of multiple equipment parameters on small deviations. Through regression analysis, it is determined that one or more equipment parameters have the greatest contribution to deviation changes. Equipment failure judgment unit: Based on the results of the multivariate regression analysis unit, the final judgment of equipment failure is generated. If the fluctuation of a certain equipment parameter has a large impact on the deviation and is significantly correlated with the cumulative effect of small deviations, the equipment will be marked as a potential fault source and fault warning information will be generated.

8. The wind turbine fault prediction system based on machine learning according to claim 7, characterized in that: The correlation analysis unit uses the Pearson correlation coefficient to measure the linear relationship between each device parameter and the yaw angle deviation. The Pearson correlation coefficient compares the operating parameter of each device with the deviation between the yaw angle and the wind direction, specifically including: Calculate differences: The correlation analysis unit calculates the overall trend of equipment operating parameters and deviation data; Measuring correlation: Calculate the degree of correlation between the equipment operating parameters and the deviation. The Pearson correlation coefficient outputs whether the two variables change at the same time. If the parameter change of a device is positively correlated with the deviation, it is considered that the device may have a strong impact on the deviation. If the correlation coefficient is negative, it indicates the opposite relationship. If the correlation coefficient is close to zero, it means that there is no linear relationship between the two. Preliminary screening of equipment: By calculating the correlation between multiple equipment parameters and deviations, the equipment operating parameters that have a great impact on the deviation can be quickly identified.

9. The wind turbine fault prediction system based on machine learning according to claim 8, characterized in that: The linear regression model evaluates the impact of multiple equipment parameters on the deviation change simultaneously, and the expression is: Dth t =β0+β1x 1,t +β2x 2,t +…+b i x i,t +∈ t , among which; Δθ t Indicates the deviation value at time t, which is the difference between the yaw angle and the wind direction angle, x 1,t ,x 2,t ,…,x i,t Indicates the parameter values ​​of each device collected at time t, which respectively represent the real-time data of the device related to the yaw system, including the current x of the yaw motor 1,t , voltage x 2,t , torque x 3,t and the hydraulic system pressure x 4,t and temperature x 5,t , β0 is the constant term of the linear regression model, which represents the baseline value of the deviation when there is no equipment parameter fluctuation, β1,β2,…,β i Represents the regression coefficient, representing each device parameter x i,t The weight of the impact on the change in deviation, each β i Indicates device parameter x i,t Contribution to the deviation change. The larger the weight, the greater the impact of the device on the deviation, ∈ t is a random error term, which quantifies the impact of each device parameter on the deviation change through a linear regression model.

10. The wind turbine fault prediction system based on machine learning according to claim 9, characterized in that: In the equipment fault judgment unit, a fault judgment is generated based on the following conditions according to the result calculated by the regression model: Contribution judgment: If a device parameter x i,t The regression coefficient β on the deviation i If it is greater than other equipment parameters, the equipment is judged to be the main source of failure, and it is determined that the equipment parameters cause deviation accumulation and cause failure; Comprehensive fault score: Use the normalized regression coefficient to generate the comprehensive fault score S of the device i , expressed as follows: Among them, S i represents the fault score of the ith device, β i is the regression coefficient of the ith device parameter, β j is the regression coefficient of the jth device parameter, k is the total number of devices, the sum of the fault scores of all devices is 1, and the device with a high score is the source of the fault.

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