Safety protection and monitoring method and system for transmission chain of wind turbine generator

Abstract

The invention relates to the technical field of computer wireless communication, and discloses a safety protection and monitoring method and system for a transmission chain of a wind turbine generator, and the method comprises the steps: collecting the operation data of the wind turbine generator; establishing a dynamic model of the wind turbine generator transmission system; fault features are extracted through a signal processing method, and an early warning model of the system stability degradation trend is constructed; and making a maintenance strategy based on an early warning result to carry out equipment predictive maintenance. According to the method, the operation reliability of the wind turbine generator is remarkably improved, the fault rate and the maintenance cost are reduced, and the method has important practical application value and wide popularization prospects. Based on prediction value analysis of multiple time points, a maintenance strategy can be dynamically adjusted, preventive maintenance can be implemented before the system state is deteriorated, the non-planned downtime is effectively shortened, and the overall operation efficiency and the service life of the wind turbine generator are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wind turbine maintenance, and in particular to a safety protection and monitoring method and system for a transmission chain of a wind turbine. Background Art

[0002] As a clean and renewable energy source, wind power generation has been widely used and developed rapidly. As the core equipment of wind power generation, the stability and reliability of wind turbine operation are directly related to the efficiency and safety of power generation. Among the many components of wind turbines, the transmission chain is one of the key components for converting wind energy into electrical energy, including the main shaft, gearbox, coupling, bearings and other components. These components operate for a long time under complex working conditions and are susceptible to multiple factors such as fatigue, wear, vibration, etc., which can lead to failures or even shutdown accidents. Therefore, the safety protection and monitoring of the transmission chain of wind turbines is particularly important.

[0003] Existing wind turbine transmission chain monitoring technologies mainly focus on condition monitoring and fault diagnosis. Common methods include vibration analysis, oil analysis, temperature monitoring, etc. These technologies can detect abnormal conditions of equipment and provide fault warnings to a certain extent, but they have certain limitations. First, traditional monitoring methods are mostly passive monitoring, which can only be identified after a fault occurs, and lack effective prediction capabilities. Secondly, due to the complex operating environment of wind turbines, traditional methods are easily affected by noise and external interference, resulting in inaccurate monitoring results. In addition, most existing technologies rely on a single monitoring parameter, such as vibration or temperature, and fail to fully consider the combined effects of multiple factors, making it difficult to fully reflect the health of the transmission chain. Therefore, existing technologies still have significant deficiencies in improving the accuracy, comprehensiveness and real-time nature of fault prediction. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is that the prior art still has great deficiencies in improving the accuracy, comprehensiveness and real-time performance of fault prediction.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a safety protection and monitoring method for a wind turbine transmission chain, comprising:

[0007] Collect wind turbine operation data;

[0008] Establish a dynamic model of the wind turbine transmission system;

[0009] Extract fault features through signal processing methods and build an early warning model for system stability degradation trends;

[0010] Formulate maintenance strategies based on early warning results to perform predictive maintenance on equipment.

[0011] As a preferred solution of the wind turbine transmission chain safety protection and monitoring method of the present invention, the wind turbine operation data includes: selecting sensors and placing them at key positions of the transmission chain, including gear boxes, bearings, and main shafts, to collect data; selecting a suitable sampling frequency to ensure that signals within the fault frequency range can be captured;

[0012] Collect historical data sets from wind turbine transmission systems, including various features and corresponding timestamps.

[0013] As a preferred solution of the wind turbine transmission chain safety protection and monitoring method of the present invention, the dynamic model of the wind turbine transmission system includes mathematical modeling of the main shaft, gear box, and bearings to obtain the dynamic equations of the main shaft, gear box, and bearings;

[0014] The dynamic equation of the main axis is expressed as:

[0015]

[0016] Among them, J s Represents the rotational inertia of the main shaft; θ s (t) represents the angular displacement of the main axis; and They represent angular velocity and angular acceleration respectively; c s represents the main shaft damping coefficient; k s Indicates the spindle stiffness; T w (t) represents the input torque applied by the wind wheel on the main shaft; T g (t) represents the torque output from the main shaft to the gearbox;

[0017] The dynamic equation of the gearbox, assuming that the transmission ratio of the gearbox is r, the dynamic equation of the low-speed shaft of the gearbox is expressed as:

[0018]

[0019] The dynamic equation of the high-speed shaft of the gearbox is expressed as:

[0020]

[0021] Among them, J g1 Indicates the inertia moment of the low-speed shaft of the gearbox; J g2 Represents the moment of inertia of the high-speed shaft of the gearbox; θ g1 (t) represents the angular displacement of the low-speed shaft; θ g2 (t) represents the angular displacement of the high-speed shaft; and represents angular velocity; cg1 and c g2 represents the damping coefficient; k g1 and k g2 represents the stiffness coefficient; T g1 (t) represents the torque of the low-speed shaft; T g2 (t) represents the torque of the high-speed shaft; T gen (t) represents the torque output from the gearbox to the generator;

[0022] The dynamic equation of the generator is:

[0023]

[0024] Among them, J gen represents the inertia moment of the generator; θ gen (t) represents the angular displacement of the generator rotor; c gen Represents the damping coefficient of the generator; T elec (t) represents the electromagnetic torque generated by the generator.

[0025] As a preferred embodiment of the wind turbine transmission chain safety protection and monitoring method of the present invention

[0026] The signal processing method includes performing denoising on the collected data and performing denoising on the signal using wavelet transform; assuming that the original signal is x(t), and the signal after wavelet transform is W x (a, b), the formula is:

[0027]

[0028] Where a and b represent the scale and translation parameters respectively, and ψ represents the mother wavelet function. Perform empirical mode decomposition denoising to decompose the signal x(t) into several intrinsic mode functions c i (t), reconstruct the signal after removing high-frequency noise:

[0029]

[0030] Among them, r n (t) represents the residual term;

[0031] Frequency domain feature extraction, Fourier transform converts the signal from time domain to frequency domain, and the spectrum calculation formula is expressed as:

[0032]

[0033] Where X(f) represents the spectrum, f represents the frequency; time-frequency domain feature extraction, characteristic frequency extraction: extract key frequency components, gear meshing frequency f g , bearing failure frequency f b; Use short-time Fourier transform to analyze the time-frequency characteristics of the signal, use the moving window h(t) to segment it in time, and get the short-time spectrum X(t,f) formula as follows:

[0034]

[0035] The time-frequency information of the signal is obtained through wavelet transform.

[0036] As a preferred solution of the wind turbine transmission chain safety protection and monitoring method of the present invention, the fault feature extraction includes using the linear discriminant analysis intra-class scatter matrix S W and the between-class scatter matrix

[0037] The intra-class scatter matrix S W , the formula is:

[0038]

[0039] Where C represents the total number of categories; χ c represents the sample set in category c; x i represents the i-th sample in category c; μ c Represents the mean vector of category c:

[0040]

[0041] Among them, N c represents the number of samples in category c; (x i -μ c ) represents the sample x i Deviation from its class mean; inter-class scatter matrix S B The formula is:

[0042]

[0043] Among them, μ represents the overall mean vector of all samples:

[0044]

[0045] Where N represents the total number of all samples; (μ c -μ) represents the deviation between the mean vector of category c and the overall mean vector;

[0046] The formula of the LDA transformation matrix W is expressed as:

[0047]

[0048] Calculate the intra-class scatter matrix S W and the inter-class scatter matrix S B, by solving the generalized eigenvalue problem, the formula is expressed as:

[0049]

[0050] Among them, Λ represents the eigenvalue diagonal matrix, and W represents the corresponding eigenvector matrix; the eigenvector matrix W corresponding to the largest eigenvalue is selected as the optimal projection direction of LDA.

[0051] As a preferred solution of the wind turbine transmission chain safety protection and monitoring method of the present invention, the early warning model of the system stability degradation trend includes: setting the target to predict the output value y through the regression model i ; The regression model formula is expressed as:

[0052] f(x)=w·φ(x)+b

[0053] Among them, w represents the weight vector, φ(x) represents the feature mapping function, which maps the input feature x to a high-dimensional space, and b represents the bias term;

[0054] Select a loss function so that when the error ∈ is within the allowable range, the error is ignored and the model is as smooth as possible. The loss function formula is expressed as:

[0055]

[0056] Among them, ||w|| 2 represents the regularization term; C represents the regularization parameter, which is used to balance the model complexity and training error; ξ i , represents the slack variable, which is used to deal with intolerable deviations;

[0057] For each training sample (x i ,y i )The constraints satisfied by are expressed as:

[0058] y i -(w·φ(x i )+b)≤∈+ξ i

[0059]

[0060] Among them, ∈ represents the tolerance of the model; let A=∈+ξ i -y i +w·φ(x i )+b,

[0061] Introducing the Lagrange multiplier α i and The optimization problem is transformed into a dual problem, and the formula is expressed as:

[0062]

[0063] By solving the dual problem, we get the optimal values ​​of w and b, and introduce the kernel function to deal with the nonlinear feature mapping problem. i ,x j ) is defined as:

[0064] K(x i ,x j )=φ(x i )·φ(x j )

[0065] Using training data Perform model training and obtain the optimal w and b, as well as the support vector, by optimizing the dual problem.

[0066] As a preferred solution of the wind turbine transmission chain safety protection and monitoring method of the present invention, the predictive maintenance of the equipment includes using a radial basis function kernel for improvement and selecting the optimal γ parameter value through cross-validation; γ determines the size of the influence range in the high-dimensional space, and the improved kernel function formula is expressed as:

[0067] K(x i ,x j )=exp(-γ||x i -x j || 2 )

[0068] The trained support vector machine model can be used to predict the output corresponding to the new input feature x The formula is:

[0069]

[0070] in, Represents the predicted output corresponding to time t; α i and represents the Lagrange multiplier obtained through training;

[0071] The degradation trend of the wind turbine transmission system is analyzed based on the predicted values ​​y(t) at multiple time points; if the predicted y(t) increases over time, it means that the system status is deteriorating, and maintenance should be performed in advance based on the trend.

[0072] A wind turbine transmission chain safety protection and monitoring system using any method described in the present invention, wherein:

[0073] The data acquisition module is responsible for collecting various data during the operation of the wind turbine, including sensor data, operating parameters, and environmental conditions;

[0074] The dynamic modeling module builds a dynamic model of the wind turbine transmission system based on the collected operating data to describe the dynamic behavior and response of the system;

[0075] The signal processing and feature extraction module pre-processes the collected signals and extracts system fault features to provide a basis for building a degradation trend model;

[0076] The early warning and maintenance strategy formulation module builds an early warning model of degradation trends based on the extracted fault characteristics, and formulates a predictive maintenance strategy for the equipment according to the early warning results.

[0077] A computer device comprises: a memory and a processor; the memory stores a computer program, comprising: the steps of implementing any one of the methods of the present invention when the processor executes the computer program.

[0078] A computer-readable storage medium stores a computer program, comprising: when the computer program is executed by a processor, the steps of implementing any one of the methods of the present invention are implemented.

[0079] Beneficial effects of the invention: Through systematic design and scientific analysis methods, the invention significantly improves the operational reliability of wind turbines, reduces failure rates and maintenance costs, and has important practical application value and broad promotion prospects. Based on the analysis of predicted values ​​at multiple time points, the invention can dynamically adjust the maintenance strategy and implement preventive maintenance before the system status deteriorates, effectively reducing unplanned downtime and improving the overall operating efficiency and service life of wind turbines. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0081] Figure 1 An overall flow chart of a wind turbine transmission chain safety protection and monitoring method provided in the first embodiment of the present invention. DETAILED DESCRIPTION

[0082] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0083] Example 1, reference Figure 1 , which is an embodiment of the present invention, provides a safety protection and monitoring method for a wind turbine transmission chain, comprising:

[0084] S1: Collect wind turbine operation data.

[0085] Furthermore, the wind turbine operation data includes selecting sensors and placing them at key positions in the transmission chain, including the gearbox, bearings, and main shaft, for data collection; selecting a suitable sampling frequency to ensure that signals within the fault frequency range can be captured.

[0086] Furthermore, historical data sets in wind turbine transmission systems are collected, including various features and corresponding timestamps.

[0087] It should be noted that the collection of historical data sets provides a basis for long-term monitoring and trend analysis of the system. By collecting historical data and combining it with timestamp information, the system can perform more accurate fault prediction and status assessment. This step not only enhances the system's predictability, but also provides important data support for subsequent signal processing, feature extraction, and trend analysis. By comparing historical data with current operating data, the system can identify potential abnormal trends and take preventive measures before the problem develops into a serious failure.

[0088] S2: Establish a dynamic model of the wind turbine transmission system.

[0089] Furthermore, the dynamic model of the wind turbine transmission system includes mathematical modeling of the main shaft, the gear box, and the bearings to obtain the dynamic equations of the main shaft, the gear box, and the bearings.

[0090] Furthermore, the dynamic equation of the main axis is expressed as:

[0091]

[0092] Among them, J s Represents the rotational inertia of the main shaft; θ s (t) represents the angular displacement of the main axis; and They represent angular velocity and angular acceleration respectively; c s represents the main shaft damping coefficient; k sIndicates the spindle stiffness; T w (t) represents the input torque applied by the wind wheel on the main shaft; T g (t) represents the torque output from the main shaft to the gearbox.

[0093] Furthermore, the dynamic equation of the gearbox, assuming that the transmission ratio of the gearbox is r, the dynamic equation of the low-speed shaft of the gearbox is expressed as:

[0094]

[0095] Furthermore, the dynamic equation of the high-speed shaft of the gearbox is expressed as:

[0096]

[0097] Among them, J g1 Indicates the inertia moment of the low-speed shaft of the gearbox; J g2 Represents the moment of inertia of the high-speed shaft of the gearbox; θ g1 (t) represents the angular displacement of the low-speed shaft; θ g2 (t) represents the angular displacement of the high-speed shaft; and represents angular velocity; c g1 and c g2 represents the damping coefficient; k g1 and k g2 represents the stiffness coefficient; T g1 (t) represents the torque of the low-speed shaft; T g2 (t) represents the torque of the high-speed shaft; T gen (t) represents the torque output from the gearbox to the generator.

[0098] Furthermore, the dynamic equation of the generator is:

[0099]

[0100] Among them, J gen represents the inertia moment of the generator; θ gen (t) represents the angular displacement of the generator rotor; c gen Represents the damping coefficient of the generator; T elec (t) represents the electromagnetic torque generated by the generator.

[0101] Furthermore, in order to obtain the response of the system under different working conditions, a numerical solution method is used for calculation, and the formula is expressed as:

[0102] k1=f(t n ,θ n )

[0103]

[0104] k4=f(t n +h,θ n +h·k3)

[0105]

[0106] Among them, t n represents the current time point; h represents the time step, θ n represents the state variable; f represents the derivative function of the dynamic system.

[0107] It should be noted that by dynamically solving the system through numerical solution methods (such as the Euler method), the response of the system in the time dimension can be simulated. This method can handle complex nonlinear dynamic equations and obtain the state variable x(t) of the system at different time steps Δt, thereby achieving precise control and real-time monitoring of the wind turbine transmission system. This improvement can significantly improve the response speed and prediction accuracy of the system in practical applications, and further optimize the overall performance of the wind turbine.

[0108] S3: Extract fault features through signal processing methods and build an early warning model for system stability degradation trends.

[0109] Furthermore, the signal processing method includes performing denoising on the collected data, and performing denoising on the signal using wavelet transform; assuming that the original signal is x(t), and the signal after wavelet transform is W x (a, b), the formula is:

[0110]

[0111] Where a and b represent the scale and translation parameters respectively, and ψ represents the mother wavelet function. Perform empirical mode decomposition denoising to decompose the signal x(t) into several intrinsic mode functions c i (t), reconstruct the signal after removing high-frequency noise:

[0112]

[0113] Among them, r n (t) represents the residual term.

[0114] Furthermore, for frequency domain feature extraction, Fourier transform converts the signal from time domain to frequency domain, and the spectrum calculation formula is expressed as:

[0115]

[0116] Where X(f) represents the spectrum, f represents the frequency; time-frequency domain feature extraction, characteristic frequency extraction: extract key frequency components, gear meshing frequency f g , bearing failure frequency f b; Use short-time Fourier transform to analyze the time-frequency characteristics of the signal, use the moving window h(t) to segment it in time, and get the short-time spectrum X(t,f) formula as follows:

[0117]

[0118] Furthermore, the time-frequency information of the signal is obtained through wavelet transform.

[0119] Furthermore, the fault feature extraction includes using the linear discriminant analysis intra-class scatter matrix S W and the between-class scatter matrix

[0120] Furthermore, the intra-class scatter matrix S W , the formula is:

[0121]

[0122] Where C represents the total number of categories; x c represents the sample set in category c; x i represents the i-th sample in category c; μ c Represents the mean vector of category c:

[0123]

[0124] Among them, N c represents the number of samples in category c; (x i -μ c ) represents the sample x i Deviation from its class mean; inter-class scatter matrix S B The formula is:

[0125]

[0126] Among them, μ represents the overall mean vector of all samples:

[0127]

[0128] Where N represents the total number of all samples; (μ c -μ) represents the deviation between the mean vector of category c and the overall mean vector.

[0129] Furthermore, the formula of the LDA transformation matrix W is expressed as:

[0130]

[0131] Furthermore, the intra-class scatter matrix S is calculated W and the inter-class scatter matrix S B, by solving the generalized eigenvalue problem, the formula is expressed as:

[0132]

[0133] Among them, Λ represents the eigenvalue diagonal matrix, and W represents the corresponding eigenvector matrix; the eigenvector matrix W corresponding to the largest eigenvalue is selected as the optimal projection direction of LDA.

[0134] It should be noted that in LDA, the calculation of the intra-class scatter matrix and the inter-class scatter matrix ensures the maximum separation between features of different categories. By selecting the eigenvector with the largest eigenvalue for projection, the separability of the fault features is significantly improved, so that different fault modes can be clearly distinguished in low-dimensional space. This feature projection method solves the problems of information redundancy and classification difficulties in high-dimensional data, and improves the accuracy and efficiency of the classifier.

[0135] S4: Formulate maintenance strategies based on early warning results to perform predictive maintenance of equipment.

[0136] Furthermore, the early warning model of the system stability degradation trend includes setting the goal to predict the output value y through the regression model i ; The regression model formula is expressed as:

[0137] f(x)=w·φ(x)+b

[0138] Among them, w represents the weight vector, φ(x) represents the feature mapping function, which maps the input feature x to a high-dimensional space, and b represents the bias term.

[0139] Select a loss function so that when the error ∈ is within the allowable range, the error is ignored and the model is as smooth as possible. The loss function formula is expressed as:

[0140]

[0141] Among them, ||w|| 2 represents the regularization term; C represents the regularization parameter, which is used to balance the model complexity and training error; ξ i , Represents the slack variable, which is used to deal with intolerable deviations.

[0142] Furthermore, for each training sample (x i ,y i )The constraints satisfied by are expressed as:

[0143] y i -(w·φ(x i )+b)≤∈+ξ i

[0144]

[0145] Among them, ∈ represents the tolerance of the model; let A=∈+ξ i -y i +w·φ(x i )+b,

[0146] Furthermore, the Lagrange multiplier α is introduced i and The optimization problem is transformed into a dual problem, and the formula is expressed as:

[0147]

[0148] Furthermore, by solving the dual problem, we can obtain the optimal values ​​of w and b and introduce the kernel function to deal with the nonlinear feature mapping problem. i ,x j ) is defined as:

[0149] K(x i ,x j )=φ(x i )·φ(x j )

[0150] Furthermore, using training data Perform model training and obtain the optimal w, b and support vector by optimizing the dual problem.

[0151] Furthermore, the predictive maintenance of equipment includes using the radial basis function kernel for improvement and selecting the optimal γ parameter value through cross-validation; γ determines the size of the influence range in the high-dimensional space, and the improved kernel function formula is expressed as:

[0152] K(x i ,x j )=exp(-γ||x i -x j || 2 )

[0153] Furthermore, the trained support vector machine model can be used to predict the output corresponding to the new input feature x The formula is:

[0154]

[0155] in, Represents the predicted output corresponding to time t; α i and represents the Lagrange multiplier obtained through training.

[0156] Furthermore, by observing the forecast trend over a period of time, if Continues to increase and exceeds a certain predetermined threshold y th , the early warning mechanism can be triggered to carry out maintenance in advance:

[0157]

[0158] Among them, y th It represents the degradation index threshold set according to historical data or expert experience; [t0, t1] represents the observation time interval.

[0159] Furthermore, the degradation trend of the wind turbine transmission system is analyzed based on the predicted values ​​y(t) at multiple time points; if the predicted y(t) increases over time, it means that the system state is deteriorating, and maintenance can be performed in advance based on the trend.

[0160] It should be noted that by introducing Lagrange multipliers and converting them into dual problems, the difficulty of solving nonlinear optimization problems is greatly simplified. The use of kernel functions enables the model to handle complex nonlinear features. By performing feature mapping in high-dimensional space, the model can more accurately capture the changing trend of the system state. The radial basis function kernel (RBF kernel) is particularly suitable for state prediction of complex systems such as wind turbines because it can accurately perform classification and regression in nonlinear feature space. The improvement of this method significantly improves the flexibility and adaptability of the model, and solves the technical obstacle that traditional linear models cannot handle complex nonlinear problems.

[0161] On the other hand, this embodiment also provides a safety protection and monitoring system for a wind turbine transmission chain, which includes:

[0162] The data acquisition module is responsible for collecting various data during the operation of the wind turbine, including sensor data, operating parameters, and environmental conditions.

[0163] The dynamic modeling module builds a dynamic model of the wind turbine transmission system based on the collected operating data to describe the dynamic behavior and response of the system.

[0164] The signal processing and feature extraction module pre-processes the collected signals and extracts system fault features, providing a basis for building a degradation trend model.

[0165] The early warning and maintenance strategy formulation module builds an early warning model of degradation trends based on the extracted fault characteristics, and formulates a predictive maintenance strategy for the equipment according to the early warning results.

[0166] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0167] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0168] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0169] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0170] Example 2: The following is an embodiment of the present invention, which provides a safety protection and monitoring method for a wind turbine transmission chain. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0171] In this embodiment, the purpose is to verify the effectiveness of a safety protection and monitoring method for the transmission chain of a wind turbine, focusing on the ability to achieve predictive maintenance by collecting wind turbine operation data, establishing a dynamic model of the transmission system, extracting fault characteristics, and constructing a system stability degradation trend warning model. For the purpose of the experiment, three wind turbines in a wind farm (labeled as A, B, and C) were selected as test objects. These units have different degrees of transmission chain failure risks in actual operation. The key steps of the experiment are as follows:

[0172] First, the test team installed high-precision vibration sensors, temperature sensors, and pressure sensors at key locations of the wind turbine (including gearboxes, main shafts, bearings, etc.) to ensure that operating data closely related to the state of the transmission system can be collected in real time. The sampling frequency of the sensor was set to 10kHz to ensure that signals can be accurately captured within the frequency range of various faults. In addition, based on historical operating data and combined with data collected by sensors, a dynamic model of the transmission system was constructed. The model includes dynamic equations for the main shaft, gearbox, and generator to simulate system responses under different operating conditions.

[0173] Secondly, the collected data is denoised using wavelet transform to remove noise interference in the operating environment and retain the key features of the fault signal. At the same time, frequency domain features are extracted through Fourier transform and empirical mode decomposition (EMD) technology to further identify key fault features such as gear meshing frequency and bearing fault frequency. On this basis, linear discriminant analysis (LDA) is used to classify the extracted features to build an early warning model for the stability degradation trend of the wind turbine transmission system.

[0174] Finally, according to the output results of the early warning model, the corresponding maintenance strategy is formulated. Specifically, when the system predicts that the fault characteristics continue to strengthen and exceed the set threshold, the system will automatically trigger an early warning and generate a detailed maintenance plan, including the parts that need to be inspected, the recommended maintenance time, and the relevant spare parts requirements. The test cycle is six months, and the key parameters of each wind turbine are recorded and analyzed once a month. Some test data are shown in Table 1.

[0175]

[0176]

[0177] Through the analysis of the test data in Table 1, from the change trends of key parameters such as vibration frequency, temperature, gear meshing frequency, and bearing failure frequency, the three wind turbines all showed a certain degree of degradation during the test, especially in the third month, the key parameters of units A, B, and C all reached a high level. This phenomenon shows that as the wind turbines continue to operate, the wear and fatigue of the transmission system gradually increases, resulting in a decrease in system stability.

[0178] Secondly, the change of the early warning index also demonstrates the creativity of the present invention. Taking unit A as an example, the early warning index gradually increased from 20% in the first month to 50% in the third month, and finally triggered the maintenance plan. This early warning mechanism successfully identified potential risks before the failure occurred and avoided possible serious failures. Compared with traditional regular maintenance, the present invention can more accurately judge the actual operating status of the equipment through real-time monitoring and analysis, avoid unnecessary shutdown and maintenance operations, and thus improve the utilization rate of the equipment.

[0179] In addition, by comparing the data of units A, B, and C, it can be seen that the model of the present invention can adapt to the individual differences of different wind turbines. Even under similar operating conditions, the warning indexes and maintenance triggering time points of the three units are different, which reflects the high adaptability of the present invention to complex industrial environments. This personalized predictive maintenance strategy significantly improves the accuracy of maintenance work and reduces the waste of resources and equipment damage risks caused by excessive or delayed maintenance in traditional technologies.

[0180] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A safety protection and monitoring method for a wind turbine transmission chain, characterized in that: include: Collect wind turbine operation data; Establish a dynamic model of the wind turbine transmission system; Extract fault features through signal processing methods and build an early warning model for system stability degradation trends; Formulate maintenance strategies based on early warning results to perform predictive maintenance on equipment.

2. The wind turbine transmission chain safety protection and monitoring method according to claim 1, characterized in that: The wind turbine operation data includes selecting sensors and placing them at key positions of the transmission chain, including the gearbox, bearings, and main shaft, to collect data; selecting a suitable sampling frequency to ensure that signals within the fault frequency range can be captured; Collect historical data sets from wind turbine transmission systems, including various features and corresponding timestamps.

3. The wind turbine transmission chain safety protection and monitoring method according to claim 2, characterized in that: The dynamic model of the wind turbine transmission system includes mathematical modeling of the main shaft, gear box, and bearings to obtain the dynamic equations of the main shaft, gear box, and bearings; The dynamic equation of the main axis is expressed as: Among them, J s Represents the rotational inertia of the main shaft; θ s (t) represents the angular displacement of the main axis; and They represent angular velocity and angular acceleration respectively; c s represents the main shaft damping coefficient; k s Indicates the spindle stiffness; T w (t) represents the input torque applied by the wind wheel on the main shaft; T g (t) represents the torque output from the main shaft to the gearbox; The dynamic equation of the gearbox, assuming that the transmission ratio of the gearbox is r, the dynamic equation of the low-speed shaft of the gearbox is expressed as: The dynamic equation of the high-speed shaft of the gearbox is expressed as: Among them, J g1 Indicates the inertia moment of the low-speed shaft of the gearbox; J g2 Represents the moment of inertia of the high-speed shaft of the gearbox; θ g1 (t) represents the angular displacement of the low-speed shaft; θ g2 (t) represents the angular displacement of the high-speed shaft; and represents angular velocity; c g1 and c g2 represents the damping coefficient; k g1 and k g2 represents the stiffness coefficient; T g1 (t) represents the torque of the low-speed shaft; T g2 (t) represents the torque of the high-speed shaft; T gen (t) represents the torque output from the gearbox to the generator; The dynamic equation of the generator is: Among them, J gen represents the inertia moment of the generator; θ gen (t) represents the angular displacement of the generator rotor; c gen Represents the damping coefficient of the generator; T elec (t) represents the electromagnetic torque generated by the generator.

4. The wind turbine transmission chain safety protection and monitoring method according to claim 3, characterized in that: The signal processing method includes performing denoising on the collected data, and performing denoising on the signal using wavelet transform; assuming that the original signal is x(t), and the signal after wavelet transform is W x (a, b), the formula is: Where a and b represent the scale and translation parameters respectively, and ψ represents the mother wavelet function. Perform empirical mode decomposition denoising to decompose the signal x(t) into several intrinsic mode functions c i (t), reconstruct the signal after removing high-frequency noise: Among them, r n (t) represents the residual term; Frequency domain feature extraction, Fourier transform converts the signal from time domain to frequency domain, and the spectrum calculation formula is expressed as: Where X(f) represents the spectrum, f represents the frequency; time-frequency domain feature extraction, characteristic frequency extraction: extract key frequency components, gear meshing frequency f g , bearing failure frequency f b ; Use short-time Fourier transform to analyze the time-frequency characteristics of the signal, use the moving window h(t) to segment it in time, and get the short-time spectrum X(t,f) formula as follows: The time-frequency information of the signal is obtained through wavelet transform.

5. The wind turbine transmission chain safety protection and monitoring method according to claim 4, characterized in that: The extraction of fault features includes using a linear discriminant analysis intra-class scatter matrix S W and the between-class scatter matrix The intra-class scatter matrix S W , the formula is: Where C represents the total number of categories; χ c represents the sample set in category c; x i represents the i-th sample in category c; μ c Represents the mean vector of category c: Among them, N c represents the number of samples in category c; (x i -μ c ) represents the sample x i Deviation from its class mean; inter-class scatter matrix S B The formula is: Among them, μ represents the overall mean vector of all samples: Where N represents the total number of all samples; (μ c -μ) represents the deviation between the mean vector of category c and the overall mean vector; The formula of the LDA transformation matrix W is expressed as: Calculate the intra-class scatter matrix S W and the inter-class scatter matrix S B , by solving the generalized eigenvalue problem, the formula is expressed as: Among them, Λ represents the eigenvalue diagonal matrix, and W represents the corresponding eigenvector matrix; the eigenvector matrix W corresponding to the largest eigenvalue is selected as the optimal projection direction of LDA.

6. The wind turbine transmission chain safety protection and monitoring method according to claim 5, characterized in that: The early warning model of the system stability degradation trend includes: setting the target to predict the output value y through the regression model i ; The regression model formula is expressed as: f(x)=w·φ(x)+b Among them, w represents the weight vector, φ(x) represents the feature mapping function, which maps the input feature x to a high-dimensional space, and b represents the bias term; Select a loss function so that when the error ∈ is within the allowable range, the error is ignored and the model is as smooth as possible. The loss function formula is expressed as: Among them, ||w|| 2 represents the regularization term; C represents the regularization parameter, which is used to balance the model complexity and training error; represents the slack variable, which is used to deal with intolerable deviations; For each training sample (x i ,y i )The constraints satisfied by are expressed as: y i -(w·φ(x i )+b)≤∈+ξ i Among them, ∈ represents the tolerance of the model; let A=∈+ξ i -y i +w·φ(x i )+b, Introducing the Lagrange multiplier α i and The optimization problem is transformed into a dual problem, and the formula is expressed as: By solving the dual problem, we get the optimal values ​​of w and b, and introduce the kernel function to deal with the nonlinear feature mapping problem. i ,x j ) is defined as: K(x i ,x j )=φ(x i )·φ(x j ) Using training data Perform model training and obtain the optimal w and b, as well as the support vector, by optimizing the dual problem.

7. The wind turbine transmission chain safety protection and monitoring method according to claim 6, characterized in that: The equipment predictive maintenance includes using the radial basis function kernel for improvement and selecting the optimal γ parameter value through cross-validation; γ determines the size of the influence range in the high-dimensional space, and the improved kernel function formula is expressed as: K(x i ,x j )=exp(-γ||x i -x j || 2 ) The trained support vector machine model can be used to predict the output corresponding to the new input feature x The formula is: in, Represents the predicted output corresponding to time t; α i and represents the Lagrange multiplier obtained through training; The degradation trend of the wind turbine transmission system is analyzed based on the predicted values ​​y(t) at multiple time points; if the predicted y(t) increases over time, it means that the system status is deteriorating, and maintenance should be performed in advance based on the trend.

8. A wind turbine transmission chain safety protection and monitoring system using the method according to any one of claims 1 to 7, characterized in that: The data acquisition module is responsible for collecting various data during the operation of the wind turbine, including sensor data, operating parameters, and environmental conditions; The dynamic modeling module builds a dynamic model of the wind turbine transmission system based on the collected operating data to describe the dynamic behavior and response of the system; The signal processing and feature extraction module pre-processes the collected signals and extracts system fault features to provide a basis for building a degradation trend model; The early warning and maintenance strategy formulation module builds an early warning model of degradation trends based on the extracted fault characteristics, and formulates a predictive maintenance strategy for the equipment according to the early warning results.

9. A computer device comprising: Memory and processor; The memory stores a computer program, characterized in that the processor implements the steps of the safety protection and monitoring method for the transmission chain of a wind turbine generator set when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The computer program, when executed by a processor, implements the steps of a method for safety protection and monitoring of a wind turbine transmission chain.

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