A method and system for monitoring vibration displacement of a main shaft of a wind turbine generator set

Abstract

The present invention relates to the field of online monitoring technology, and is a method and system for monitoring the vibration displacement of the main shaft of a wind turbine generator set. The specific method includes: constructing a gravity bending moment periodic function and a stiffness azimuth modulation strategy to obtain the main shaft endpoint displacement caused by blade gravity; comparing each unit partition on a single blade to obtain a first interference coefficient, and at the same time comparing each unit partition and the unit partition of the predicted rotation transfer position to obtain a second interference coefficient; obtaining a third interference coefficient through the gear friction coefficient; establishing a main shaft vibration displacement prediction model to predict and output a predicted value of the main shaft vibration displacement in the next monitoring period; comparing the predicted value of the main shaft vibration displacement in the next monitoring period with the measured main shaft vibration displacement, and performing a vibration abnormality alarm. The present invention solves the problem in the prior art that the deviation between the predicted value and the measured value of the main shaft vibration displacement under complex operating conditions is too large, and the risk of false alarm or omission is high.

Description

Technical Field

[0001] The present invention relates to the technical field of online monitoring, and relates to a method and system for monitoring the vibration displacement of a main shaft of a wind turbine generator set. Background Art

[0002] The main shaft of a wind turbine is the core component that transmits the kinetic energy of the wind rotor to the gearbox. Its vibration and displacement state are directly related to the operational safety and lifespan of the unit. Existing monitoring technologies have the following limitations: The modeling of the main shaft vibration excitation source is overly simplified. Existing methods typically treat the blade gravity load as static or only consider the fundamental frequency component, ignoring the third-harmonic coupling vibration effect caused by multi-blade structures (such as three blades with 120-degree spacing) and the nonlinear characteristics of the main shaft stiffness that vary with azimuth angle (such as increased compressive stiffness at 0 degrees and decreased tensile stiffness at 180 degrees). This results in insufficient accuracy in predicting the periodic function of the gravity-induced bending moment and the corresponding endpoint displacements. The errors are particularly significant at critical positions, such as when the blades are pointing vertically downward or upward. Secondly, there is insufficient refined assessment of uneven blade load distribution. Existing technologies are mostly based on overall blade loads or rough segmentation, lacking spatial resolution for dynamic factors such as blade surface corrosion and local wind speed differences. They are unable to accurately capture the maximum load fluctuation within the radial zone of the blade and its transfer trend, and are difficult to quantify the interference torque of neighboring blades caused by non-uniform aerodynamic loads and the additional excitation of the main shaft due to sudden changes in environmental parameters when the partition rotates to a new position. In addition, the vibration coupling mechanism of the gear transmission chain is not well considered. Especially under high variable load or unbalanced conditions, the existing methods do not effectively associate the additional heat loss of the gearbox caused by the unbalanced centrifugal force of the blades and the viscosity index attenuation caused by the temperature rise of the lubricating oil, resulting in distortion of the dynamic prediction of the gear meshing friction excitation. In summary, the problems existing in the existing technology lead to excessive deviation between the predicted value and the measured value of the main shaft vibration displacement under complex operating conditions, with a high risk of false alarm or omission, making it difficult to meet the needs of high-reliability condition monitoring. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to address the problem in the prior art that under complex operating conditions, the deviation between the predicted value and the measured value of the main shaft vibration displacement is too large, and the risk of false alarm or missed alarm is high. A method and system for monitoring the main shaft vibration displacement of a wind turbine are proposed.

[0004] In order to achieve the above-mentioned object, the technical solution of a method for monitoring the vibration displacement of a main shaft of a wind turbine generator set of the present invention comprises the following steps:

[0005] S1: Construct the gravity bending moment periodic function and stiffness azimuth modulation strategy to obtain the main axis endpoint displacement caused by blade gravity;

[0006] S2: Divide the blade into a number of unit partitions, and predict the rotation transfer position of each unit partition in the next monitoring period based on the load difference indicator of each unit partition in the current monitoring period;

[0007] Comparing each unit partition on the single blade to obtain a first interference coefficient, and comparing each unit partition with the unit partition of the predicted rotation transfer position to obtain a second interference coefficient;

[0008] S3: Extracts gear torque and lubricating oil temperature, evaluates the thermal fluctuation effect of oil temperature in real time, and dynamically predicts the gear friction coefficient. The third interference coefficient is obtained from the gear friction coefficient.

[0009] S4: establishing a spindle vibration displacement prediction model, inputting the spindle endpoint displacement, the first interference coefficient, the second interference coefficient, and the third interference coefficient, and predicting and outputting a predicted value of the spindle vibration displacement in the next monitoring period;

[0010] S5: Compare the predicted value of the spindle vibration displacement in the next monitoring period with the measured spindle vibration displacement, and calculate the difference. When the calculated difference is greater than the alarm threshold, a vibration abnormality alarm is issued.

[0011] Preferably, S11: obtaining the total mass and equivalent arm length of a single blade of the wind turbine generator set, evaluating the influence of blade gravity on main shaft vibration according to a periodic gravity bending moment evaluation strategy, and obtaining a periodic gravity bending moment;

[0012] S12: Get the spindle base stiffness , the directional anisotropy of the main axis at different azimuth angles is quantified by the azimuth modulation function, and the dynamic stiffness of the main axis at different azimuth angles is obtained;

[0013] S13: The ratio of the periodic gravity bending moment output in step S11 to the dynamic stiffness of the main shaft at different azimuth angles output in step S12 is used as a linear response term, and a Sigmoid function is introduced to correct the nonlinear deformation of the main shaft under high load to obtain the main shaft endpoint displacement caused by blade gravity.

[0014] Preferably, S21: dynamically partitioning each blade of the wind turbine generator set, obtaining state parameters of each unit partition in a current monitoring period, and predicting the rotation transfer position of each unit partition in a next monitoring period based on the state parameters of each unit partition in the current monitoring period;

[0015] S211: Dynamically partition each blade of the wind turbine generator, including:

[0016] Each blade of the wind turbine generator set is divided into J unit partitions along the radial direction, where j is the unit partition index, j is an integer and ;

[0017] Establish a polar coordinate grid on the wind rotor plane, including: dividing the radial direction from the hub radius to the blade tip radius into M equally spaced rings;

[0018] In terms of circumferential angle, the 360 ​​degrees of the wind rotor rotation is divided into N equal parts;

[0019] S212: Extract the state parameters of each unit partition and construct the state matrix of each unit partition , , is the radial position of the center point of the jth unit partition, distributed along the blade from the hub to the tip; is the circumferential angle of the jth unit partition at the current moment; is the load value of the jth unit partition in the current monitoring period; is the local speed of the jth unit partition, and its initial speed is equal to the spindle speed;

[0020] S213: Calculate the average load of all partitions on a single blade during the current monitoring period, and simultaneously calculate the average load of adjacent partitions on the inner partition and boundary partition of the single blade, where when 2≤j≤J-1, it indicates that the partition belongs to the inner partition; when j=1 or j=J, it indicates that the partition belongs to the boundary partition;

[0021] S214: evaluating a load difference indicator based on average loads of adjacent partitions on the inner partition and the boundary partition, and importing the load difference indicator into a rotation position offset angle quantization strategy to obtain a rotation position offset angle;

[0022] S215: predicting the rotation transfer position of each unit partition in the next monitoring period according to the rotation position offset angle of each unit partition, and synchronously outputting the target grid index corresponding to the rotation transfer position of each unit partition in the next monitoring period.

[0023] S22: traverse all unit partitions on all blades of the wind turbine generator set to obtain the rotation transfer positions of all unit partitions in the next monitoring period, and form a rotation transfer position sequence;

[0024] S23: extracting the state parameters of each unit partition in the current monitoring period, and screening out the radial zone group with the largest load fluctuation on a single blade of the wind turbine generator set through a radial load uniformity evaluation strategy;

[0025] The unit partitions in the radial zone group with the largest load fluctuation on a single blade of the wind turbine are compared one by one to obtain the first interference coefficient of the neighboring blades on the main shaft vibration displacement during operation.

[0026] S24: extracting the real-time environmental parameters of the unit partition and the real-time environmental parameters of the unit partition corresponding to the predicted rotational transfer position within the current monitoring period according to the rotational transfer position sequence outputted in step S22;

[0027] Synchronously extracting surface morphological parameters of the unit partition and surface morphological parameters of the unit partition corresponding to the predicted rotational transfer position;

[0028] S25: According to S24, the second interference coefficient of each unit partition on a single blade of the wind turbine generator set on the main shaft vibration displacement during operation is quantified to obtain the second interference coefficient .

[0029] Preferably, S31: obtaining the second interference coefficient outputted in step S25, the main shaft speed of the wind turbine generator set, and the energy conversion loss coefficient during the operation of the wind turbine generator set, and introducing them into the unbalanced energy loss assessment strategy to obtain the unbalanced heat loss generated by the gearbox due to the unbalanced operation of the blades;

[0030] S32: Extract the unbalanced heat loss output in step S31 and simultaneously obtain the current ambient temperature of the gearbox , the current real-time oil temperature of the inter-tooth lubricating oil , the current gear heat dissipation coefficient h and the current tooth surface heat dissipation area A, build a quantitative model of the lubricating oil temperature change trend, and predict the real-time change rate of the lubricating oil temperature;

[0031] S33: Obtain the real-time change rate of lubricating oil temperature based on the prediction, and predict and update the real-time oil temperature in the next monitoring period ;

[0032] S34: According to the updated real-time oil temperature in the next monitoring period , dynamically predict the gear friction coefficient and obtain the predicted value of the gear friction coefficient;

[0033] S35: Based on the predicted value of the gear friction coefficient output in step S34, evaluate the third interference coefficient of the gear teeth meshing on the main shaft vibration displacement in the next monitoring period compared with the current monitoring period. .

[0034] Preferably, S41: extract the first interference coefficient outputted from step S23, the second interference coefficient outputted from step S25 and the third interference coefficient outputted from step S3, and simultaneously extract the spindle end displacement caused by the blade gravity outputted from step S13 , Under the balanced working condition of the wind turbine, the blade end connected to the main shaft produces the blade response baseline displacement relative to the main shaft and the average baseline displacement of the meshing response of each meshing tooth pair in the gearbox connected to the main shaft relative to the main shaft ;

[0035] S42: importing the data extracted in S41 into the spindle vibration displacement prediction model, and predicting and outputting the predicted value of the spindle vibration displacement in the next monitoring period;

[0036] In addition, the present invention provides a wind turbine main shaft vibration displacement monitoring system comprising the following modules:

[0037] Gravity bending moment quantification module, first interference coefficient acquisition module, second interference coefficient acquisition module, third interference coefficient acquisition module, vibration displacement prediction module and alarm module;

[0038] The gravity bending moment quantification module is used to construct a gravity bending moment periodic function and a stiffness azimuth modulation strategy to obtain the main shaft endpoint displacement caused by blade gravity;

[0039] The first interference coefficient acquisition module is used to compare each unit partition on a single blade to obtain a first interference coefficient;

[0040] The second interference coefficient acquisition module compares each unit partition with the unit partition of the predicted rotation transfer position to obtain a second interference coefficient;

[0041] The third interference coefficient acquisition module extracts the gear torque and lubricating oil temperature, evaluates the thermal fluctuation effect of the oil temperature in real time, and dynamically predicts the gear friction coefficient, and obtains the third interference coefficient based on the gear friction coefficient;

[0042] The vibration displacement prediction module is used to establish a spindle vibration displacement prediction model and predict and output a predicted value of the spindle vibration displacement in the next monitoring period;

[0043] The alarm module is used to compare the predicted value of the spindle vibration displacement in the next monitoring period with the measured spindle vibration displacement and calculate the difference. When the calculated difference is greater than the alarm threshold, a vibration abnormality alarm is issued.

[0044] Compared with the prior art, the technical effects of the present invention are as follows:

[0045] 1. The present invention constructs a periodic function of gravity bending moment (including the third harmonic) and a stiffness azimuth modulation strategy to accurately quantify the nonlinear changes in the dynamic stiffness of the main shaft at critical positions such as the blade's vertical downward / upward position (for example, increased compressive stiffness at 0 degrees and decreased tensile stiffness at 180 degrees). The Sigmoid function is then used to correct plastic deformation under high loads, thereby reducing the prediction error of endpoint displacement caused by gravity.

[0046] 2. This invention uses dynamic zoning and rotational transfer position prediction mechanisms to divide zones into radial groups and calculate load difference indicators, accurately capturing extreme load fluctuations on the blade surface. A rotational position offset angle quantization strategy is used to predict high-load zone transfer trajectories in advance, achieving collaborative decoupling of the first interference coefficient (load imbalance interference from neighboring blades) and the second interference coefficient (pressure difference interference caused by sudden environmental parameter changes), thereby improving the accuracy of identifying non-uniform aerodynamic load excitations.

[0047] 3. The present invention constructs a full-link model from blade unbalanced centrifugal force → gearbox heat loss → lubricating oil temperature rise → nonlinear sharp increase in tooth surface friction coefficient, solving the problem of gear excitation misjudgment caused by ignoring the temperature rise-viscosity-friction coupling effect in the existing technology.

[0048] The present invention integrates the triple interference coefficients of gravity periodic modulation, blade unit partition load transfer, and gear thermal friction time-varying to construct a main shaft vibration displacement prediction model. Under complex working conditions such as variable pitch imbalance and turbulent mutation, the deviation between the predicted value and the measured value is reduced, the false alarm rate is reduced, abnormal main shaft vibration is identified in advance, and a key decision-making window is provided for the safe regulation of the unit. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] 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 those skilled in the art, other drawings can be derived from these drawings without inventive effort. Among them:

[0050] Figure 1 Schematic diagram of a flow chart of a method for monitoring the vibration displacement of a main shaft of a wind turbine generator set according to the present invention;

[0051] Figure 2 Schematic diagram of the process of step S21 of the present invention;

[0052] Figure 3 The figure is a structural diagram of a wind turbine main shaft vibration displacement monitoring system according to the present invention. DETAILED DESCRIPTION

[0053] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0054] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0055] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0056] Embodiment 1;

[0057] like Figure 1 As shown, a method for monitoring the vibration displacement of the main shaft of a wind turbine generator set according to an embodiment of the present invention is as follows: Figure 1 As shown, the specific steps are as follows:

[0058] S1: Construct the gravity bending moment periodic function and stiffness azimuth modulation strategy to obtain the main axis endpoint displacement caused by blade gravity;

[0059] S11: Obtain the total mass and equivalent arm length of a single blade of the wind turbine generator set, evaluate the influence of blade gravity on main shaft vibration according to the periodic gravity bending moment evaluation strategy, and obtain the periodic gravity bending moment;

[0060] For example, in this embodiment, a periodic gravity bending moment evaluation strategy is provided, specifically:

[0061] ;

[0062] in, is the periodic gravity bending moment of a single blade of a wind turbine;

[0063] is the total mass of a single blade, representing the inertia of the blade;

[0064] g is the acceleration of gravity, which represents the real-time gravitational field strength of a single blade;

[0065] is the equivalent force arm length, the distance from the center of mass to the principal axis, which determines the gravity force arm;

[0066] is the blade azimuth angle, which changes with time and is used to reflect the blade rotation position;

[0067] It should be noted that the component of gravity in the direction perpendicular to the principal axis is ,when (i.e., when the blades are pointing vertically downward), the gravitational bending moment is the largest, which is in line with the principles of statics. At the same time, in this embodiment, a three-blade wind turbine is taken as an example. In the three-blade structure, each blade is spaced 120 degrees apart. During rotation, vibrations are superimposed at positions of 120 degrees and 240 degrees. The third harmonic is introduced to describe the periodic coupled vibration, based on the decomposition of the periodic load using the Fourier series.

[0068] It should also be noted that, in this embodiment, the periodic gravity bending moment evaluation strategy provided describes the periodic variation of the bending moment caused by gravity with the azimuth angle, and the fundamental frequency component Reflecting the change in gravity direction, the third harmonic characterizes the vibration coupling of the three-blade structure and provides load input for subsequent displacement calculations.

[0069] S12: Get the spindle base stiffness , the directional anisotropy of the main axis at different azimuth angles is quantified by the azimuth modulation function, and the dynamic stiffness of the main axis at different azimuth angles is obtained;

[0070] For example, in this embodiment, a strategy for obtaining the dynamic stiffness of the main shaft in different directions is provided, specifically:

[0071] ;

[0072] in, The main axis is at the blade azimuth angle The dynamic stiffness corresponding to the unit displacement under ;

[0073] In this embodiment, is the blade azimuth modulation function;

[0074] It should be noted that the main shaft exhibits nonlinear stiffness changes at different blade azimuths due to changes in force direction or structural deformation. When it is equal to 0 degrees (the blades are pointing vertically downward), the main shaft is under compressive stress and has a high stiffness; when When it is equal to 180 degrees (the blades are pointing vertically upward), the stiffness will decrease due to structural relaxation or gap effect.

[0075] S13: The ratio of the periodic gravity bending moment output in step S11 to the dynamic stiffness of the main shaft at different azimuth angles output in step S12 is used as a linear response term, and a Sigmoid function is introduced to correct the nonlinear deformation of the main shaft under high load to obtain the main shaft endpoint displacement caused by blade gravity.

[0076] For example, in this embodiment, a strategy for obtaining the displacement of the main shaft end point caused by blade gravity is provided, specifically:

[0077] ;

[0078] in, is the displacement of the main shaft end point caused by the blade gravity; is a linear response term; it should be noted that the Sigmoid function, that is ,exist When , it approaches 1, , approaches 0.

[0079] Here through To ensure that the nonlinearity is only related to the load amplitude, When it is large, the denominator approaches 1 and the displacement approaches a linear response; when When it is small, the denominator increases and the displacement is suppressed. It is used to simulate the stiffness softening caused by the material entering the plastic stage under high load or contact nonlinearity (such as gap closure).

[0080] S2: Divide the blade into a number of unit partitions, and predict the rotation transfer position of each unit partition in the next monitoring period based on the load difference indicator of each unit partition in the current monitoring period;

[0081] S21: Dynamically partition each blade of the wind turbine generator set, obtain state parameters of each unit partition in the current monitoring period, and predict the rotation transfer position of each unit partition in the next monitoring period based on the state parameters of each unit partition in the current monitoring period;

[0082] S22: traverse all unit partitions on all blades of the wind turbine generator set to obtain the rotation transfer positions of all unit partitions in the next monitoring period, and form a rotation transfer position sequence;

[0083] S23: extracting the state parameters of each unit partition in the current monitoring period, and screening out the radial zone group with the largest load fluctuation on a single blade of the wind turbine generator set through a radial load uniformity evaluation strategy;

[0084] The unit partitions in the radial zone group with the largest load fluctuation on a single blade of the wind turbine are compared one by one to obtain the first interference coefficient of the neighboring blades on the main shaft vibration displacement during operation.

[0085] For example, in this embodiment, an implementation strategy for radial load uniformity assessment strategy is provided, specifically:

[0086] For each blade, the unit partition is divided into multiple radial groups along the direction from the blade root to the blade tip. The width of each radial group is equal to the width of the unit partition, wherein each radial group contains Unit partitions;

[0087] Collect the load values ​​of all unit partitions in each radial zone group during the current monitoring period, and calculate the average load level of the load values ​​of all unit partitions;

[0088] For radial grouping, calculate the difference between the load value of each unit partition and the average load level, and at the same time count the sum of the ratios of the difference between the load value of all unit partitions and the average load level and the load fluctuation allowable range. Divide the sum of the ratios by the number of unit partitions in the radial grouping to obtain the load fluctuation degree of this radial grouping;

[0089] Traversing all radial groups on a single blade, obtaining a set of load fluctuation degrees of all radial groups on a single blade, and screening out the radial group with the largest load fluctuation degree on a single blade of the wind turbine generator set;

[0090] In S2, the first interference coefficient is obtained by comparing each unit partition on the single blade, including:

[0091] For example, in this embodiment, a first interference coefficient is provided. The acquisition strategy is as follows:

[0092] ;

[0093] in, These are the index numbers of different blades of wind turbine generator sets. , I is the total number of blades of the wind turbine;

[0094] are the load values ​​of the jth unit partition in the radial zone group with the largest load fluctuation on the ath blade and the bth blade of the wind turbine generator set;

[0095] The maximum load value and the minimum load value of all unit partitions of the radial zone group with the largest load fluctuation on the a-th blade and the b-th blade of the wind turbine generator set are respectively;

[0096] In S2, each unit partition and the unit partition of the predicted rotation transfer position are compared to obtain a second interference coefficient, including:

[0097] S24: extracting the real-time environmental parameters of the unit partition and the real-time environmental parameters of the unit partition corresponding to the predicted rotational transfer position within the current monitoring period according to the rotational transfer position sequence outputted in step S22;

[0098] Synchronously extracting surface morphological parameters of the unit partition and surface morphological parameters of the unit partition corresponding to the predicted rotational transfer position;

[0099] S25: According to S24, the second interference coefficient of each unit partition on a single blade of the wind turbine generator set on the main shaft vibration displacement during operation is quantified to obtain the second interference coefficient ;

[0100] For example, in this embodiment, a second interference coefficient is provided. The quantitative strategy is as follows:

[0101] ;

[0102] in, The target grid index of the unit partition corresponding to the rotation transfer position of the j-th unit partition in the next monitoring period;

[0103] They are the jth unit partition and the jth unit partition on the blade in the current monitoring period. The ratio of the eroded area of ​​the blade surface per unit partition;

[0104] They are the jth unit partition and the jth unit partition on the blade in the current monitoring period. Real-time ambient wind speed value of each unit partition;

[0105] It should be noted that corrosion on the blade surface can alter the blade's aerodynamic shape and affect its aerodynamic performance. Even in different regions under the same real-time environmental parameters, the changes in aerodynamic performance due to corrosion are more significant, further affecting the blade's load conditions. Therefore, the second interference coefficient quantification strategy provided in this embodiment compares the corrosion area ratio to assess the degree of change in aerodynamic performance in different regions, thereby predicting and inferring load fluctuations in those regions.

[0106] It should also be noted that the aerodynamic load on wind turbine blades is primarily generated by the force of the airflow on the blades. According to the basic principles of aerodynamics, the aerodynamic forces (such as lift and drag) acting on the blades are proportional to the square of the wind speed. In common wind turbine aerodynamic models, the formula for calculating aerodynamic forces can usually be expressed as:

[0107] ;

[0108] Where F is the aerodynamic force, is the air density, C is the relevant aerodynamic coefficient (such as lift coefficient, drag coefficient), is the frontal area of ​​the blade, It is the ambient wind speed. From this formula, it can be clearly seen that when other parameters remain unchanged, the ambient wind speed The change of will directly and significantly affect the size of the aerodynamic force F. A slight increase in wind speed will increase the aerodynamic force by multiples of the square, causing the load on the blade to increase sharply.

[0109] Although parameters such as wind direction, turbulence intensity and air density also have an impact on blade load, their impact is relatively indirect or needs to work together with other factors such as wind speed to be clearly reflected. For example, wind direction mainly affects the distribution of load on the blades; turbulence intensity mainly affects the fluctuation characteristics of the load and the fatigue life of the blades; although air density is proportional to the load, its changes are relatively slow and limited compared to wind speed. Therefore, wind speed is the parameter that most directly affects the load value of the wind turbine blades. Therefore, in the quantification strategy of the second interference coefficient provided in this embodiment, the real-time environmental wind speed is selected as the evaluation parameter;

[0110] S3: Extracts gear torque and lubricating oil temperature, evaluates the thermal fluctuation effect of oil temperature in real time, and dynamically predicts the gear friction coefficient. The third interference coefficient is obtained from the gear friction coefficient.

[0111] S31: Obtain the second interference coefficient output in step S25, the main shaft speed of the wind turbine generator set, and the energy conversion loss coefficient during the operation of the wind turbine generator set, and introduce them into the unbalanced energy loss assessment strategy to obtain the unbalanced heat loss generated by the gearbox operating under unbalanced blade conditions;

[0112] For example, in this embodiment, an unbalanced heat loss is provided. The calculation strategy is as follows: ,in, is the energy conversion loss coefficient during the operation of the wind turbine generator set; is the main shaft speed of the wind turbine;

[0113] It should be noted that for a rotating wind turbine, the centrifugal force generated by the unbalanced force for , where m is mass and e is eccentricity, so the energy loss is related to the work done by centrifugal force, that is, At the same time, the second interference coefficient reflects the load imbalance degree of the wind turbine during the working process from the current monitoring period to the next monitoring period. It should be noted that the higher the main shaft speed and the greater the imbalance degree, the greater the vibration and friction loss caused by the centrifugal force, so the unbalanced heat loss Proportional to the product of the two.

[0114] S32: Extract the unbalanced heat loss output in step S31 and simultaneously obtain the current ambient temperature of the gearbox , the current real-time oil temperature of the inter-tooth lubricating oil , the current gear heat dissipation coefficient h and the current tooth surface heat dissipation area A, build a quantitative model of the lubricating oil temperature change trend, and predict the real-time change rate of the lubricating oil temperature;

[0115] For example, in this embodiment, an output formula of a lubricating oil temperature variation trend quantitative model is provided, specifically:

[0116] ;

[0117] in, is the predicted value of the real-time change rate of the lubricating oil temperature; is the mass of lubricating oil on the gears in the gearbox; is the specific heat capacity of the lubricating oil on the gear;

[0118] It should be noted that the prediction of the real-time rate of change of lubricating oil temperature is based on the first law of thermodynamics (i.e. conservation of energy), input energy (unbalanced heat loss ) minus the heat dissipation energy ( ) is equal to the change in internal energy of the oil.

[0119] It should be noted that under low loads, the contact pressure between the tooth surfaces is low, making the lubricating film relatively easy to form and maintain. At this point, the friction coefficient depends primarily on the properties of the lubricant and the surface roughness of the tooth surfaces. Due to the low contact pressure, the microscopic deformation between the tooth surfaces is also small, and the lubricating oil temperature is relatively balanced, resulting in a relatively low and stable friction coefficient. However, with increasing loads or unbalanced operating conditions, heat builds between the tooth surfaces, causing the oil temperature to rise. Increased oil temperature reduces the viscosity of the lubricant. When the viscosity drops to a certain level, the lubricating film's load-bearing capacity decreases, making it more susceptible to damage, further increasing the friction coefficient.

[0120] S33: Obtain the real-time change rate of lubricating oil temperature based on the prediction, and predict and update the real-time oil temperature in the next monitoring period ;

[0121] Among them, the real-time oil temperature in the next monitoring period To predict the real-time change rate of lubricating oil temperature The product of the unit monitoring period and the current real-time oil temperature of the inter-tooth lubricating oil The sum of

[0122] S34: According to the updated real-time oil temperature in the next monitoring period , dynamically predict the gear friction coefficient and obtain the predicted value of the gear friction coefficient;

[0123] For example, in this embodiment, a strategy for obtaining a predicted value of a gear friction coefficient is provided, specifically:

[0124] ;

[0125] in, is the predicted value of the gear friction coefficient; is the average friction coefficient between all teeth on the gear under equilibrium conditions;

[0126] is the temperature sensitivity coefficient of the lubricating oil; is the real-time load torque of the gear, is the critical contact load torque;

[0127] In this embodiment, it should be noted that, referring to the Andrade formula, the viscosity of the lubricating oil decreases exponentially with increasing temperature, and the friction coefficient is positively correlated with the viscosity under the fluid lubrication state, so , its physical meaning is that when the lubricating oil temperature increases, the lubricating oil viscosity decreases and the friction coefficient decreases;

[0128] In this embodiment, it should be noted that is the empirical coefficient of nonlinear response. In mixed lubrication or boundary lubrication, the friction coefficient increases with the increase of contact pressure. That is, when the real-time load torque of the gear exceeds the critical contact load, the direct contact of the surface micro-asperities increases, and the friction coefficient increases nonlinearly with the load.

[0129] S35: Based on the predicted value of the gear friction coefficient output in step S34, evaluate the third interference coefficient of the gear teeth meshing on the main shaft vibration displacement in the next monitoring period compared with the current monitoring period. .

[0130] For example, in this embodiment, a strategy for obtaining the third interference coefficient is provided, specifically:

[0131] ;

[0132] in, is the gear friction coefficient when the gear teeth are meshing during the current monitoring period;

[0133] This is the predicted value of the gear friction coefficient output in step S34.

[0134] S4: establishing a spindle vibration displacement prediction model, inputting the spindle endpoint displacement, the first interference coefficient, the second interference coefficient, and the third interference coefficient, and predicting and outputting a predicted value of the spindle vibration displacement in the next monitoring period;

[0135] S41: Extract the first interference coefficient output from step S23, the second interference coefficient output from step S25, and the third interference coefficient output from step S3, and simultaneously extract the spindle end displacement caused by the blade gravity output from step S13 , Under the balanced working condition of the wind turbine, the blade end connected to the main shaft produces the blade response baseline displacement relative to the main shaft and the average baseline displacement of the meshing response of each meshing tooth pair in the gearbox connected to the main shaft relative to the main shaft ;

[0136] S42: importing the data extracted in S41 into the spindle vibration displacement prediction model, and predicting and outputting the predicted value of the spindle vibration displacement in the next monitoring period;

[0137] For example, in this embodiment, a strategy for outputting the predicted value of the spindle vibration displacement in the next monitoring period is provided, specifically:

[0138]

[0139] in, It is the predicted value of the spindle vibration displacement in the next monitoring period.

[0140] S5: Compare the predicted value of the spindle vibration displacement in the next monitoring period with the measured spindle vibration displacement, and calculate the difference. When the calculated difference is greater than the alarm threshold, a vibration abnormality alarm is issued.

[0141] Embodiment 2:

[0142] like Figure 2 As shown, an embodiment of the present invention is an implementation method for predicting the rotation transfer position of each unit partition in the next monitoring period based on the load difference indicator of each unit partition in the current monitoring period, specifically:

[0143] S211: Dynamically partition each blade of the wind turbine generator, including:

[0144] Each blade of the wind turbine generator set is divided into J unit partitions along the radial direction, where j is the unit partition index, j is an integer and ;

[0145] Establish the polar coordinate grid of the wind wheel plane, including: in the radial direction, from the hub radius Radius to blade tip , is evenly divided into M equally spaced rings, where the radial step length is ;

[0146] In terms of circumferential angle, the 360 ​​degrees of the wind wheel rotation is divided into N equal parts, where the circumferential angle step is ;

[0147] S212: Extract the state parameters of each unit partition and construct the state matrix of each unit partition. , is the state matrix of the j-th unit partition; is the radial position of the center point of the jth unit partition, distributed along the blade from the hub to the tip; is the circumferential angle of the jth unit partition at the current moment; is the load value of the jth unit partition in the current monitoring period; is the local speed of the jth unit partition, and its initial speed is equal to the spindle speed;

[0148] For example, in this embodiment, , is the overall azimuth angle of the i-th blade; is the installation deflection angle of the jth unit partition on the blade, which is used to reflect the structural characteristics of the blade such as torsion;

[0149] S213: Calculate the average load of all partitions on a single blade during the current monitoring period, and simultaneously calculate the average load of adjacent partitions on the inner partition and boundary partition of the single blade, where when 2≤j≤J-1, it indicates that the partition belongs to the inner partition; when j=1 or j=J, it indicates that the partition belongs to the boundary partition;

[0150] For example, in this embodiment, the calculation strategy for the average load of adjacent partitions on the inner partition and the boundary partition is:

[0151] ;

[0152] in, is the average load of the adjacent partitions of the jth unit partition;

[0153] is the load value of the j-1th and j+1th unit partitions in the current monitoring period;

[0154] S214: evaluating a load difference indicator based on average loads of adjacent partitions on the inner partition and the boundary partition, and importing the load difference indicator into a rotation position offset angle quantization strategy to obtain a rotation position offset angle;

[0155] For example, in this embodiment, a calculation strategy for the load difference indicator is provided, specifically: ;

[0156] in, is the load difference indicator of the j-th unit partition;

[0157] For example, in this embodiment, an implementation of a rotation position offset angle quantization strategy is also provided, specifically:

[0158] ;

[0159] in, is the rotation position offset angle of the jth unit partition;

[0160] is the offset coefficient. In this embodiment, based on the calibration results of wind tunnel tests, structural strain tests, and simulations of 200 wind turbines, a value range of the offset coefficient is provided, specifically: ;

[0161] The maximum allowable load per unit partition of the blade;

[0162] It should be noted that the physical meaning of the rotational position offset angle quantization strategy provided in this embodiment is as follows: if the load of the current partition is higher than that of the adjacent partition, that is, the load difference indicator is greater than 0, then when the blade rotates from the current monitoring period to the next monitoring period, compared with a normal blade with a uniform load distribution, a negative rotational position offset angle (lag) will be generated; conversely, if the load of the current partition is lower than that of the adjacent partition, that is, the load difference indicator is less than 0, then when the blade rotates from the current monitoring period to the next monitoring period, compared with a normal blade with a uniform load distribution, a positive rotational position offset angle (advance) will be generated;

[0163] S215: predicting the rotational transfer position of each unit partition in the next monitoring period based on the rotational position offset angle of each unit partition output in step S214, and synchronously outputting the target grid index corresponding to the rotational transfer position of each unit partition in the next monitoring period;

[0164] For example, in this embodiment, a prediction strategy for the rotation transfer position of each unit partition in the next monitoring period is provided, specifically:

[0165] Calculate the blade base rotation angle , ; is the main shaft speed of the wind turbine; is the monitoring unit time step;

[0166] The overall rotation angle of each unit partition is calculated based on the basic rotation angle of the blade, specifically:

[0167] ,

[0168] is the overall rotation angle of the j-th unit partition;

[0169] is the circumferential angle of the jth unit partition at the current moment;

[0170] is the blade base rotation angle;

[0171] is the rotation position offset angle of the jth unit partition;

[0172] It should be noted that the total rotation angle is the superposition of the "basic rotation" and the "load disturbance", and is finally The operation constrains the angle to be within the period range (0≤θ<2π).

[0173] According to the overall rotation angle of each unit partition, the target grid index corresponding to the rotation transfer position of each unit partition in the next monitoring period is output. Specifically, the ratio of the overall rotation angle to the circumferential angle step is rounded down using a floor function to obtain the target grid index.

[0174] Embodiment 3;

[0175] like Figure 3 As shown, a wind turbine main shaft vibration displacement monitoring system according to an embodiment of the present invention is as follows: Figure 3 As shown, it includes the following modules:

[0176] Gravity bending moment quantification module, first interference coefficient acquisition module, second interference coefficient acquisition module, third interference coefficient acquisition module, vibration displacement prediction module and alarm module;

[0177] The gravity bending moment quantification module is used to construct a gravity bending moment periodic function and a stiffness azimuth modulation strategy to obtain the main shaft endpoint displacement caused by blade gravity;

[0178] The first interference coefficient acquisition module is used to compare each unit partition on a single blade to obtain a first interference coefficient;

[0179] The second interference coefficient acquisition module compares each unit partition with the unit partition of the predicted rotation transfer position to obtain a second interference coefficient;

[0180] The third interference coefficient acquisition module extracts the gear torque and lubricating oil temperature, evaluates the thermal fluctuation effect of the oil temperature in real time, and dynamically predicts the gear friction coefficient, and obtains the third interference coefficient based on the gear friction coefficient;

[0181] The vibration displacement prediction module is used to establish a spindle vibration displacement prediction model and predict and output a predicted value of the spindle vibration displacement in the next monitoring period;

[0182] The alarm module is used to compare the predicted value of the spindle vibration displacement in the next monitoring period with the measured spindle vibration displacement and calculate the difference. When the calculated difference is greater than the alarm threshold, a vibration abnormality alarm is issued.

[0183] Embodiment 4;

[0184] This embodiment provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0185] The processor executes the above-mentioned method for monitoring the vibration displacement of the main shaft of a wind turbine generator set by calling the computer program stored in the memory.

[0186] This electronic device can vary significantly depending on its configuration or performance, and can include one or more processors (Central Processing Units, CPUs) and one or more memories, wherein the memories store at least one computer program, which is loaded and executed by the processor to implement the wind turbine main shaft vibration displacement monitoring method provided in the above-mentioned method embodiment. The electronic device can also include other components for implementing the device's functions. For example, the electronic device can also include components such as wired or wireless network interfaces and input / output interfaces for data input and output. This embodiment is not described in detail here.

[0187] Embodiment 5;

[0188] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0189] When the computer program is run on a computer device, the computer device is caused to execute the above-mentioned method for monitoring the vibration displacement of a main shaft of a wind turbine generator set.

[0190] For example, computer-readable storage media can be read-only memory (ROM), random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device.

[0191] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0192] It should be understood that determining B based on A does not mean determining B based solely on A. B can also be determined based on A and / or other information.

[0193] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are fully or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0194] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0195] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0196] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for monitoring the vibration displacement of a main shaft of a wind turbine generator set, characterized in that: The method comprises: S1: Construct the gravity bending moment periodic function and stiffness azimuth modulation strategy to obtain the main axis endpoint displacement caused by blade gravity; S2: Divide the blade into a number of unit partitions, and predict the rotation transfer position of each unit partition in the next monitoring period based on the load difference indicator of each unit partition in the current monitoring period; Comparing each unit partition on the single blade to obtain a first interference coefficient, and comparing each unit partition with the unit partition of the predicted rotation transfer position to obtain a second interference coefficient; S3: Extracts gear torque and lubricating oil temperature, evaluates the thermal fluctuation effect of oil temperature in real time, and dynamically predicts the gear friction coefficient. The third interference coefficient is obtained from the gear friction coefficient. S4: establishing a spindle vibration displacement prediction model, inputting the spindle endpoint displacement, the first interference coefficient, the second interference coefficient, and the third interference coefficient, and predicting and outputting a predicted value of the spindle vibration displacement in the next monitoring period; S5: Compare the predicted value of the spindle vibration displacement in the next monitoring period with the measured spindle vibration displacement, and calculate the difference. When the calculated difference is greater than the alarm threshold, a vibration abnormality alarm is issued.

2. A method for monitoring the vibration displacement of a main shaft of a wind turbine generator set according to claim 1, characterized in that: S1 includes: S11: Obtain the total mass and equivalent arm length of a single blade of the wind turbine generator set, evaluate the influence of blade gravity on main shaft vibration according to the periodic gravity bending moment evaluation strategy, and obtain the periodic gravity bending moment; S12: Get the spindle base stiffness , the directional anisotropy of the main axis at different azimuth angles is quantified by the azimuth modulation function, and the dynamic stiffness of the main axis at different azimuth angles is obtained; S13: The ratio of the periodic gravity bending moment output in step S11 to the dynamic stiffness of the main shaft at different azimuth angles output in step S12 is used as a linear response term, and a Sigmoid function is introduced to correct the nonlinear deformation of the main shaft under high load to obtain the main shaft endpoint displacement caused by blade gravity.

3. A method for monitoring the vibration displacement of a main shaft of a wind turbine generator set according to claim 2, characterized in that S2 include: S21: Dynamically partition each blade of the wind turbine generator set, obtain state parameters of each unit partition in the current monitoring period, and predict the rotation transfer position of each unit partition in the next monitoring period based on the state parameters of each unit partition in the current monitoring period; S22: traverse all unit partitions on all blades of the wind turbine generator set to obtain the rotation transfer positions of all unit partitions in the next monitoring period, and form a rotation transfer position sequence; S23: extracting the state parameters of each unit partition in the current monitoring period, and screening out the radial zone group with the largest load fluctuation on a single blade of the wind turbine generator set through a radial load uniformity evaluation strategy; The unit partitions in the radial zone group with the largest load fluctuation on a single blade of the wind turbine are compared one by one to obtain the first interference coefficient of the neighboring blades on the main shaft vibration displacement during operation.

4. A method for monitoring the vibration displacement of a main shaft of a wind turbine generator set according to claim 3, characterized in that: S2 also includes: S24: extracting the real-time environmental parameters of the unit partition and the real-time environmental parameters of the unit partition corresponding to the predicted rotational transfer position within the current monitoring period according to the rotational transfer position sequence outputted in step S22; Synchronously extracting surface morphological parameters of the unit partition and surface morphological parameters of the unit partition corresponding to the predicted rotational transfer position; S25: According to S24, the second interference coefficient of each unit partition on a single blade of the wind turbine generator set on the main shaft vibration displacement during operation is quantified to obtain the second interference coefficient .

5. A method for monitoring the vibration displacement of a main shaft of a wind turbine generator set according to claim 4, characterized in that: S21 includes the following specific steps: S211: Dynamically partition each blade of the wind turbine generator, including: Each blade of the wind turbine generator set is divided into J unit partitions along the radial direction, where j is the unit partition index, j is an integer and ; Establish a polar coordinate grid on the wind rotor plane, including: dividing the radial direction from the hub radius to the blade tip radius into M equally spaced rings; In terms of circumferential angle, the 360 ​​degrees of the wind rotor rotation is divided into N equal parts; S212: Extract the state parameters of each unit partition and construct the state matrix of each unit partition , , is the radial position of the center point of the jth unit partition, distributed along the blade from the hub to the tip; is the circumferential angle of the jth unit partition at the current moment; is the load value of the jth unit partition in the current monitoring period; is the local speed of the jth unit partition, and its initial speed is equal to the spindle speed; S213: Calculate the average load of all partitions on a single blade during the current monitoring period, and simultaneously calculate the average load of adjacent partitions on the inner partition and boundary partition of the single blade, where when 2≤j≤J-1, it indicates that the partition belongs to the inner partition; when j=1 or j=J, it indicates that the partition belongs to the boundary partition; S214: evaluating a load difference indicator based on average loads of adjacent partitions on the inner partition and the boundary partition, and importing the load difference indicator into a rotation position offset angle quantization strategy to obtain a rotation position offset angle; S215: predicting the rotation transfer position of each unit partition in the next monitoring period according to the rotation position offset angle of each unit partition, and synchronously outputting the target grid index corresponding to the rotation transfer position of each unit partition in the next monitoring period.

6. A method for monitoring the vibration displacement of a main shaft of a wind turbine generator set according to claim 5, characterized in that S3 include: S31: Obtain the second interference coefficient output in step S25, the main shaft speed of the wind turbine generator set, and the energy conversion loss coefficient during the operation of the wind turbine generator set, and introduce them into the unbalanced energy loss assessment strategy to obtain the unbalanced heat loss generated by the gearbox operating under unbalanced blade conditions; S32: Extract the unbalanced heat loss output in step S31 and simultaneously obtain the current ambient temperature of the gearbox , the current real-time oil temperature of the inter-tooth lubricating oil , the current gear heat dissipation coefficient h and the current tooth surface heat dissipation area A, build a quantitative model of the lubricating oil temperature change trend, and predict the real-time change rate of the lubricating oil temperature; S33: Obtain the real-time change rate of lubricating oil temperature based on the prediction, and predict and update the real-time oil temperature in the next monitoring period ; S34: According to the updated real-time oil temperature in the next monitoring period , dynamically predict the gear friction coefficient and obtain the predicted value of the gear friction coefficient.

7. A method for monitoring the vibration displacement of a main shaft of a wind turbine generator set according to claim 6, characterized in that: S3 also includes: S35: Based on the predicted value of the gear friction coefficient output in step S34, evaluate the third interference coefficient of the gear teeth meshing on the main shaft vibration displacement in the next monitoring period compared with the current monitoring period. .

8. A method for monitoring the vibration displacement of a main shaft of a wind turbine generator set according to claim 7, characterized in that S4 include: S41: Extract the first interference coefficient output from step S23, the second interference coefficient output from step S25, and the third interference coefficient output from step S3, and simultaneously extract the spindle endpoint displacement caused by the blade gravity output from step S13 , Under the balanced working condition of the wind turbine, the blade end connected to the main shaft produces the blade response baseline displacement relative to the main shaft and the average baseline displacement of the meshing response of each meshing tooth pair in the gearbox connected to the main shaft relative to the main shaft ; S42: Importing the data extracted in S41 into the spindle vibration displacement prediction model, and predicting and outputting a predicted value of the spindle vibration displacement in the next monitoring period.

9. A wind turbine generator main shaft vibration displacement monitoring system, used to implement a wind turbine main shaft vibration displacement monitoring method according to any one of claims 1 to 8, characterized in that: The system includes the following modules: Gravity bending moment quantification module, first interference coefficient acquisition module, second interference coefficient acquisition module, third interference coefficient acquisition module, vibration displacement prediction module and alarm module; The gravity bending moment quantification module is used to construct a gravity bending moment periodic function and a stiffness azimuth modulation strategy to obtain the main shaft endpoint displacement caused by blade gravity; The first interference coefficient acquisition module is used to compare each unit partition on a single blade to obtain a first interference coefficient; The second interference coefficient acquisition module compares each unit partition with the unit partition of the predicted rotation transfer position to obtain a second interference coefficient; The third interference coefficient acquisition module extracts the gear torque and lubricating oil temperature, evaluates the thermal fluctuation effect of the oil temperature in real time, and dynamically predicts the gear friction coefficient, and obtains the third interference coefficient based on the gear friction coefficient; The vibration displacement prediction module is used to establish a spindle vibration displacement prediction model and predict and output a predicted value of the spindle vibration displacement in the next monitoring period; The alarm module is used to compare the predicted value of the spindle vibration displacement in the next monitoring period with the measured spindle vibration displacement and calculate the difference. When the calculated difference is greater than the alarm threshold, a vibration abnormality alarm is issued.

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