CN120969089A - Blade fault diagnosis method and device based on vibration of wind generating set - Google Patents
Blade fault diagnosis method and device based on vibration of wind generating setInfo
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- CN120969089A CN120969089A CN202511494495.2A CN202511494495A CN120969089A CN 120969089 A CN120969089 A CN 120969089A CN 202511494495 A CN202511494495 A CN 202511494495A CN 120969089 A CN120969089 A CN 120969089A
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
- F03D17/005—Monitoring or testing of wind motors, e.g. diagnostics using computation methods, e.g. neural networks
- F03D17/0065—Monitoring or testing of wind motors, e.g. diagnostics using computation methods, e.g. neural networks for diagnostics
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
- F03D17/009—Monitoring or testing of wind motors, e.g. diagnostics characterised by the purpose
- F03D17/013—Monitoring or testing of wind motors, e.g. diagnostics characterised by the purpose for detecting abnormalities or damage
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
- F03D17/009—Monitoring or testing of wind motors, e.g. diagnostics characterised by the purpose
- F03D17/015—Monitoring or testing of wind motors, e.g. diagnostics characterised by the purpose for monitoring vibrations
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
- F03D17/027—Monitoring or testing of wind motors, e.g. diagnostics characterised by the component being monitored or tested
- F03D17/028—Blades
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/10—Pre-processing; Data cleansing
- G06F18/15—Statistical pre-processing, e.g. techniques for normalisation or restoring missing data
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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Abstract
The invention provides a blade fault diagnosis method and device based on wind generating set vibration, which relates to the technical field of blade fault diagnosis of wind generating set vibration, the invention synchronously collects vibration signals at the cabin foundation and the blade root, after filtering and steady-state working condition screening, the first-order modal frequency and damping ratio of the blade under different working conditions are accurately identified through phase compensation and frequency response function calculation, and sequentially connecting modal parameters of a plurality of working points to construct a dynamic response track, forming a local mode feature vector by calculating relative vectors of each point and other points in the track, integrating the local mode feature vector into a global mode feature matrix, obtaining a track mode deviation index by calculating Frobenius norms of a matrix to be monitored and a reference matrix, and judging structural damage according to a statistics rule of continuous super-threshold of the index.
Description
Technical Field
The invention relates to the technical field of blade fault diagnosis of vibration of a wind generating set, in particular to a blade fault diagnosis method and device based on vibration of the wind generating set.
Background
The wind turbine generator system blade is used as a core component for capturing wind energy, is subjected to complex alternating load for a long time, is easy to generate structural damage such as cracks, layering and the like, and can cause huge economic loss and safety accidents if not detected in time, so that vibration-based blade state monitoring and fault diagnosis are carried out, and the wind turbine generator system blade has important engineering value for guaranteeing safe operation and realizing predictive maintenance.
In the prior art, vibration-based blade diagnosis methods mainly rely on identifying the modal parameters of a blade, such as the natural frequency and damping ratio, under specific operation conditions, and implementing fault identification by monitoring the deviation of the parameters relative to a healthy reference, such methods generally assume that the change of the modal parameters is isolated, namely only whether the absolute deviation of the frequency or damping ratio exceeds a threshold value or not, however, the modal parameters of the blade can show inherent fluctuation due to load change under different operation conditions such as wind speed, power and the like, and the prior methods treat the data points under different operation conditions as isolated or attempt to establish a fixed threshold range allowing fluctuation, which essentially neglects the continuity and the integrity of the dynamic characteristics of the blade along with the evolution of the operation conditions.
The defects of the prior art are that natural fluctuation of modal parameters caused by normal operation load change and abnormal change caused by structural damage are difficult to distinguish effectively, and for early and tiny damage, parameter change signals caused by the natural fluctuation are weak and are very easy to submerge in normal working condition fluctuation, so that diagnosis sensitivity is low, early warning cannot be realized, and in addition, the inherent relevance among modal parameters cannot be captured, the directionality of the damage is not strong, the damage is easy to be interfered by environmental noise, and the false alarm rate is high.
The above information disclosed in the background section is only for enhancement of understanding of the background of the disclosure and therefore it may include information that does not form the prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
The invention aims to provide a blade fault diagnosis method and device based on vibration of a wind generating set, so as to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a blade fault diagnosis method based on vibration of a wind generating set comprises the following specific steps:
Step 1, arranging sensors at the basic position of a cabin and the root of a blade of a wind generating set, synchronously acquiring cabin reference vibration signals and blade response vibration signals, and screening steady-state effective data segments through a preset time window;
Step 2, analyzing time delay between a cabin reference signal and a blade response signal according to the effective data segments, and identifying modal parameters of the first order of the blade through phase compensation and frequency response function calculation, wherein the modal parameters comprise modal frequencies and modal damping ratios, and repeating the operations on a plurality of different effective data segments to obtain a series of modal parameters;
step 3, sequentially connecting a series of modal parameters in a coordinate system with the modal frequency as an abscissa and the modal damping ratio as an ordinate, constructing a track of dynamic response, calculating relative vectors of each modal parameter on the track and all other modal parameters in the track to form local feature vectors, integrating all the local feature vectors, and constructing a global feature matrix representing the overall track morphology;
And 4, calculating a reference global feature matrix when the blade is healthy and lossless according to the steps 1 to 3, calculating a Frobenius norm between the global feature matrix and the reference global feature matrix, defining the norm value as a track deviation degree, and judging whether the wind generating set blade has structural damage according to a track deviation construction state transition probability matrix.
Further, a time window with the current time T as an end point and a fixed length T is traced backWherein,For the time length of 10 complete rotation periods, synchronous signal acquisition is carried out through a reference acceleration sensor positioned on a main frame of the engine room and a response acceleration sensor positioned on the inner side of a blade root flange in a time window so as to acquire an engine room reference vibration signal and a blade response vibration signal;
The method comprises the steps of filtering signals by band-pass filtering, wherein the band-pass frequency range covers the first-order waving modal frequency of the blade, namely, the vibration signals are band-pass filtered to retain the main modal frequency components of the blade, and the wind speed and output power signals are monitored in real time and set to be the length in the running process of the unit The sliding window is made to be in the time windowAnd sliding in the range, when the fluctuation ranges of the wind speed and the output power are respectively smaller than the respective preset thresholds in the sliding window, judging that the unit enters a steady-state working condition, and taking the time interval corresponding to the sliding window as an effective data segment.
Further, identifying modal parameters of the first order of the blade based on each valid data segment specifically includes calculating a nacelle reference signal under each valid data segmentResponsive to blade signalsCross-power spectrum of (a)Self-power spectrum of cabin reference signalWhereinAndRespectively isAndIs used for the fourier transform of (a),Wherein for the desired operation(s),Is complex conjugated;
By aligning Performing inverse Fourier transform to obtain a cross-correlation function: Wherein Is an inverse fourier transform;
Searching for a lead Initial time delay to take maximum valueBy using、、Parabolic interpolation of cross-correlation function values at three points to accurately determine time delayThe interpolation formula is as follows:
;
wherein, the Is the sampling time interval.
Further, to eliminate time delayInfluence on phase information, cross-power spectrum of both in frequency domainAnd (3) compensating, wherein the compensation formula is as follows:
;
wherein, the In units of imaginary numbers,For frequency, the compensated positive sign is used to counteract the time delay in the blade response signal relative to the nacelle reference signalThereby correcting the phase of the cross power spectrum;
And uses the compensated cross power spectrum Self-power spectrum with cabin reference signalCalculating a frequency response function from the nacelle to the blade: in the following And (3) identifying the highest peak value in the theoretical frequency range of the first-order waving mode of the blade, determining the frequency corresponding to the peak value as the mode frequency, and calculating the mode damping ratio of the first-order mode by using a half-power bandwidth method.
Further, when constructing the locus of dynamic response, the modal frequency and modal damping ratio corresponding to each effective data segment are taken as one data pointN data points are obtained in total and are sequentially connected according to the starting time sequence of the effective data segments to form a track, whereinIs the modal frequency of the ith data point,Modal damping ratio for the i-th data point;
for the ith data point on the trace Its local feature vectorIs oneThe column vector of the dimension has the mathematical expression:
;
wherein, the I is the index of the data point, and。
Further, performing Z-score normalization processing on the modal frequencies and modal damping ratios in all effective data segments in a time window, and performing normalization on all N local feature vectors according to the corresponding data pointsSequentially stacked as row vectors to form a global feature matrixProcessing the signals of the same unit blade in the healthy and lossless state by the same method to obtain a reference global feature matrix;
Defining track departure indexThe Frobenius norm, which is the difference between the two matrices, is calculated as follows:
;
wherein, the As a reference global feature matrix,As a global feature matrix of the system,Representing the Frobenius norm of the matrix.
Further, under the condition that the same unit blade is healthy and lossless, historical data of track deviation indexes are obtained through a plurality of historical time windows, and the average value of the historical data is calculatedAnd standard deviationReal-time monitoring the obtained track deviation indexAbnormal probability of (2)Calculated from the following formula:
;
wherein, the A cumulative distribution function that is a standard normal distribution;
and when the abnormal probability is continuously higher than a set threshold value in K continuous time windows, judging that the blade has structural damage, wherein K is more than or equal to 3.
The invention further provides a blade fault diagnosis device based on the vibration of the wind generating set, which is used for executing the blade fault diagnosis method based on the vibration of the wind generating set, and comprises the following steps:
The signal acquisition module is used for arranging sensors at the basic position of a cabin and the root of a blade of the wind generating set, synchronously acquiring cabin reference vibration signals and blade response vibration signals, and screening steady-state effective data segments through a preset time window;
The modal calculation module is used for analyzing the time delay between the cabin reference signal and the blade response signal according to the effective data segments, and identifying the modal parameters of the first order of the blade through phase compensation and frequency response function calculation, wherein the modal parameters comprise modal frequencies and modal damping ratios, and repeating the operations on a plurality of different effective data segments to obtain a series of modal parameters;
the characteristic construction module is used for sequentially connecting a series of modal parameters in a coordinate system taking modal frequency as an abscissa and modal damping ratio as an ordinate to construct a track of dynamic response, calculating relative vectors of each modal parameter on the track and all other modal parameters in the track to form local feature vectors, integrating all the local feature vectors, and constructing a global feature matrix representing the overall track shape;
the result judging module is used for calculating a reference global feature matrix when the blade is healthy and lossless according to the steps 1 to 3, calculating a Frobenius norm between the global feature matrix and the reference global feature matrix, defining the norm value as a track deviation degree, and judging whether the wind turbine blade has structural damage according to a track deviation construction state transition probability matrix.
Compared with the prior art, the invention has the beneficial effects that:
According to the invention, through accurate phase compensation and modal parameter identification, the accuracy of basic data points of the constructed track is ensured; the technical characteristic of the method enables the method to capture track form distortion caused by structural damage sharply, and the change of the integral form is incomparable by weak deviation of single parameter points, thereby realizing high-sensitivity detection of early and tiny damage;
The diagnosis basis of the invention is the morphological similarity of the whole track, namely, the method is realized by calculating the Frobenius norms of two global mode feature matrixes, so that the method is insensitive to fluctuation of single data points due to random noise or instantaneous disturbance, the track form of healthy blades keeps intrinsic consistency even under different normal running loads, once damage occurs, the consistency is destroyed, the deviation index is obviously increased, and the diagnosis result is not easily influenced by normal working condition fluctuation due to the judgment logic based on the mode similarity rather than the absolute value of the parameters;
According to the invention, through setting the continuous monitoring judgment conditions based on statistics, the accidental interference signals can be effectively filtered while the real fault trend is accurately captured, and highly reliable early warning information is provided for wind farm operation and maintenance personnel, so that the real and effective predictive maintenance is realized.
Drawings
FIG. 1 is a schematic flow chart of the overall method of the present invention;
FIG. 2 is a histogram of trace deviation index versus anomaly probability in accordance with the present invention;
FIG. 3 is a plot of modal frequencies versus modal damping ratios for the present invention;
FIG. 4 is a graph of trace deviation index versus anomaly probability fit according to the present invention;
FIG. 5 is a normal probability map of anomaly probabilities in accordance with the present invention;
FIG. 6 is a flow chart of the overall device structure of the present invention.
Detailed Description
The present invention will be further described in detail with reference to specific embodiments in order to make the objects, technical solutions and advantages of the present invention more apparent.
It is to be noted that unless otherwise defined, technical or scientific terms used herein should be taken in a general sense as understood by one of ordinary skill in the art to which the present invention belongs. The terms "first," "second," and the like, as used herein, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "up", "down", "left", "right" and the like are used only to indicate a relative positional relationship, and when the absolute position of the object to be described is changed, the relative positional relationship may be changed accordingly.
Examples:
Referring to fig. 1-5, the present invention provides a technical solution:
a blade fault diagnosis method based on vibration of a wind generating set comprises the following specific steps:
Step 1, arranging sensors at the basic position of a cabin and the root of a blade of a wind generating set, synchronously acquiring cabin reference vibration signals and blade response vibration signals, and screening steady-state effective data segments through a preset time window;
For the cabin foundation, the position sensing is the comprehensive vibration transmitted to the cabin by the whole transmission chain (comprising a wind wheel, a main shaft, a gear box and a generator), and the comprehensive vibration is taken as a reference signal and represents the combined action of an excitation source and a transmission path; for the root of the blade, the position directly senses the local dynamic response of the blade, and because the blade is a flexible body, the vibration of the blade contains rich structural dynamics information, and the root response is combined with a cabin reference signal to separate the dynamic characteristics of the blade, the synchronous acquisition is used for ensuring that two signals are strictly aligned in time, which is the basis for carrying out time delay estimation, cross-correlation analysis and frequency response function calculation subsequently, and if the two signals are not synchronous, systematic errors exist in all subsequent analysis;
the time window refers to a total duration frame of data acquisition, the effective data segment refers to a high-quality short data segment which is screened from total acquired data and is suitable for subsequent precise modal analysis, and the screening standard is that the unit is in a steady-state working condition;
Setting a time window with the current time T as an end point and tracing back a fixed length T Wherein,For the time length of 10 complete rotation periods, synchronous signal acquisition is carried out through a reference acceleration sensor positioned on a main frame of the engine room and a response acceleration sensor positioned on the inner side of a blade root flange in a time window to acquire an engine room reference vibration signal and a blade response vibration signal, wherein the sampling frequency of the reference acceleration sensor and the response acceleration sensor is set to be 100Hz, namely sampling time intervalSecond, this arrangement satisfies the Nyquist sampling theorem, effectively covering the principal frequency component of blade vibration (below 50 Hz), and providing sufficient resolution for subsequent cross-correlation calculations and time delay interpolation, over a time windowIn, synchronize signal acquisitionTo be performed at intervals, ensuring that the nacelle reference vibration signal has a consistent time base with the time series of blade response vibration signals
WhereinFor the current rotational speed of the wind turbine blades,Ensuring that the data buffer is large enough, e.g. ifSecond (about 0.33Hz for a typical fan, 10 turns, 30 seconds), then T needs at least more than 90 seconds; the 10 rotation periods are selected to be representative in statistics, namely, the stable vibration period cannot be captured when the time is too short and is easily influenced by random fluctuation, the response speed of the system to the working condition change is reduced when the time is too long, and the 10 periods are empirical values for balancing the stability and the timeliness;
The band-pass filtering is adopted to filter the signals, the passband frequency range covers the waving modal frequency of the blade, namely the vibration signals are subjected to the band-pass filtering to keep the main modal frequency components of the blade, the wind speed and the output power signals are monitored in real time in the running process of the unit, and a length is set as The sliding window is made to be in the time windowSliding in the range, judging that the unit enters a steady-state working condition when the fluctuation ranges of the wind speed and the output power are respectively smaller than the respective preset thresholds in the sliding window continuously, and taking the time interval corresponding to the sliding window as an effective data segment;
The filter uses a zero-phase digital filter to avoid phase distortion, and removes high-frequency noise such as vibration of a gear box and a generator and low-frequency interference such as tower swing, so that vibration components related to a blade waving mode are highlighted, and the signal to noise ratio is improved;
the blade is most easily excited and is most sensitive to damage, and is usually a low-order mode, particularly a first-order waving mode, firstly, the frequency range of the first-order waving mode of the blade is estimated through simulation calculation or unit design data, for example, for a large-scale wind turbine blade, the range is between 0.8Hz and 1.5Hz, then the passband of a bandpass filter is set in the range, for example, 0.5Hz-2.0Hz, so that the signal-to-noise ratio can be remarkably improved, and the subsequent mode identification is more accurate;
Length of Is the execution unit of steady state judgment toFor step length, atAnd (3) sliding the window and checking the data in each window to evaluate the steady state characteristics, wherein the steady state quantization standard is based on the statistical fluctuation of the data in the window, the fluctuation range is required to be smaller than a preset threshold value, and when the method is concretely implemented, for wind speed data, the variation coefficient (the ratio of standard deviation to mean value) of the wind speed value in the window is calculated, the variation coefficient is required to be smaller than the preset threshold value (such as not more than 0.1), for power data, the variation coefficient (the ratio of standard deviation to mean value) of the power value in the window is calculated, the variation coefficient is required to be smaller than the preset threshold value (such as not more than 0.03), wherein the variation coefficient is defined as the ratio of standard deviation to mean value, and the time window is judged to be in steady state working condition only when the fluctuation statistics of all monitoring parameters in the window meet the respective preset threshold value requirements.
Step 2, analyzing time delay between a cabin reference signal and a blade response signal according to the effective data segments, and identifying modal parameters of the first order of the blade through phase compensation and frequency response function calculation, wherein the modal parameters comprise modal frequencies and modal damping ratios, and repeating the operations on a plurality of different effective data segments to obtain a series of modal parameters;
time delay analysis physically takes time for vibrations to travel from the nacelle to the blade root, this delay containing information about the propagation of the signal in the structural path, accurate estimation of it, being the first step to ensure accuracy of subsequent analysis, the blade responding to the signal due to the time delay Will lag in phase behind the nacelle reference signalThis phase lag distorts the calculated frequency response function, and phase compensation is an alignment operation aimed at eliminating this propagation effect, allowing us to observe pure blade dynamics subsystem characteristics;
The frequency response function describes the relation between the output (response) and the input (excitation) of the unit blade on the frequency domain, the unit blade resonates at the modal frequency, the response is greatly amplified, the frequency response function is represented as a prominent peak value, and the key modal frequency and damping ratio can be extracted by analyzing the peak value;
the method for identifying the modal parameters of the first order of the blade based on each effective data segment comprises the steps of calculating a nacelle reference signal under each effective data segment Responsive to blade signalsCross-power spectrum of (a)Self-power spectrum of cabin reference signalWhereinAndRespectively isAndIs used for the fourier transform of (a),Wherein for the desired operation(s),Is complex conjugated;
Is itself a complex number, representing the frequency content in both signals Common power and phase relationship on it, which is reflected in frequencyWhere the magnitude of the linear correlation of the blade response signal with the nacelle reference signal is greater, the frequency component is representedFrom the slaveTo transfer toThe stronger the energy, the frequency is the natural frequency of the system,Is directly dependent on the size of (2)AndIf input is made of the amplitude of (2)At which the energy is present and at which the system is readily excited (i.e., near the modal frequency), then the outputWill be large in amplitude, resulting inThe amplitude of (2) is also very large;
is a real number representing the blade reference signal The distribution of its own power in the frequency domain, which reflects the individual frequency components in the excitation signalThe amount of energy involved, which indicates at which frequencies the system is excited,Directly byIs determined by the square of the magnitude of (a),The larger the amplitude of (a) is,The greater the value at that frequency;
By aligning Performing inverse Fourier transform to obtain a cross-correlation function: Wherein Is an inverse fourier transform;
Representing delaying two signals at different times The following similarity, which reflects the signalWith a time delayTo a similar extent whenEqual to the real time delay between the two signals,A maximum value is reached and is therefore a critical function indicating the time delay between signals; the larger the value of (2) is, the time delay is represented The more similar the waveforms of the two signals are, the corresponding global maximumIs the primary time delay to be found;
Searching for a lead Initial time delay to take maximum valueBy using、、Parabolic interpolation of cross-correlation function values at three points to accurately determine time delayThe interpolation formula is as follows:
;
wherein, the Is a sampling time interval;
Since the signal is discretely sampled, the maximum point is initially found Can only be accurate to the sampling intervalThe real maximum point is positioned between two sampling points, parabolic interpolation utilizes the maximum point and the left and right adjacent points thereof to fit a parabola and find the vertex of the parabola, thereby delaying the timeThe accuracy of (2) is improved to a sub-sampling level;
The above formula passes through the three function values 、、Calculating the vertex abscissa of the parabola ifThe larger the value relative to the left and right points (i.e., the sharper the peak), then the calculatedThe closer the approach isIf the peak is asymmetric, the peak is not symmetric,Will shift to the higher function side;
to eliminate time delay Influence on phase information, cross-power spectrum of both in frequency domainAnd (3) compensating, wherein the compensation formula is as follows:
;
wherein, the In units of imaginary numbers,For frequency, the compensated positive sign is used to counteract the time delay in the blade response signal relative to the nacelle reference signalThereby correcting the phase of the cross-power spectrum, the formula being multiplied byTo compensate (cancel) for time delays in response signalsWith resultant phase lagThis positive sign compensates for the delay itself, which has the effect of advancing the response signal in phase so as to align with the reference signal;
And uses the compensated cross power spectrum Self-power spectrum with cabin reference signalCalculating a frequency response function from the nacelle to the blade: in the following Identifying the highest peak value in the theoretical frequency range of the first-order waving mode of the blade, determining the frequency corresponding to the peak value as the mode frequency, and calculating the mode damping ratio of the first-order mode by using a half-power bandwidth method;
Is a complex number containing amplitude and phase information describing the blade as a system whose vibrational response is related to the frequency domain of the excited, at the modal frequencies of the blade, A significant peak occurs in the amplitude of (c) because the system resonates at this frequency, a small excitation can produce a large response,The larger the amplitude of (a) is, the more the system is at that frequencyThe stronger the ability of the vibration response to amplify, which strongly suggests that this frequency is a natural frequency of the system;
within a predicted theoretical frequency range, e.g. obtained by design drawing or simulation, find Peak in the amplitude spectrum, the peak corresponding to the frequencyI.e. the identified first order flapping modal frequency, which is a fundamental inherent property of the blade;
at the identified modal frequencies Find the amplitude at the peak of (2)Calculating the-3 d point, i.e. the amplitude drops toTwo frequencies corresponding to each otherAnd,Calculating half power bandwidthModal damping ratioThe calculation formula of (2) is as follows: The damping ratio reflects the capacity of the structural system to consume vibration energy, the larger the damping ratio is, the stronger the capacity of the system to dissipate energy is, the vibration is attenuated faster, the damping ratio is generally in a stable and smaller range for a healthy blade, if the damping ratio is obviously increased, the damping ratio means that friction type damages exist in the structure, such as the mutual friction of crack surfaces to consume energy, if the damping ratio is obviously reduced, the damping ratio means that the structural connection is loosened, and the energy consumption mechanism is weakened, so that the damping ratio is the same as the modal frequency and is a key index for diagnosing the health state of the structure.
Step 3, sequentially connecting a series of modal parameters in a coordinate system with the modal frequency as an abscissa and the modal damping ratio as an ordinate, constructing a track of dynamic response, calculating relative vectors of each modal parameter on the track and all other modal parameters in the track to form local feature vectors, integrating all the local feature vectors, and constructing a global feature matrix representing the overall track morphology;
Each data point represents the 'dynamic fingerprint' of the blade under a specific steady-state working condition, the starting time sequence is essentially working condition sequence, and the sequence implies the change of the load from small to large because the data are collected under the steady-state working condition with different wind speeds (i.e. different loads), so the track reveals the inherent rule of the change of the modal parameter (frequency and damping) of the blade along with the wind load, and a healthy blade has stable and repeatable track form;
when constructing the track of dynamic response, the modal frequency and modal damping ratio corresponding to each effective data segment are taken as one data point N data points are obtained in total and are sequentially connected according to the starting time sequence of the effective data segments to form a track, whereinIs the modal frequency of the ith data point,Modal damping ratio for the i-th data point;
for the ith data point on the trace Its local feature vectorIs oneThe column vector of the dimension has the mathematical expression:
;
wherein, the I is the index of the data point, and;
In order to quantify the geometry of the entire trajectory, a mathematical tool is required that describes the global relationship of each data point, local feature vectorIt is to create a "global context description" for each data point on the trace;
Itself a high-dimensional vector whose elements are the current data points The difference in coordinates from all other data points on the track,Reflecting data pointsThe unique position and role in the overall track shape is that it is not just a data point information, but encodesRelative to all other data points on the trace ifIs an abnormal point (e.g., due to injury), its relative relationship to most points on a healthy track changes dramatically, resulting in itA significant change occurs;
The size of each element in (a) represents The Euclidean distance component in the modal parameter space from other data points on each axis (frequency, damping) ifThese differences are then severely deviated from the healthy track position in which it should be due to injuryThe absolute value of (2) will generally increase;
Directly dependent on these coordinate values, if the unit blade is healthy, the track form constituted by all data points is stable, then each Is also stable, if damage occurs, the coordinates of certain data points will change, which will result in that pointAlmost all elements in the data are changed, and other data points are slightly influencedThis general nature of traction makes the feature extremely sensitive to local injury;
Performing Z-score normalization processing on the modal frequencies and modal damping ratios in all effective data segments in a time window, and performing normalization on all N local feature vectors according to the corresponding data points Sequentially stacked as row vectors to form a global feature matrixProcessing the signals of the same unit blade in the healthy and lossless state by the same method to obtain a reference global feature matrix;
The global feature matrix integrates N vectors describing the local feature vector of each point into a mathematical object capable of describing the complete shape of the whole track;
Z-score normalization is used for eliminating deviation caused by different dimension and magnitude of two parameters of modal frequency and damping ratio, the frequency value is usually about 1Hz, the damping ratio is only 0.01, if the frequency is not processed, tiny fluctuation of the frequency can be dominant in a characteristic vector to cover the change of the damping ratio;
The mean value and standard deviation used for normalization must come from data monitored for a long time under the health state of the blade, the data to be monitored is normalized by using the fixed health reference statistic, and the new mean value and standard deviation are calculated by using the data of the data to be monitored, so that all the data can be compared under the same reference;
Rather than just a "digitized template" of the health track morphology, it is more precisely a relative spatial relationship-based health track behavior code embedded with temporal sequence information, which contains the relative vector relationships (spatial information) between all data points, while by the exact order of the rows, The time stamp sequence (time information) when these data points were confirmed is retained in strong association; Defining the specific spatial configuration formed by the dynamic parameters of healthy leaves under the time sequence, and any factor (such as structural damage) destroying the spatial-temporal configuration can lead to the currently constructed global feature matrix And (3) withCreating a large difference.
Step 4, calculating a reference global feature matrix when the blade is healthy and lossless according to the steps 1 to 3, calculating a Frobenius norm between the global feature matrix and the reference global feature matrix, defining the norm value as a track deviation degree, and judging whether the wind generating set blade has structural damage according to a track deviation construction state transition probability matrix;
Defining track departure index The Frobenius norm, which is the difference between the two matrices, is calculated as follows:
;
wherein, the As a reference global feature matrix,As a global feature matrix of the system,The Frobenius norm of the matrix;
compressing the overall difference between the two complex matrices into a single, comparable scalar value, the Frobenius norm is the mathematical tool to achieve this goal; Quantitatively reflecting the comprehensive deviation degree between the overall dynamic behavior mode of the blade to be monitored and the health reference mode thereof, and calculating the overall level of the difference value of all corresponding position elements in the two global feature matrixes; The larger the value of (i) is, the larger the difference between the current dynamic response track and the healthy track of the blade to be monitored in the overall shape is, the difference is caused by the change of the structural characteristics of the blade (namely, the occurrence of damage), and the difference is approximately 0 The value means that the current behavior is almost consistent with the healthy behavior;
Is the square root of the sum of these difference squares, and therefore, AndThe difference between any corresponding elements increases, which results inIs increased by a value of (a),Sensitive to any tiny change in the matrix, and can comprehensively and comprehensively capture any local or global morphological distortion caused by damage;
Under the same unit blade health lossless state, acquiring historical data of track deviation index through a plurality of historical time windows, and calculating the average value of the historical data And standard deviationReal-time monitoring the obtained track deviation indexAbnormal probability of (2)Calculated from the following formula:
;
wherein, the A cumulative distribution function that is a standard normal distribution;
the partial time window and the specific data of the anomaly probability are shown in table 1.
TABLE 1 statistical graph of anomaly probabilities
Through analysis of the data, a certain correlation is observed between different characteristic parameters, for example, as can be seen from the data, a certain negative correlation trend exists between the modal frequency and the track deviation index, the modal frequency gradually decreases (from 0.5995 to 0.5858) along with the increase of a time window, and the track deviation index increases (from 0.027 to 0.064). This indicates that the vibration characteristics of the blade change under the condition of low modal frequency, resulting in an increase in the degree of trajectory deviation, reflecting deterioration of the structural state;
When the relation between the modal frequency and the anomaly probability is analyzed, the anomaly probability shows a gradually increasing trend along with the reduction of the modal frequency, and the anomaly probability is increased due to the fact that the reduction of the modal frequency is related to damage of the blade structure, so that the sensitivity of anomaly detection is improved, for example, the modal frequency of a time window 1 is 0.5995, the corresponding anomaly probability is 0.556, the modal frequency of a time window 15 is 0.5858, the anomaly probability is increased to 0.811, and the comprehensive influence of the modal frequency and the track deviation degree is considered to optimize the accuracy and the reliability of blade health monitoring.
When the anomaly probability continues to be higher than a set threshold for a succession of K time windows, then a structural damage is determined to exist in the leaf, where K is 3 or more, this determination strategy perfectly balances sensitivity and reliability, which is sufficiently sensitive to early damage (byCapture), has strong anti-interference capability through persistence requirements, effectively avoids false alarm, and makes a final diagnosis conclusion very stable enough to be used as a decision basis for shutdown maintenance;
when the blade is healthy, repeatedly executing the steps 1-4 for a long time to calculate a large number of Values that constitute a state of healthDistribution of samples by means of their averageAnd standard deviationTo fully describe, meanRepresenting healthy leavesThe normal average level of the value, which should ideally be close to 0, is usually a value slightly greater than 0 due to various random factors; representing healthy leaves The normal fluctuation range of the value is set,The larger the indication of healthUnstable and large fluctuation; And Together define a reference for health status, e.g.Is the limit range of normal fluctuations;
Will be current The value is converted into a more visual and explanatory index-which is the probability of an outlier-which is compared with the direct useThe absolute value of (2) is more finely judged by threshold value, and the abnormality probability is higherIs a value between 0 and 1,Indicating the deviation of the track currently observedPossibility of belonging to health distribution; The larger the value of (2) is, the current is represented The value comes from an abnormal state, so that the blade state is extremely likely to be abnormal (damaged), e.gIndicating that in a healthy state, only a 5% probability of the current situation is observedValues or greater, and therefore with high confidence, the unit blades are considered abnormal;
is normalized Values, i.e.It represents the currentBy how many standard deviations from the healthy mean,The larger the value of (c) is,The closer to 1 the value is (i.e., the cumulative probability of the value in a normal distribution), and thereforeThe closer to 1, which is a monotonically increasing relationship, normalizedThe greater the value, the probability of abnormalityThe larger the size;
When (when) Continuously below the set threshold for a continuous K time windowsFor exampleWhen the blade is judged to have structural damage, wherein K is more than or equal to 3, the judgment strategy balances sensitivity and specificity, and requires that an abnormal signal is continuous, false alarms caused by instantaneous interference are avoided, for example, 3 continuous time windows are all providedThere is a high degree of confidence in determining the impairment.
Referring to fig. 6, the present invention further provides a device for diagnosing a blade fault based on vibration of a wind turbine generator, where the device is configured to perform the above method for diagnosing a blade fault based on vibration of a wind turbine generator, and the method includes:
The signal acquisition module is used for arranging sensors at the basic position of a cabin and the root of a blade of the wind generating set, synchronously acquiring cabin reference vibration signals and blade response vibration signals, and screening steady-state effective data segments through a preset time window;
The modal calculation module is used for analyzing the time delay between the cabin reference signal and the blade response signal according to the effective data segments, and identifying the modal parameters of the first order of the blade through phase compensation and frequency response function calculation, wherein the modal parameters comprise modal frequencies and modal damping ratios, and repeating the operations on a plurality of different effective data segments to obtain a series of modal parameters;
the characteristic construction module is used for sequentially connecting a series of modal parameters in a coordinate system taking modal frequency as an abscissa and modal damping ratio as an ordinate to construct a track of dynamic response, calculating relative vectors of each modal parameter on the track and all other modal parameters in the track to form local feature vectors, integrating all the local feature vectors, and constructing a global feature matrix representing the overall track shape;
the result judging module is used for calculating a reference global feature matrix when the blade is healthy and lossless according to the steps 1 to 3, calculating a Frobenius norm between the global feature matrix and the reference global feature matrix, defining the norm value as a track deviation degree, and judging whether the wind turbine blade has structural damage according to a track deviation construction state transition probability matrix.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. Those of skill in the art will appreciate that the elements and algorithm steps described in connection with the embodiments disclosed herein can be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application.
Claims (8)
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| CN115496102A (en) * | 2022-09-23 | 2022-12-20 | 西安热工研究院有限公司 | Wind turbine generator blade fault diagnosis method and device, equipment and storage medium |
| CN116542101A (en) * | 2023-05-09 | 2023-08-04 | 重庆大学 | Wind turbine generator blade load dynamic estimation method |
| CN120369093A (en) * | 2025-04-15 | 2025-07-25 | 江苏国电南自海吉科技有限公司 | Wind turbine generator blade vibration detection method based on optical fiber whispering gallery mode singular point microcavity model |
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