CN112629838B - Wind turbine blade fault monitoring method - Google Patents
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Abstract
A wind turbine blade fault monitoring method comprises the steps of establishing a wind turbine blade model according to actually detected wind turbine blades; determining the number of sampling points for solving the backward scattering electric field of the wind turbine blade; solving a backscattering electric field of the wind turbine blade; obtaining a radar echo signal of the wind turbine blade according to the backscattering electric field of the wind turbine blade; carrying out short-time Fourier transform according to the radar echo signals of the wind turbine blade to obtain a time-frequency graph of the radar echo signals of the wind turbine blade; monitoring the wind turbine blade which actually runs in real time by using a radar, and acquiring a radar echo of the monitored wind turbine blade; carrying out short-time Fourier transform on the obtained radar echo signals of the monitored wind turbine blade to obtain a time-frequency diagram of the radar echo signals of the monitored wind turbine blade; and comparing the obtained time-frequency graphs of the radar echo signals of the wind motor, and judging whether the blades of the wind motor are in failure. The invention solves the technical problem that the running state of the blades of the wind motor cannot be accurately acquired in real time in the running process of the wind motor.
Description
Technical Field
The invention belongs to the technical field of wind turbine blade fault monitoring, and particularly relates to a wind turbine blade fault monitoring method.
Background
With the initiation of national energy internet engineering targeting 'transmission of clean energy as a main network frame by using an extra-high voltage power grid and transportation of the clean energy as a leading factor and global interconnection of ubiquitous and strong smart power grids', the energy internet engineering serves as the basis of an energy strategy in China and is also an important component of clean and renewable energy, and the development and utilization of wind power energy are more and more emphasized by the nation. A large number of large and medium-sized wind power plant projects built or regularly planned in the construction are generated in areas rich in wind energy resources in China, the wind power produced in 2017 reaches 1503 ten thousand kW, and the accumulated grid-connected installed capacity reaches 1.64 hundred million kW.
With the construction of large and medium-sized wind power plants, the number of wind motors put into use is increased very rapidly, and it is very important to acquire the operating state of the blades of the wind motors in real time.
Disclosure of Invention
In view of the technical problems in the background art, the method for monitoring the faults of the blades of the wind turbine provided by the invention solves the technical problem that the running state of the blades of the wind turbine cannot be accurately acquired in real time in the running process of the wind turbine.
In order to solve the technical problems, the invention adopts the following technical scheme to realize:
a wind turbine blade fault monitoring method comprises the following steps:
the method comprises the following steps: establishing a wind turbine blade model according to the actual monitored wind turbine blade;
according to the wind turbine blade fault monitoring method provided by the invention, the micro Doppler characteristic of the radar echo when the monitored wind turbine operates normally needs to be obtained, so that a wind turbine model needs to be established according to the actual monitored wind turbine for obtaining the micro Doppler characteristic of the wind turbine blade in the normal operation state.
Step two: and determining the number of sampling points for solving the backward scattering electric field of the wind turbine blade according to the Nyquist sampling theorem.
The "Nyquist sampling theorem" requires a sampling frequency fcSatisfy fc＞2fdmax，fdmaxIs the maximum Doppler frequency, f, generated during the operation of the wind turbine bladedmax2f ω R/c; ω is the angular velocity of the wind turbine blade rotation; r is the wind turbine blade length. When sampling frequency fcWhen the electric field is determined, the number of sampling points for solving the backward scattering electric field of the wind turbine blade in a corresponding period is 360fc/ω。
Step three: and solving the backscattering electric field of the wind turbine blade by using a mixed algorithm formed by a physical optics method (PO) and a moment method (MOM).
The hybrid algorithm adopts a triangular surface element to disperse the wind turbine blade, and in PO areas such as the front surface of the wind turbine blade and the front surface of an engine room, induced current generated by radar signals on the triangular surface element on the surface of the wind turbine blade is used as a secondary scattering source to replace the wind turbine blade by a physical optical method (PO), and then the induced current on the surface is integrated to calculate the scattering electric field of the wind turbine blade; and establishing a matrix equation to solve the induced current of the MOM region in the MOM region at the back of the blade, the back of the engine room, the discontinuous part of the surface of the wind turbine blade and some complex edge parts, and further solving the scattering electric field of the MOM region. And finally, obtaining the scattering electric field of the wind turbine blade through vector superposition. And solving the scattering electric field of the wind motor blade in a certain fixed attitude, and combining a quasi-static method to obtain the dynamic scattering electric field of the wind motor blade.
Step four: obtaining radar echo signals of the wind turbine blade according to the solved backward scattering electric field of the wind turbine blade;
the echo at the scattering point is expressed as: s (t) exp (j2 pi ft) · σ (t) exp (-j4 pi r (t)/λ), where f and λ are radar signal frequency and wavelength, respectively; r (t) and sigma (t) are respectively the distance from a scattering point to the radar at the moment t and the scattering coefficient of the scattering point, wherein sigma (t) is generally subjected to homogenization treatment. Constant carrier term exp (j2 pi f)ct) is the carrier of the signal, the useful information of the echo is stored in the baseband signal exp (-j4 π R (t)/λ);
the expression in time-harmonic electromagnetic fields is: es(r,t)＝Es(r)cos(2πfct + φ (r)), using a complex number to take the real part of this equation, we can:
in the formula
Is EsA complex amplitude vector of (r, t), wherein Es(r) is the amplitude of the electric field, and phi (r) is the phase of the electric field; comparing the echo expression of the scattering point with the formula, the baseband signal in the echo corresponds to the complex amplitude vector in the scattering electric field of the target,therefore, the obtained scattering electric field of the target can be converted into an echo signal of the target.
Step five: carrying out short-time Fourier transform (STFT) on the obtained radar echo signal of the wind turbine blade, and obtaining a time-frequency diagram of the radar echo signal of the wind turbine blade;
and (2) to acquire a Time-frequency diagram of the radar echo signals of the wind turbine blade, processing the radar echo signals of the wind turbine blade obtained in the step three by adopting Short Time Fourier Transform (STFT), wherein the calculation formula is as follows:
wherein x (k) is a radar echo signal of the wind turbine blade; n is the maximum sampling point number; k. m and n are 0,1,2,3L N-1; Δ t is the sampling interval of the time variable; ω (t) is a window function; the superscript "+" indicates the complex conjugate.
Step six: and monitoring the wind motor blade in actual operation in real time by using a radar, and acquiring a radar echo of the monitored wind motor blade. And carrying out short-time Fourier transform (STFT) on the obtained radar echo signals of the monitored wind turbine blade to obtain a time-frequency diagram of the radar echo signals of the monitored wind turbine blade.
Step seven: comparing the time-frequency graphs of the radar echo signals of the wind motor obtained in the fifth step with the time-frequency graphs of the radar echo signals of the wind motor obtained in the sixth step, and judging whether the blades of the wind motor break down or not;
when the wind motor runs, because the blades of the wind motor rotate periodically, a time-frequency diagram of a radar echo signal of the wind motor presents a unique micro Doppler characteristic. And fifthly, obtaining an echo time-frequency diagram of the blade of the wind turbine in normal operation, and obtaining an echo time-frequency diagram of the blade of the monitored wind turbine in the sixth step. Comparing the two signals, and if the micro Doppler characteristics of the two signals are the same, indicating that the blade of the monitored wind turbine is not in fault; if the micro Doppler characteristics of the two are different, the monitored wind turbine blade is judged to be in fault.
Step eight: after the operation state of the wind outlet motor blade is judged according to the seventh step, if the wind motor blade fails, an alarm is sent out in time; and if the wind motor blade normally runs, continuing to monitor the running state of the wind motor blade.
This patent can reach following beneficial effect:
1. the method solves the problem that the running state of the blades of the wind motor cannot be accurately acquired in real time in the running process of the wind motor, and can quickly and accurately judge the running state of the blades of the wind motor by comparing the micro Doppler characteristics of the echoes of the blades of the wind motor.
2. The invention monitors the running state of the wind turbine blade in real time, can send out an alarm in time when the blade is in fault, transmits the information to workers, reminds the workers to process in time and reduces loss.
Drawings
The invention is further illustrated by the following examples in conjunction with the accompanying drawings:
FIG. 1 is a flow chart of a wind turbine fault monitoring method of the present invention;
FIG. 2 is a wind turbine blade model constructed according to the present invention from a Vestas-V82 wind turbine;
FIG. 3 is a time domain diagram of the blade echo of the Vestas-V82 wind turbine of the present invention during normal operation;
FIG. 4 is a time-frequency diagram of the blade echo of the Vestas-V82 wind turbine in normal operation;
FIG. 5 is a wind turbine blade model after a blade fracture fault occurs in a Vestas-V82 wind turbine blade simulated by the method;
FIG. 6 is a time domain diagram of blade echoes of a Vestas-V82 wind turbine blade after the blade is broken and then fails;
FIG. 7 is a time-frequency diagram of blade echoes of a Vestas-V82 wind turbine blade after the blade is broken and then fails.
Detailed Description
The preferable scheme is as shown in fig. 1 to 7, and the wind motor fault monitoring method is used for judging the operation state of the wind motor blade based on the micro doppler characteristics of the blade echo signal of the wind motor during operation. The method comprises the following steps:
the method comprises the following steps: establishing a wind turbine blade model according to the actual monitored wind turbine blade;
according to the wind turbine blade fault monitoring method provided by the invention, the micro Doppler characteristic of the radar echo when the monitored wind turbine operates normally needs to be obtained, so that a wind turbine model needs to be established according to the actual monitored wind turbine for obtaining the micro Doppler characteristic of the wind turbine blade in the normal operation state.
Step two: and determining the number of sampling points for solving the backward scattering electric field of the wind turbine blade according to the Nyquist sampling theorem.
The "Nyquist sampling theorem" requires a sampling frequency fcSatisfy fc＞2fdmax，fdmaxIs the maximum Doppler frequency, f, generated during the operation of the wind turbine bladedmax2f ω R/c; ω is the angular velocity of the wind turbine blade rotation; r is the wind turbine blade length. When sampling frequency fcWhen the electric field is determined, the number of sampling points for solving the backward scattering electric field of the wind turbine blade in a corresponding period is 360fc/ω。
Step three: and solving the backscattering electric field of the wind turbine blade by using a mixed algorithm formed by a physical optics method (PO) and a moment method (MOM).
The hybrid algorithm adopts a triangular surface element to disperse the wind turbine blade, and in PO areas such as the front surface of the wind turbine blade and the front surface of an engine room, induced current generated by radar signals on the triangular surface element on the surface of the wind turbine blade is used as a secondary scattering source to replace the wind turbine blade by a physical optical method (PO), and then the induced current on the surface is integrated to calculate the scattering electric field of the wind turbine blade; and establishing a matrix equation to solve the induced current of the MOM region in the MOM region at the back of the blade, the back of the engine room, the discontinuous part of the surface of the wind turbine blade and some complex edge parts, and further solving the scattering electric field of the MOM region. And finally, obtaining the scattering electric field of the wind turbine blade through vector superposition. And solving the scattering electric field of the wind motor blade in a certain fixed attitude, and combining a quasi-static method to obtain the dynamic scattering electric field of the wind motor blade.
Step four: obtaining radar echo signals of the wind turbine blade according to the solved backward scattering electric field of the wind turbine blade;
the echo at the scattering point is expressed as: s (t) ═ exp (j2 π ft). σ (t) exp (-j4 π R (t)/λ), itF and lambda are radar signal frequency and wavelength respectively; r (t) and sigma (t) are respectively the distance from a scattering point to the radar at the moment t and the scattering coefficient of the scattering point, wherein sigma (t) is generally subjected to homogenization treatment. Constant carrier term exp (j2 pi f)ct) is the carrier of the signal, the useful information of the echo is stored in the baseband signal exp (-j4 π R (t)/λ);
the expression in time-harmonic electromagnetic fields is: es(r,t)＝Es(r)cos(2πfct + φ (r)), using a complex number to take the real part of this equation, we can:
in the formula
Is EsA complex amplitude vector of (r, t), wherein Es(r) is the amplitude of the electric field, and phi (r) is the phase of the electric field; comparing the echo expression of the scattering point with the formula, the baseband signal in the echo corresponds to the complex amplitude vector in the scattering electric field of the target, so that the obtained scattering electric field of the target can be converted into the echo signal of the target.
Step five: carrying out short-time Fourier transform (STFT) on the obtained radar echo signal of the wind turbine blade, and obtaining a time-frequency diagram of the radar echo signal of the wind turbine blade;
and (2) to acquire a Time-frequency diagram of the radar echo signals of the wind turbine blade, processing the radar echo signals of the wind turbine blade obtained in the step three by adopting Short Time Fourier Transform (STFT), wherein the calculation formula is as follows:
wherein x (k) is a radar echo signal of the wind turbine blade; n is the maximum sampling point number; k. m and n are 0,1,2,3L N-1; Δ t is the sampling interval of the time variable; ω (t) is a window function; the superscript "+" indicates the complex conjugate.
Step six: and monitoring the wind motor blade in actual operation in real time by using a radar, and acquiring a radar echo of the monitored wind motor blade. And carrying out short-time Fourier transform (STFT) on the obtained radar echo signals of the monitored wind turbine blade to obtain a time-frequency diagram of the radar echo signals of the monitored wind turbine blade.
Step seven: comparing the time-frequency graphs of the radar echo signals of the wind motor obtained in the fifth step with the time-frequency graphs of the radar echo signals of the wind motor obtained in the sixth step, and judging whether the blades of the wind motor break down or not;
when the wind motor runs, because the blades of the wind motor rotate periodically, a time-frequency diagram of a radar echo signal of the wind motor presents a unique micro Doppler characteristic. And fifthly, obtaining an echo time-frequency diagram of the blade of the wind turbine in normal operation, and obtaining an echo time-frequency diagram of the blade of the monitored wind turbine in the sixth step. Comparing the two signals, and if the micro Doppler characteristics of the two signals are the same, indicating that the blade of the monitored wind turbine is not in fault; if the micro Doppler characteristics of the two are different, the monitored wind turbine blade is judged to be in fault.
Step eight: after the operation state of the wind outlet motor blade is judged according to the seventh step, if the wind motor blade fails, an alarm is sent out in time; and if the wind motor blade normally runs, continuing to monitor the running state of the wind motor blade.
The following description will be made in detail by taking a Vestas-V82 wind motor as an example:
a simulation model of a wind turbine blade is established according to a Vestas-V82 wind turbine, the wind turbine blade model is shown in figure 2, the number of blades is 3, the length of the blade is 44 meters, and it is assumed that a radar signal is incident on a yoz plane, and an incident angle theta and the rotation direction of the blade are shown in figure 2. The transmitting frequency of the radar signal is 1GHZ, the number of sampling points for solving a backward scattering electric field of the wind turbine blade is 6000 according to the Nyquist sampling theorem, the polarization direction of electromagnetic waves is vertical polarization, and a time domain graph and a time frequency graph of blade echoes of the Vestas-V82 wind turbine during normal operation are shown in FIGS. 3-4.
The micro Doppler characteristic of the wind motor blade echo mainly comprises a zero frequency band, a sinusoidal envelope curve and time-frequency flicker. The "zero band" is due to the nacelle being relatively stationary with the radar at the wind turbine blade axis. The blade tips are equivalent to circular motion, and the "sinusoidal envelope" corresponds to the echoes of the blade tips. When the radar sight is perpendicular to the wind turbine blade, the blade is completely irradiated by a radar signal, the echo reaches the peak value, the time-frequency domain waveform generates a flickering phenomenon, the blade presents positive time-frequency flickering when being close to the radar, and the blade presents negative time-frequency flickering when being far away from the radar. The blade is completely irradiated, and when the echo reaches the peak value, the time domain waveform can generate a wave peak, so that the time when the time-frequency flicker occurs corresponds to the time when the wave peak of the time domain waveform occurs. When positive Doppler flicker occurs, the radar irradiates the front edge of the wind turbine blade, the front edge structure is thick, the energy of an echo is large, the energy of the positive Doppler flicker is higher than that of the negative Doppler flicker, and the complex shape of the wind turbine blade enables the time-frequency domain waveform to have the phenomenon of time-frequency flicker bending.
FIG. 5 is a wind turbine blade model after blade breakage failure of a simulated Vestas-V82 wind turbine blade, and a time domain graph and a time frequency graph of blade echo of the Vestas-V82 wind turbine blade when blade breakage failure occurs are shown in FIGS. 6-7.
When the radar sight is perpendicular to the blades of the wind motor, the time-frequency domain waveform has a flickering phenomenon, and the Vestas-V82 wind motor has three blades, so that the time-frequency domain waveform has three groups of time-frequency flickering, and the maximum Doppler frequency is equal. However, when a certain blade breaks down, the time-frequency domain waveform of the radar echo changes to a certain extent, so that the blade of the wind turbine can be judged to break down.
In conclusion, the wind motor fault monitoring method can effectively solve the technical problem that the running state of the wind motor blade cannot be accurately acquired in real time in the running process of the wind motor, and can remind workers to process the wind motor blade in time when the wind motor blade fails, so that loss is reduced.
The above-described embodiments are merely preferred embodiments of the present invention, and should not be construed as limiting the present invention, and the scope of the present invention is defined by the claims, and equivalents including technical features described in the claims. I.e., equivalent alterations and modifications within the scope hereof, are also intended to be within the scope of the invention.
Claims (10)
1. A wind turbine blade fault monitoring method is characterized by comprising the following steps:
the method comprises the following steps: establishing a wind motor blade model according to the actually detected wind motor blade;
step two: determining the number of sampling points for solving the backward scattering electric field of the wind turbine blade according to the Nyquist sampling theorem;
step three: solving a backscattering electric field of the wind turbine blade by using a hybrid algorithm formed by a physical optical method and a moment method;
step four: obtaining radar echo signals of the wind turbine blade according to the solved backward scattering electric field of the wind turbine blade;
step five: carrying out short-time Fourier transform on the obtained radar echo signal of the wind turbine blade to obtain a time-frequency graph of the radar echo signal of the wind turbine blade;
step six: monitoring the wind turbine blade which actually runs in real time by using a radar, and acquiring a radar echo of the monitored wind turbine blade; carrying out short-time Fourier transform on the obtained radar echo signals of the monitored wind turbine blade to obtain a time-frequency diagram of the radar echo signals of the monitored wind turbine blade;
step seven: comparing the time-frequency graphs of the radar echo signals of the wind motor obtained in the fifth step with the time-frequency graphs of the radar echo signals of the wind motor obtained in the sixth step, and judging whether the blades of the wind motor break down or not;
step eight: after the running state of the wind outlet motor blade is judged, if the wind motor blade breaks down, an alarm is sent out in time; and if the wind motor blade normally runs, continuing to monitor the running state of the wind motor blade.
2. The wind turbine blade fault monitoring method according to claim 1, characterized in that: the step of obtaining the radar echo signal of the wind motor blade by four-way passing through the backward scattering electric field of the wind motor blade is that a baseband signal in the echo corresponds to a complex amplitude vector of a target scattering electric field.
3. The wind turbine blade fault monitoring method according to claim 2, characterized in that: fifthly, obtaining an echo time-frequency diagram of the wind turbine blade in normal operation, and sixthly obtaining an echo time-frequency diagram of the monitored wind turbine blade; when the wind turbine runs, because the blades of the wind turbine rotate periodically, a time-frequency diagram of a radar echo signal of the wind turbine can present a unique micro Doppler characteristic; and fifthly, acquiring the micro Doppler characteristics of the radar echo signals of the wind turbine blade, namely performing short-time Fourier transform on the radar echo signals of the wind turbine blade obtained in the fourth step.
4. The wind turbine blade fault monitoring method according to claim 3, characterized in that: and sixthly, obtaining the radar echo and the micro Doppler characteristic of the monitored wind turbine blade through radar real-time monitoring.
5. The wind turbine blade fault monitoring method according to claim 1, characterized in that: and judging whether the wind motor blade has a fault or not by comparing whether the blade micro Doppler characteristics obtained in the fifth step and the sixth step are the same or not.
6. The wind turbine blade fault monitoring method according to claim 1, characterized in that: after the running state of the wind outlet motor blade is judged, if the wind motor blade breaks down, an alarm is sent out in time; and if the wind motor blade normally runs, continuing to monitor the running state of the wind motor blade.
7. The wind turbine blade fault monitoring method according to claim 1, characterized in that: in the second step, the number of sampling points for solving the backward scattering electric field of the wind turbine blade is determined according to the Nquist sampling theorem, and the method comprises the following steps:
the "Nquist sampling theorem" requires a sampling frequency fcSatisfy fc＞2fdmax，fdmaxIs the maximum Doppler frequency, f, generated during the operation of the wind turbine bladedmax＝2fωR/c；
ω is the angular velocity of the wind turbine blade rotation; r is the wind turbine blade length; when sampling frequency fcWhen determined, thenThe number of sampling points for solving the backward scattering electric field of the wind turbine blade in a corresponding period is 360fc/ω；
f is the radar signal frequency.
8. The wind turbine blade fault monitoring method according to claim 7, wherein: in the third step, a hybrid algorithm formed by a physical optical method and a moment method is used for solving the backscattering electric field of the wind turbine blade, and the method comprises the following steps:
in the hybrid algorithm, a triangular surface element is adopted to disperse the wind turbine blade, induced current generated by radar signals on the triangular surface element on the surface of the wind turbine blade is used as a secondary scattering source to replace the wind turbine blade in PO areas such as the front surface of the wind turbine blade and the front surface of an engine room by a physical optical method, and then the surface induced current is integrated to calculate the scattering electric field of the wind turbine blade; establishing a matrix equation in MOM areas such as the back of a blade, the back of an engine room, the surface discontinuity of a wind turbine blade and some complex edge parts to solve induced currents of the MOM areas so as to obtain a scattering electric field of the MOM areas; finally, a scattering electric field of the wind motor blade can be obtained through vector superposition; and solving the scattering electric field of the wind motor blade in a certain fixed attitude, and combining a quasi-static method to obtain the dynamic scattering electric field of the wind motor blade.
9. The wind turbine blade fault monitoring method according to claim 8, wherein: obtaining radar echo signals of the wind turbine blade according to the solved backward scattering electric field of the wind turbine blade;
the echo at the scattering point is expressed as: s (t) exp (j2 pi ft) · σ (t) exp (-j4 pi r (t)/λ), where f and λ are radar signal frequency and wavelength, respectively; r (t) and sigma (t) are respectively the distance from a scattering point to the radar at the moment t and the scattering coefficient of the scattering point, wherein sigma (t) is generally subjected to homogenization treatment; constant carrier term exp (j2 pi f)ct) is the carrier of the signal, the useful information of the echo is stored in the baseband signal exp (-j4 π R (t)/λ);
the expression in time-harmonic electromagnetic fields is: es(r,t)＝Es(r)cos(2πfct + φ (r)), using a complex number to take the real part of this equation, we can:
in the formula
Is EsA complex amplitude vector of (r, t), wherein Es(r) is the amplitude of the electric field, and phi (r) is the phase of the electric field; comparing the echo expression of the scattering point with the formula, the baseband signal in the echo corresponds to the complex amplitude vector in the scattering electric field of the target, so that the obtained scattering electric field of the target can be converted into the echo signal of the target.
10. The wind turbine blade fault monitoring method of claim 9, wherein: carrying out short-time Fourier transform on the obtained radar echo signal of the wind turbine blade to obtain a time-frequency graph of the radar echo signal of the wind turbine blade;
and (3) acquiring a time-frequency diagram of the radar echo signals of the wind turbine blade, wherein the radar echo signals of the wind turbine blade acquired in the third short-time Fourier transform processing step need to be processed, and the calculation formula is as follows:
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