CN114856935A - Wind turbine state analysis and control method - Google Patents
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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
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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
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- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/72—Wind turbines with rotation axis in wind direction
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Abstract
The invention provides a method for analyzing and controlling the state of a wind turbine, which belongs to the technical field of wind turbine monitoring and comprises the following steps: collecting state parameters of each blade of the wind turbine; preprocessing the state parameters of each blade to obtain a load range value, an equivalent fatigue load and a frequency band signal; analyzing the load range value and the equivalent fatigue load of each blade based on a preset load limit threshold, a preset fatigue limit value and a preset blade load mutual difference limit threshold, and regulating and controlling the operation of the wind turbine according to the analysis result; and performing machine learning, calculating the distribution of equivalent fatigue loads and frequency band signals under different wind wheel rotating speeds in real time, comparing the distribution parameters of the target data with the corresponding health data distribution parameters, and performing early warning according to the comparison result. The invention can monitor the running state of the wind turbine in real time, carry out overrun alarm and state prediction, provide a regulation and control strategy, reduce the fault occurrence rate, reduce the impact load received by the wind turbine, prolong the running life of the wind turbine, reduce the shutdown time and improve the power generation capacity.
Description
Technical Field
The invention relates to the technical field of wind turbine monitoring, in particular to a method for analyzing and controlling the state of a wind turbine.
Background
The development direction of the wind turbine is that the blades are longer and the relative mass is lighter after long-term development; wind turbine towers are also becoming taller and taller, and are relatively more flexible in order to control manufacturing processes and costs. In order to monitor the running state of the wind turbine, the prior art generally adopts local storage type monitoring, sensors are arranged on the positions of the blades needing to be monitored and other important parts of the wind turbine, data are collected and stored in real time, a storage position is placed at the cabin or the bottom of the wind turbine, and the blade is manually analyzed and judged whether to run normally or not and whether to need maintenance or not, and is adjusted according to the running problem, so that the efficiency is low.
Disclosure of Invention
The invention aims to provide a method for analyzing and controlling the state of a wind turbine, which can monitor the running state parameters of the wind turbine in real time and carry out prejudgment and automatic regulation and control on the running state of the wind turbine according to the running state parameters of the wind turbine.
In order to achieve the purpose, the invention adopts the technical scheme that:
the wind turbine state analysis and control method comprises the following steps: acquiring state parameters of each blade of the wind turbine according to a preset frequency periodically according to a preset period; wherein the state parameters include: strain data, vibration data, and inclination data; strain data are acquired under different azimuths, angles and pitch angles of each blade; preprocessing the state parameters of each blade to obtain load data and a vibration spectrogram of each blade; calculating target data of each blade according to the load data and the vibration spectrum characteristic value, wherein the target data comprises: a load range value, an equivalent fatigue load and a frequency band signal; analyzing the load range value and the equivalent fatigue load of each blade based on a preset load limit threshold value, a preset fatigue limit value and a blade load mutual difference limit threshold value, and regulating and controlling the operation of the wind turbine according to the analysis result; performing machine learning on target data, respectively calculating the distribution of equivalent fatigue loads and frequency band signals under different wind wheel rotating speeds in real time, comparing the distribution parameters of the target data with the corresponding distribution parameters of health data, and performing early warning according to the comparison result; wherein the health data distribution parameters refer to state distribution parameters during fault-free operation of the wind turbine.
Further, the load data calculation method comprises the following steps: calculating to obtain a conversion matrix through known blade section gravity moment and corresponding strain data; and carrying out linear calculation on the corresponding variable data through the conversion matrix to obtain load data.
Further, the calculation method of the load range value is as follows: acquiring load data within a preset duration range, carrying out limit statistics, and acquiring a load range value; wherein the load range values include: load maximum and load minimum.
Further, the method for calculating the equivalent fatigue load comprises the following steps: obtaining a load cycle range value and the occurrence times within a preset time range through rain flow calculation; and calculating to obtain the 1Hz equivalent fatigue load in the preset duration range through the load cycle range value in the preset duration range and the occurrence times respectively.
Further, the frequency band signal includes RMS values of full frequency band, 1P frequency band, 2P frequency band, 3P frequency band, first order flap frequency band, and first order shimmy frequency band.
Further, analyzing the load range value and the equivalent fatigue load of each blade based on a preset load limit threshold, a preset fatigue limit value and a blade load mutual difference limit threshold, and regulating and controlling the operation of the wind turbine according to an analysis result, wherein the method comprises the following steps: comparing the maximum load value of each blade with a load limit threshold value, and if the maximum load value is greater than the load limit threshold value, sending out an overrun alarm; comparing the equivalent fatigue load of each blade with the fatigue limit value, and if the equivalent fatigue load is greater than the fatigue limit value, sending out an overrun alarm; and calculating the load peak value of each blade, and if the difference between the load peak values of any two blades is greater than the mutual difference limit threshold value of the blade loads, giving out an overrun alarm.
Further, the regulation and control method comprises the following steps: dividing a 360-degree yaw position of the operation of the wind turbine into a plurality of sectors, and calculating load data and equivalent fatigue loads of the wind turbine in different sectors; reducing the power generation amount of the sector with the load data larger than the load limit threshold or the sector with the equivalent fatigue load larger than the fatigue limit value; and improving the power generation amount of the sector of which the load data is smaller than the load limit threshold or the sector of which the equivalent fatigue load is smaller than the fatigue limit value.
Further, machine learning is carried out on target data, distribution of equivalent fatigue loads and frequency band signals under different wind wheel rotating speeds is calculated in real time respectively, distribution parameters of the target data are compared with corresponding health data distribution parameters, and early warning is carried out according to comparison results; wherein the health data distribution parameters refer to state distribution parameters of the wind turbine during fault-free operation, and comprise: performing machine learning on target data, calculating the distribution of the equivalent fatigue load of each blade at the rotating speed of the wind wheel in real time, and obtaining the mean value and the standard deviation of the normal distribution of the equivalent fatigue load; calculating the distribution of RMS values of different frequency bands of each blade under the rotating speed of the wind wheel in real time to obtain the mean value and standard deviation of the normal distribution of the RMS values of different frequency bands; and comparing the mean value of the equivalent fatigue load normal distribution, the standard deviation of the equivalent fatigue load normal distribution, the mean value of the RMS value normal distribution of different frequency bands and the standard deviation of the RMS value normal distribution of different frequency bands of each blade with corresponding health data distribution parameters during the fault-free operation of the wind turbine, and if the deviation value exceeds 1 time of the standard deviation, sending an abnormal alarm.
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The invention and its features, aspects and advantages will become more apparent from reading the following detailed description of non-limiting embodiments with reference to the accompanying drawings. Like reference symbols in the various drawings indicate like elements. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the invention.
FIG. 1 is a schematic flow chart of a method for analyzing and controlling a condition of a wind turbine according to the present invention.
Detailed Description
The invention will be further described with reference to the following drawings and specific examples, which are not intended to limit the invention thereto.
In view of the defects of the prior art, the invention provides a method for analyzing and controlling the state of a wind turbine so as to detect, analyze and regulate the running state of the wind turbine in real time, and as shown in fig. 1, the method comprises the following steps:
acquiring state parameters of each blade of the wind turbine according to a preset frequency periodically according to a preset period; wherein the state parameters include: strain data, vibration data, and inclination data; strain data are acquired under different azimuths, angles and pitch angles of each blade;
the strain data is converted into signals which CAN be directly received by a PLC data acquisition system after passing through an amplifier, and the amplifier CAN output analog signals or transmit the signals through digital communication protocols such as RS485, CAN, EtherCAT and the like. Obtaining load data through linear calculation through a conversion matrix obtained in advance after strain signals are obtained
Wherein epsilon 1 And epsilon 2 Bending strain obtained for a section, M x And M y For the obtained section bending moment, the matrixes A and B, strain is collected under different azimuth angles and pitch angles of the blade by knowing the gravity moment of the section of the blade, the strain can be obtained by calculating the corresponding gravity moment and strain, specifically, different gravity moments and corresponding strain values of the section of the blade under the action of gravity are obtained by adjusting the pitch angle and the azimuth angle of a wind wheel of the blade, and thus a conversion matrix is obtained by solving a linear equation set and the like, wherein the linear equation set is as follows
Wherein:
the ith data required for the calculation of the strain acquisition channel 1 is represented,
A ij and B i Is a transformation matrix.
Then load data are collected according to a fixed frequency, preferably 50Hz, data processing is carried out according to a fixed time length, the time period can be set according to needs, the time length of more than 1 minute is preferred, limit statistics is carried out on the load data in the time period, and load range values, namely the maximum value and the minimum value of the load, are obtained.
Then carrying out rain flow counting to obtain the load cycle range value and the occurrence times in the time period; passing the load cycle range value and the respective occurrence times within the time period; calculating to obtain T second time sequence 1Hz equivalent fatigue load in a preset time length range
Wherein R is i For the i-th interval of the load spectrum, n i For the number of load range values, T is the data duration in seconds, m is the material slope, and 10 is typically selected for the blade.
Then, performing spectrum analysis, and calculating RMS values of a full frequency band, a 1P frequency band, a 2P frequency band, a 3P frequency band, a first-order swing frequency band and a first-order swing frequency band, wherein the calculation formula is as follows:
wherein S is i Is f in the frequency spectrum i The corresponding amplitude.
After the data are obtained, the following strategies are executed:
comparing the maximum load value of each blade with a load limit threshold value, and if the maximum load value is greater than the load limit threshold value, sending out an overrun alarm; comparing the equivalent fatigue load of each blade with the fatigue limit value, and if the equivalent fatigue load is greater than the fatigue limit value, sending out an overrun alarm;
calculating load peak values of all blades, comparing the difference between the load peak values of any two blades with a blade load mutual difference limiting threshold value, wherein the mutual difference limiting is to compare the load difference between three blades, the blade load difference is usually small in a certain time, but when the blades have the problems of abnormal pitch variation, breakage, icing of a single blade and the like, the load difference of the three blades is increased, so that a wind wheel is unbalanced, damage is further increased, if the difference between the load peak values of any two blades is larger than the blade load mutual difference limiting threshold value, an overrun alarm needs to be sent out, after the alarm is given, operation and maintenance personnel are reported, and a unit carries out pitch variation, rotation speed reduction, power reduction and other operations to avoid blade damage.
The method divides the 360-degree yaw position of the operation of the wind turbine into a plurality of sectors, and calculates the load data, the equivalent fatigue load, the wind energy and other indexes of the wind turbine in different sectors; reducing the power generation amount of the sector with the load data larger than the load limit threshold or the sector with the equivalent fatigue load larger than the fatigue limit value; and increasing the power generation capacity of the sector with the load data smaller than the load limit threshold or the sector with the equivalent fatigue load smaller than the fatigue limit value in the load bearable range of the blade of the sector, namely performing power-limited operation in the sector with low power generation and high fatigue damage.
In addition, the method can also carry out machine learning on the target data, respectively calculate the distribution of equivalent fatigue load and frequency band signals under different wind wheel rotating speeds in real time, compare the distribution parameters of the target data with the corresponding distribution parameters of the health data, and carry out early warning according to the comparison result; wherein the health data distribution parameters refer to state distribution parameters of the wind turbine during fault-free operation, and comprise: performing machine learning on target data, calculating the distribution of the equivalent fatigue load of each blade at the rotating speed of the wind wheel in real time, and obtaining the mean value and the standard deviation of the normal distribution of the equivalent fatigue load; calculating the distribution of RMS values of different frequency bands of each blade at the rotating speed of the wind wheel in real time to obtain the mean value and standard deviation of normal distribution of the RMS values of different frequency bands; and comparing the mean value of the equivalent fatigue load normal distribution, the standard deviation of the equivalent fatigue load normal distribution, the mean value of the RMS value normal distribution of different frequency bands and the standard deviation of the RMS value normal distribution of different frequency bands of each blade with corresponding health data distribution parameters during the fault-free operation of the wind turbine, and if the deviation value exceeds 1 time of the standard deviation, giving an abnormal alarm to prompt operation and maintenance personnel to detect abnormal blades.
In conclusion, the invention can monitor the running state parameters of the wind turbine in real time, alarm the running state of the wind turbine in an overrun mode according to the running state parameters of the wind turbine, provide a regulation and control strategy, so that operation and maintenance personnel can regulate the running state of the wind turbine in real time, ensure that the wind turbine runs in a safe and reliable state constantly, and can realize the advance judgment of abnormity after a period of machine learning, thereby taking prevention and control measures in advance, fundamentally reducing the fault occurrence probability, reducing the impact load received by the wind turbine, prolonging the running life of the wind turbine, reducing the shutdown time and improving the power generation capacity.
The above description is of the preferred embodiment of the invention; it is to be understood that the invention is not limited to the particular embodiments described above, in that devices and structures not described in detail are understood to be implemented in a manner common in the art; any person skilled in the art can make many possible variations and modifications, or modify equivalent embodiments, without departing from the technical solution of the invention, without affecting the essence of the invention; therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention are still within the scope of the protection of the technical solution of the present invention, unless the contents of the technical solution of the present invention are departed.
Claims (8)
1. The wind turbine state analysis and control method is characterized by comprising the following steps of:
acquiring state parameters of each blade of the wind turbine according to a preset frequency periodically according to a preset period; wherein the state parameters include: strain data, vibration data, and inclination data; the strain data are acquired under different azimuths, angles and pitch angles of each blade;
preprocessing the state parameters of each blade to obtain load data and a vibration spectrogram of each blade; calculating target data of each blade according to the load data and the vibration spectrum characteristic value, wherein the target data comprises: a load range value, an equivalent fatigue load and a frequency band signal;
analyzing the load range value and the equivalent fatigue load of each blade based on a preset load limit threshold value, a preset fatigue limit value and a blade load mutual difference limit threshold value, and regulating and controlling the operation of the wind turbine according to an analysis result;
performing machine learning on the target data, respectively calculating the distribution of the equivalent fatigue load and the frequency band signal under different wind wheel rotating speeds in real time, comparing the distribution parameters of the target data with the corresponding health data distribution parameters, and performing early warning according to the comparison result; wherein the health data distribution parameter refers to a state distribution parameter during fault-free operation of the wind turbine.
2. The wind turbine condition analyzing and controlling method as claimed in claim 1, wherein the load data is calculated by:
calculating to obtain a conversion matrix through known blade section gravity moment and corresponding strain data;
and carrying out linear calculation on the corresponding variable data through the conversion matrix to obtain load data.
3. The method for analyzing and controlling the condition of the wind turbine as claimed in any one of claims 1 and 2, wherein the load range value is calculated by:
acquiring load data within a preset duration range, carrying out limit statistics, and acquiring a load range value; wherein the load range values include: load maximum and load minimum.
4. The method for analyzing and controlling the state of a wind turbine as claimed in claim 3, wherein the method for calculating the equivalent fatigue load comprises:
obtaining a load cycle range value and the occurrence times within a preset time range through rain flow calculation;
and calculating to obtain the 1Hz equivalent fatigue load in the preset duration range through the load cycle range value in the preset duration range and the occurrence times respectively.
5. The wind turbine condition analysis and control method of claim 4, wherein the band signals include RMS values for full, 1P, 2P, 3P, first-order flap, and first-order shimmy bands.
6. The method for analyzing and controlling the state of a wind turbine as claimed in claim 5, wherein the analyzing the load range value and the equivalent fatigue load of each blade based on the preset load limit threshold, the preset fatigue limit value and the blade load difference limit threshold, and controlling the operation of the wind turbine according to the analysis result comprises:
comparing the maximum load value of each blade with a load limit threshold value, and if the maximum load value is greater than the load limit threshold value, sending out an overrun alarm;
comparing the equivalent fatigue load of each blade with the fatigue limit value, and if the equivalent fatigue load is greater than the fatigue limit value, sending out an overrun alarm;
and calculating the load peak value of each blade, and if the difference between the load peak values of any two blades is greater than the mutual difference limit threshold value of the blade loads, sending out an overrun alarm.
7. The wind turbine condition analysis and control method of claim 6, wherein the regulation and control method comprises:
dividing a 360-degree yaw position of the operation of the wind turbine into a plurality of sectors, and calculating load data and equivalent fatigue loads of the wind turbine in different sectors;
reducing the power generation amount of the sector with the load data larger than the load limit threshold or the sector with the equivalent fatigue load larger than the fatigue limit value;
and improving the power generation amount of the sector of which the load data is smaller than the load limit threshold or the sector of which the equivalent fatigue load is smaller than the fatigue limit value.
8. The method for analyzing and controlling the state of the wind turbine as claimed in claim 7, wherein the target data is subjected to machine learning, the equivalent fatigue load and the distribution of the frequency band signals at different wind turbine rotation speeds are respectively calculated in real time, the distribution parameters of the target data are compared with the corresponding distribution parameters of the health data, and early warning is performed according to the comparison result; wherein the health data distribution parameters refer to state distribution parameters during fault-free operation of the wind turbine, and comprise:
performing machine learning on the target data, calculating the distribution of the equivalent fatigue load of each blade at the rotating speed of the wind wheel in real time, and obtaining the mean value and the standard deviation of the normal distribution of the equivalent fatigue load; calculating the distribution of RMS values of different frequency bands of each blade under the rotating speed of the wind wheel in real time to obtain the mean value and standard deviation of the normal distribution of the RMS values of different frequency bands;
and comparing the mean value of the equivalent fatigue load normal distribution, the standard deviation of the equivalent fatigue load normal distribution, the mean value of the RMS value normal distribution of different frequency bands and the standard deviation of the RMS value normal distribution of different frequency bands of each blade with corresponding health data distribution parameters during the fault-free operation of the wind turbine, and if the deviation value exceeds 1 time of the standard deviation, sending an abnormal alarm.
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| CN115750204A (en) * | 2022-11-15 | 2023-03-07 | 中国华能集团清洁能源技术研究院有限公司 | Method and system for adjusting rotating speed of generator of wind turbine generator |
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