Wind turbine generator blade icing detection method, medium and system
By preprocessing and FFT analysis of the SCADA data of the wind turbine, 1P feature frequency is identified, the accuracy and reliability of blade ice detection are solved, scientific and rigorous detection methods are provided, and the safe operation of the wind turbine is improved.
Patent Information
- Application Number
- CN202411602778.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-11
- Publication Date
- 2025-01-10
AI Technical Summary
Wind turbines are prone to ice on the blades in cold and humid environments, resulting in a decrease in power output and safety risks. It is difficult to accurately and promptly detect the existing technology, which affects the stable operation of the unit.
By obtaining SCADA data for preprocessing and aggregation, scatter points that do not match wind speed and power, frequency conversion analysis is performed using Fast Fourier transform (FFT) to determine whether there is a 1P feature frequency to determine whether the blade is covered with ice.
It improves the accuracy and reliability of leaf ice-covering detection, provides a new perspective to reveal the unit ice-covering condition, and clearly displays the frequency components in the power timing through spectrum analysis to ensure the scientificity and rigor of the detection.
Smart Images
Figure CN119288791A_ABST
Abstract
Description
Technical Field
[0001] The present invention mainly relates to the field of wind turbine operating status monitoring and fault diagnosis, and in particular to a wind turbine blade ice coating detection method, medium and system. Background Art
[0002] Blade icing is a major challenge faced by wind turbines in cold and humid environments. It not only significantly reduces the power output and power generation of the units, but also greatly increases the safety risks caused by ice falling, posing a serious threat to the stable operation of wind farms.
[0003] Under certain climatic conditions, especially in winter, ice easily forms on the surface of wind turbine blades. Ice increases the weight of blades and deteriorates aerodynamic performance, which in turn significantly reduces the power output of wind turbines. At the same time, ice shedding may also pose a threat to the surrounding environment and personnel safety. Therefore, accurate and timely detection of blade ice coverage is crucial to ensure the safe operation of wind turbines. Summary of the invention
[0004] In view of the technical problems existing in the prior art, the present invention provides a method, medium and system for detecting ice coating on blades of a wind turbine generator set with high accuracy and high reliability.
[0005] In order to solve the above technical problems, the technical solution proposed by the present invention is:
[0006] A method for detecting ice coating on blades of a wind turbine generator system comprises the following steps:
[0007] Acquire SCADA data and pre-process the SCADA data to obtain power generation condition data for analysis and diagnosis;
[0008] Aggregate the power generation condition data, identify the scattered points where the wind speed and power do not match, and obtain the power anomaly data set;
[0009] Identify whether the necessary conditions for icing in the power anomaly data set are met, and obtain a suspected icing data set that meets the necessary conditions for icing;
[0010] Steady-state data is extracted from the suspected icing data set, and frequency analysis is performed using fast Fourier transform to determine whether the power has 1P characteristics. If the power has 1P characteristics, it is determined that the wind turbine blades are iced.
[0011] Preferably, the characteristic frequency calculation formula of 1P is as follows:
[0012] F=R gen / 60 / tr
[0013] Where F represents the characteristic frequency; R gen represents the generator speed; tr is the transmission ratio.
[0014] Preferably, the SCADA data includes one or more of wind speed, active power, blade angle, temperature, humidity, generator set speed, torque and active power instruction.
[0015] Preferably, preprocessing the SCADA second-level data specifically includes: eliminating shutdown data, fault data, power limit and power limit data.
[0016] Preferably, the necessary conditions for ice accumulation include humidity and temperature reaching set thresholds.
[0017] Preferably, the necessary conditions for ice accumulation also include a time period.
[0018] Preferably, the steady-state data includes a steady-state rotational speed, and the steady-state data includes a grid-connected rotational speed and a rated rotational speed.
[0019] The present invention also discloses a computer program product, comprising a computer program, wherein the computer program executes the steps of the method described above when executed by a processor.
[0020] The embodiment of the present invention further discloses a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method described above are executed.
[0021] The present invention also discloses a wind turbine blade ice detection method system, comprising a memory and a processor connected to each other, wherein a computer program is stored in the memory, and when the computer program is run by the processor, the steps of the method described above are executed.
[0022] Compared with the prior art, the advantages of the present invention are:
[0023] The present invention analyzes the formation mechanism and manifestation of wind turbine icing, and proposes to use the performance deviation of the unit and the information processing method to analyze the 1P characteristic value of the power time series data to effectively determine the occurrence of unit icing. This method has the following advantages:
[0024] (1) Spectral analysis provides a new perspective
[0025] As a rotating component, ice on the blades of a wind turbine will cause changes in the aerodynamic characteristics of the wind rotor and uneven mass distribution (changes in wing shape and differences in ice position and speed), which will in turn affect the periodic rotation characteristics of the wind rotor and ultimately be reflected in the generator and unit output power. By using fast Fourier transform (FFT) for spectrum analysis, these time domain signals can be converted into frequency domain signals to clearly display their frequency components. The purpose of performing spectrum analysis on the unit output power is to extract the hidden key information in the power time series and reveal the physical laws therein. In the case of ice, power frequency analysis using FFT reveals that both the wind rotor and the output power present the same 1P characteristics. Therefore, spectrum analysis provides an unprecedented new perspective for unit ice detection.
[0026] (2) Improved the accuracy and reliability of blade icing detection
[0027] Since the mechanism of this method is clear, and the principle and process of the analysis method are clear, the blade icing detection work can be carried out on a more scientific and rigorous basis. As a precise signal processing technology, spectrum analysis can accurately capture the subtle changes caused by blade icing by deeply analyzing the spectrum characteristics of key parameters such as unit output power. This change may be difficult to detect in the time domain signal, but it can be clearly presented in the frequency domain, which greatly improves the accuracy of detection. At the same time, since the analysis process of this method is clear and each step has a clear basis and purpose, it also helps to improve the reliability of detection. In practical applications, the blade icing situation can also be comprehensively judged based on the spectrum analysis results combined with other relevant information, so as to draw more accurate and reliable conclusions. BRIEF DESCRIPTION OF THE DRAWINGS
[0028] Figure 1 It is a schematic diagram of the wind wheel mass imbalance of the present invention.
[0029] Figure 2 It is the wind speed-power curve diagram of the present invention.
[0030] Figure 3 It is a schematic diagram of the mismatch between wind speed and power of the present invention.
[0031] Figure 4 It is a schematic diagram (3D) of the mismatch between wind speed and power of the present invention.
[0032] Figure 5 This is a diagram of the wind speed and power mismatch period of the present invention.
[0033] Figure 6 Schematic diagram of the Fourier transform process in the present invention.
[0034] Figure 7This is the FFT spectrum diagram of the wind turbine A in the present invention.
[0035] Figure 8 The present invention is a flow chart of the wind turbine blade ice detection method. DETAILED DESCRIPTION
[0036] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments.
[0037] In order to better describe the technical solution of the present invention, the blade icing phenomenon is now explained:
[0038] Ice on the surface of wind turbine blades is a random process with non-uniform distribution, which will lead to impeller mass imbalance. To treat the impeller mass imbalance, it is equivalent to attaching a virtual mass block m to three blades with equal mass. The mass block moves with the impeller at an angular velocity ω. M Rotation, such as Figure 1 As shown. During the rotation process, the forces on the mass block mainly include its own gravity mg and centrifugal force F e Since the rigidity of the tower in the vertical direction is very large, the centrifugal force of the unbalanced mass block of the wind rotor mainly causes the vibration of the wind rotor, the nacelle and the tower in the horizontal direction of the nacelle. The horizontal component of the centrifugal force can be expressed as:
[0039] Fx=Fe·sin(ω M t+φ) (1)
[0040]
[0041] Where r is the distance between the unbalanced mass block and the center of the hub (i.e. the radius of circular motion), ω M is the impeller rotation angular velocity, φ is the impeller initial position angle.
[0042] The equivalent mass m moves with the impeller at ω M The gravity torque generated by the impeller will cause the wind turbine main shaft speed to fluctuate. The unbalanced mass block will accelerate the main shaft when it rotates from the top to the bottom; and when the impeller rotates from the bottom to the top, the equivalent mass block will decelerate the main shaft. The frequency of such periodic changes is equal to 1 times the impeller rotation frequency.
[0043] Then the power performance deviation characteristics of wind turbines under icing conditions are analyzed:
[0044] When the wind turbine is operating normally, the corresponding scattered points of wind speed and power should fluctuate within a certain range above and below the theoretical curve, such as Figure 2 However, during the operation of wind turbines, there will be a phenomenon of wind speed and power mismatch, such as Figure 3There are many reasons for power anomalies, such as power limitation, stall, icing operation, etc. After excluding factors such as power limitation, power limitation, stall, etc., combined with ambient temperature, humidity and other conditions, if the icing threshold is reached, it can be initially determined that icing causes blade load operation, thereby reducing output power.
[0045] Further analysis of its temperature characteristics such as Figure 4 ,It can be seen that the abnormal power data is often accompanied by the ,characteristic of temperature being below 0℃.
[0046] Furthermore, the outlier detection method based on statistical distribution is used to detect outliers in the power scattered points when the blades are covered with ice, and the time range in which the abnormal data set appears is determined. For example, a wind farm in the northern plains of China selects unit A (unit parameters are shown in Table 1) and has an obvious abnormally low power condition from January 19 to 20, 2024. Its power distribution is as follows Figure 5 shown.
[0047] Table 1 Unit A parameters
[0048]
[0049] As mentioned in the previous section on “blade icing phenomenon”, blade icing will have a periodic effect with a frequency equal to 1 times the impeller rotation frequency. Steady-state data is extracted for the suspected icing data time period, and the rotation frequency is analyzed using the fast Fourier transform (FFT) to detect whether its power has 1P characteristics, thereby detecting the blade icing condition.
[0050] Any continuously measured time series or signal can be expressed as an infinite superposition of sine wave signals of different frequencies. The Fourier transform algorithm based on this principle uses the original signal directly measured to calculate the frequency, amplitude and phase of different sine wave signals in the signal in an accumulative manner. The conversion process is as follows: Figure 6 shown.
[0051] After the original time domain signal is decomposed by Fourier transform, the frequency of these sine waves is analyzed for the superposition of different sine wave signals to transform a time domain signal into a frequency domain signal. Some signals are difficult to see any characteristics in the time domain, but after being transformed into the frequency domain, the characteristics are easy to see. This is the basic principle of using FFT to detect 1P detection of the power generation of suspected ice-covered units.
[0052] The characteristic frequency calculation formula of 1P is as follows:
[0053] F = R gen / 60 / tr (3)
[0054] Where F represents the characteristic frequency, R gen Indicates the generator speed, Rgen / 60 is due to R gen is the generator speed per minute, the frequency should be converted into seconds, and tr is the transmission ratio (see Table 1).
[0055] Wind turbines have two steady-state speeds, one is the grid-connected speed when they are just connected to the grid, and the other is the rated speed (see Table 1). In the case of icing, the wind turbine usually maintains the rated speed and cannot go higher. Taking the wind turbine A mentioned above as an example, the grid-connected speed of 1050 and the transmission ratio of 195 in Table 1 are substituted into the characteristic frequency expression (3) of 1P to obtain F≈0.090Hz.
[0056] The FFT screening results of the power data of wind turbine A under grid-connected speed conditions from 2024 / 01 / 19 to 2024 / 01 / 20 are shown in Table 2.
[0057] Table 2 FFT screening results of unit A from 2024 / 01 / 19 to 2024 / 01 / 20
[0058]
[0059] Therefore, there is a 1-fold frequency fluctuation in the power signal of wind turbine A. The spectrum of some segments is drawn from the selected segments, such as Figure 7 shown.
[0060] The power of the samples randomly selected for the time period in Table 2 is abnormal and has 1P characteristics, which is consistent with the basic characteristics of wind turbine icing.
[0061] like Figure 8 As shown, based on the above analysis process, the wind turbine blade ice detection method provided by the embodiment of the present invention has the following specific steps:
[0062] Select SCADA second-level data, including variables for analysis: wind speed, active power, blade angle, temperature, humidity, generator speed, torque and active power command;
[0063] According to the control strategy, the shutdown data, fault data, power limit and power limit data are eliminated to obtain the power generation condition data for analysis and diagnosis. The data is aggregated as required to identify the scattered points where the wind speed VS power does not match and obtain the power abnormality data set.
[0064] Identify whether the necessary conditions for icing in the power anomaly data set are met, that is, whether the humidity and temperature reach the set thresholds. If so, obtain a suspected icing data set;
[0065] Steady-state data is extracted from the suspected icing data set, and frequency analysis is performed using Fast Fourier Transform (FFT) to determine whether the power has 1P characteristics. If so, it is determined that the blades are iced and the output power is reduced.
[0066] The present invention analyzes the formation mechanism and manifestation of wind turbine icing, and proposes to use the performance deviation of the unit and the information processing method to analyze the 1P characteristic value of the power time series data to effectively determine the occurrence of unit icing. This method has the following advantages:
[0067] 1) Spectral analysis provides new perspectives
[0068] As a rotating component, ice on the blades of a wind turbine will cause changes in the aerodynamic characteristics of the wind rotor and uneven mass distribution (changes in wing shape and differences in ice position and speed), which will in turn affect the periodic rotation characteristics of the wind rotor and ultimately be reflected in the generator and unit output power. By using fast Fourier transform (FFT) for spectrum analysis, these time domain signals can be converted into frequency domain signals to clearly display their frequency components. The purpose of performing spectrum analysis on the unit output power is to extract the hidden key information in the power time series and reveal the physical laws therein. In the case of ice, power frequency analysis using FFT reveals that both the wind rotor and the output power present the same 1P characteristics. Therefore, spectrum analysis provides an unprecedented new perspective for unit ice detection.
[0069] 2) Improved the accuracy and reliability of blade icing detection
[0070] Since the mechanism of this method is clear, and the principle and process of the analysis method are clear, the blade icing detection work can be carried out on a more scientific and rigorous basis. As a precise signal processing technology, spectrum analysis can accurately capture the subtle changes caused by blade icing by deeply analyzing the spectrum characteristics of key parameters such as unit output power. This change may be difficult to detect in the time domain signal, but it can be clearly presented in the frequency domain, which greatly improves the accuracy of detection. At the same time, since the analysis process of this method is clear and each step has a clear basis and purpose, it also helps to improve the reliability of detection. In practical applications, the blade icing situation can also be comprehensively judged based on the spectrum analysis results combined with other relevant information, so as to draw more accurate and reliable conclusions.
[0071] The present invention also discloses a computer program product, including a computer program, which executes the steps of the method described above when the computer program is executed by a processor. An embodiment of the present invention further discloses a computer-readable storage medium, on which a computer program is stored, and the computer program executes the steps of the method described above when the computer program is executed by a processor. The present invention also discloses a wind turbine blade ice detection method system, including an interconnected memory and a processor, on which a computer program is stored, and the computer program executes the steps of the method described above when the computer program is executed by the processor. The product, medium and system of the present invention correspond to the above method and also have the advantages described in the above method.
[0072] The present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by hardware related to computer program instructions. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiment when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable storage media include: any entity or device that can carry computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. The memory is used to store computer programs and / or modules, and the processor implements various functions by running or executing computer programs and / or modules stored in the memory, and calling data stored in the memory. The memory may include high-speed random access memory and may also include non-volatile memory, such as a hard disk, an internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.
[0073] Glossary:
[0074] FFT: The abbreviation of Fast Fourier Transformation, which is an efficient algorithm of discrete Fourier transform (DFT).
[0075] 1P: 1Periodic (period), this term is usually related to the periodic characteristics of rotating parts, and "1P" refers to the signal characteristics corresponding to a complete rotation cycle.
[0076] Grid-connected speed: refers to the rotation speed of a wind turbine when it is connected to the grid. Grid-connected speed is an important indicator for evaluating the performance of a wind turbine. It determines whether the generator can efficiently and stably convert wind energy into electrical energy and meet the grid-connected requirements.
[0077] Rated speed: refers to the speed of the wind turbine at rated power, that is, the motor speed at full load, so it is also called full load speed. It is one of the important indicators for evaluating the performance of wind turbines.
[0078] Transmission ratio: The transmission ratio of wind power generation refers to the ratio of the speed of the wind turbine shaft to the speed of the wind rotor. It is an important parameter of the transmission system in the wind turbine, which reflects the proportional relationship between the generator shaft speed and the wind rotor speed.
[0079] The above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present invention.
Claims
1. A method for detecting ice coating on blades of a wind turbine generator set, characterized in that: Includes steps: Acquire SCADA data and pre-process the SCADA data to obtain power generation condition data for analysis and diagnosis; Aggregate the power generation condition data, identify the scattered points where the wind speed and power do not match, and obtain the power anomaly data set; Identify whether the necessary conditions for icing in the power anomaly data set are met, and obtain a suspected icing data set that meets the necessary conditions for icing; Steady-state data is extracted from the suspected icing data set, and frequency analysis is performed using fast Fourier transform to determine whether the power has 1P characteristics. If the power has 1P characteristics, it is determined that the wind turbine blades are iced.
2. The wind turbine blade ice detection method according to claim 1, characterized in that: The characteristic frequency calculation formula of 1P is as follows: F=R gen / 60 / tr Where F represents the characteristic frequency; R gen represents the generator speed; tr is the transmission ratio.
3. The wind turbine blade ice detection method according to claim 1 or 2, characterized in that: The SCADA data includes one or more of wind speed, active power, blade angle, temperature, humidity, generator set speed, torque and active power command.
4. The wind turbine blade ice detection method according to claim 1 or 2, characterized in that: The specific preprocessing of SCADA second-level data is to eliminate shutdown data, fault data, power limitation and power limitation data.
5. The wind turbine blade ice detection method according to claim 1 or 2, characterized in that: Necessary conditions for icing include humidity and temperature reaching set thresholds.
6. The wind turbine blade ice detection method according to claim 1 or 2, characterized in that: Necessary conditions for ice cover also include time period.
7. The wind turbine blade ice detection method according to claim 1 or 2, characterized in that: Steady-state data includes steady-state speed, and steady-state data includes grid-connected speed and rated speed.
8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are performed.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the computer program performs the steps of the method according to any one of claims 1 to 7.
10. A wind turbine blade ice detection method system, comprising a memory and a processor connected to each other, wherein a computer program is stored in the memory, characterized in that: When the computer program is executed by a processor, the computer program performs the steps of the method according to any one of claims 1 to 7.