A fan blade lightning wire fault online detection method and device based on high-frequency characteristics

Abstract

A fan blade lightning conductor fault online detection method and device based on high frequency characteristics, the method comprising: 1) during the operation of the fan, the pulse generator module periodically generates a pulse signal, and transmits the pulse signal to the fan blade tip through the lightning conductor; 2) the signal acquisition module acquires the response current at the fan blade tip collected by the passive current sensor through the wire, and acquires the pulse signal; 3) determine whether the lightning conductor is in a normal working state, if yes, return to step 1), if not, go to step 4); 4) pre-process the signal acquired by the signal acquisition module to obtain a transfer function curve; 5) compare the characteristic quantity of the transfer function curve with the baseline data to detect the fault of the lightning conductor. The device comprises a pulse generation module, a passive current sensor, a signal acquisition module, a wire, and a remote monitoring system. The present application has the potential for online application, has high sensitivity, and can realize real-time monitoring of the fan blade lightning conductor fault.

Description

Technical Field

[0001] This invention relates to the field of wind power generation technology, specifically to an online detection method and device for wind turbine blade lightning protection wire faults based on high-frequency characteristics. Background Technology

[0002] With the continuous advancement of wind power technology and the increasing demand for clean energy in my country, the number of wind power companies in my country has been increasing year by year, and the capacity of wind turbine generators has continued to expand. Lightning protection wires are a crucial part of the wind turbine power system, their function being to prevent lightning strikes from damaging the wind turbine generators and their electrical equipment.

[0003] However, lightning protection wires may break or be damaged during long-term operation, leading to a loss of lightning protection and increasing the safety risks of wind power equipment. If the lightning protection wire fails to conduct lightning, an isolated wind turbine will experience electric field distortion under the influence of the thunderstorm cloud's electric field, causing a large amount of non-uniform charge from the thunderstorm cloud to accumulate at the blade tips, resulting in point discharge. When the lightning current enters the blade's cavity, it is conducted along the internal channels of the blade shell or to the external blade surface, generating a lightning arc within the shell. The high temperature can sometimes scorch the blade shell near the material along the arc path, and this high temperature may also generate a high-voltage shock wave, causing cracks or breakage of the blade shell after a lightning strike. Therefore, the proper condition of the lightning protection wires on wind turbine blades is crucial for ensuring the safe, reliable, and economical operation of wind turbines, and timely and effective detection of lightning protection wire faults is key to ensuring the safe operation of wind power generation.

[0004] Currently, traditional methods for detecting faults in wind turbine blade lightning arresters mainly include current analysis-based detection, manual inspection, and resistance-based detection. For example, current analysis-based detection can determine whether a fault has occurred by monitoring the current in the wind turbine blade lightning arrester. When the lightning arrester experiences grounding, open circuit, or other faults, the current will change. By comparing the current changes before and after the fault, it can be determined whether a lightning arrester fault has occurred. Manual inspection relies on maintenance personnel visually inspecting the blade surface or using basic tools under shutdown conditions to assess the mechanical damage or aging of the exposed parts and connection points of the lightning arrester. Resistance-based detection uses a resistance tester to measure the resistance value of the entire length or sections of the lightning arrester and compares it with standard resistance values ​​or historical records to identify any abnormalities. However, in the initial stage of a fault, the internal current change in the wind turbine's lightning arrester is not obvious enough, and current analysis detection methods may not be sensitive enough for minor or short-term faults. Manual inspection is inefficient, relies on personnel experience, is difficult to detect hidden or early-stage faults, and can only be performed when the wind turbine is shut down. Resistance-based detection methods must also be performed when the wind turbine is not in operation, cannot identify dynamic faults or potential problems during equipment operation, and have low sensitivity. In summary, existing lightning arrester fault detection methods have poor sensitivity, are complex to implement, have high detection costs, and cannot achieve online detection of wind turbine blade lightning arrester faults. Summary of the Invention

[0005] The purpose of this invention is to provide an online detection method and device for wind turbine blade lightning protection wire faults based on high-frequency characteristics.

[0006] The technical solution adopted to achieve the technical objective of this invention is as follows: an online detection method for wind turbine blade lightning arrester faults based on high-frequency characteristics, comprising the following steps:

[0007] 1) During wind turbine operation, the pulse generator module periodically generates pulse signals and transmits the pulse signals to the tips of the wind turbine blades through the lightning protection wires arranged inside the wind turbine blades.

[0008] 2) The passive current sensor collects the response current at the tip of the wind turbine blade and transmits it to the signal acquisition module.

[0009] The pulse generator module transmits the pulse signal to the signal acquisition module.

[0010] 3) The signal acquisition module transmits the acquired response current and pulse signal to the remote monitoring system.

[0011] 4) The remote monitoring system analyzes and processes the pulse signals and response currents collected by the signal acquisition module to determine whether the lightning protection wire is in normal working condition. If yes, return to step 1); otherwise, proceed to step 5.

[0012] 5) The pulse signal and response current acquired by the signal acquisition module are preprocessed to obtain the transfer function curve.

[0013] 6) Compare the characteristic quantities of the transfer function curve with the baseline data, and determine the fault type of the lightning protection wire based on the difference in characteristic quantities.

[0014] Furthermore, the amplitude, frequency, and pulse width of the pulse signal are all adjustable.

[0015] Furthermore, the preprocessing includes filtering out low-frequency components and obtaining spectral characteristics.

[0016] The processing method for filtering out low-frequency components includes wavelet transform.

[0017] The wavelet functions of the wavelet transform method include Haar wavelet, Daubechies wavelet, and Morlet wavelet.

[0018] The processing method for obtaining spectral characteristics includes the Fast Fourier Transform method.

[0019] Furthermore, the transfer function curve is shown below:

[0020]

[0021] In the formula, H(k) is the transfer function curve.

[0022] Among them, the fast Fourier transform V of the pulse signal in (k) Fast Fourier Transform of Response Current I out (k) is shown below:

[0023]

[0024] In the formula, v in (n) represents the pulse signal acquired by the signal acquisition module. out (n) represents the response current acquired by the signal acquisition module. n is the signal number. N is the signal length. k is the discrete frequency.

[0025] Furthermore, the baseline data is the transfer function curve of the lightning protection line under normal operating conditions.

[0026] The baseline data is pre-stored in the remote monitoring system.

[0027] Furthermore, the characteristic quantities include, but are not limited to, the number of peaks, the number of troughs, the resonant frequency of the peaks, the resonant frequency of the troughs, the amplitude of the peaks, the amplitude of the troughs, and the shape of the transfer function curve.

[0028] The formulas for calculating the characteristic quantities of the comparison transfer function curve and baseline data include:

[0029]

[0030] In the formula, ED, SD, CC, DABS, and RMSE represent the Euclidean distance, standard deviation, correlation coefficient, absolute difference, and root mean square error between the transfer function curve and the baseline data, respectively. X and Y are the characteristic quantities of the transfer function curve and the baseline data, respectively. i is the frequency index, and N... i Let be the total number of frequencies. X(i) and Y(i) are the amplitudes of the transfer function curve and the baseline data at frequency i, respectively.

[0031] Furthermore, the lightning protection wire is determined to be in normal working condition by using the similarity factor between the pulse signal and the response current obtained by the signal acquisition module.

[0032] The fault types of the lightning protection wire include grounding of the lightning protection wire and open circuit of the lightning protection wire.

[0033] A detection device using the above method includes a pulse generation module, a passive current sensor, a signal acquisition module, wires, and a remote monitoring system.

[0034] The pulse generation module is used to generate pulse signals and transmit the pulse signals to the tip of the wind turbine blades through the lightning protection wire inside the wind turbine blades.

[0035] The passive current sensor is used to detect the response current at the tip of the wind turbine blade.

[0036] The signal acquisition module is used to acquire pulse signals and response currents.

[0037] The wire is used to transmit the response current to the signal acquisition module.

[0038] The pulse generation module and signal acquisition module are both installed at the root of the wind turbine blades, while the passive current sensor is installed at the tip of the wind turbine blades.

[0039] The wire is installed inside the wind turbine blade.

[0040] The remote monitoring system is used to analyze the signals collected by the signal acquisition module and detect faults in the lightning protection wire.

[0041] Furthermore, the detection device also includes a power module.

[0042] The power supply module provides power to the pulse generation module and the signal acquisition module.

[0043] Furthermore, the remote monitoring system includes a function curve acquisition module, a comparison module, and a fault type assessment module.

[0044] The function curve acquisition module is used to preprocess the pulse signal and response current acquired by the signal acquisition module to obtain the transfer function curve.

[0045] The comparison module is used to compare the characteristic quantities of the transfer function curve with the baseline data.

[0046] The fault type assessment module determines the fault type of the lightning protection wire based on the differences in characteristic quantities.

[0047] The technical effects of this invention are undeniable. This invention proposes an online detection method and device for wind turbine blade lightning arrester faults based on high-frequency characteristics. During wind turbine operation, a pulse signal with specific amplitude, frequency, and pulse width is applied at the blade root. This pulse propagates through the lightning arrester wire, generating a corresponding current signal at the blade tip on the other side. By acquiring the initial voltage signal at the blade root and the response current signal at the blade tip, and analyzing the signals at both ends of the lightning arrester wire, a wide-bandwidth transfer function response curve can be obtained. By comparing the measured transfer function curve with the transfer function curve under normal operating conditions, the fault status of the wind turbine blade lightning arrester wire can be determined. This method can monitor wind turbine blade lightning arrester wire faults online, has the potential to detect faults in the early stages of fault occurrence, provides timely feedback on the operating status of the wind turbine blade lightning arrester wire, realizes monitoring of the wind turbine power system, and improves the power system's control over critical power equipment.

[0048] This invention overcomes the problems of existing wind turbine blade lightning protection wire fault monitoring methods, such as current analysis-based detection method, manual inspection method, and resistance-based detection method, which are complex to implement, have low sensitivity, and are costly. It has the potential for online application, and due to its wide bandwidth characteristics, it has high sensitivity and can realize real-time monitoring of wind turbine blade lightning protection wire faults.

[0049] This invention proposes an online detection method and device for wind turbine blade lightning protection wire faults based on high-frequency characteristics. This technology can realize online monitoring of wind turbine blade lightning protection wire faults, enabling timely prevention of accidents caused by such faults. Furthermore, compared to periodic maintenance, this proposed technology offers higher real-time performance. It can achieve online detection of wind turbine blade lightning protection wire faults after lightning strikes, and compared to traditional manual inspection methods, it does not require the assistance of cranes or robots, solving the problems of low efficiency, high cost, missed detections, and complex operation associated with traditional methods. Simultaneously, online monitoring of the wind turbine blade lightning protection wire status allows for timely fault detection, automatic early warning, and notification of relevant personnel, improving fault response speed and reducing downtime and maintenance costs due to sudden faults. Attached Figure Description

[0050] Figure 1 A flowchart illustrating the online detection method for lightning protection wire faults in wind turbine blades;

[0051] Figure 2 A schematic diagram of the modules of the online fault detection device for wind turbine blade lightning protection wires;

[0052] Figure 3 A schematic diagram showing the installation location of the online fault detection device module for wind turbine blade lightning protection wires;

[0053] Figure 4 Schematic diagram of the test principle for online detection method of lightning protection wire fault of wind turbine blade;

[0054] Figure 5 This is a structural block diagram of an online fault detection device for wind turbine blade lightning protection wires.

[0055] Figure 6 This is a schematic diagram comparing the transfer function curve of the lightning arrester wire of a faulty wind turbine blade in one embodiment with the reference transfer function curve.

[0056] In the diagram: 1. Pulse generation module; 2. Passive current sensor; 3. Signal acquisition module; 4. Lightning protection wire; 5. Wire; 6. Remote monitoring system; 601. Function curve acquisition module; 602. Comparison module; 603. Fault type assessment module; 7. Power supply module. Detailed Implementation

[0057] The present invention will be further described below with reference to embodiments, but it should not be construed that the scope of the present invention is limited to the following embodiments. Various substitutions and modifications made based on ordinary technical knowledge and common practices in the art without departing from the above-described technical concept of the present invention should be included within the scope of protection of the present invention.

[0058] Example 1:

[0059] See Figures 1 to 6 An online fault detection method for wind turbine blade lightning arrester wires based on high-frequency characteristics includes the following steps:

[0060] 1) During wind turbine operation, the pulse generator module periodically generates pulse signals and transmits the pulse signals to the tips of the wind turbine blades through the lightning protection wires arranged inside the wind turbine blades.

[0061] 2) The passive current sensor collects the response current at the tip of the wind turbine blade and transmits it to the signal acquisition module.

[0062] The pulse generator module transmits the pulse signal to the signal acquisition module.

[0063] 3) The signal acquisition module transmits the acquired response current and pulse signal to the remote monitoring system.

[0064] 4) The remote monitoring system analyzes and processes the pulse signals and response currents collected by the signal acquisition module to determine whether the lightning protection wire is in normal working condition. If yes, return to step 1); otherwise, proceed to step 5.

[0065] 5) The pulse signal and response current acquired by the signal acquisition module are preprocessed to obtain the transfer function curve.

[0066] 6) Compare the characteristic quantities of the transfer function curve with the baseline data, and determine the fault type of the lightning protection wire based on the difference in characteristic quantities.

[0067] Example 2:

[0068] An online detection method for wind turbine blade lightning protection wire faults based on high-frequency characteristics is described in Example 1. Furthermore, the amplitude, frequency, and pulse width of the pulse signal are all adjustable.

[0069] Example 3:

[0070] An online detection method for wind turbine blade lightning protection wire faults based on high frequency characteristics is provided. The main technical contents are described in any one of Embodiments 1 to 2. Further, the preprocessing includes filtering out low frequency components and obtaining spectral characteristics.

[0071] The processing method for filtering out low-frequency components includes wavelet transform.

[0072] The wavelet functions of the wavelet transform method include Haar wavelet, Daubechies wavelet, and Morlet wavelet.

[0073] The processing method for obtaining spectral characteristics includes the Fast Fourier Transform method.

[0074] Example 4:

[0075] An online fault detection method for wind turbine blade lightning arrester wires based on high-frequency characteristics, the main technical contents of which are described in any one of Embodiments 1 to 3, and further, the transfer function curve is shown below:

[0076]

[0077] In the formula, H(k) is the transfer function curve.

[0078] Among them, the fast Fourier transform V of the pulse signal in (k) Fast Fourier Transform of Response Current I out (k) is shown below:

[0079]

[0080] In the formula, v in (n) represents the pulse signal acquired by the signal acquisition module.out (n) represents the response current acquired by the signal acquisition module. n is the signal number. N is the signal length. k is the discrete frequency.

[0081] Example 5:

[0082] An online fault detection method for wind turbine blade lightning protection wire based on high frequency characteristics is provided. The main technical contents are described in any one of Examples 1 to 4. Furthermore, the baseline data is the transfer function curve of the lightning protection wire under normal working conditions.

[0083] The baseline data is pre-stored in the remote monitoring system.

[0084] Example 6:

[0085] An online detection method for lightning protection wire faults of wind turbine blades based on high-frequency characteristics is provided. The main technical contents are described in any one of Examples 1 to 5. Furthermore, the characteristic quantities include, but are not limited to, the number of peaks, the number of troughs, the resonant frequency of the peaks, the resonant frequency of the troughs, the amplitude of the peaks, the amplitude of the troughs, and the shape of the transfer function curve.

[0086] The formulas for calculating the characteristic quantities of the comparison transfer function curve and baseline data include:

[0087]

[0088] In the formula, ED, SD, CC, DABS, and RMSE represent the Euclidean distance, standard deviation, correlation coefficient, absolute difference, and root mean square error between the transfer function curve and the baseline data, respectively. X and Y are the characteristic quantities of the transfer function curve and the baseline data, respectively. i is the frequency index, and N... i Let be the total number of frequencies. X(i) and Y(i) are the amplitudes of the transfer function curve and the baseline data at frequency i, respectively.

[0089] Example 7:

[0090] An online fault detection method for wind turbine blade lightning protection wire based on high frequency characteristics is provided. The main technical contents are described in any one of Embodiments 1 to 6. Further, the lightning protection wire is determined to be in normal working condition by the similarity factor between the pulse signal and the response current obtained by the signal acquisition module.

[0091] The fault types of the lightning protection wire include grounding of the lightning protection wire and open circuit of the lightning protection wire.

[0092] Example 8:

[0093] A detection device for applying the method of any one of embodiments 1 to 7 includes a pulse generation module 1, a passive current sensor 2, a signal acquisition module 3, a wire 5, and a remote monitoring system 6.

[0094] The pulse generation module 1 is used to generate pulse signals and transmit the pulse signals to the tip of the wind turbine blades through the lightning protection wire 4 inside the wind turbine blades.

[0095] The passive current sensor 2 is used to detect the response current at the tip of the wind turbine blade.

[0096] The signal acquisition module 3 is used to acquire pulse signals and response currents.

[0097] The wire 5 is used to transmit the response current to the signal acquisition module 3.

[0098] The pulse generation module 1 and the signal acquisition module 3 are both installed at the root of the wind turbine blade, and the passive current sensor 2 is installed at the tip of the wind turbine blade.

[0099] The conductor 5 is installed inside the wind turbine blade.

[0100] The remote monitoring system 6 is used to analyze the signals collected by the signal acquisition module 3 and detect faults in the lightning protection wire.

[0101] Example 9:

[0102] An online fault detection device for wind turbine blade lightning protection wire based on high frequency characteristics is described in Embodiment 8. Furthermore, the detection device also includes a power supply module 7.

[0103] The power supply module 7 supplies power to the pulse generation module 1 and the signal acquisition module 3.

[0104] Example 10:

[0105] An online fault detection device for wind turbine blade lightning protection wire based on high frequency characteristics, the main technical contents of which are described in any one of embodiments 8 to 9. Further, the remote monitoring system 6 includes a function curve acquisition module 601, a comparison module 602, and a fault type evaluation module 603.

[0106] The function curve acquisition module 601 is used to preprocess the pulse signal and response current acquired by the signal acquisition module 3 to obtain the transfer function curve.

[0107] The comparison module 602 is used to compare the characteristic quantities of the transfer function curve with the baseline data.

[0108] The fault type assessment module 603 determines the fault type of the lightning protection wire based on the differences in characteristic quantities.

[0109] Example 11:

[0110] See Figures 1 to 6 An online fault detection method for wind turbine blade lightning arrester wires based on high-frequency characteristics includes the following steps:

[0111] A stable DC voltage is provided to power the pulse generator module by installing a power module at the blade root hub. Lightning protection wires and conductors are arranged within the blade, extending from the blade root to the blade tip. A passive current sensor is installed at the blade tip, and a data acquisition card is installed at the blade root. The passive current sensor is connected to the data acquisition card at the blade root via conductors within the blade.

[0112] 1) During wind turbine operation, the pulse generator module periodically generates pulse signals with specific amplitude, frequency and pulse width at the blade root, and propagates the pulse signals to the blade tip of the wind turbine blade through the lightning protection wire arranged in the wind turbine blade.

[0113] 2) The passive current sensor collects the response current at the tip of the wind turbine blade and transmits it to the signal acquisition module.

[0114] The pulse generator module transmits the pulse signal to the signal acquisition module.

[0115] 3) The signal acquisition module transmits the acquired response current and pulse signal to the remote monitoring system.

[0116] 4) The remote monitoring system analyzes and processes the pulse signals and response currents collected by the signal acquisition module to determine whether the lightning protection wire is in normal working condition. If yes, return to step 1); otherwise, proceed to step 5.

[0117] 5) The pulse signal and response current acquired by the signal acquisition module are preprocessed to obtain the transfer function curve.

[0118] 6) Compare the characteristic quantities of the transfer function curve with the baseline data, and determine the fault type of the lightning protection wire based on the difference in characteristic quantities.

[0119] Example 12:

[0120] An online detection method for wind turbine blade lightning protection wire faults based on high-frequency characteristics is described in Example 11. Furthermore, the amplitude, frequency, and pulse width of the pulse signal are all adjustable.

[0121] Example 13:

[0122] An online detection method for wind turbine blade lightning protection wire faults based on high frequency characteristics is provided. The main technical contents are described in any one of Examples 11 to 12. Further, the preprocessing includes filtering out low frequency components and obtaining spectral characteristics.

[0123] The processing method for filtering out low-frequency components includes wavelet transform.

[0124] Wavelet transform denoising is a technique that uses wavelet transform algorithms to filter out low-frequency components from a signal. Its basic process is as follows:

[0125] 1. Wavelet Function Selection: The first step is to select a suitable wavelet function as the basis for signal transformation. The wavelet functions used in the wavelet transform method include Haar wavelet, Daubechies wavelet, and Morlet wavelet, each providing different characteristics to capture signal features at different scales.

[0126] 2. Signal Decomposition: The signal is decomposed using a selected wavelet. This process involves filtering the signal at different resolution levels. The Continuous Wavelet Transform (CWT) computes wavelet coefficients by convolving the signal with a scaled and translated version of a wavelet function. The Discrete Wavelet Transform (DWT) operates on discrete samples, decomposing the signal into approximate and detail coefficients at each scale.

[0127] 3. Scaling and Translation: For each decomposition level, the wavelet function is scaled by a factor (adjusting the width of the wavelet) and translated over the signal. The scaling factor controls the frequency resolution; larger scales correspond to lower frequencies, and smaller scales correspond to higher frequencies.

[0128] 4. Coefficient Extraction: The generated coefficients represent signals at different scales and locations. These coefficients are used to reconstruct the original signal or extract features such as edges, patterns, or anomalies from the data.

[0129] 5. Reconstruction: After decomposition, the signal can be reconstructed using inverse wavelet transform, which combines approximation coefficients and detail coefficients. This allows the signal to be reconstructed back to its original state, or modified by manipulating certain scales of the wavelet coefficients (e.g., denoising).

[0130] 6. Analysis and Interpretation: The final step involves analyzing the wavelet coefficients to gain insights. In practice, wavelet transform is commonly used in applications such as signal compression, feature extraction, denoising, and time-frequency analysis.

[0131] The processing method for obtaining spectral characteristics includes the Fast Fourier Transform method.

[0132] Example 14:

[0133] An online fault detection method for wind turbine blade lightning arrester wires based on high-frequency characteristics, the main technical contents of which are described in any one of Examples 11 to 13, and further, the transfer function curve is shown below:

[0134]

[0135] In the formula, H(k) is the transfer function curve.

[0136] Among them, the fast Fourier transform V of the pulse signal in (k) Fast Fourier Transform of Response Current I out (k) is shown below:

[0137]

[0138] In the formula, v in (n) represents the pulse signal acquired by the signal acquisition module. out (n) represents the response current acquired by the signal acquisition module. n is the signal number. N is the signal length. k is the discrete frequency.

[0139] Example 15:

[0140] An online fault detection method for wind turbine blade lightning protection wire based on high frequency characteristics is provided. The main technical contents are described in any one of Examples 11 to 14. Furthermore, the baseline data is the transfer function curve of the lightning protection wire under normal working conditions.

[0141] The baseline data is pre-stored in the remote monitoring system.

[0142] Example 16:

[0143] An online detection method for lightning protection wire faults of wind turbine blades based on high-frequency characteristics is provided. The main technical contents are described in any one of Examples 11 to 15. Furthermore, the characteristic quantities include, but are not limited to, the number of peaks, the number of troughs, the resonant frequency of the peaks, the resonant frequency of the troughs, the amplitude of the peaks, the amplitude of the troughs, and the shape of the transfer function curve.

[0144] The formulas for calculating the characteristic quantities of the comparison transfer function curve and baseline data include:

[0145]

[0146]

[0147] In the formula, ED, SD, CC, DABS, and RMSE represent the Euclidean distance, standard deviation, correlation coefficient, absolute difference, and root mean square error between the transfer function curve and the baseline data, respectively. X and Y are the characteristic quantities of the transfer function curve and the baseline data, respectively. i is the frequency index, and N... i Let be the total number of frequencies. X(i) and Y(i) are the amplitudes of the transfer function curve and the baseline data at frequency i, respectively.

[0148] Example 17:

[0149] An online fault detection method for wind turbine blade lightning protection wire based on high frequency characteristics is provided. The main technical contents are described in any one of Examples 11 to 16. Further, the lightning protection wire is determined to be in normal working condition by the similarity factor between the pulse signal and the response current obtained by the signal acquisition module.

[0150] The pulse voltage signal and response current collected in the current operation are compared with the pulse voltage signal and response current collected in the initial operation, i.e., fingerprint data. If any similarity factor of the voltage and current time domain waveforms is less than 0.9, the lightning protection line is not in normal working condition; otherwise, it is in normal working condition.

[0151] The fault types of the lightning protection wire include grounding of the lightning protection wire and open circuit of the lightning protection wire.

[0152] The correlation coefficient can be used as an indicator to determine the fault type. If the correlation coefficient is greater than 0.9, the fault is in normal condition; if the correlation coefficient is between 0.5 and 0.9, the fault type is grounding fault; and if the correlation coefficient is between 0 and 0.5, the fault type is open circuit fault.

[0153] Example 18:

[0154] A detection device for the method described in any one of embodiments 11 to 17 includes a pulse generation module 1, a passive current sensor 2, a signal acquisition module 3, a wire 5, and a remote monitoring system 6.

[0155] The pulse generation module 1 is used to generate pulse signals and transmit the pulse signals to the tip of the wind turbine blades through the lightning protection wire 4 inside the wind turbine blades.

[0156] The passive current sensor 2 is used to detect the response current at the tip of the wind turbine blade.

[0157] A passive current sensor detects changes in the magnetic field caused by current flowing through the wind turbine's lightning arrester wire, converting this change into an output signal proportional to the current magnitude. Since the passive current sensor requires no external power supply, it directly generates an electrical signal through magnetic field changes, thus enabling current monitoring. It can sense even minute current changes, making it suitable for high-precision current monitoring. When mounted at the tip of the wind turbine blade, it is primarily used to capture the response current of the wind turbine's lightning arrester wire under pulse excitation.

[0158] The signal acquisition module 3 is used to acquire pulse signals and response currents.

[0159] The wire 5 is used to transmit the response current to the signal acquisition module 3.

[0160] The pulse generation module 1 and the signal acquisition module 3 are both installed at the root of the wind turbine blade, and the passive current sensor 2 is installed at the tip of the wind turbine blade.

[0161] The conductor 5 is installed inside the wind turbine blade.

[0162] The remote monitoring system 6 is used to analyze the signals collected by the signal acquisition module 3 and detect faults in the lightning protection wire.

[0163] Example 19:

[0164] An online fault detection device for wind turbine blade lightning protection wire based on high frequency characteristics is described in Embodiment 18. Furthermore, the detection device also includes a power supply module 7.

[0165] The power supply module 7 supplies power to the pulse generation module 1 and the signal acquisition module 3.

[0166] Example 20:

[0167] An online fault detection device for wind turbine blade lightning protection wire based on high frequency characteristics, the main technical contents of which are described in any one of embodiments 18 to 19. Further, the remote monitoring system 6 includes a function curve acquisition module 601, a comparison module 602, and a fault type evaluation module 603.

[0168] The function curve acquisition module 601 is used to preprocess the pulse signal and response current acquired by the signal acquisition module 3 to obtain the transfer function curve.

[0169] The comparison module 602 is used to compare the characteristic quantities of the transfer function curve with the baseline data.

[0170] The fault type assessment module 603 determines the fault type of the lightning protection wire based on the differences in characteristic quantities.

[0171] Example 21:

[0172] See Figures 1 to 6 A method and device for online detection of faults in the lightning arrester wires of wind turbine blades based on high-frequency characteristics, the main technical contents of which include:

[0173] Step 1: A stable DC voltage is provided to power the pulse generator module by installing a power module at the blade root hub. Lightning protection wires and conductors are arranged inside the blade, extending from the blade root to the blade tip. A passive current sensor is installed at the blade tip, and a data acquisition card is installed at the blade root. The passive current sensor is connected to the data acquisition card at the blade root via conductors inside the blade.

[0174] Step 2: During wind turbine operation, a pulse signal with a specific amplitude, frequency, and pulse width is applied at the blade root. This pulse propagates through the lightning protection wire and generates a corresponding current signal at the blade tip on the other side. The response current is collected by a passive current sensor, and the initial voltage signal at the blade root and the response current signal at the blade tip are collected by a data acquisition card.

[0175] Step 3: Judge the voltage and current signals collected in real time on both sides of the wind turbine blade lightning arrester. If they are within the preset normal operating range of the wind turbine lightning arrester, the collected data is left unprocessed, and the process returns to Step 2. If they exceed the preset normal operating range of the wind turbine blade lightning arrester, proceed to Step 4. This involves comparing the currently collected pulse voltage signal and response current with the initially collected pulse voltage signal and response current, i.e., the fingerprint data. If any similarity factor of the voltage and current time-domain waveforms is less than 0.9, the lightning arrester is not in normal operating condition; otherwise, it is in normal operating condition.

[0176] Step 4: Preprocess the voltage signal at the root of the wind turbine blade lightning arrester and the current signal at the tip of the blade: including filtering out low-frequency components and obtaining the spectral characteristics, thereby obtaining the transfer function curve represented by the current-voltage ratio.

[0177] Step 5: Use the similarity coefficient method to compare the transfer function curve with the baseline data (transfer function curve of the wind turbine blade lightning protection wire in a healthy state). By analyzing the changes in the peak and valley values ​​of the transfer function curve, possible faults can be detected.

[0178] Example 22:

[0179] See Figures 1 to 6 A method and device for online detection of faults in the lightning arrester wires of wind turbine blades based on high-frequency characteristics, the main technical contents of which include:

[0180] Step 1: A stable DC voltage is provided to power the pulse generator module by installing a power module at the blade root hub. Lightning protection wires and conductors are arranged inside the blade, extending from the blade root to the blade tip. A passive current sensor is installed at the blade tip, and a data acquisition card is installed at the blade root. The passive current sensor is connected to the data acquisition card at the blade root via conductors inside the blade.

[0181] Step Two: During wind turbine operation, a pulse signal with specific amplitude, frequency, and pulse width is applied to the blade root. This pulse propagates through the lightning arrester wire and generates a corresponding current signal at the blade tip on the other side. The response current at the blade tip is collected by a passive current sensor. The initial voltage signal at the blade root and the response current signal at the blade tip are then collected by a data acquisition card.

[0182] Step 3: Judge the voltage and current signals collected in real time on both sides of the wind turbine blade lightning arrester. If they are within the preset normal operating range of the wind turbine lightning arrester, the collected data is left unprocessed, and the process returns to Step 2. If they exceed the preset normal operating range of the wind turbine blade lightning arrester, proceed to Step 4. This involves comparing the currently collected pulse voltage signal and response current with the initially collected pulse voltage signal and response current, i.e., the fingerprint data. If any similarity factor of the voltage and current time-domain waveforms is less than 0.9, the lightning arrester is not in normal operating condition; otherwise, it is in normal operating condition.

[0183] Step 4: Preprocess the voltage signal at the root of the wind turbine blade lightning arrester and the current signal at the tip of the blade: including filtering out low-frequency components and obtaining the spectral characteristics, thereby obtaining the transfer function curve represented by the current-voltage ratio.

[0184] Step 5: Use the similarity coefficient method to compare the transfer function curve with the baseline data (transfer function curve of the wind turbine blade lightning arrester under healthy conditions). Analyze the characteristic quantities of the transfer function curve, such as the changes in peak and valley values, to detect possible faults.

[0185] In step two, the current measuring device is primarily a passive current sensor. This sensor detects the magnetic field changes caused by the current flowing through the wind turbine's lightning arrester wire and converts it into an output signal proportional to the current magnitude. Since the passive current sensor does not require an external power source, it directly generates an electrical signal through magnetic field changes, thus enabling current monitoring. It can also sense minute current changes, making it suitable for high-precision current monitoring. It is installed at the tip of the wind turbine blade, primarily to capture the response current of the lightning arrester wire under pulse excitation. Simultaneously, a data acquisition card needs to be installed at the root of the blade, near the wind turbine bearing and motor, to measure the initial voltage signal at the blade root and the response current signal at the blade tip.

[0186] In step four, the low-frequency components of the acquired time-domain signal are filtered out using a wavelet transform algorithm, and the spectral characteristics of the signal in the time domain are obtained using a fast Fourier transform algorithm.

[0187] The characteristic quantities mentioned in step five include, but are not limited to, the increase or decrease in the number of peaks and troughs of the transfer function curve, the change in the resonant frequencies of the peaks and troughs, the change in the amplitude of the peaks and troughs, and the change in the shape of the transfer function curve. The baseline data is the transfer function curve obtained experimentally under normal conditions of the wind turbine blade lightning arrester. The comparison of characteristic quantities between the transfer function curve and the baseline data mainly uses the similarity coefficient method. Based on the changes in the transfer function curve, it is determined whether a fault has occurred in the wind turbine blade lightning arrester, thereby achieving online monitoring.

[0188] When using this method to detect faults in the lightning arrester wires of wind turbine blades, a pulse signal with a certain amplitude, frequency, and pulse width is applied at the blade root. When a fault occurs in the lightning arrester wire, the corresponding inductance and capacitance parameters inside will change, and the corresponding response curve will also change. By performing spectral analysis on the response curve, the type of fault in the lightning arrester wire can be roughly determined. The internal inductance and capacitance parameters are closely related to its transfer function. The circuit diagram of the test principle is shown below. Figure 4 As shown, where R s Resistance per unit length; L s Inductance per unit length; C s Capacitance per unit length; C g R is the capacitance to ground per unit length; R is the measured impedance.

[0189] In addition, such as Figure 5 This application also provides an online fault detection device for wind turbine blade lightning arrester wires based on high-frequency characteristics, comprising:

[0190] The power module provides a stable DC voltage to the pulse generator and data acquisition card at the leaf root.

[0191] The pulse generation module is used to generate pulse signals with specific amplitude, frequency, and pulse width at the leaf root, thereby generating a response current signal at the leaf tip.

[0192] The signal acquisition module is used to acquire the voltage signal at the root of the blade and the current signal at the response side of the blade tip.

[0193] The function curve acquisition module is used to obtain the measured transfer function curve represented by the current-voltage ratio based on the spectral characteristics of the voltage signal at the leaf root and the current signal at the leaf tip response side when the voltage signal at the leaf root and the current signal at the leaf tip are outside the preset operating range.

[0194] The comparison module is used to compare the tested transfer function curve with the reference transfer function curve according to preset statistical indicators to obtain statistical indicator data; the statistical indicator data is used to characterize the similarity between the tested transfer function curve and the reference transfer function curve; the statistical indicators include a first type of indicator and a second type of indicator; the statistical indicator data corresponding to the first type of indicator is extracted from the amplitude vector of the transfer function curve.

[0195] The fault type assessment module is used to determine the fault detection result of the lightning protection wire of the tested wind turbine blade based on the statistical index data.

[0196] Example 23:

[0197] See Figures 1 to 6A method and device for online detection of faults in the lightning arrester wires of wind turbine blades based on high-frequency characteristics, the main technical contents of which include:

[0198] like Figure 2 and Figure 3 In the aforementioned steps, this embodiment describes a fault testing device for the lightning protection wire of a wind turbine blade. The hardware of this device mainly consists of the following parts: a lightning protection wire and a conductor within the wind turbine blade. The lightning protection wire is a lead wire laid from the lightning arrester at the blade tip to the blade root. The conductor is a new lead wire connecting the passive current sensor at the blade root to the acquisition card at the blade tip. A passive current sensor is installed at the blade tip via bolt connection to acquire the response-side current signal from the lightning protection wire. A pulse generator is arranged at the blade tip to generate pulse signals with specific amplitude, frequency, and pulse width. In this example, the acquisition card has at least six analog input channels, 16-bit resolution, and a maximum sampling rate of 10 MS / s. The remote monitoring system wirelessly receives the blade number and acquired signal from the signal detection module, analyzes the acquired signal, and then determines the fault status of the lightning protection wire.

[0199] In step three, the voltage and current signals on both sides of the wind turbine blade lightning arrester are judged in real time: if they are within the preset normal operating range of the wind turbine blade lightning arrester, the collected data is not processed and the process returns to step two; if they exceed the normal operating range of the wind turbine blade lightning arrester, step four is performed. That is, the pulse voltage signal and response current collected in this instance are compared with the pulse voltage signal and response current collected initially, i.e., fingerprint data. If any similarity factor of the voltage and current time-domain waveforms is less than 0.9, the lightning arrester is not in normal operating condition; otherwise, it is in normal operating condition.

[0200] In step four, since the acquired signal also includes various on-site interference noises from the wind turbine blade lightning arrester, it is necessary to preprocess it to provide reliable analytical data for subsequent fault analysis. The filtering of low-frequency components is achieved using a wavelet algorithm. The acquisition of spectral characteristics is achieved using a Fast Fourier Transform algorithm.

[0201] Wavelet transform denoising is a technique that uses wavelet transform algorithms to filter out low-frequency components from a signal. Its basic process is as follows:

[0202] 1. Wavelet Function Selection: The first step is to select a suitable wavelet function as the basis for signal transformation. Common wavelets include the Haar wavelet, Daubechies wavelet, and Morlet wavelet, each providing different characteristics to capture signal features at different scales.

[0203] 2. Signal Decomposition: The signal is decomposed using a selected wavelet. This process involves filtering the signal at different resolution levels. The Continuous Wavelet Transform (CWT) computes wavelet coefficients by convolving the signal with a scaled and translated version of a wavelet function. The Discrete Wavelet Transform (DWT) operates on discrete samples, decomposing the signal into approximate and detail coefficients at each scale.

[0204] 3. Scaling and Translation: For each decomposition level, the wavelet function is scaled by a factor (adjusting the width of the wavelet) and translated over the signal. The scaling factor controls the frequency resolution; larger scales correspond to lower frequencies, and smaller scales correspond to higher frequencies.

[0205] 4. Coefficient Extraction: The generated coefficients represent signals at different scales and locations. These coefficients are used to reconstruct the original signal or extract features such as edges, patterns, or anomalies from the data.

[0206] 5. Reconstruction: After decomposition, the signal can be reconstructed using inverse wavelet transform, which combines approximation coefficients and detail coefficients. This allows the signal to be reconstructed back to its original state, or modified by manipulating certain scales of the wavelet coefficients (e.g., denoising).

[0207] 6. Analysis and Interpretation: The final step involves analyzing the wavelet coefficients to gain insights. In practice, wavelet transform is commonly used in applications such as signal compression, feature extraction, denoising, and time-frequency analysis.

[0208] The Fast Fourier Transform (FFT) can be used to convert a time-domain signal into a frequency-domain signal, thereby obtaining the signal's spectral characteristics. in (n) represents the input voltage sampled at the leaf root, I out (k) Output current through the lightning protection wire collected at the leaf tip, V in (k) is V in Fast Fourier Transform of (n), I out (k) is i out The Fast Fourier Transform of (n), and the measured transfer function curve H(k). Its mathematical formula is as follows:

[0209]

[0210]

[0211] In step five, the transfer function curve of the wind turbine blade lightning arrester is analyzed to extract characteristic quantities. These are then compared with the original transfer function and its characteristic quantities to analyze the fault status of the wind turbine blade lightning arrester. The characteristic quantities include the increase or decrease in the number of peak and trough values ​​of the response curve, the change in the resonant frequency, and the change in the shape of the transfer function curve. The original transfer function curve is the transfer function curve obtained experimentally under normal conditions of the wind turbine blade lightning arrester. The comparison with the original transfer function curve and characteristic quantities uses the similarity coefficient method. Specifically, the calculation method compares different types of fault sample data with the healthy state, using the data under the healthy state as the standard value to calculate the characteristic value of the fault. The preset statistical indicators may include Euclidean distance (ED), standard deviation (SD), correlation coefficient (CC), absolute difference (DABS), and root mean square error (RMSE). The data comparison operation rules corresponding to the statistical indicators are as follows:

[0212] The formulas for calculating various eigenvalues ​​are given below:

[0213] 1. European distance ED

[0214]

[0215] 2. Standard deviation (SD)

[0216]

[0217] 3. Correlation coefficient (CC)

[0218]

[0219] 4. Absolute difference DABS

[0220]

[0221] 5. Root Mean Square Error (RMSE)

[0222]

[0223] In the formula, X(i) and Y(i) are the amplitudes of the corresponding fault state and healthy state at the same frequency, respectively.

[0224] In the process of comparing the measured characteristic quantity and the benchmark characteristic quantity according to the preset statistical indicators to obtain statistical indicator data, the change range of the measured characteristic quantity relative to the benchmark characteristic quantity can be determined according to the preset data comparison operation rules corresponding to the statistical indicators, thus obtaining the change range of the characteristic quantity corresponding to each statistical indicator; and the statistical indicator data is obtained based on the change range of the characteristic quantity corresponding to each statistical indicator. Furthermore, the correlation coefficient can be used as an indicator to determine the fault type: a correlation coefficient greater than 0.9 indicates a normal state; a correlation coefficient between 0.5 and 0.9 indicates a ground fault; and a correlation coefficient between 0 and 0.5 indicates an open circuit fault.

[0225] In step five, this example uses a passive current sensor to collect the current at the blade tip, and then uses a data acquisition card to collect the initial voltage and response current signals at the blade root and tip, respectively. If the wind turbine blade lightning protection wire malfunctions, it will cause a change in the transfer function curve waveform. The transfer function curve is analyzed using the similarity coefficient method based on the above formula. This indicator is used to determine the fault status of the wind turbine blade lightning protection wire. The actual measured and compared waveforms are as follows: Figure 6 As shown.

Claims

1. A method for online detection of faults in the lightning arrester wire of a wind turbine blade based on high-frequency characteristics, characterized in that, Includes the following steps: 1) During wind turbine operation, the pulse generator module periodically generates pulse signals and transmits the pulse signals to the tips of the wind turbine blades through the lightning protection wires arranged inside the wind turbine blades. 2) A passive current sensor collects the response current at the tip of the wind turbine blades and transmits it to the signal acquisition module; The pulse generator module transmits the pulse signal to the signal acquisition module; 3) The signal acquisition module transmits the acquired response current and pulse signals to the remote monitoring system; 4) The remote monitoring system analyzes and processes the pulse signals and response currents collected by the signal acquisition module to determine whether the lightning protection wire is in normal working condition. If yes, return to step 1); otherwise, proceed to step 5. The lightning protection wire is determined to be in normal working condition by using the similarity factor between the pulse signal and the response current obtained by the signal acquisition module. 5) The pulse signal and response current acquired by the signal acquisition module are preprocessed to obtain the transfer function curve; 6) Compare the characteristic quantities of the transfer function curve with the baseline data, and determine the fault type of the lightning protection wire based on the differences in the characteristic quantities; The fault types of the lightning protection wire include lightning protection wire grounding and lightning protection wire open circuit; The similarity factor includes the correlation coefficient (CC); When the correlation coefficient satisfies If so, the lightning protection wire is in normal condition; When the correlation coefficient satisfies Then the lightning protection wire is in the lightning protection wire grounding state; When the correlation coefficient satisfies If the lightning protection wire is open, then the lightning protection wire is in an open circuit state.

2. The online detection method for wind turbine blade lightning arrester faults based on high-frequency characteristics according to claim 1, characterized in that, The amplitude, frequency, and pulse width of the pulse signal are all adjustable.

3. The online detection method for wind turbine blade lightning arrester faults based on high-frequency characteristics according to claim 1, characterized in that, The preprocessing includes filtering out low-frequency components and obtaining spectral characteristics; The processing method for filtering out low-frequency components includes wavelet transform; The wavelet functions of the wavelet transform method include Haar wavelet, Daubechies wavelet and Morlet wavelet; The processing method for obtaining spectral characteristics includes the Fast Fourier Transform method.

4. The online detection method for wind turbine blade lightning arrester faults based on high-frequency characteristics according to claim 1, characterized in that, The transfer function curve is shown below: （1） In the formula, The transfer function curve; Among them, the fast Fourier transform of pulse signals Fast Fourier Transform of Response Current As shown below: （2） （3） In the formula, The pulse signal acquired by the signal acquisition module; The response current acquired by the signal acquisition module; Signal number; The signal length; For discrete frequencies.

5. The online detection method for wind turbine blade lightning arrester faults based on high-frequency characteristics according to claim 1, characterized in that, The baseline data is the transfer function curve of the lightning protection wire under normal operating conditions; The baseline data is pre-stored in the remote monitoring system.

6. The online detection method for wind turbine blade lightning arrester faults based on high-frequency characteristics according to claim 1, characterized in that, The characteristic quantities include, but are not limited to, the number of peaks, the number of troughs, the resonant frequency of the peaks, the resonant frequency of the troughs, the amplitude of the peaks, the amplitude of the troughs, and the shape of the transfer function curve. The formulas for calculating the characteristic quantities of the comparison transfer function curve and baseline data include: （4） （5） （6） （7） （8） In the formula, SD, CC, DABS, and RMSE represent the Euclidean distance, standard deviation, correlation coefficient, absolute difference, and root mean square error between the transfer function curve and the baseline data, respectively. , These are the characteristic quantities of the transfer function curve and the baseline data, respectively. For frequency serial number, The total number of frequencies; , The transfer function curve and baseline data at frequency are respectively. The amplitude at that time.

7. A detection apparatus applying the method according to any one of claims 1 to 6, characterized in that, Includes a pulse generation module (1), a passive current sensor (2), a signal acquisition module (3), a wire (5), and a remote monitoring system (6); The pulse generation module (1) is used to generate pulse signals and transmit the pulse signals to the tip of the wind turbine blades through the lightning protection wire (4) inside the wind turbine blades. The passive current sensor (2) is used to detect the response current at the tip of the wind turbine blade; The signal acquisition module (3) is used to acquire pulse signals and response currents; The wire (5) is used to transmit the response current to the signal acquisition module (3); The pulse generation module (1) and the signal acquisition module (3) are both installed at the root of the wind turbine blade, and the passive current sensor (2) is installed at the tip of the wind turbine blade. The conductor (5) is installed inside the wind turbine blade; The remote monitoring system (6) is used to analyze the signals collected by the signal acquisition module (3) and detect faults in the lightning protection line.

8. The online fault detection device for wind turbine blade lightning arrester wire based on high-frequency characteristics according to claim 7, characterized in that, The detection device also includes a power module (7). The power supply module (7) supplies power to the pulse generation module (1) and the signal acquisition module (3).

9. The online fault detection device for wind turbine blade lightning arrester wire based on high-frequency characteristics according to claim 7, characterized in that, The remote monitoring system (6) includes a function curve acquisition module (601), a comparison module (602), and a fault type assessment module (603). The function curve acquisition module (601) is used to preprocess the pulse signal and response current acquired by the signal acquisition module (3) to obtain the transfer function curve; The comparison module (602) is used to compare the characteristic quantities of the transfer function curve with the baseline data; The fault type assessment module (603) determines the fault type of the lightning protection wire based on the difference in characteristic quantities.

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