Miniature wind generating set

Abstract

The invention relates to the technical field of wind driven generators, in particular to a miniature wind driven generator set which is used for solving the problems that in the prior art, the standard of abnormal sound of a wind driven generator is not determined, the abnormal sound is mostly found manually, and the abnormal sound is often noticed when the problem appears. Vibration parameters of a tower, a wind wheel and a generator are monitored in real time, a vibration abnormal value is analyzed based on the vibration parameters, a sound signal in a monitoring environment is acquired, an abnormal sound generation value is analyzed, and whether abnormity exists or not is determined according to data output by a vibration monitoring analysis subsystem and an abnormal sound monitoring control subsystem. According to the invention, fault sources can be identified and positioned more accurately, false alarms and missing alarms are reduced, and the accuracy of abnormality diagnosis is improved.

Description

Technical Field

[0001] This invention relates to the field of wind turbines, and more specifically, to a micro wind turbine generator set. Background Technology

[0002] Existing micro wind turbines can be broadly classified into two types: turbine-type micro wind turbines, which utilize wind power to drive a turbine to rotate and then output electrical energy through an electromagnetic conversion mechanism; and wind-induced vibration-type micro wind turbines, which use wind-induced structural vibrations and utilize vibration energy harvesting methods to achieve energy conversion. Both types of generators require a wind turbine, a generator, and a tower—the tower supports the wind turbine and generator, positioning them at a higher level to capture more stable wind energy. However, regardless of the structure, vibrations of the wind turbine and generator during actual operation often lead to loosening or even detachment of the tower's fixing bolts, resulting in abnormal noises. Reference 1 (Teng W, et al., Vibration Analysis for Fault Detection of Wind Turbine Drivetrains—A Comprehensive Investigation. Sensors, 2021) points out that due to complex mechanical structures and changing operating conditions, fault identification and location of wind turbine mechanical components is a difficult task, prone to false alarms and missed alarms.

[0003] Reference 2 (Zhang Y, et al., Wind turbine blade damage detection based on acoustic signals. Scientific Reports, 2025) further reveals the limitations of acoustic signal monitoring technology. This study points out that during sound signal acquisition, wind noise, aerodynamic noise, and mechanical noise mix to form the sound signal of a wind turbine. Spectral subtraction calculations under extreme noise intensities are challenging, requiring the use of pre-trained source separation neural networks to distinguish between random wind noise and mechanical noise. This indicates that existing acoustic monitoring technologies suffer from high complexity and poor practicality in signal processing.

[0004] In summary, existing monitoring technologies lack effective automated means for abnormal noise identification and cannot establish standardized criteria for such identification, relying primarily on manual auditory judgment. These technological limitations mean that faults are often only discovered after the problem has already manifested, at which point the structural stability of the tower, turbine, and generator may have already decreased, potentially leading to equipment collapse or component detachment, causing personal injury and equipment damage. More importantly, existing vibration and acoustic monitoring technologies operate independently, lacking comprehensive analytical capabilities; a single monitoring method is insufficient to comprehensively and accurately assess the true operating status of the equipment. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, the present invention provides a micro wind turbine generator set. By analyzing vibration abnormal values ​​based on vibration parameters and analyzing abnormal noise values, and determining whether there is an anomaly based on the data output by the vibration monitoring and analysis subsystem and the abnormal noise monitoring and control subsystem, the present invention can more accurately identify and locate the source of the fault, reduce false alarms and missed alarms, and improve the accuracy of anomaly diagnosis, thereby solving the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A micro wind turbine generator set includes a vibration monitoring and analysis subsystem, an abnormal noise monitoring and control subsystem, and an anomaly diagnosis subsystem. The abnormal noise monitoring and control subsystem includes a three-level abnormal noise parameter acquisition module and an abnormal noise analysis module. The three-level abnormal noise parameter acquisition module is used to acquire a fifth parameter of the sound signal in the monitoring environment. The abnormal noise analysis module is used to extract the fifth parameter, import the fifth parameter into the abnormal noise generation value analysis model, and calculate the abnormal noise generation value. The calculation process of the abnormal noise generation value analysis model includes the following steps:

[0008] Step S1: Obtain the maximum and minimum sound frequencies in the monitoring environment during a single sound signal acquisition, and calculate the difference between them; at the same time, obtain the maximum and minimum sound amplitudes in the monitoring environment during the acquisition, and calculate the difference between them.

[0009] Step S2: For the sound signal at a specific moment in this acquisition, obtain its sound frequency and corresponding sound amplitude;

[0010] Step S3: Calculate the frequency difference calculated in step S1 and the sound frequency obtained in step S2 to obtain the frequency characteristic ratio; simultaneously, calculate the amplitude difference calculated in step S1 and the sound amplitude obtained in step S2 to obtain the amplitude characteristic ratio.

[0011] Step S4: Multiply the frequency characteristic ratio and amplitude characteristic ratio obtained in step S3 to obtain the single abnormal noise characteristic value collected in this step.

[0012] Step S5: Repeat steps S1 to S4 until all preset number of sound signal acquisitions are completed, and record the single abnormal sound characteristic value for each time.

[0013] Step S6: Sum the recorded single abnormal noise feature values, and then divide the sum by the total number of sound signal acquisitions to obtain the final abnormal noise generation value.

[0014] The abnormal noise generation analysis model achieves accurate abnormal sound identification through multi-dimensional sound signal processing. The model first establishes the baseline range characteristics of the sound signal. During a single acquisition process, acoustic sensors simultaneously acquire the fluctuation range of sound frequency and the variation range of sound amplitude within the monitoring environment, determining the extreme differences of each as evaluation benchmarks. Subsequently, the model precisely samples key moments in the acquisition process, capturing the specific sound frequency value and its corresponding sound amplitude value at that moment, forming instantaneous sound feature data.

[0015] During the feature extraction phase, the system calculates the standardized ratio of the acquired frequency range differences to the instantaneous frequency values, and performs the same standardized processing on the amplitude range differences to the instantaneous amplitude values, thereby generating feature indicators in the frequency and amplitude dimensions. By correlating these two feature indicators, a comprehensive abnormal noise feature value for this acquisition is formed, which reflects the degree of abnormality of the sound signal in both the frequency and amplitude dimensions.

[0016] To ensure the stability and accuracy of abnormal noise identification, the model employs a multi-data fusion approach, repeatedly executing the aforementioned analysis process for a preset number of sound signal acquisitions to accumulate multiple sets of abnormal noise characteristic values. Finally, statistical methods are used to comprehensively process the abnormal noise characteristic values ​​from all acquisition cycles, calculating their average as the final abnormal noise generation value. This effectively eliminates potential random interference from single acquisitions, improving the overall reliability of the abnormal noise monitoring and control subsystem and the accuracy of fault early warning.

[0017] As a further aspect of the present invention, when no carrier signal is injected, the vibration monitoring and analysis subsystem includes a first-level parameter acquisition module, a second-level parameter acquisition module, a first-level anomaly analysis module, and a second-level anomaly analysis module.

[0018] As a further aspect of the present invention, the anomaly diagnosis subsystem includes a first-level response control module, a second-level response control module, and a third-level response control module.

[0019] As a further aspect of the present invention, the primary parameter acquisition module is used to acquire the first parameter within the monitoring environment; the secondary parameter acquisition module is used to acquire the second parameter of tower vibration, the third parameter of wind turbine vibration, and the fourth parameter of generator vibration within the monitoring environment.

[0020] As a further aspect of the present invention, the first parameter includes the real-time wind speed in the monitored environment; the second parameter includes the real-time vibration parameters of the bottom, middle and top of the tower; the third parameter includes the real-time vibration parameters of the center of the wind turbine hub; and the fourth parameter includes the real-time vibration parameters of the generator base and the top of the casing.

[0021] As a further aspect of the present invention, the first-level anomaly analysis module is used to analyze the environmental stability impact value through the first parameter; the second-level anomaly analysis module is used to extract the second, third, and fourth parameters, and import the extracted second, third, and fourth parameters into the vibration anomaly value calculation model to calculate the vibration anomaly value.

[0022] As a further aspect of the present invention, the secondary anomaly analysis module is used to extract real-time vibration parameters of the tower bottom, middle and top from the second parameter, real-time vibration parameters of the wind turbine hub center from the third parameter, and real-time vibration parameters of the generator base and the top of the casing from the fourth parameter. The extracted real-time vibration parameters of the tower bottom, middle and top, the wind turbine hub center, the generator base and the top of the casing are imported into the vibration anomaly value calculation model to calculate the vibration anomaly value. The calculation of the vibration anomaly value includes calculating the first vibration anomaly value, the second vibration anomaly value and the third vibration anomaly value. The calculated first vibration anomaly value, the second vibration anomaly value and the third vibration anomaly value are added together and divided by the total number of samplings to obtain the final vibration anomaly value.

[0023] As a further aspect of the present invention, the calculation step of the first vibration anomaly value includes:

[0024] Step A1: Calculate the ratio of the real-time vibration parameters at the bottom of the tower at a specific moment to the difference between the maximum and minimum real-time vibration parameters at the bottom of the tower before that moment. Then, calculate the ratio of the real-time vibration parameters at the middle of the tower at a specific moment to the difference between the maximum and minimum real-time vibration parameters at the middle of the tower before that moment. Finally, calculate the absolute difference between the two ratios to obtain the first absolute difference.

[0025] Step A2: Calculate the ratio of the real-time vibration parameters of the middle part of the tower at a specific moment to the difference between the maximum and minimum real-time vibration parameters of the middle part of the tower before that moment. Then, calculate the ratio of the real-time vibration parameters of the top of the tower at a specific moment to the difference between the maximum and minimum real-time vibration parameters of the top of the tower before that moment. Finally, calculate the absolute difference between the two ratios to obtain the second absolute difference.

[0026] Step A3: Add the first absolute difference value and the second absolute difference value to obtain the instantaneous vibration difference value at the sampling time. Accumulate the instantaneous vibration difference values ​​at all sampling times to obtain the final first vibration anomaly value.

[0027] As a further aspect of the present invention, the calculation step of the second vibration anomaly value includes: performing a ratio calculation on the real-time vibration parameter of the wind turbine hub center at a specific moment and the difference between the maximum and minimum real-time vibration parameters of the wind turbine hub center at that moment, and accumulating the ratios at all moments to obtain the second vibration anomaly value.

[0028] As a further aspect of the present invention, the calculation steps for the third vibration anomaly value include:

[0029] Step B1: Calculate the ratio of the real-time vibration parameters of the generator base at a specific moment to the difference between the maximum and minimum real-time vibration parameters of the generator base at that moment; and then calculate the ratio of the real-time vibration parameters of the top of the generator casing at a specific moment to the difference between the maximum and minimum real-time vibration parameters of the top of the generator casing at that moment.

[0030] Step B2: Calculate the absolute difference between the two ratios, and sum the absolute differences at all times to obtain the third vibrational anomaly value.

[0031] The vibration anomaly calculation model employs a hierarchical analysis strategy to achieve accurate assessment of the overall vibration state of the wind turbine generator. This model constructs three independent dimensions for calculating vibration anomalies, specifically analyzing the overall coordination of the tower structure, the independent operating state of the turbine system, and the internal vibration correlation of the generator components.

[0032] In tower vibration analysis, the model focuses on the vibration transmission characteristics and mutual influence relationships between the bottom, middle, and top of the tower. By assessing the degree of vibration difference between adjacent monitoring points, it identifies potential uneven stress or local anomalies in the tower structure. Wind turbine vibration analysis independently assesses the vibration characteristics at the hub center, specifically monitoring the balance and operational stability of the wind turbine rotation system. Generator vibration analysis focuses on the vibration correlation of two key components: the base and the top of the casing. By analyzing the distribution characteristics of vibrations inside the generator, it determines whether there are any mechanical anomalies during generator operation.

[0033] By statistically fusing the vibration anomalies from the three dimensions, the model can generate a comprehensive index that reflects the vibration state of the entire wind turbine generator set. This maintains the independence of the vibration characteristics of each subsystem while achieving a unified assessment of the overall equipment vibration anomalies, providing reliable vibration state data support for the anomaly diagnosis subsystem.

[0034] The technical effects and advantages of the micro wind turbine generator set of the present invention are as follows:

[0035] This invention monitors the vibration parameters of the tower, wind turbine, and generator in real time, analyzes abnormal vibration values ​​based on these parameters, acquires sound signals within the monitoring environment, analyzes abnormal noise values, and determines whether any anomalies exist based on the data output by the vibration monitoring and analysis subsystem and the abnormal noise monitoring and control subsystem. This allows for more accurate identification and location of fault sources, reduces false alarms and missed alarms, and improves the accuracy of anomaly diagnosis. Attached Figure Description

[0036] Figure 1 This refers to the online vibration data acquisition and analysis system for the wind turbine transmission system described in Reference 1.

[0037] Figure 2 This is a portable data acquisition system for the wind turbine transmission system described in Reference 1.

[0038] Figure 3 This is a schematic diagram of the structure of a micro wind turbine generator set according to the present invention. Detailed Implementation

[0039] The technical solutions of this invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described technical solutions are only a part of this invention, and not all of it. All other technical solutions obtained by those skilled in the art based on the technical solutions of this invention without inventive effort are within the scope of protection of this invention.

[0040] Example 1

[0041] Figure 3 A schematic diagram of a micro wind turbine generator set according to Embodiment 1 of the present invention is shown. Figure 3 As shown, a micro wind turbine generator set in this embodiment includes a vibration monitoring and analysis subsystem, an abnormal noise monitoring and control subsystem, and an anomaly diagnosis subsystem. The vibration monitoring and analysis subsystem is connected to the anomaly diagnosis subsystem, the abnormal noise monitoring and control subsystem is connected to the anomaly diagnosis subsystem, and the anomaly diagnosis subsystem is connected to both the vibration monitoring and analysis subsystem and the abnormal noise monitoring and control subsystem.

[0042] The vibration monitoring and analysis subsystem is used to monitor the vibration parameters of the tower, wind turbine and generator in real time, and analyze vibration anomalies based on the vibration parameters;

[0043] The abnormal noise monitoring and control subsystem is used to acquire sound signals within the monitoring environment and analyze the abnormal noise generation value.

[0044] The anomaly diagnosis subsystem is used to determine whether an anomaly exists based on the data output by the vibration monitoring and analysis subsystem and the abnormal noise monitoring and control subsystem.

[0045] By combining vibration and sound data, a more comprehensive monitoring of wind turbine generators can be achieved. The anomaly diagnosis subsystem comprehensively analyzes vibration and abnormal noise data, enabling more accurate identification and location of fault sources, reducing false alarms and missed alarms. Real-time monitoring and analysis of vibration and sound signals can issue alarms in the early stages of faults, preventing small problems from escalating into major failures. Through continuous monitoring and data analysis, it can predict possible equipment failure trends, formulate scientific maintenance plans, and avoid unplanned downtime. The anomaly diagnosis subsystem automatically issues alarms when anomalies are detected, ensuring that maintenance personnel can take timely measures to protect the safety of equipment and personnel. Early identification and handling of potential problems reduces equipment failure rates and improves the overall reliability of the system. Through early warning and accurate fault diagnosis, the number and duration of unplanned equipment downtime are reduced, saving operating costs. Predictive maintenance and accurate diagnosis help optimize the use of maintenance resources and avoid unnecessary maintenance and repairs.

[0046] The vibration monitoring and analysis subsystem includes a primary parameter acquisition module, a secondary parameter acquisition module, a primary anomaly analysis module, and a secondary anomaly analysis module; the primary parameter acquisition module is connected to the primary anomaly analysis module, and the secondary parameter acquisition module is connected to the secondary anomaly analysis module.

[0047] The primary parameter acquisition module is used to acquire the first parameter within the monitoring environment;

[0048] The secondary parameter acquisition module is used to acquire the second parameter of tower vibration, the third parameter of wind turbine vibration, and the fourth parameter of generator vibration within the monitoring environment.

[0049] The first-level anomaly analysis module is used to analyze the environmental stability impact value through the first parameter;

[0050] The secondary anomaly analysis module is used to extract the second, third, and fourth parameters, and then import these parameters into the vibration anomaly calculation model to calculate the vibration anomaly value.

[0051] By analyzing the first parameter, stable environmental influences are identified, improving the accuracy of anomaly detection. Considering the second, third, and fourth parameters, a vibration anomaly calculation model is used to accurately calculate vibration anomalies, further enhancing fault diagnosis accuracy. Specific vibration parameters obtained through the secondary parameter acquisition module allow for more precise determination of equipment fault location and cause, avoiding blind maintenance and unnecessary repairs. Real-time monitoring and precise analysis enable timely detection and handling of potential problems, improving equipment reliability and stability. Based on predictive maintenance and anomaly analysis, many potential faults and damages can be prevented, extending equipment lifespan.

[0052] The first parameter includes the real-time wind speed in the monitoring environment, which is obtained through an ultrasonic anemometer.

[0053] The second parameter includes real-time vibration parameters at the bottom, middle, and top of the tower; the third parameter includes real-time vibration parameters at the center of the wind turbine hub; and the fourth parameter includes real-time vibration parameters at the generator base and the top of the casing.

[0054] The second parameter includes real-time vibration parameters at the bottom, middle, and top of the tower. The real-time vibration parameters at the bottom of the tower are obtained by installing vibration sensors at the connection between the tower and the foundation, i.e., on the ground surface. This reflects the overall stability of the tower and detects vibrations caused by ground instability or external vibrations. The real-time vibration parameters at the middle of the tower are obtained by installing vibration sensors at the midpoint of the tower's height. This detects the vibration in the middle of the tower and is used to assess the impact of wind loads on the middle section of the tower. The real-time vibration parameters at the top of the tower are obtained by installing vibration sensors at the top of the tower, i.e., near the installation point of the wind turbine and generator. This monitors the vibration in the area at the top of the tower close to the wind turbine and generator.

[0055] The third parameter includes real-time vibration parameters at the center of the wind turbine hub. These parameters are obtained by installing a vibration sensor at the center of the hub and can be used to assess the overall balance and vibration during rotation of the wind turbine. The fourth parameter includes real-time vibration parameters at the generator base and the top of the generator housing. Real-time vibration parameters at the generator base are obtained by installing a vibration sensor at the connection point between the generator base and the tower. This is used to monitor the vibration at the connection point and detect vibrations caused by unstable installation or structural problems. Real-time vibration parameters at the top of the generator housing are obtained by installing a vibration sensor at the top of the generator housing. This is used to monitor the vibration of the generator housing and detect mechanical vibrations generated during operation.

[0056] Real-time wind speed is a critical factor in the operation of wind turbine generators. Obtaining real-time wind speed using an ultrasonic anemometer provides accurate wind speed data. The second parameter includes real-time vibration parameters from the bottom, middle, and top of the tower, covering the vibration conditions of different parts of the tower and enabling a comprehensive assessment of the tower's stability and the impact of wind loads. The third parameter assesses the balance of the wind turbine and its vibration during rotation.

[0057] The fourth parameter includes real-time vibration parameters of the generator base and the top of the casing, which can monitor the installation stability of the generator and the mechanical vibration generated during operation. The real-time acquired vibration parameters can reflect the operating status of the equipment in a timely manner, which helps to quickly diagnose and handle abnormal situations. Through the vibration anomaly calculation model, abnormal vibration values ​​can be accurately calculated, improving the accuracy of anomaly diagnosis.

[0058] The first-level anomaly analysis module analyzes the environmental stability impact value using the first parameter. It imports the real-time wind speed within the monitored environment (the first parameter) into the environmental stability impact analysis model to calculate the impact value. This model first determines each monitoring moment and obtains the real-time wind speed value within the monitored environment at that moment. Then, the system statistically analyzes the maximum and minimum real-time wind speed values ​​recorded before that moment, calculating the range of their differences. Next, it compares the current real-time wind speed value with this range of differences to obtain the standardized wind speed change index for that moment. Finally, the system sums the standardized wind speed change indices for all monitoring moments to obtain a comprehensive environmental stability impact value, serving as a key indicator for assessing the stability of the monitored environment.

[0059] By considering the historical maximum and minimum values ​​of real-time wind speed within the monitored environment, the stability of the environment can be comprehensively assessed, reflecting the magnitude and frequency of environmental changes. When environmental stability declines significantly, it may be a sign of environmental anomalies, such as sudden and drastic fluctuations in wind speed, which may indicate an approaching storm. This allows for timely detection of such anomalies and the issuance of early warning signals, enabling appropriate measures to be taken. The calculation of environmental stability impact values ​​can help predict the operating environment of wind turbine generators, thereby predicting potential environmental impacts and facilitating preventative maintenance measures to ensure the safe and stable operation of the equipment.

[0060] The secondary anomaly analysis module is used to extract real-time vibration parameters from the tower bottom, middle, and top (second parameter), the wind turbine hub center (third parameter), and the generator base and top of the casing (fourth parameter). These extracted parameters are then imported into the vibration anomaly calculation model to calculate the vibration anomaly values. The calculation includes the calculation of the first, second, and third vibration anomaly values. The calculated values ​​are then summed and divided by the total number of samplings to obtain the final vibration anomaly value.

[0061] The calculation steps for the first vibration anomaly value include:

[0062] Step A1: Calculate the ratio of the real-time vibration parameters at the bottom of the tower at a specific moment to the difference between the maximum and minimum real-time vibration parameters at the bottom of the tower before that moment. Then, calculate the ratio of the real-time vibration parameters at the middle of the tower at a specific moment to the difference between the maximum and minimum real-time vibration parameters at the middle of the tower before that moment. Finally, calculate the absolute difference between the two ratios to obtain the first absolute difference.

[0063] Step A2: Calculate the ratio of the real-time vibration parameters of the middle part of the tower at a specific moment to the difference between the maximum and minimum real-time vibration parameters of the middle part of the tower before that moment. Then, calculate the ratio of the real-time vibration parameters of the top of the tower at a specific moment to the difference between the maximum and minimum real-time vibration parameters of the top of the tower before that moment. Finally, calculate the absolute difference between the two ratios to obtain the second absolute difference.

[0064] Step A3: Add the first absolute difference value and the second absolute difference value to obtain the instantaneous vibration difference value at the sampling time. Accumulate the instantaneous vibration difference values ​​at all sampling times to obtain the final first vibration anomaly value.

[0065] The calculation steps for the second vibration anomaly value include: performing a ratio calculation on the real-time vibration parameter of the wind turbine hub center at a specific moment and the difference between the maximum and minimum real-time vibration parameters of the wind turbine hub center at that moment, and summing the ratios at all moments to obtain the second vibration anomaly value.

[0066] The calculation steps for the third vibration anomaly value include:

[0067] Step B1: Calculate the ratio of the real-time vibration parameters of the generator base at a specific moment to the difference between the maximum and minimum real-time vibration parameters of the generator base at that moment; and then calculate the ratio of the real-time vibration parameters of the top of the generator casing at a specific moment to the difference between the maximum and minimum real-time vibration parameters of the top of the generator casing at that moment.

[0068] Step B2: Calculate the absolute difference between the two ratios, and sum the absolute differences at all times to obtain the third vibrational anomaly value.

[0069] By extracting real-time vibration parameters from the bottom, middle, and top of the tower, the center of the wind turbine hub, and the generator base and top of the casing, the vibration of key parts of the wind turbine generator is comprehensively considered, providing a more complete assessment of the equipment's operating status. Through a vibration anomaly calculation model, vibration parameters from different parts are imported into the model for calculation, yielding anomaly values. This allows for accurate diagnosis of abnormal vibrations, improving the accuracy of anomaly diagnosis. By monitoring the vibration of each part and calculating anomaly values, potential fault signs can be detected promptly, enabling early maintenance measures to prevent further development of faults and reducing maintenance costs and risks. Based on the anomaly values ​​of vibration parameters from different parts, relevant parameters can be automatically adjusted to adapt to operating conditions under varying vibration levels, improving the system's adaptability and stability.

[0070] The abnormal noise monitoring and control subsystem includes a three-level abnormal noise parameter acquisition module and an abnormal noise analysis module; the three-level abnormal noise parameter acquisition module and the abnormal noise analysis module are connected.

[0071] The Level 3 Abnormal Noise Parameter Acquisition Module is used to acquire the fifth parameter of the sound signal within the monitoring environment;

[0072] The abnormal noise analysis module is used to extract the fifth parameter and import it into the abnormal noise generation value analysis model to calculate the abnormal noise generation value.

[0073] The fifth parameter includes the sound frequency and amplitude in the monitored environment, which are obtained through acoustic sensors.

[0074] The abnormal noise analysis module is used to extract the sound frequency and amplitude of the monitored environment from the fifth parameter, and import the sound frequency and amplitude of the monitored environment into the abnormal noise generation value analysis model to calculate the abnormal noise generation value. The calculation process of the abnormal noise generation value analysis model includes the following steps:

[0075] Step S1: Obtain the maximum and minimum sound frequencies in the monitoring environment during a single sound signal acquisition, and calculate the difference between them; at the same time, obtain the maximum and minimum sound amplitudes in the monitoring environment during the acquisition, and calculate the difference between them.

[0076] Step S2: For the sound signal at a specific moment in this acquisition, obtain its sound frequency and corresponding sound amplitude;

[0077] Step S3: Calculate the frequency difference calculated in step S1 and the sound frequency obtained in step S2 to obtain the frequency characteristic ratio; simultaneously, calculate the amplitude difference calculated in step S1 and the sound amplitude obtained in step S2 to obtain the amplitude characteristic ratio.

[0078] Step S4: Multiply the frequency characteristic ratio and amplitude characteristic ratio obtained in step S3 to obtain the single abnormal noise characteristic value collected in this step.

[0079] Step S5: Repeat steps S1 to S4 until all preset number of sound signal acquisitions are completed, and record the single abnormal sound characteristic value for each time.

[0080] Step S6: Sum the recorded single abnormal noise feature values, and then divide the sum by the total number of sound signal acquisitions to obtain the final abnormal noise generation value.

[0081] The abnormal noise analysis module can extract the frequency and amplitude of the sound signal and, combined with multiple data collections, calculate the abnormal noise value, thereby more accurately assessing the abnormality of the sound signal. By monitoring the frequency and amplitude of the sound signal in real time, the abnormal noise analysis module can detect abnormal sounds in a timely manner, which helps to detect equipment malfunctions or failures in advance.

[0082] The anomaly diagnosis subsystem includes a first-level response control module, a second-level response control module, and a third-level response control module;

[0083] The first-level response control module compares the environmental stability impact value with the preset environmental stability impact value. If the environmental stability impact value is greater than or equal to the preset environmental stability impact value, the second-level anomaly analysis module is then executed. If the environmental stability impact value is less than the preset environmental stability impact value, the first-level response alarm unit is triggered.

[0084] The secondary response control module compares the abnormal vibration value with the preset abnormal vibration value. If the abnormal vibration value is greater than or equal to the preset abnormal vibration value, the abnormal noise monitoring and control subsystem continues to operate. If the abnormal vibration value is less than the preset abnormal vibration value, the secondary response alarm unit is triggered.

[0085] The three-level response control module compares the abnormal noise generation value with the preset abnormal noise generation value. If the abnormal noise generation value is greater than or equal to the preset abnormal noise generation value, the three-level response alarm unit will be triggered; if the abnormal noise generation value is less than the preset abnormal noise generation value, the vibration monitoring and analysis subsystem will continue.

[0086] When the environmental stability impact value, abnormal vibration value, or abnormal noise generation value exceeds the preset threshold, the corresponding response control module will automatically trigger the alarm unit to remind maintenance personnel to pay attention to the abnormal situation. The preset environmental stability impact value, abnormal vibration value, and abnormal noise generation value are set, and the alarm is only triggered when the monitoring result exceeds the preset threshold, which can reduce the false alarm rate and avoid unnecessary interference and handling. Once an abnormal situation is detected, the system can respond in a timely manner and trigger the corresponding alarm unit, enabling maintenance personnel to take measures quickly to ensure the safe operation of the equipment. By setting the threshold, early warning can be given before the fault occurs, which helps to prevent accidents and reduce risks.

[0087] This invention, through real-time monitoring of the vibration parameters of the tower, wind turbine, and generator, analyzes abnormal vibration values ​​based on these parameters, acquires sound signals within the monitoring environment, analyzes abnormal noise values, and determines the presence of anomalies based on the data output by the vibration monitoring and analysis subsystem and the abnormal noise monitoring and control subsystem. This approach can more accurately identify and locate fault sources, reduce false alarms and missed alarms, and improve the accuracy of anomaly diagnosis.

[0088] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0089] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A micro wind turbine generator set, comprising a vibration monitoring and analysis subsystem, an abnormal noise monitoring and control subsystem, and an anomaly diagnosis subsystem, characterized in that, The abnormal noise monitoring and control subsystem includes a three-level abnormal noise parameter acquisition module and an abnormal noise analysis module. The three-level abnormal noise parameter acquisition module is used to acquire the fifth parameter of the sound signal in the monitoring environment. The abnormal noise analysis module is used to extract the fifth parameter, import the fifth parameter into the abnormal noise generation value analysis model, and calculate the abnormal noise generation value. The calculation process of the abnormal noise generation value analysis model includes the following steps: Step S1: Obtain the maximum and minimum sound frequencies in the monitoring environment during a single sound signal acquisition, and calculate the difference between them; at the same time, obtain the maximum and minimum sound amplitudes in the monitoring environment during the acquisition, and calculate the difference between them. Step S2: For the sound signal at a specific moment in this acquisition, obtain its sound frequency and corresponding sound amplitude; Step S3: Calculate the frequency difference calculated in step S1 and the sound frequency obtained in step S2 to obtain the frequency characteristic ratio; simultaneously, calculate the amplitude difference calculated in step S1 and the sound amplitude obtained in step S2 to obtain the amplitude characteristic ratio. Step S4: Multiply the frequency characteristic ratio and amplitude characteristic ratio obtained in step S3 to obtain the single abnormal noise characteristic value collected in this step. Step S5: Repeat steps S1 to S4 until all preset number of sound signal acquisitions are completed, and record the single abnormal sound characteristic value for each time. Step S6: Sum the recorded single abnormal noise feature values, and then divide the sum by the total number of sound signal acquisitions to obtain the final abnormal noise generation value.

2. A micro wind turbine generator set according to claim 1, characterized in that, The vibration monitoring and analysis subsystem includes a primary parameter acquisition module, a secondary parameter acquisition module, a primary anomaly analysis module, and a secondary anomaly analysis module.

3. A micro wind turbine generator set according to claim 1, characterized in that, The anomaly diagnosis subsystem includes a first-level response control module, a second-level response control module, and a third-level response control module.

4. A micro wind turbine generator set according to claim 2, characterized in that, The first-level parameter acquisition module is used to acquire the first parameter within the monitoring environment; the second-level parameter acquisition module is used to acquire the second parameter of tower vibration, the third parameter of wind turbine vibration, and the fourth parameter of generator vibration within the monitoring environment.

5. A micro wind turbine generator set according to claim 4, characterized in that, The first parameter includes the real-time wind speed in the monitored environment; the second parameter includes the real-time vibration parameters of the bottom, middle and top of the tower; the third parameter includes the real-time vibration parameters of the center of the wind turbine hub; and the fourth parameter includes the real-time vibration parameters of the generator base and the top of the casing.

6. A micro wind turbine generator set according to claim 4, characterized in that, The first-level anomaly analysis module is used to analyze the environmental stability impact value through the first parameter; the second-level anomaly analysis module is used to extract the second, third, and fourth parameters, and import the extracted second, third, and fourth parameters into the vibration anomaly value calculation model to calculate the vibration anomaly value.

7. A micro wind turbine generator set according to claim 6, characterized in that, The secondary anomaly analysis module is used to extract real-time vibration parameters from the tower bottom, middle, and top in the second parameter; real-time vibration parameters from the wind turbine hub center in the third parameter; and real-time vibration parameters from the generator base and the top of the casing in the fourth parameter. These extracted real-time vibration parameters are then imported into the vibration anomaly calculation model to calculate the vibration anomaly values. This includes calculating the first, second, and third vibration anomaly values. The calculated first, second, and third vibration anomaly values ​​are then summed and divided by the total number of samplings to obtain the final vibration anomaly value.

8. A micro wind turbine generator set according to claim 7, characterized in that, The calculation steps for the first vibration anomaly value include: Step A1: Calculate the ratio of the real-time vibration parameters at the bottom of the tower at a specific moment to the difference between the maximum and minimum real-time vibration parameters at the bottom of the tower before that moment. Then, calculate the ratio of the real-time vibration parameters at the middle of the tower at a specific moment to the difference between the maximum and minimum real-time vibration parameters at the middle of the tower before that moment. Finally, calculate the absolute difference between the two ratios to obtain the first absolute difference. Step A2: Calculate the ratio of the real-time vibration parameters of the middle part of the tower at a specific moment to the difference between the maximum and minimum real-time vibration parameters of the middle part of the tower before that moment. Then, calculate the ratio of the real-time vibration parameters of the top of the tower at a specific moment to the difference between the maximum and minimum real-time vibration parameters of the top of the tower before that moment. Finally, calculate the absolute difference between the two ratios to obtain the second absolute difference. Step A3: Add the first absolute difference value and the second absolute difference value to obtain the instantaneous vibration difference value at the sampling time. Accumulate the instantaneous vibration difference values ​​at all sampling times to obtain the final first vibration anomaly value.

9. A micro wind turbine generator set according to claim 7, characterized in that, The calculation steps for the second vibration anomaly value include: performing a ratio calculation on the real-time vibration parameter of the wind turbine hub center at a specific moment and the difference between the maximum and minimum real-time vibration parameters of the wind turbine hub center at that moment, and summing the ratios at all moments to obtain the second vibration anomaly value.

10. A micro wind turbine generator set according to claim 7, characterized in that, The calculation steps for the third vibration anomaly value include: Step B1: Calculate the ratio of the real-time vibration parameters of the generator base at a specific moment to the difference between the maximum and minimum real-time vibration parameters of the generator base at that moment; and then calculate the ratio of the real-time vibration parameters of the top of the generator casing at a specific moment to the difference between the maximum and minimum real-time vibration parameters of the top of the generator casing at that moment. Step B2: Calculate the absolute difference between the two ratios, and sum the absolute differences at all times to obtain the third vibrational anomaly value.

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