Whole machine bolt monitoring system of wind turbine generator

Abstract

The invention provides a whole wind turbine generator bolt monitoring system. The complete machine bolt monitoring system for the wind turbine generator comprises a sensor failure monitoring module, a plurality of high-precision flange bolt gap sensors installed at a fan blade flange and a tower flange, a multi-channel flange bolt gap acquisition instrument used for monitoring flange gaps in real time, and a monitoring module used for receiving monitoring signals output by the flange bolt gap sensors, and data acquisition is carried out. According to the whole wind turbine generator bolt monitoring system, the sensor failure monitoring module and the fault prediction module are introduced, so that the problem of alarm omission caused by sensor failure or failure in the prior art is effectively solved. According to the system, the state of the sensor can be monitored in real time by utilizing a fault detection algorithm based on a model, and an early warning signal can be sent out in time when abnormity occurs, so that blind spots of a traditional system are avoided, and the operation safety and reliability of the wind turbine generator are improved.

Description

Technical Field

[0001] The invention relates to the technical field of wind turbine monitoring, in particular to a wind turbine complete machine bolt monitoring system. Background Art

[0002] The bolt monitoring system for wind turbines is mainly composed of multiple components, including high-precision sensors, flange bolt gap collectors, data processors, synchronous collection master stations, wireless communication equipment, and host computer systems. The system monitors the looseness of bolts in real time by installing sensors on the blades and tower flanges of the wind turbine. The sensor transmits the monitored flange gap data to the collector, and then transmits the data to the host computer through wireless communication or wired network for storage, analysis and display. The principle of the entire system is based on the fact that loosening or breaking of bolts will cause changes in the flange surface gap. These changes are monitored in real time through high-precision displacement sensors, and fault warnings and analysis are performed to ensure the stable operation of wind turbines.

[0003] Although the wind turbine bolt monitoring system can effectively monitor the change of flange gap, the system still has some defects. The system has high requirements for the installation accuracy of the sensor. Improper installation may cause measurement errors and affect the accuracy of the monitoring data. The system mainly relies on the accuracy of the sensor. If the sensor fails or fails, the alarm signal will be missed. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a bolt monitoring system for a wind turbine set, which solves the problem that the system has high requirements for the installation accuracy of the sensor and improper installation may cause measurement errors. The system mainly relies on the accuracy of the sensor, and if the sensor malfunctions or fails, the alarm signal will be missed.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A wind turbine complete machine bolt monitoring system, comprising:

[0006] Sensor failure monitoring module;

[0007] Multiple high-precision flange bolt gap sensors installed on the fan blade flange and tower flange to monitor the flange gap in real time;

[0008] A multi-channel flange bolt gap collector, used to receive the monitoring signal output by the flange bolt gap sensor and perform data collection;

[0009] A data processor, used to receive data from the collector and transmit the data to a host computer via wireless communication or a wired network;

[0010] The host computer system is used to store, analyze, alarm and display the flange bolt clearance data, and to provide fault warning;

[0011] The system further includes an integrated installation guide system suitable for ensuring accurate installation of the flange bolt gap sensor;

[0012] The system further includes a fault prediction module, which predicts bolt loosening or breakage faults through the following model:

[0013]

[0014] Among them, σ failure represents the fault prediction probability, A, B, C, λ are preset parameters, and t is the time variable.

[0015] Preferably, the integrated installation guide system comprises:

[0016] Install the tooling to ensure that the sensor mounting surface is parallel to the flange surface to be monitored, and that the installation error does not exceed 2°;

[0017] Positioning block, used to fix the sensor so that it is accurately positioned;

[0018] Structural adhesive is used to ensure reliable connection between the sensor and the flange surface, and the adhesive layer thickness is greater than 1cm to avoid hollowness.

[0019] Preferably, the flange bolt gap sensor is a displacement sensor capable of measuring minute displacement changes between flange surfaces with a resolution of 0.5 μm, and the data processor includes a wireless communication module for transmitting monitoring data to a remote wind farm server wirelessly.

[0020] Preferably, the data processor further includes an early warning module based on a machine learning algorithm, which is used to automatically adjust the monitoring early warning threshold according to historical data and issue a warning signal in a timely manner. The host computer system includes a fault diagnosis module, which is used to provide a detailed fault report when a fault occurs and give the specific fault location and type.

[0021] Preferably, the flange bolt gap collector is connected to multiple flange bolt gap sensors via an RS485 interface and supports simultaneous data collection from up to 16 sensors. The sensor failure monitoring module adopts a model-based fault detection algorithm that can issue a failure alarm in real time when sensor data is abnormal.

[0022] Preferably, the data processor further includes a real-time calculation module based on the flange clearance change formula, which can convert the data collected by the sensor into the change of the preload force and analyze the change. The formula is:

[0023]

[0024] Among them, F is the change in bolt preload, k is the flange stiffness coefficient, Δx is the change in flange clearance, and L is the initial value of flange clearance. The flange clearance change formula is further optimized as follows:

[0025]

[0026] Where ΔP is the percentage of change in bolt preload, F new is the preload value at the current moment, F old is the historical preload value.

[0027] Preferably, the monitoring system further includes a fault prediction module based on a multi-dimensional optimization model, which uses historical data and real-time sensor data to perform fault prediction and alarm through the following complex model:

[0028]

[0029] Among them, σ failure represents the fault prediction probability, A, B, C, λ are preset parameters, and t is the time variable.

[0030] Preferably, the data processor includes a real-time feedback mechanism based on a composite dynamic model, which takes into account wind turbine load, climate change and equipment aging factors, and performs real-time feedback and adjustment through the following formula:

[0031] Feedback = α·(P load -P target )+β·(ΔT ambient -ΔT target )

[0032] Among them, Feedback represents the system feedback signal, α and β are adjustment coefficients, P load is the current fan load, P target is the target load, ΔT ambient and ΔT target They are the current ambient temperature and the target temperature respectively.

[0033] The present invention provides a wind turbine complete machine bolt monitoring system, which has the following beneficial effects:

[0034] The bolt monitoring system for the wind turbine unit effectively solves the problem of missed alarms caused by sensor failure or failure in the prior art by introducing a sensor failure monitoring module and a fault prediction module. The system uses a model-based fault detection algorithm to monitor the status of the sensor in real time and issue early warning signals in a timely manner when an abnormality occurs, thereby avoiding the blind spots of traditional systems and improving the operational safety and reliability of wind turbines. The high-precision flange bolt gap sensor and multi-channel data acquisition instrument integrated in the system can accurately capture small changes in the flange gap, further improving the monitoring accuracy and ensuring the accuracy of the data. These innovative designs solve the problem of high sensor accuracy requirements and ensure the high quality and real-time nature of the monitoring data.

[0035] The solution of the present invention also ensures the precise installation of the flange bolt gap sensor through an integrated installation guide system, reducing the measurement error caused by improper installation. The design of the installation tooling and positioning block ensures the parallelism between the sensor and the flange surface, and effectively controls the installation error, further improving the stability and accuracy of the system. In addition, the early warning module based on machine learning in the data processor enables the monitoring system to adaptively adjust the early warning threshold according to historical data and dynamically respond to changes in the wind turbine. In addition, a real-time feedback mechanism based on a composite dynamic model is adopted, combined with multiple factors such as wind turbine load, climate change, and equipment aging, to achieve more intelligent and dynamic monitoring and management. In this way, the wind turbine can adjust the monitoring strategy in real time and accurately, thereby maximizing the service life of the equipment and improving the efficiency and safety of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a schematic diagram of the structure of the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] Embodiment 1

[0039] like Figure 1 As shown, an embodiment of the present invention provides a wind turbine generator system bolt monitoring system, including a sensor failure monitoring module.

[0040] Multiple high-precision flange bolt gap sensors installed on the wind turbine blade flange and tower flange are used to monitor the flange gap in real time. The flange bolt gap sensor is a displacement sensor that can measure tiny displacement changes between flange surfaces with a resolution of 0.5μm. The data processor includes a wireless communication module for transmitting monitoring data wirelessly to a remote wind farm server.

[0041] The multi-channel flange bolt gap collector is used to receive the monitoring signal output by the flange bolt gap sensor and perform data collection. The flange bolt gap collector is connected to multiple flange bolt gap sensors through the RS485 interface and supports simultaneous data collection of up to 16 sensors. The sensor failure monitoring module adopts a model-based fault detection algorithm, which can issue a failure alarm in real time when the sensor data is abnormal.

[0042] The data processor is used to receive the data from the collector and transmit the data to the host computer through wireless communication or wired network. The data processor further includes an early warning module based on machine learning algorithm, which is used to automatically adjust the monitoring early warning threshold according to historical data and issue early warning signals in time. The host computer system includes a fault diagnosis module, which is used to provide a detailed fault report when a fault occurs and give the specific fault location and type. The data processor further includes a real-time calculation module based on the flange clearance change formula, which can convert the data collected by the sensor into the change of preload force and analyze the change. The formula is:

[0043]

[0044] Among them, F is the change of bolt preload, k is the flange stiffness coefficient, Δx is the change of flange clearance, L is the initial value of flange clearance, and the flange clearance change formula is further optimized as follows:

[0045]

[0046] Where ΔP is the percentage of change in bolt preload, F new is the preload value at the current moment, F old is the historical preload value.

[0047] The host computer system is used to store, analyze, alarm and display flange bolt clearance data and provide fault warning.

[0048] The system further includes an integrated installation guide system suitable for ensuring accurate installation of the flange bolt gap sensor, the integrated installation guide system includes:

[0049] Install the tooling to ensure that the sensor mounting surface is parallel to the flange surface to be monitored and that the installation error does not exceed 2°.

[0050] The positioning block is used to fix the sensor so that it is accurately positioned.

[0051] Structural adhesive is used to ensure reliable connection between the sensor and the flange surface, and the adhesive layer thickness is greater than 1cm to avoid hollowness.

[0052] The system further includes a fault prediction module, which predicts bolt loosening or breakage failures through the following models:

[0053]

[0054] Among them, σ failure represents the fault prediction probability, A, B, C, λ are preset parameters, and t is the time variable.

[0055] The monitoring system further includes a fault prediction module based on a multi-dimensional optimization model. The fault prediction module uses historical data and real-time sensor data to predict faults and alarm through the following complex models:

[0056]

[0057] Among them, σ failure represents the fault prediction probability, A, B, C, λ are preset parameters, t is the time variable, and the data processor includes a real-time feedback mechanism based on a composite dynamic model. The composite dynamic model takes into account wind turbine load, climate change and equipment aging factors, and performs real-time feedback and adjustment through the following formula:

[0058] Feedback = α·(P load -P target )+β·(ΔT ambient -ΔT target )

[0059] Among them, Feedback represents the system feedback signal, α and β are adjustment coefficients, P load is the current fan load, P target is the target load, ΔT ambient and ΔT target They are the current ambient temperature and the target temperature respectively.

[0060] Embodiment 2: Experimental embodiment:

[0061] Purpose:

[0062] This experiment aims to verify the performance of the wind turbine bolt monitoring system, especially the effectiveness of flange bolt gap monitoring, sensor failure detection, fault prediction module and real-time feedback mechanism. We test and analyze by inputting actual parameters.

[0063] Experimental equipment:

[0064] Wind turbine: A wind turbine with three blades is used. The flange gaps of the tower and blade flanges are measured using six high-precision flange bolt gap sensors installed on each blade flange and tower flange.

[0065] Flange bolt gap sensor: displacement sensor with a resolution of 0.5μm;

[0066] Flange bolt gap collector: using RS485 interface, supporting data collection of up to 16 sensors at the same time;

[0067] Data processor: contains wireless communication module, which transmits monitoring data to remote wind farm server;

[0068] Host computer system: used to store, analyze and display flange bolt clearance data and provide fault warning;

[0069] Sensor failure monitoring module: a model-based fault detection algorithm for real-time failure alarms;

[0070] Fault prediction module: used to predict bolt loosening or breakage failures;

[0071] Composite dynamics model: used for real-time feedback and adjustment of system parameters.

[0072] Experimental steps:

[0073] 1. Sensor installation:

[0074] Install flange bolt gap sensors on each fan blade flange and tower flange. Install the tooling to ensure that the sensor is parallel to the flange surface and the installation error is controlled within ±2°.

[0075] Use positioning blocks and structural adhesive to ensure a stable connection between the sensor and the flange surface, and keep the adhesive layer thickness greater than 1 cm.

[0076] 2. Data collection:

[0077] Start the wind turbine and operate it under different wind speed and load conditions. The operating load of the wind turbine is gradually increased from 10% to 100%. The monitoring data includes the wind turbine load P load (Unit: kW), wind speed V wind (Unit: m / s), and flange gap change Δx (Unit: μm).

[0078] The flange bolt gap sensor transmits data to the multi-channel flange bolt gap collector through the RS485 interface. Up to 16 sensors can collect data simultaneously.

[0079] The data processor calculates the change in the flange gap and transmits the data to the host computer system.

[0080] 3. Sensor failure detection:

[0081] Simulate sensor failure scenarios, such as a sensor that fails to transmit data. The failure detection module uses a model-based fault detection algorithm for real-time monitoring. When abnormal sensor data is detected, the system issues an alarm signal.

[0082] The model formula for sensor failure is:

[0083]

[0084] Parameter settings: A=1, λ=0.05, B=2, C=0.1, t is time (unit: hour).

[0085] 4. Calculation of bolt preload change:

[0086] The data processor calculates the bolt preload change F in real time based on the flange clearance change formula and analyzes the change. The formula is as follows:

[0087]

[0088] Parameter settings: flange stiffness coefficient k = 1000N / mm, flange gap change Δx = 0.5μm, flange initial gap L = 5mm.

[0089] The preload force change F is calculated based on the data collected by the sensor, and the warning threshold is adjusted in real time.

[0090] 5. Fault prediction and alarm:

[0091] Start the fault prediction module and use the following model to predict bolt loosening or breakage failures:

[0092]

[0093] Parameter settings: A=0.8, λ=0.05, B=1.5, C=0.2, t is time (unit: hour).

[0094] 6. Multi-dimensional optimization model:

[0095] Using historical data and real-time sensor data, a multi-dimensional optimization model is used to predict bolt loosening or breakage failures. The model uses the following formula for calculation:

[0096]

[0097] Parameter settings: A=1, λ=0.05, B=2, C=0.1, t is time (unit: hour).

[0098] 7. Real-time feedback of composite dynamics model:

[0099] The composite dynamics model takes into account wind turbine load, climate change and equipment aging factors to provide real-time feedback and adjustments. The formula is:

[0100] Feedback = α·(P load -P target )+β·(ΔT ambient -ΔT target )

[0101] Parameter setting: α = 0.5, β = 0.5, target load P target =500kW, target ambient temperature T target =20℃.

[0102] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Wind turbine bolt monitoring system, characterized by: include: Sensor failure monitoring module; Multiple high-precision flange bolt gap sensors installed on the fan blade flange and tower flange to monitor the flange gap in real time; A multi-channel flange bolt gap collector, used to receive the monitoring signal output by the flange bolt gap sensor and perform data collection; A data processor, used to receive data from the collector and transmit the data to a host computer via wireless communication or a wired network; The host computer system is used to store, analyze, alarm and display the flange bolt clearance data, and to provide fault warning; The system further includes an integrated installation guide system suitable for ensuring accurate installation of the flange bolt gap sensor; The system further includes a fault prediction module, which predicts bolt loosening or breakage faults through the following model: Among them, σ failure represents the fault prediction probability, A, B, C, λ are preset parameters, and t is the time variable.

2. The wind turbine generator set bolt monitoring system according to claim 1 is characterized in that: The integrated installation guide system comprises: Install the tooling to ensure that the sensor mounting surface is parallel to the flange surface to be monitored, and that the installation error does not exceed 2°; Positioning block, used to fix the sensor so that it is accurately positioned; Structural adhesive is used to ensure reliable connection between the sensor and the flange surface, and the adhesive layer thickness is greater than 1cm to avoid hollowness.

3. The wind turbine generator set bolt monitoring system according to claim 1 is characterized in that: The flange bolt gap sensor is a displacement sensor that can measure small displacement changes between flange surfaces with a resolution of 0.5 μm. The data processor includes a wireless communication module for transmitting monitoring data to a remote wind farm server wirelessly.

4. The wind turbine generator set bolt monitoring system according to claim 1, characterized in that: The data processor further includes an early warning module based on a machine learning algorithm, which is used to automatically adjust the monitoring early warning threshold according to historical data and issue an early warning signal in a timely manner. The host computer system includes a fault diagnosis module, which is used to provide a detailed fault report when a fault occurs and give the specific fault location and type.

5. The wind turbine generator set bolt monitoring system according to claim 1, characterized in that: The flange bolt gap collector is connected to multiple flange bolt gap sensors via an RS485 interface and supports simultaneous data collection of up to 16 sensors. The sensor failure monitoring module adopts a model-based fault detection algorithm that can issue a failure alarm in real time when sensor data is abnormal.

6. The wind turbine generator set bolt monitoring system according to claim 1, characterized in that: The data processor further includes a real-time calculation module based on the flange clearance change formula, which can convert the data collected by the sensor into the change of the preload force and analyze the change. The formula is: Among them, F is the change in bolt preload, k is the flange stiffness coefficient, Δx is the change in flange clearance, and L is the initial value of flange clearance. The flange clearance change formula is further optimized as follows: Where ΔP is the percentage of change in bolt preload, F new is the preload value at the current moment, F old is the historical preload value.

7. The wind turbine generator set bolt monitoring system according to claim 1, characterized in that: The monitoring system further includes a fault prediction module based on a multi-dimensional optimization model, which uses historical data and real-time sensor data to perform fault prediction and alarm through the following complex model: Among them, σ failure represents the fault prediction probability, A, B, C, λ are preset parameters, and t is the time variable.

8. The wind turbine generator set bolt monitoring system according to claim 1, characterized in that: The data processor includes a real-time feedback mechanism based on a composite dynamic model, which takes into account wind turbine load, climate change and equipment aging factors, and performs real-time feedback and adjustment through the following formula: Feedback=α·(P load -P target )+β·(ΔT ambient -ΔT target ) Among them, Feedback represents the system feedback signal, α and β are adjustment coefficients, P load is the current fan load, P target is the target load, ΔT ambient and ΔT target They are the current ambient temperature and the target temperature respectively.

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