A wind turbine monitoring and early warning device based on internet of things sensing technology

Abstract

The utility model discloses a kind of wind turbine generator unit monitoring and early warning device based on internet perception technology, it is related to wind turbine generator unit monitoring and early warning technical field, including processing controller shell, the processing controller shell side is equipped with jack, by local operation AI health assessment model, millisecond level response is realized, the real-time of wind turbine fault monitoring is greatly improved, the influence of sudden failure to wind turbine operation is reduced.Secondly, traditional cloud PHM system needs to transmit a large number of original data, resulting in excessive bandwidth consumption, while the device uses event-triggered data upload mechanism, only uploads key feature data, reduces communication cost, improves the efficiency and reliability of the system.In addition, most of the existing monitoring schemes rely on a single sensor type, cannot accurately identify complex fault patterns, while the device integrates multi-modal data such as vibration, temperature, current, acoustics and vision, improves the accuracy of fault identification and reduces false positives and false negatives.

Description

Technical Field

[0001] This utility model relates to the field of wind turbine monitoring and early warning technology, specifically a wind turbine monitoring and early warning device based on Internet of Things sensing technology. Background Technology

[0002] Wind power, as a crucial component of clean energy, has experienced rapid development in recent years. However, due to the long-term operation of wind turbines at high altitudes, offshore, or remote areas, their maintenance and monitoring face numerous challenges. Core components of wind turbines (such as gearboxes, main shafts, generators, and pitch systems) are subjected to complex mechanical loads, environmental influences (such as high wind speeds, temperature variations, and humidity corrosion), and electrical stresses, making them prone to fatigue damage, lubrication failure, and electrical faults. These problems not only affect turbine operating efficiency but can also lead to significant economic losses and safety hazards. Traditional wind turbine operation and maintenance primarily rely on manual inspections and periodic maintenance. However, due to the large number and wide distribution of wind turbines, manual maintenance is costly, inefficient, and struggles to detect potential faults in a timely manner. Therefore, leveraging emerging technologies such as the Internet of Things (IoT), artificial intelligence (AI), and edge computing to improve the intelligent monitoring capabilities of wind turbines, reduce unplanned downtime, and optimize maintenance strategies has become a critical issue that the wind power industry urgently needs to address.

[0003] Currently, there are some wind turbine monitoring solutions on the market that involve the Internet of Things and intelligent monitoring and early warning. These mainly include the following technical solutions: traditional SCADA (Supervisory Control and Data Acquisition) systems, where data needs to be transmitted to a remote control center for calculation and decision-making, resulting in slow response speeds and making them unsuitable for scenarios requiring real-time processing; and remote cloud-based health management systems, which have high bandwidth and communication costs, data processing delays, and strong cloud dependence. Utility Model Content

[0004] (a) Technical problems to be solved

[0005] To address the shortcomings of existing technologies, this utility model provides a wind turbine monitoring and early warning device based on IoT sensing technology, which solves the problems of slow response speed, high bandwidth and communication costs, and data processing delays.

[0006] Technical solution

[0007] To achieve the above objectives, this utility model is implemented through the following technical solution: A wind turbine monitoring and early warning device based on IoT sensing technology, comprising: a processing controller housing, a socket on one side of the processing controller housing, a plug inserted into the socket, multiple sets of connecting wires on the side of the plug away from the socket, a vibration sensor fixedly connected to one end of each set of connecting wires, a display screen fixedly connected to the surface of the processing controller housing, a control button fixedly connected to the side of the processing controller housing near the display screen, two sets of wires fixedly connected to the side of the processing controller housing away from the display screen, a terminal block fixedly connected to one end of each set of wires, multiple sets of signal wires uniformly fixedly connected to the side of the processing controller housing near the wires, a temperature sensor fixedly connected to one end of each set of signal wires, an external interface on the side of the processing controller housing away from the socket, an embedded AI chip fixedly installed inside the processing controller housing, a power cord fixedly connected to the surface of the processing controller housing, and a power supply connector fixedly connected to one end of the power cord.

[0008] Preferably, an emergency backup battery is fixedly connected to the side of the processing controller housing near the external interface, and the emergency backup battery is electrically connected to the power cord.

[0009] Preferably, signal transceiver antennas are symmetrically rotatably connected to both sides of the processing controller housing.

[0010] Preferably, the top of the processing controller housing has multiple sets of heat dissipation slots evenly distributed, and a dustproof plate is fixedly connected to the top of the processing controller housing, with a gap between the dustproof plate and the top of the processing controller housing.

[0011] Preferably, the processing controller housing is fixedly connected to multiple sets of data transmission lines on the side near the power cord, and one end of each data transmission line is fixedly connected to a monitoring camera.

[0012] Preferably, the bottom of the processing controller housing is symmetrically provided with support feet, and the support feet are provided with insulating rubber pads.

[0013] Preferably, the external interface includes an audio input / output interface, a network cable interface, a USB interface, and a Type-C interface.

[0014] (III) Beneficial Effects

[0015] This utility model provides a wind turbine monitoring and early warning device based on Internet of Things sensing technology, which has at least the following advantages compared with the prior art:

[0016] By running an AI health assessment model locally, millisecond-level response is achieved, significantly improving the real-time performance of wind turbine fault monitoring and reducing the impact of sudden failures on turbine operation. Secondly, traditional cloud-based PHM systems require the transmission of large amounts of raw data, leading to excessive bandwidth consumption. This device, however, employs an event-triggered data upload mechanism, uploading only key feature data, reducing communication costs and improving system efficiency and reliability. Furthermore, most existing monitoring solutions rely on single sensor types, failing to accurately identify complex fault modes. This device, through the fusion of multimodal data including vibration, temperature, current, acoustics, and vision, improves the accuracy of fault identification and reduces false alarms and missed alarms. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the structure of this utility model;

[0018] Figure 2 This is a schematic diagram of the display screen, control buttons, and emergency backup battery of this utility model.

[0019] Figure 3 This is a schematic diagram of the external interface and power supply connector of this utility model;

[0020] Figure 4 This is a schematic diagram of the structure of the socket, plug, and connecting wire of this utility model.

[0021] In the diagram: 1. Controller housing; 2. Power cord; 3. Power connector; 4. Display screen; 5. Control buttons; 6. Emergency backup battery; 7. Signal transceiver antenna; 8. Socket; 9. Plug; 10. Connecting cable; 11. Vibration sensor; 12. Data transmission line; 13. Surveillance camera; 14. Signal line; 15. Temperature sensor; 16. Wire; 17. Terminal block; 18. Heat sink; 19. Dustproof plate; 20. Support feet; 21. External interface. Detailed Implementation

[0022] The technical solutions of the present utility model will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present utility model, and not all embodiments. Based on the embodiments of the present utility model, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of the present utility model.

[0023] Example 1:

[0024] Please see Figures 1-4This utility model provides a technical solution: a processing controller housing 1, with a socket 8 on one side of the processing controller housing 1, into which a plug 9 is inserted. Multiple sets of connecting wires 10 are arranged on the side of the plug 9 away from the socket 8, each set of connecting wires 10 having a vibration sensor 11 fixedly connected to one end. A display screen 4 is fixedly connected to the surface of the processing controller housing 1. A control button 5 is fixedly connected to the side of the processing controller housing 1 near the display screen 4. Two sets of wires 16 are fixedly connected to the side of the processing controller housing 1 away from the display screen 4, each set of wires 16 having a terminal block 17 fixedly connected to one end. Multiple sets of signal lines 14 are evenly fixedly connected to the side of the processing controller housing 1 near the wires 16, each set of signal lines 14 having a temperature sensor 15 fixedly connected to one end. An external interface 21 is provided on the side of the processing controller housing 1 away from the socket 8. An embedded AI chip is fixedly installed inside the processing controller housing 1. A power cord 2 is fixedly connected to the surface of the processing controller housing 1, with a power connector 3 fixedly connected to one end of the power cord 2. The external interface 21 includes an audio input / output interface, a network cable interface, a USB interface, and a Type-C interface.

[0025] Analysis of the above: Vibration sensors 11 are fixed to bearings, gearboxes, and other parts of the wind turbine to monitor vibration data. Terminal 17 is connected to the generator to monitor generator current and other data. Temperature sensors 15 are used to detect temperature data. The data is sent to the AI ​​chip for local computation and compared with a large model to improve the accuracy of fault identification and reduce false alarms and missed alarms. Various IoT sensors are connected in real time to collect the operating status of the wind turbine and transmit the data wirelessly or via wired network. Machine learning / deep learning algorithms are run on the local device for real-time data analysis, such as vibration signal analysis, temperature anomaly detection, and fault mode identification, reducing the cloud computing burden and improving response speed. Based on the AI ​​model, it determines whether there are any abnormalities in the wind turbine and makes early warning decisions, such as adjusting operating parameters, sending alarm information, and triggering self-test processes. Through remote connection, the device can interact with the cloud-based operation and maintenance platform to achieve remote diagnosis and expert-assisted decision-making functions. All components are general standard parts or components known to those skilled in the art, and their structure and principles can be learned by those skilled in the art through technical manuals or conventional experimental methods.

[0026] Example 2:

[0027] Please see Figures 1-4 Based on Embodiment 1, this utility model provides a technical solution: an emergency backup battery 6 is fixedly connected to the side of the controller housing 1 near the external interface 21, and the emergency backup battery 6 is electrically connected to the power cord 2.

[0028] Analysis of the above: The emergency backup battery 6 provides emergency power to the controller housing 1 and promptly issues alarm information. During normal power supply, the emergency backup battery 6 is charged via power cord 2. After a power outage, the emergency backup battery 6 provides power to the equipment.

[0029] Example 3:

[0030] Please see Figures 1-4 Based on Embodiment 1, this utility model provides a technical solution: signal transceiver antennas 7 are symmetrically and rotatably connected to both sides of the controller housing 1.

[0031] Analysis of the above content: Setting up signal transceiver antenna 7 improves signal transmission and reception performance.

[0032] Example 4:

[0033] Please see Figures 1-4 Based on Embodiment 1, this utility model provides a technical solution: multiple sets of heat dissipation grooves 18 are evenly opened on the top of the processing controller housing 1, and a dustproof plate 19 is fixedly connected to the top of the processing controller housing 1, with a gap between the dustproof plate 19 and the top of the processing controller housing 1.

[0034] Analysis of the above content: The heat sink 18 is used to dissipate heat for the internal components of the controller housing 1, and the dustproof plate 19 is used to prevent dust from falling into the heat sink 18.

[0035] Example 5:

[0036] Please see Figures 1-4 Based on Embodiment 1, this utility model provides a technical solution: multiple sets of data transmission lines 12 are fixedly connected to the side of the controller housing 1 near the power line 2, and a monitoring camera 13 is fixedly connected to one end of the data transmission line 12.

[0037] Analysis of the above content: The surveillance camera 13 is used to monitor a specific location via video, and the data is sent to the processing controller housing 1. The device status is then analyzed by the chip.

[0038] Example 6:

[0039] Please see Figures 1-4 Based on Embodiment 1, this utility model provides a technical solution: the bottom of the controller housing 1 is symmetrically provided with support feet 20, and the support feet 20 are set as insulating rubber pads.

[0040] Analysis of the above: Support feet 20 provide a buffering effect for the controller housing 1, reducing the impact of vibration on it. Vibration sensors 11 are installed on key components such as gearboxes and bearings. Abnormal patterns, such as bearing fatigue and gear wear, are analyzed through vibration data, and preliminary warnings are issued locally. The system connects to the generator via wires 16 and terminals 17 to monitor the wind turbine's current, voltage, harmonics, and other electrical parameters in real time. AI algorithms detect abnormalities in the motor, main shaft, gearbox, or cables, such as mechanical failures, partial discharge, and short circuit hazards. Data preprocessing is performed locally, uploading only critical information to optimize communication resources. An AI health assessment model is used to analyze the health status of the wind turbine equipment based on multimodal data. A high-performance embedded AI chip supports deep learning algorithms, improving diagnostic accuracy. Local encrypted storage and blockchain data tamper-proofing are used to improve system security. Integrating data from multiple sensors enables more accurate fault detection and trend prediction. AI algorithms are run to perform real-time analysis of equipment status, such as anomaly detection and fault prediction. The system combines a pre-defined health assessment model (PHM, Prognostics, and Health Management) to calculate device health status scores and provide early warning decisions. It interacts with edge computing devices via the internet to receive key monitoring data. Long-term trend analysis and health prediction algorithms provide maintenance recommendations to operations and maintenance personnel.

[0041] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0042] Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the present invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A wind turbine monitoring and early warning device based on IoT sensing technology, characterized in that, include: The processing controller housing (1) has a socket (8) on one side, into which a plug (9) is inserted. Multiple sets of connecting wires (10) are arranged on the side of the plug (9) away from the socket (8). A vibration sensor (11) is fixedly connected to one end of each set of connecting wires (10). A display screen (4) is fixedly connected to the surface of the processing controller housing (1). A control button (5) is fixedly connected to the side of the processing controller housing (1) near the display screen (4). Two sets of wires (11) are fixedly connected to the side of the processing controller housing (1) away from the display screen (4). 6) Each set of wires (16) is fixedly connected to a terminal block (17) at one end. Multiple sets of signal lines (14) are evenly fixedly connected to the side of the processing controller housing (1) near the wires (16). A temperature sensor (15) is fixedly connected to one end of each set of signal lines (14). An external interface (21) is provided on the side of the processing controller housing (1) away from the socket (8). An embedded AI chip is fixedly installed inside the processing controller housing (1). A power cord (2) is fixedly connected to the surface of the processing controller housing (1). A power supply connector (3) is fixedly connected to one end of the power cord (2).

2. The wind turbine monitoring and early warning device based on IoT sensing technology according to claim 1, characterized in that: An emergency backup battery (6) is fixedly connected to the side of the processing controller housing (1) near the external interface (21), and the emergency backup battery (6) is electrically connected to the power cord (2).

3. The wind turbine monitoring and early warning device based on IoT sensing technology according to claim 1, characterized in that: The processing controller housing (1) is symmetrically rotated and connected to signal transceiver antennas (7) on both sides.

4. The wind turbine monitoring and early warning device based on IoT sensing technology according to claim 1, characterized in that: The top of the processing controller housing (1) has multiple sets of heat dissipation slots (18) evenly distributed. A dustproof plate (19) is fixedly connected to the top of the processing controller housing (1), and a gap is left between the dustproof plate (19) and the top of the processing controller housing (1).

5. A wind turbine monitoring and early warning device based on IoT sensing technology according to claim 1, characterized in that: The processing controller housing (1) is fixedly connected to a number of data transmission lines (12) on the side near the power line (2), and a monitoring camera (13) is fixedly connected to one end of the data transmission line (12).

6. A wind turbine monitoring and early warning device based on IoT sensing technology according to claim 1, characterized in that: The bottom of the processing controller housing (1) is symmetrically provided with support feet (20), and the support feet (20) are set as insulating rubber pads.

7. A wind turbine monitoring and early warning device based on IoT sensing technology according to claim 1, characterized in that: The external interface (21) includes an audio input / output interface, a network cable interface, a USB interface, and a Type-C interface.

Images