Fire prevention monitoring method and system for old wind turbine based on wireless temperature measurement technology

Abstract

The invention discloses an old wind turbine anti-fire monitoring method and system based on a wireless temperature measurement technology, and relates to the technical field of automatic monitoring, and the method comprises the steps: collecting temperature data, and transmitting the temperature data to a central monitoring system; constructing a temperature normal value range model; carrying out anomaly detection and alarm based on a temperature normal value range model; and performing intervention according to the alarm information. Through real-time monitoring and dynamic analysis of temperature and current data, potential fire risks can be quickly and accurately identified, and accidents caused by overheating of equipment or abnormal current are remarkably reduced. And secondly, the real-time performance of data acquisition and the maintenance convenience of the system are improved by using a wireless technology, monitoring blind spots caused by equipment aging are reduced, and the integrity and real-time updating of monitoring data are ensured. In addition, the adaptive capacity of the system to the environment change and the foresight of early warning are further enhanced through the dynamic threshold adjustment and abnormal mode recognition functions, and false alarm and missing alarm are effectively avoided.

Description

Technical Field

[0001] The present invention relates to the field of automated monitoring technology, and in particular to a fire prevention monitoring method and system for old wind turbines based on wireless temperature measurement technology. Background Art

[0002] In the field of wind power generation, as the global demand for renewable energy increases, wind power generation technology is also constantly improving and developing. As the core component of the wind power generation system, the operation safety and maintenance efficiency of wind turbines have a vital impact on the overall power generation efficiency. Traditional wind turbine monitoring systems mainly rely on wired sensing technology for real-time monitoring of equipment status. However, old wind turbines often face many challenges due to design and technical limitations, including equipment aging, maintenance difficulties, and non-real-time monitoring data. Especially in terms of temperature monitoring, traditional methods are often difficult to accurately capture temperature anomalies of key components in real time due to complex wiring, inconvenient maintenance, and data delays, making it impossible to prevent and deal with potential safety hazards in a timely manner.

[0003] With the development of wireless sensor technology and data encryption technology, new solutions have been provided for the safety monitoring of old wind turbines. Although existing technologies have made certain progress in wireless monitoring and data transmission security, there are still deficiencies in practical applications. For example, existing monitoring systems often cannot effectively integrate and analyze the collected data, resulting in the inability to accurately determine the abnormal status of the equipment. At the same time, there is also a lack of effective predictive maintenance mechanisms to optimize the operation and maintenance plans of the equipment. In addition, the existing system still has room for improvement in data security protection, especially in the data encryption and verification mechanisms during wireless transmission, to prevent data from being tampered with or leaked during transmission.

[0004] In response to the above problems, our invention provides a fire prevention monitoring method for old wind turbines based on wireless temperature measurement technology. This method collects temperature and current data in real time through wireless sensors, and uses advanced AES encryption technology to ensure the security of data during transmission. By establishing a normal temperature range model and combining it with current data for comprehensive analysis, the system can more accurately identify abnormal conditions of the equipment and achieve early warning. In addition, the preprocessing module in our invention can effectively process and correct errors and omissions in the data to ensure the accuracy and completeness of the data. These improvements not only improve the efficiency and reliability of the monitoring system, but also provide strong technical support for the safe operation and maintenance of wind turbines. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by the present invention is: how to monitor the temperature and current anomalies of old wind turbines in real time and accurately during operation to prevent the risk of fire caused by equipment overheating or current anomalies. In particular, the present invention aims to solve the problems of non-real-time data collection, insufficient monitoring system integration and analysis capabilities, and insufficient security protection during data transmission in the prior art.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: a fire prevention monitoring method for old wind turbines based on wireless temperature measurement technology, which comprises the following steps:

[0008] Collect temperature data and transmit it to the central monitoring system; build a normal temperature range model; perform anomaly detection and alarm based on the normal temperature range model; and intervene based on the alarm information.

[0009] As a preferred solution of the fire prevention monitoring method for old wind turbines based on wireless temperature measurement technology described in the present invention, the temperature data is encrypted when transmitted to the central monitoring system.

[0010] The transmission to the central monitoring system is then pre-processed.

[0011] As a preferred solution of the fire prevention monitoring method for old wind turbines based on wireless temperature measurement technology described in the present invention, the encryption includes generating an AES key through a central management system and distributing it to sensor nodes. After the sensor collects the temperature data, it immediately uses the pre-assigned AES key to encrypt it. A MAC is generated during the encryption operation. After the encryption is completed, the MAC and the encrypted temperature data are sent to the central monitoring system. After the central monitoring system receives the data, it uses the same AES key to decrypt it and calculate the received MAC. If the calculated MAC matches the generated MAC, it means that the temperature data has not been modified.

[0012] As a preferred solution of the old wind turbine fire prevention monitoring method based on wireless temperature measurement technology described in the present invention, the preprocessing includes checking whether the decrypted temperature data meets expectations, whether the temperature data contains a timestamp and whether it is continuous.

[0013] If the timestamp format is incorrect, it will be corrected according to the predetermined format and synchronized with the standard time source to automatically correct the time deviation.

[0014] If there are missing data points, interpolation methods are used to estimate the missing values.

[0015] As a preferred solution of the old wind turbine fire prevention monitoring method based on wireless temperature measurement technology described in the present invention, the construction of the normal temperature value range model includes collecting historical temperature data, performing data preprocessing, standardizing temperature data from different sources, analyzing the distribution of historical temperature data after preprocessing, establishing a normal value range, and verifying the correctness of the normal temperature value range model based on historical fault data and normal operation data.

[0016] As a preferred solution of the fire prevention monitoring method for old wind turbines based on wireless temperature measurement technology described in the present invention, the abnormality detection and alarm include real-time monitoring of temperature data and current data, and comparing the temperature data with a preset threshold value in a normal temperature range model, wherein if the temperature data and the current data individually exceed a preset first-level threshold value, it indicates the presence of a primary abnormality.

[0017] If combination mode A is triggered, it indicates that a secondary anomaly exists.

[0018] If combination mode B is triggered, it indicates that a high-level anomaly exists.

[0019] The combination mode A includes that the temperature exceeds the first threshold and is less than or equal to the second threshold, and the current rises to exceed the limit value within a limited time. In the process of the temperature rising to exceed the limit value, continuous measurement points are set. If the temperature continues to rise during the measurement, it is combination mode A.

[0020] The combination mode B includes that the temperature or the current exceeds the preset three-level threshold value alone.

[0021] The abnormality detection and alarming also include automatically adjusting the alarm thresholds of temperature and current using an adaptive learning algorithm.

[0022] As a preferred solution of the fire prevention monitoring method for old wind turbines based on wireless temperature measurement technology described in the present invention, the intervention based on the alarm information includes, when the primary abnormality is triggered, recording the equipment data where the abnormality occurs, increasing the monitoring frequency of related equipment, and conducting on-site confirmation by personnel.

[0023] When the secondary abnormality is triggered, technicians are arranged to conduct inspections, evaluate the operating status and safety status of the equipment, and adjust the load according to the specific status of the equipment.

[0024] When the advanced abnormality is triggered, the emergency shutdown procedure is immediately executed. After the emergency shutdown, the equipment is inspected and maintained to check whether there are damaged parts that need to be replaced, whether there are signs of internal short circuits or overheating, and a detailed safety assessment is performed. After confirming that the equipment is safe, the system is reset and restarted to monitor the process of the equipment returning to normal operating status.

[0025] Another object of the present invention is to provide an old wind turbine fire prevention monitoring system based on wireless temperature measurement technology, which can solve the problems of poor adaptability and slow response speed of existing monitoring systems to old equipment by real-time and dynamic monitoring of temperature and current changes of key components.

[0026] To solve the above technical problems, the present invention provides the following technical solutions: an old wind turbine fire prevention monitoring system based on wireless temperature measurement technology, including a data acquisition module, a data transmission and encryption module, a data analysis and model building module, and an alarm and intervention module.

[0027] The data acquisition module is responsible for collecting the temperature data of the wind turbine.

[0028] The data transmission and encryption module is responsible for defining the format and protocol of data transmission, using AES encryption and MAC verification.

[0029] The data analysis and model building module builds a model of normal operating temperature based on historical temperature data and defines alarm thresholds of different levels.

[0030] The alarm and intervention module automatically or manually implements intervention measures according to the alarm level.

[0031] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the fire prevention monitoring method for old wind turbines based on wireless temperature measurement technology are implemented as described above.

[0032] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the fire prevention monitoring method for old wind turbines based on wireless temperature measurement technology as described above.

[0033] Beneficial effects of the present invention: Through real-time monitoring and dynamic analysis of temperature and current data, the present invention can quickly and accurately identify potential fire risks, significantly reducing accidents caused by equipment overheating or current anomalies. Secondly, the use of wireless technology improves the real-time nature of data acquisition and the convenience of system maintenance, reduces monitoring blind spots caused by equipment aging, and ensures the integrity and real-time updating of monitoring data. In addition, the dynamic threshold adjustment and abnormal pattern recognition functions in the present invention further enhance the system's adaptability to environmental changes and the foresight of early warning, effectively avoiding false alarms and missed alarms, and providing wind farm operators with an efficient and reliable risk management tool. These beneficial effects jointly improve the operational safety of wind turbines, extend equipment life, and reduce potential maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0035] Figure 1 An overall flow chart of a fire prevention monitoring method for old wind turbines based on wireless temperature measurement technology provided in the first embodiment of the present invention.

[0036] Figure 2 The overall framework diagram of the old wind turbine fire prevention monitoring system based on wireless temperature measurement technology provided in the second embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0038] Example 1, reference Figure 1 , is an embodiment of the present invention, and provides a fire prevention monitoring method for old wind turbines based on wireless temperature measurement technology, which is characterized by:

[0039] S1: Collect temperature data and transmit it to the central monitoring system.

[0040] The temperature data is encrypted when transmitted to the central monitoring system.

[0041] After being transmitted to the central monitoring system, it is pre-processed.

[0042] The encryption process includes generating an AES key through the central management system and distributing it to the sensor nodes. After the sensor collects the temperature data, it immediately encrypts it using the pre-assigned AES key. A MAC is generated during the encryption operation. After the encryption is completed, the MAC and the encrypted temperature data are sent to the central monitoring system. After the central monitoring system receives the data, it uses the same AES key to decrypt it and calculate the received MAC. If the calculated MAC matches the generated MAC, it means that the temperature data has not been modified.

[0043] The preprocessing includes checking whether the decrypted temperature data is consistent with expectations, including whether the temperature data contains a timestamp and whether it is continuous.

[0044] If the timestamp format is incorrect, it will be corrected according to the predetermined format and synchronized with the standard time source to automatically correct the time deviation.

[0045] If there are missing data points, interpolation methods are used to estimate the missing values.

[0046] S2: Construct a model of normal temperature range.

[0047] Constructing a normal temperature range model includes collecting historical temperature data, performing data preprocessing, standardizing temperature data from different sources, analyzing the distribution of preprocessed historical temperature data, establishing a normal value range, and verifying the correctness of the normal temperature range model based on historical fault data and normal operation data.

[0048] S3: Anomaly detection and alarm based on the normal temperature range model.

[0049] Abnormal detection and alarm include real-time monitoring of temperature data and current data, and comparing the temperature data with a preset threshold in a normal temperature range model. If the temperature data and the current data individually exceed a preset first-level threshold, it indicates a primary abnormality.

[0050] If combination mode A is triggered, it indicates that a secondary anomaly exists.

[0051] If combination mode B is triggered, it indicates that a high-level anomaly exists.

[0052] Combination mode A includes that the temperature exceeds the first threshold and is less than or equal to the second threshold, and the current rises to exceed the limit value within a limited time. In the process of the temperature rising to exceed the limit value, continuous measurement points are set. If it continues to rise within the measurement, it is combination mode A.

[0053] Combination mode B includes that the temperature or current exceeds the preset three-level threshold value alone.

[0054] Anomaly detection and alerting also includes automatically adjusting alarm thresholds for temperature and current using adaptive learning algorithms.

[0055] S4: Intervene based on the alarm information.

[0056] Intervention based on alarm information includes recording the data of the equipment where the abnormality occurred, increasing the monitoring frequency of related equipment, and conducting on-site confirmation by personnel when the primary abnormality is triggered.

[0057] When a secondary abnormality is triggered, technicians are arranged to conduct inspections, evaluate the operating status and safety status of the equipment, and adjust the load according to the specific status of the equipment.

[0058] When a high-level abnormality is triggered, the emergency shutdown procedure is immediately executed. After the emergency shutdown, the equipment is inspected and maintained to check whether there are damaged parts that need to be replaced, whether there are signs of internal short circuits or overheating, and a detailed safety assessment is performed. After confirming that the equipment is safe, the system is reset and restarted, and the process of the equipment returning to normal operating status is monitored.

[0059] Example 2, reference Figure 2 , which is an embodiment of the present invention, provides a system for monitoring the fire prevention of old wind turbines based on wireless temperature measurement technology, which is characterized by: including a data acquisition module 100, a data transmission and encryption module 200, a data analysis and model building module 300 and an alarm and intervention module 400.

[0060] The data acquisition module 100 is responsible for collecting the temperature data of the wind turbine.

[0061] The data transmission and encryption module 200 is responsible for defining the format and protocol of data transmission, using AES encryption and MAC authentication.

[0062] The data analysis and model building module 300 builds a model of normal operating temperature based on historical temperature data and defines alarm thresholds of different levels.

[0063] The alarm and intervention module 400 automatically or manually implements intervention measures based on the alarm level.

[0064] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0065] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0066] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0067] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0068] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A fire prevention monitoring method for old wind turbines based on wireless temperature measurement technology, characterized in that: include: Collect temperature data and transmit it to the central monitoring system; Construct a model of normal temperature range; Anomaly detection and alerting based on the normal temperature range model; Intervene based on the alarm information.

2. The fire prevention monitoring method for old wind turbines based on wireless temperature measurement technology according to claim 1 is characterized in that: The temperature data is encrypted when transmitted to the central monitoring system; The transmission to the central monitoring system is followed by preprocessing.

3. The fire prevention monitoring method for old wind turbines based on wireless temperature measurement technology according to claim 2 is characterized in that: The encryption includes generating an AES key through a central management system and distributing it to sensor nodes. After the sensor collects the temperature data, it immediately encrypts it using a pre-assigned AES key. A MAC is generated during the encryption operation. After the encryption is completed, the MAC and the encrypted temperature data are sent to the central monitoring system. After the central monitoring system receives the data, it uses the same AES key to decrypt it and calculate the received MAC. If the calculated MAC matches the generated MAC, it means that the temperature data has not been modified.

4. The fire prevention monitoring method for old wind turbines based on wireless temperature measurement technology according to claim 3 is characterized in that: The preprocessing includes checking whether the decrypted temperature data meets expectations, wherein the temperature data contains a timestamp and whether it is continuous; If the timestamp format is incorrect, it will be corrected according to the predetermined format and synchronized with the standard time source to automatically correct the time deviation; If there are missing data points, interpolation methods are used to estimate the missing values.

5. The fire prevention monitoring method for old wind turbines based on wireless temperature measurement technology according to claim 4 is characterized in that: The construction of the normal temperature range model includes collecting historical temperature data, performing data preprocessing, standardizing temperature data from different sources, analyzing the distribution of the preprocessed historical temperature data, establishing a normal value range, and verifying the correctness of the normal temperature range model based on historical fault data and normal operation data.

6. The fire prevention monitoring method for old wind turbines based on wireless temperature measurement technology according to claim 5 is characterized in that: The abnormality detection and alarm includes real-time monitoring of temperature data and current data, and comparing the temperature data with a preset threshold value in a temperature normal value range model, wherein if the temperature data and the current data individually exceed a preset primary threshold value, it indicates that a primary abnormality exists; If combination mode A is triggered, it indicates that a secondary anomaly exists; If combination mode B is triggered, it indicates that a high-level anomaly exists; The combination mode A includes that the temperature exceeds the primary threshold and is less than or equal to the secondary threshold, and the current rises to exceed the limit value within a limited time. In the process of the temperature rising to exceed the limit value, continuous measurement points are set. If the temperature continues to rise within the measurement, it is combination mode A; The combination mode B includes that the temperature or the current exceeds the preset three-level threshold value alone; The abnormality detection and alarming also include automatically adjusting the alarm thresholds of temperature and current using an adaptive learning algorithm.

7. The fire prevention monitoring method for old wind turbines based on wireless temperature measurement technology according to claim 6 is characterized in that: The intervention according to the alarm information includes, when the primary abnormality is triggered, recording the data of the device where the abnormality occurs, increasing the monitoring frequency of the relevant equipment, and conducting on-site confirmation by personnel; When the secondary abnormality is triggered, arrange technicians to conduct inspections, evaluate the operating status and safety status of the equipment, and adjust the load according to the specific status of the equipment; When the advanced abnormality is triggered, the emergency shutdown procedure is immediately executed. After the emergency shutdown, the equipment is inspected and maintained to check whether there are damaged parts that need to be replaced, whether there are signs of internal short circuits or overheating, and a detailed safety assessment is performed. After confirming that the equipment is safe, the system is reset and restarted to monitor the process of the equipment returning to normal operating status.

8. A system using the fire prevention monitoring method for old wind turbines based on wireless temperature measurement technology as described in any one of claims 1 to 7, characterized in that: It includes a data acquisition module (100), a data transmission and encryption module (200), a data analysis and model building module (300), and an alarm and intervention module (400); The data acquisition module (100) is responsible for collecting temperature data of the wind turbine; The data transmission and encryption module (200) is responsible for defining the format and protocol of data transmission, using AES encryption and MAC authentication; The data analysis and model building module (300) builds a model of normal operating temperature based on historical temperature data and defines alarm thresholds of different levels; The alarm and intervention module (400) automatically or manually implements intervention measures according to the alarm level.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the old wind turbine fire prevention monitoring method based on wireless temperature measurement technology described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the fire prevention monitoring method for old wind turbines based on wireless temperature measurement technology described in any one of claims 1 to 7 are implemented.

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