Transformer winding deformation monitoring method based on wireless low-power-consumption distribution

Abstract

The invention discloses a transformer winding deformation monitoring method based on wireless low-power-consumption distribution, relates to the technical field of power equipment monitoring and fault diagnosis, and is used for solving the problem that a comprehensive solution capable of giving consideration to efficient data acquisition, low-power-consumption operation and wireless transmission is lacked. A weighted clustering algorithm is utilized to divide a monitoring area, a three-dimensional model is generated by combining spectral analysis and multi-channel signal fusion, and wavelet transform or Kalman filtering is adopted to process signals. According to the application, the energy consumption of the sensor nodes and the wireless communication efficiency can be optimized, the distributed deployment flexibility is improved, meanwhile, high-precision winding state monitoring and analysis are realized, the requirements of real-time performance, reliability and economy are met, and the transformer winding deformation monitoring effect is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment monitoring and fault diagnosis, and more particularly to a transformer winding deformation monitoring method based on wireless low-power distributed technology. Background Art

[0002] Transformer winding deformation monitoring technology plays a vital role in ensuring the safe operation of power systems. Currently, transformer winding deformation monitoring methods rely on data collection from wired sensor networks or local monitoring points. However, these methods have limitations in distributed deployment, low-power design, and wireless communication capabilities, potentially hindering real-time and high-efficiency requirements under complex operating conditions.

[0003] The existing technology has the following deficiencies:

[0004] Currently, existing deformation monitoring technologies have room for improvement in distributed deployment, low-power design, and wireless communication capabilities. In particular, in the field of transformer winding deformation monitoring, there is a lack of comprehensive solutions that combine efficient data acquisition, low-power operation, and wireless transmission. Therefore, a method for transformer winding deformation monitoring based on wireless, low-power, and distributed systems is proposed.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a transformer winding deformation monitoring method based on wireless low-power distributed technology, which solves the problems raised in the above-mentioned background technology by optimizing the low-power design of sensor nodes, improving wireless communication efficiency, and realizing distributed data acquisition and processing.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a transformer winding deformation monitoring method based on wireless, low-power, distributed technology, comprising: S1: installing multiple micro-sensing units at key locations of the transformer winding, each micro-sensing unit establishing a connection with a central processing unit via a wireless communication module, the central processing unit initializing and configuring each micro-sensing unit and recording its physical location information, calculating the coverage range of each micro-sensing unit based on the physical location information and the signal strength between the micro-sensing units, and dividing the monitoring area using a weighted clustering algorithm based on the coverage range;

[0008] S2: Within the monitoring area, the vibration signal of the transformer winding is collected by the micro-sensing unit. The vibration signal is transmitted to the central processing unit after analog-to-digital conversion. The central processing unit performs spectrum analysis on the collected vibration signal, extracts the characteristic frequency, and compares it with the preset database to determine whether the current winding status is abnormal;

[0009] S3: When an abnormality is detected, the central processing unit sends instructions to the micro-sensing unit in the abnormal area, requiring it to increase the sampling frequency and synchronously collect vibration signals. The synchronously collected vibration signals are transmitted to the central processing unit through the wireless communication module. The central processing unit uses a multi-channel signal fusion algorithm to process the synchronous signals and generate a three-dimensional model of the winding deformation;

[0010] S4: According to the degree of deformation in the three-dimensional model, different signal processing algorithms are selected to further analyze the winding deformation. If the degree of deformation is small, the wavelet transform algorithm is used to denoise the signal. If the degree of deformation is large, the Kalman filter algorithm is used to dynamically compensate the signal.

[0011] In a preferred embodiment, in step S1, the micro-sensing unit is installed on the surface of the transformer winding through a magnetic fixing device, the magnetic fixing device is made of high magnetic permeability material, the wireless communication module uses a low-power Bluetooth protocol for data transmission, and the communication distance is set to within 10 meters. The central processing unit queries its factory parameters, including sensitivity, sampling rate and operating voltage range, by matching the unique identifier of the micro-sensing unit, and stores them in a local database.

[0012] In a preferred embodiment, in step S1, the coverage range refers to the maximum radius within which the micro-sensing unit can stably receive and transmit signals. The size of the coverage range is affected by the transmission power of the micro-sensing unit and environmental interference factors. The specific implementation steps of the weighted clustering algorithm are as follows:

[0013] Define input and output: Set the physical location information and signal strength of the microsensor unit as input features, and the monitoring area boundary as output features;

[0014] Select weight coefficient: Calculate the weight coefficient based on the signal strength and coverage of the micro-sensor unit. The weight coefficient is equal to the product of signal strength and coverage.

[0015] Calculate cluster centers: Select the micro-sensor unit with the highest signal strength in the input feature set as the initial cluster center;

[0016] Allocate micro-sensor units: Allocate the remaining micro-sensor units according to their distance from the cluster center. Micro-sensor units with a distance smaller than the coverage range are placed in the same monitoring area.

[0017] Update cluster centers: Recalculate the locations of cluster centers based on the allocation results until the cluster centers no longer change.

[0018] In a preferred embodiment, in step S2, the acquisition frequency of the vibration signal is set to 1 kHz, the resolution of the analog-to-digital converter is 16 bits, the spectrum analysis adopts the fast Fourier transform algorithm, the extraction of the characteristic frequency is achieved by calculating the peak position of the spectrum, and the characteristic frequency is compared with the standard frequency in a preset database. When the deviation exceeds the preset threshold, it is determined to be abnormal.

[0019] In a preferred embodiment, in step S3, after the micro-sensing unit in the abnormal area receives the instruction from the central processing unit, it increases the sampling frequency from 1kHz to 5kHz. The increase in the sampling frequency is achieved by adjusting the clock signal of the analog-to-digital converter. The synchronously collected vibration signals are aligned through timestamp marks. The accuracy of the timestamp is set to microseconds, and the multi-channel signal fusion algorithm adopts principal component analysis.

[0020] In a preferred embodiment, in step S4, a wavelet transform algorithm is used to remove high-frequency noise in the signal.

[0021] In a preferred embodiment, in step S4, a Kalman filter algorithm is used to perform dynamic compensation on the signal.

[0022] In a preferred embodiment, the magnetic fixing device is disc-shaped and has an anti-slip gasket at the bottom. Its adsorption force is calculated and designed to ensure that it can be firmly adsorbed on the metal casing of the transformer without interfering with the magnetic field environment of the transformer winding due to excessive magnetic force.

[0023] In a preferred embodiment, the installation location of the micro-sensor unit is selected at the key parts of the transformer winding, including the winding end, the middle and near the terminal, and a distance of 3 to 5 meters is maintained between each micro-sensor unit to avoid data redundancy caused by excessive signal overlap.

[0024] The technical effects and advantages of the present invention are as follows:

[0025] 1. The present invention reduces the overall energy consumption of the system and extends the operating time of the equipment by optimizing the installation method of the sensing unit and the design of the wireless communication module. The monitoring area is divided by a weighted clustering algorithm, which improves the flexibility and coverage of the distributed deployment. The vibration signal acquisition and analysis process combines spectrum analysis and multi-channel signal fusion algorithms, which can accurately identify the deformation state of the winding. The wavelet transform algorithm and Kalman filter algorithm selected for different deformation degrees are respectively suitable for high-frequency noise removal and dynamic signal compensation, which improves the accuracy and efficiency of signal processing. In summary, the present invention has significant advantages in real-time, reliability and economy, and can meet the complex needs of transformer winding deformation monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a module diagram of the transformer winding deformation monitoring method based on wireless low-power distributed technology of the present invention. DETAILED DESCRIPTION

[0027] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0028] Example 1

[0029] like Figure 1 The system architecture diagram of the wireless, low-power, distributed transformer winding deformation monitoring method provided by an embodiment of the present invention is shown. The entire system consists of a micro-sensing unit, a wireless communication module, a central processing unit, a magnetic fixing device, and a monitoring area. The micro-sensing unit is installed on the surface of the transformer winding via a magnetic fixing device. The wireless communication module is used for data transmission between the micro-sensing unit and the central processing unit. The monitoring area is the coverage area of ​​the sensor unit divided by a weighted clustering algorithm. The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0030] First, we describe the installation method of the microsensor unit and its positional relationship with the transformer winding. The microsensor unit is attached to the surface of the transformer winding via a magnetic fixture. This fixture is made of highly magnetically permeable material, is disc-shaped, and features a non-slip pad at the bottom to enhance the fixation. The magnetic fixture's suction force is calculated and designed to ensure it remains firmly attached to the transformer's metal casing while preventing excessive magnetic force from disrupting the transformer winding's magnetic field. The microsensor units are installed in key locations along the transformer winding, including the ends, middle, and near the terminals—areas where winding deformation is most likely to occur. Each microsensor unit is spaced a certain distance apart, adjusted based on its signal coverage range and typically set between 3 and 5 meters to avoid data redundancy caused by excessive signal overlap.

[0031] The micro-sensing unit integrates a vibration sensor, an analog-to-digital converter, and a low-power Bluetooth communication module. The vibration sensor is responsible for collecting vibration signals from the transformer windings. Its sensitivity range is 0.1 to 10g, the sampling frequency is initially set to 1kHz, and the resolution is 16 bits. The analog-to-digital converter converts the collected analog signal into a digital signal and transmits it to the central processing unit via the low-power Bluetooth communication module. The low-power Bluetooth communication module operates in the 2.4GHz frequency band, and the communication range is set to within 10 meters to meet the requirements of distributed deployment. Each micro-sensing unit has a unique identifier, which is preset at the factory and stored in a local database. The central processing unit queries its factory parameters, including sensitivity, sampling rate, and operating voltage range, by matching the identifier, and initializes the configuration of the sensor unit 1 based on these parameters.

[0032] The wireless communication module facilitates data transmission between the microsensor units and the central processing unit. The wireless communication module utilizes the Bluetooth Low Energy protocol, and its communication process is divided into two phases. The first phase is initialization, during which the central processing unit sends a broadcast signal to all microsensor units. Upon receiving the broadcast signal, the microsensor units return their physical location information and signal strength. The second phase is data transmission, during which the microsensor units timestamp the collected vibration signals and transmit them to the central processing unit in packets. To improve data transmission reliability, the wireless communication module employs an automatic retransmission request mechanism. If a data packet is lost or an error occurs, the central processing unit sends a retransmission instruction to the microsensor units until the data is fully received.

[0033] The central processing unit is the control core of the entire system. Its main functions include initialization configuration, data processing and anomaly detection. The central processing unit receives the vibration signal from the micro-sensor unit through the wireless communication module and performs spectrum analysis on it. The spectrum analysis uses the fast Fourier transform algorithm, and the formula is

[0034] Where X(k) is the kth frequency component in the frequency domain and the signal value corresponding to the nth sampling point in the time domain;

[0035] The characteristic frequency is extracted by calculating the peak position of the spectrum and compared with the standard frequency in a preset database. If the characteristic frequency deviates from a preset threshold, an abnormal state is detected. At this point, the central processing unit sends a command to the microsensor unit in the abnormal area, instructing it to increase the sampling frequency from 1kHz to 5kHz. This adjustment is achieved by changing the clock signal of the analog-to-digital converter. The synchronously collected vibration signals are aligned using timestamps, with timestamp accuracy set to microseconds.

[0036] In abnormal conditions, the central processing unit uses a multi-channel signal fusion algorithm to process the synchronously collected vibration signals. The multi-channel signal fusion algorithm uses principal component analysis, and the formula is Y = XW;

[0037] Where Y is the signal matrix after dimensionality reduction, X is the original signal matrix, and W is the eigenvector matrix;

[0038] Principal component analysis extracts the signal's key features and generates a 3D model of the winding deformation. This 3D model generation process includes the following steps: first, the relative displacement of each sensor unit 1 is calculated based on the synchronously collected vibration signal; second, the relative displacement is mapped into a 3D coordinate system to form the winding's geometric shape; and finally, the geometric shape is smoothed using an interpolation algorithm to generate the final 3D model.

[0039] According to the degree of deformation in the 3D model, the central processing unit selects different signal processing algorithms to further analyze the winding deformation. If the degree of deformation is small, the wavelet transform algorithm is used to denoise the signal. The formula of wavelet transform is

[0040] Where W(a,b) is the wavelet coefficient, f(t) is the input signal, ψ(t) is the wavelet basis function, a is the scale parameter, and b is the translation parameter;

[0041] The wavelet transform algorithm is mainly used to remove high-frequency noise in the signal and retain the low-frequency characteristics. If the degree of deformation is large, the Kalman filter algorithm is used to perform dynamic compensation processing on the signal. The formula of the Kalman filter is

[0042] in, is the estimated state, K k is the Kalman gain, z k is the observation value, H is the observation matrix;

[0043] The Kalman filter algorithm improves the stability and accuracy of the signal by dynamically compensating the signal.

[0044] The monitoring area is divided using a weighted clustering algorithm. The specific implementation steps are as follows: First, define the input and output, using the physical location information and signal strength of the microsensor units as input features and the boundary of the monitoring area as the output feature. Second, select a weight coefficient based on the signal strength and coverage of the microsensor units, which is equal to the product of signal strength and coverage. Then, calculate the cluster center, selecting the microsensor unit with the highest signal strength from the input feature set as the initial cluster center. Next, allocate the microsensors, assigning the remaining microsensors according to their distance from the cluster center. Microsensors with distances less than the coverage area are assigned to the same monitoring area. Finally, update the cluster center, recalculating its position based on the allocation results until it no longer changes. Through the weighted clustering algorithm, the monitoring area can be dynamically adjusted based on the actual distribution of the microsensor units, thereby improving the flexibility and coverage of distributed deployment.

[0045] In practical applications, the system of the present invention can be deployed in a substation or at a power equipment maintenance site. For example, in a substation, multiple transformers need to monitor winding deformation at the same time. Several micro-sensor units are installed on each transformer and connected to the central processing unit through a wireless communication module. The central processing unit can be deployed in the monitoring room of the substation to display the winding status of each transformer in real time through a display screen. When an abnormality occurs in the winding of a certain transformer, the central processing unit will issue an alarm and generate a detailed three-dimensional model for technical personnel to analyze and process. This deployment method not only reduces the overall energy consumption of the system and extends the operating time of the equipment, but also improves the flexibility and coverage of distributed deployment, can accurately identify the deformation status of the winding, and meet the complex needs of transformer winding deformation monitoring.

[0046] In order to better enable relevant personnel in this technical field to fully understand and implement the present invention, the specific implementation principle of the present invention is supplemented below with reference to a specific application scenario.

[0047] In actual substation deployment, micro-sensing units are first installed in key locations on the transformer winding using magnetic fixtures. These fixtures are made of highly permeable materials, and their suction force is precisely calculated and designed to ensure they adhere securely to the transformer's metal casing without disrupting the winding's magnetic field due to excessive magnetic force. The micro-sensing units are installed at the ends, center, and near the terminals of the winding, areas most susceptible to deformation. The spacing between each micro-sensing unit is adjusted to 3 to 5 meters based on signal coverage to avoid excessive signal overlap and data redundancy. This arrangement enables efficient data collection in a distributed system.

[0048] Step 1: The vibration sensor in the micro-sensing unit begins to collect vibration signals from the transformer winding. The sensitivity range of the vibration sensor is set to 0.1 to 10g, the initial sampling frequency is 1kHz, and the resolution is 16 bits. The collected analog signal is converted into a digital signal by an analog-to-digital converter and transmitted to the central processing unit through a low-power Bluetooth communication module. The operating frequency band of the low-power Bluetooth communication module is 2.4GHz, and the communication distance is set to within 10 meters, meeting the requirements of distributed deployment. At the same time, the central processing unit queries the factory parameters of the micro-sensing unit, including sensitivity, sampling rate, and operating voltage range, by matching the unique identifier of the micro-sensing unit, and initializes its configuration. This step ensures that each sensor unit 1 can operate stably and provide accurate initial data.

[0049] Step 2: After the central processing unit receives the vibration signal transmitted by the micro-sensor unit, it uses the fast Fourier transform algorithm to perform spectrum analysis on the signal. The fast Fourier transform formula is

[0050]

[0051] Where X(k) is the kth frequency component in the frequency domain and the signal value corresponding to the nth sampling point in the time domain;

[0052] By extracting the peak position of the spectrum, the characteristic frequency is determined and compared with the standard frequency in a preset database. If the characteristic frequency deviates from a preset threshold, an abnormal state is detected. At this point, the central processing unit sends a command to the microsensor unit in the abnormal area, instructing it to increase the sampling frequency from 1kHz to 5kHz. The sampling frequency adjustment is achieved by changing the clock signal of the analog-to-digital converter, thereby ensuring the accuracy of capturing high-frequency signals.

[0053] Step 3: Under abnormal conditions, the central processing unit uses a multi-channel signal fusion algorithm to process the synchronously collected vibration signals. The multi-channel signal fusion algorithm uses principal component analysis, and the formula is Y = XW;

[0054] Where Y is the signal matrix after dimensionality reduction, X is the original signal matrix, and W is the eigenvector matrix;

[0055] By extracting the key signal features, a 3D model of the winding deformation is generated. This 3D model generation process includes the following steps: first, the relative displacement of each sensor unit 1 is calculated based on the synchronously collected vibration signals; second, the relative displacements are mapped into a 3D coordinate system to form the winding geometry; and finally, the geometry is smoothed using an interpolation algorithm to generate the final 3D model. This step utilizes a multi-channel signal fusion algorithm to achieve accurate modeling of the winding deformation state.

[0056] Step 4: Based on the degree of deformation in the 3D model, the central processing unit selects different signal processing algorithms to further analyze the winding deformation. If the degree of deformation is small, the wavelet transform algorithm is used to denoise the signal. The specific formula is

[0057] Where W(a,b) is the wavelet coefficient, f(t) is the input signal, ψ(t) is the wavelet basis function, a is the scale parameter, and b is the translation parameter;

[0058] The wavelet transform algorithm is mainly used to remove high-frequency noise in the signal and retain the low-frequency characteristics. If the degree of deformation is large, the Kalman filter algorithm is used to perform dynamic compensation processing on the signal. The Kalman filter formula is

[0059] in, is the estimated state, K k is the Kalman gain, z k is the observation value, H is the observation matrix;

[0060] The Kalman filter algorithm improves the stability and accuracy of the signal by dynamically compensating the signal.

[0061] The monitoring area is divided using a weighted clustering algorithm. The specific implementation steps are as follows: First, define the input and output, using the physical location information and signal strength of the microsensor units as input features and the boundary of the monitoring area as the output feature. Second, select a weight coefficient based on the signal strength and coverage of the microsensor units, which is equal to the product of signal strength and coverage. Then, calculate the cluster center, selecting the microsensor unit with the highest signal strength from the input feature set as the initial cluster center. Next, allocate the microsensors, assigning the remaining microsensors according to their distance from the cluster center. Microsensors with distances less than the coverage area are assigned to the same monitoring area. Finally, update the cluster center, recalculating its position based on the allocation results until it no longer changes. Through the weighted clustering algorithm, the monitoring area can be dynamically adjusted based on the actual distribution of the microsensor units, thereby improving the flexibility and coverage of distributed deployment.

[0062] In actual application, the central processing unit is deployed in the substation's monitoring room, where a display screen displays the real-time status of each transformer's windings. When a transformer's windings experience an anomaly, the central processing unit issues an alarm and generates a detailed 3D model for technicians to analyze and address. This deployment approach not only reduces overall system energy consumption and extends equipment uptime, but also improves the flexibility and coverage of distributed deployments. It can accurately identify winding deformation and meet the complex requirements of transformer winding deformation monitoring.

[0063] Any content not described in detail in the specification belongs to the prior art known to those skilled in the art, and the model parameters of each electrical appliance are not specifically limited, and conventional equipment can be used. In this technical solution, electrical control components not mentioned are not shown in the figures because they belong to the prior art and will not be described here.

[0064] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0065] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0066] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0067] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0068] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0069] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0070] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0071] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0072] 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 application, 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 and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0073] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A transformer winding deformation monitoring method based on wireless low-power distributed control is characterized by: include: S1: Multiple micro-sensor units are installed at key locations on the transformer windings. Each micro-sensor unit establishes a connection with the central processing unit via a wireless communication module. The central processing unit initializes and configures each micro-sensor unit and records its physical location information. The coverage range of each micro-sensor unit is calculated based on the physical location information and the signal strength between the micro-sensor units. A weighted clustering algorithm is used to divide the monitoring area based on the coverage range. S2: Within the monitoring area, the vibration signal of the transformer winding is collected by the micro-sensing unit. The vibration signal is transmitted to the central processing unit after analog-to-digital conversion. The central processing unit performs spectrum analysis on the collected vibration signal, extracts the characteristic frequency, and compares it with the preset database to determine whether the current winding status is abnormal; S3: When an abnormality is detected, the central processing unit sends instructions to the micro-sensing unit in the abnormal area, requiring it to increase the sampling frequency and synchronously collect vibration signals. The synchronously collected vibration signals are transmitted to the central processing unit through the wireless communication module. The central processing unit uses a multi-channel signal fusion algorithm to process the synchronous signals and generate a three-dimensional model of the winding deformation; S4: According to the degree of deformation in the three-dimensional model, different signal processing algorithms are selected to further analyze the winding deformation. If the degree of deformation is small, the wavelet transform algorithm is used to denoise the signal. If the degree of deformation is large, the Kalman filter algorithm is used to dynamically compensate the signal.

2. The transformer winding deformation monitoring method based on wireless low-power distributed control according to claim 1 is characterized in that: In step S1, the micro-sensor unit is installed on the surface of the transformer winding through a magnetic fixing device. The magnetic fixing device is made of high magnetic permeability material. The wireless communication module uses a low-power Bluetooth protocol for data transmission. The communication distance is set to within 10 meters. The central processing unit queries its factory parameters, including sensitivity, sampling rate and operating voltage range, by matching the unique identifier of the micro-sensor unit, and stores them in the local database.

3. The transformer winding deformation monitoring method based on wireless low-power distributed control according to claim 1 is characterized in that: In step S1, the coverage range refers to the maximum radius within which the micro-sensor unit can stably receive and transmit signals. The size of the coverage range is affected by the micro-sensor unit's transmission power and environmental interference factors. The specific implementation steps of the weighted clustering algorithm are as follows: Define input and output: Set the physical location information and signal strength of the microsensor unit as input features, and the monitoring area boundary as output features; Select weight coefficient: Calculate the weight coefficient based on the signal strength and coverage of the micro-sensor unit. The weight coefficient is equal to the product of signal strength and coverage. Calculate cluster centers: Select the micro-sensor unit with the highest signal strength in the input feature set as the initial cluster center; Allocate micro-sensor units: Allocate the remaining micro-sensor units according to their distance from the cluster center. Micro-sensor units with a distance smaller than the coverage range are placed in the same monitoring area. Update cluster centers: Recalculate the locations of cluster centers based on the allocation results until the cluster centers no longer change.

4. The method for monitoring transformer winding deformation based on wireless low-power distributed control according to claim 3, characterized in that: In step S2, the acquisition frequency of the vibration signal is set to 1kHz, the resolution of the analog-to-digital converter is 16 bits, the spectrum analysis uses the fast Fourier transform algorithm, the extraction of the characteristic frequency is achieved by calculating the peak position of the spectrum, and the characteristic frequency is compared with the standard frequency in the preset database. When the deviation exceeds the preset threshold, it is determined to be abnormal.

5. The method for monitoring transformer winding deformation based on wireless low-power distributed control according to claim 1, characterized in that: In step S3, after receiving the instruction from the central processing unit, the micro-sensing unit in the abnormal area increases the sampling frequency from 1kHz to 5kHz. The increase in the sampling frequency is achieved by adjusting the clock signal of the analog-to-digital converter. The synchronously collected vibration signals are aligned through timestamp marks. The accuracy of the timestamp is set to microseconds. The multi-channel signal fusion algorithm adopts principal component analysis.

6. The method for monitoring transformer winding deformation based on wireless low-power distributed control according to claim 5, characterized in that: In step S4, a wavelet transform algorithm is used to remove high-frequency noise in the signal.

7. The method for monitoring transformer winding deformation based on wireless low-power distributed control according to claim 6, characterized in that: In step S4, a Kalman filter algorithm is used to perform dynamic compensation on the signal.

8. The method for monitoring transformer winding deformation based on wireless low-power distributed control according to claim 2, characterized in that: The magnetic fixture is disc-shaped and has an anti-slip pad at the bottom. Its adsorption force has been calculated and designed to ensure that it can be firmly adsorbed on the metal casing of the transformer without interfering with the magnetic field environment of the transformer winding due to excessive magnetic force.

9. The transformer winding deformation monitoring method based on wireless low-power distributed control according to claim 1 is characterized in that: The installation locations of the micro-sensor units are selected at key parts of the transformer winding, including the winding ends, the middle and near the terminal blocks. A distance of 3 to 5 meters is maintained between each micro-sensor unit to avoid data redundancy caused by excessive signal overlap.

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