A friction-electromagnetic-piezoelectric hybrid self-powered aeolian vibration identification and suppression system for power transmission lines

Abstract

This invention relates to a triboelectric-electromagnetic-piezoelectric hybrid self-powered wind vibration identification and suppression system for power transmission lines, belonging to the field of power transmission line monitoring and vibration reduction technology. The system includes an electromagnetic power generation unit, a triboelectric power generation unit, and a piezoelectric power generation unit integrated within a housing. The piezoelectric power generation unit is constructed as a damper structure with a mass block and elastic elements, used to simultaneously achieve energy recovery and vibration suppression under vibration excitation. An energy management circuit is connected to each power generation unit for rectifying, storing, and managing electrical energy. A vibration state identification module is connected to the triboelectric power generation unit, acquiring its output voltage signal, and using a pre-deployed convolutional neural network model to extract features and recognize patterns from the signal, outputting vibration state classification and graded early warning information for the power transmission line. This invention integrates energy harvesting, vibration sensing, and vibration suppression functions, utilizing deep learning to intelligently identify complex wind vibration patterns, and has advantages such as high integration, high identification accuracy, and vibration reduction capabilities.

Description

Technical Field

[0001] This invention belongs to the field of power transmission line monitoring and vibration reduction technology, and relates to a triboelectric-electromagnetic-piezoelectric hybrid self-powered wind vibration identification and suppression system for power transmission lines. Background Technology

[0002] With the rapid development of the power Internet of Things, real-time intelligent sensing of transmission line conditions has become a necessary condition for ensuring the safe and stable operation of the power grid. Overhead high-voltage transmission lines are susceptible to wind vibration under wind loads, characterized by high frequency (typically 5~40Hz) and small amplitude (typically 1~6 mm). Long-term exposure to such vibration can lead to conductor fatigue, strand breakage, and even tower collapse. Currently, vibration sensors based on acceleration, inertia, and optical principles have limitations such as power supply difficulties, high cost, and poor environmental adaptability, making them unsuitable for large-scale and long-term distributed monitoring.

[0003] To address the power supply challenge, self-powered technologies utilizing environmental mechanical energy have become a research hotspot. Currently, environmental energy harvesting mainly focuses on three methods: triboelectric nanogenerators (TENGs), electromagnetic generators (EMGs), and piezoelectric generators (PEGs). Among them, TENGs have advantages such as flexible structure, high output voltage, and high sensitivity to light wind vibrations; EMGs can provide high output current and a wide frequency response range, making them suitable for continuous power supply; PEGs have a simple structure, high output voltage, and the piezoelectric material itself can act as a structural damper during energy harvesting, showing potential for integrating energy harvesting and vibration suppression. Coupling two or three of these methods is considered an effective way to improve the reliability and stability of power supply capabilities.

[0004] In the prior art, some solutions combining the above technologies have emerged. For example, Chinese patent CN118554652B discloses a transmission line monitoring device combining electromagnetic and triboelectric technologies, realizing vibration energy harvesting and self-powered sensing. Chinese patent CN120638895A discloses a vibration energy harvesting device combining piezoelectric and triboelectric technologies. However, the prior art still has the following limitations: First, at the energy harvesting level, single or dual power generation methods are insufficient to cover the wide frequency range of transmission line vibrations, and cannot take into account the complementary advantages of high voltage output (TENG), high current output (EMG), and structural damping (PEG). Energy harvesting efficiency and power supply stability need to be improved.

[0005] Secondly, at the vibration sensing level, most studies only establish a simple mapping between basic vibration parameters and peak output electrical signals, failing to delve into the multidimensional dynamic electrical signal characteristics excited by vibration, making it difficult to identify complex vibration modes. Although some studies involve multi-sensor data fusion, they employ traditional algorithms such as weighted least squares and fuzzy comprehensive evaluation, resulting in limited recognition accuracy and intelligence.

[0006] Furthermore, at the system level, energy harvesting, state sensing, and vibration control are often studied in isolation, lacking collaborative design and integrated optimization. In particular, piezoelectric materials can act as structural dampers in the energy harvesting process, possessing the potential to integrate energy harvesting and vibration suppression, but current technologies have failed to fully utilize this property.

[0007] Therefore, how to provide a transmission line wind vibration monitoring system that integrates efficient broadband energy harvesting, high-precision intelligent identification and adaptive vibration reduction, and realize a closed-loop system covering the entire chain from energy supply to state perception and active protection, has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0008] In view of this, the present invention aims to solve the technical problems of existing transmission line wind vibration monitoring systems, such as limited functionality, low integration, inability to achieve integrated vibration suppression and energy harvesting, and insufficient accuracy in identifying complex vibration modes.

[0009] To achieve the above objectives, this invention provides a triboelectric-electromagnetic-piezoelectric hybrid self-powered wind vibration identification and suppression system for power transmission lines. The system includes an electromagnetic power generation unit, a triboelectric power generation unit, and a piezoelectric power generation unit, all integrated within a single housing. This system converts the vibration energy of the power transmission line into electrical energy. The piezoelectric power generation unit is constructed as a damper structure with a mass block and an elastic element, used to deform under vibration excitation, generating both electrical energy and dissipating vibration energy, thus integrating energy recovery and vibration suppression. An energy management circuit, connected to the output terminals of the electromagnetic, triboelectric, and piezoelectric power generation units, rectifies, stores, and manages the electrical energy generated by these units, providing a stable operating voltage for subsequent circuits. A vibration state identification module, connected to the output terminal of the triboelectric power generation unit, collects the voltage signal output by the triboelectric power generation unit and identifies the vibration state of the power transmission line based on the voltage signal.

[0010] Preferably, the piezoelectric power generation unit is a "vibration damper-like" structure, including at least one elastic beam, at least one piezoelectric element fixed on the elastic beam, and a mass block disposed at the end of the elastic beam; the elastic beam bends and deforms under vibration excitation, causing the piezoelectric element to deform, thereby generating electrical energy. Further, the elastic beam is a spring steel rod, and the piezoelectric element is symmetrically attached to the surface of the spring steel rod.

[0011] Preferably, the electromagnetic power generation unit includes a fixed part and a movable part. A magnet is provided on the fixed part, and a coil is provided on the movable part. The movable part reciprocates relative to the fixed part under vibration excitation, so that the coil cuts the magnetic field lines to generate electrical energy.

[0012] Preferably, the triboelectric power generation unit is a vertical contact-separation triboelectric nanogenerator, comprising multiple alternating electrode layers and triboelectric material layers, used to generate electrical energy through contact-separation motion between layers under vibration excitation.

[0013] Preferably, the vibration state identification module includes a microcontroller with a pre-trained convolutional neural network model deployed therein for feature extraction and pattern recognition of the voltage signal output by the triboelectric generator unit, to output a classification result of the vibration state. Further, the vibration state identification module also includes a signal processing unit connected between the triboelectric generator unit and the microcontroller, for filtering and amplitude adjustment of the voltage signal; the microcontroller is also used to execute a preset hierarchical early warning strategy based on the classification result of the vibration state.

[0014] Preferably, the energy management circuit includes rectifier circuits corresponding to the output terminals of the electromagnetic power generation unit, the triboelectric power generation unit, and the piezoelectric power generation unit, respectively, as well as a common energy storage element and voltage conversion circuit; wherein, the rectifier circuit corresponding to the triboelectric power generation unit is a full-bridge rectifier circuit, and its output terminal is connected in series with a thyristor and a diode to stabilize the trigger voltage.

[0015] Preferably, the electromagnetic power generation unit, the triboelectric power generation unit, and the piezoelectric power generation unit are stacked sequentially from top to bottom within the encapsulation housing.

[0016] Preferably, the system further includes a wireless communication module connected to the microcontroller, used to send vibration status identification results and early warning information to an external monitoring platform.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) This invention deeply integrates the three major functions of energy harvesting, vibration sensing and vibration suppression into one system. In particular, the piezoelectric power generation unit is constructed as a "vibration damper-like" structure, which enables it to recover energy through the piezoelectric effect under vibration excitation and dissipate vibration energy through the inertial damping effect of the mass block. While collecting energy, it effectively suppresses conductor vibration and extends the line life.

[0018] (2) This invention applies convolutional neural networks to the high-dimensional dynamic signal processing of the output of the triboelectric power generation unit, explores the deep mapping relationship between it and the vibration state, realizes the fine classification and high-precision identification of complex wind vibration modes, and provides a reliable basis for scientific early warning and operation and maintenance decision-making.

[0019] (3) This invention achieves efficient collection and management of wide-band, low-amplitude vibration energy by complementing three power generation units: triboelectric power generation unit provides high voltage output, electromagnetic power generation unit provides high current output, and piezoelectric power generation unit supplements energy in the high-frequency band. Combined with the design of a dedicated energy management circuit, this invention ensures long-term and stable self-powered operation of the system.

[0020] (4) The present invention integrates three power generation units, energy management circuit and identification module into a compact package housing, which is ingenious and adopts a top-to-bottom stacked spatial layout, which is ingenious and convenient for installation and deployment on power transmission lines.

[0021] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0022] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein: Figure 1 This is a schematic diagram of a triboelectric-electromagnetic-piezoelectric composite energy harvesting device for wind vibration identification and adaptive vibration suppression in outdoor power transmission line scenarios; wherein, 1-top fixed resin plate, 2-fixed connecting rod, 3-movable resin plate, 4-bottom fixed resin plate, 5-cylindrical magnet, 6-electromagnetic induction coil, 7-triboelectric power generation unit, 8-mass block, 9-piezoelectric sheet. Figure 2 This is a schematic diagram of the energy management circuit and vibration signal processing module in an embodiment of the present invention; wherein, LTC3588 is an energy management chip, BR1 and BR2 are full-bridge rectifiers, SCR is a silicon controlled rectifier, C1 is an energy storage capacitor, high-pass filter is a component of the signal processing unit, passive attenuation network is a component of the signal processing unit, zero-crossing comparator is a component of the signal processing unit, and CC2340R5 is a microcontroller. Figure 3 This shows the open-circuit voltage variation trend of the triboelectric power generation unit in the 0-50Hz frequency range after material optimization in the embodiments of the present invention. Figure 4 This is the short-circuit current variation trend of the electromagnetic power generation unit in the 0-50Hz frequency range after material optimization in the embodiment of the present invention. Figure 5 This shows the open-circuit voltage variation trend of the piezoelectric power generation unit in the embodiment of the present invention after material optimization in the frequency range of 0-50Hz; Figure 6 This is a comparison diagram of the amplitude attenuation of the conductor in the 0-50Hz frequency range under the same vibration excitation and without piezoelectric vibration reduction measures in an embodiment of the present invention. Figure 7 This is a flowchart of the micro-wind vibration state assessment in an embodiment of the present invention; Figure 8 This is a schematic diagram of the structure of the convolutional neural network model in an embodiment of the present invention; Figure 9 This is a confusion matrix diagram of the recognition results of the CNN model under nine vibration states in an embodiment of the present invention; Figure 10 This is a graph showing the changes in accuracy and loss values ​​of the CNN model on the training and validation sets in an embodiment of the present invention. Figure 11 This is a schematic diagram of the host computer interface for real-time display of vibration status classification and graded early warning in an embodiment of the present invention. Detailed Implementation

[0023] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0024] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0025] This embodiment provides a triboelectric-electromagnetic-piezoelectric hybrid self-powered wind vibration identification and suppression system for power transmission lines.

[0026] like Figure 1 As shown, the system mainly includes a cylindrical insulated and sealed housing that is installed above the transmission line via a snap-fit ​​insulation structure. Inside the housing, from top to bottom, are arranged an electromagnetic power generation unit (EMG), a triboelectric power generation unit (TENG), and a piezoelectric power generation unit (PEG), which work together to achieve energy harvesting, vibration sensing, and vibration reduction functions.

[0027] 1. An electromagnetic power generation unit (EMG) is located at the top of the housing. Specifically, it includes a top fixed resin plate 1 fixed to the inner wall of the housing, and a movable resin plate 3 located below it and connected to the bottom structure via a guide spring. The top fixed resin plate 1 is fixedly connected to the housing via a fixed connecting rod 2 to ensure its stability during vibration. Four cylindrical magnets 5 are uniformly embedded on the lower surface of the top fixed resin plate 1. Four annular coils 6 are uniformly embedded at corresponding positions on the upper surface of the movable resin plate 3. When the power transmission line experiences wind vibration, the vibration energy is transmitted through the housing, causing the movable resin plate 3 to drive the electromagnetic induction coils 6 to reciprocate vertically relative to the fixed cylindrical magnets 5. Based on Faraday's law of electromagnetic induction, the magnetic flux in the electromagnetic induction coils 6 changes, thereby generating an induced current. This induced current is led out through a wire as the electrical energy output of the electromagnetic power generation unit. The guide spring is used to provide a restoring force for the movable resin plate 3 and guide its direction of movement.

[0028] 2. The triboelectric generator unit (TENG) adopts a vertical contact-separation mode and consists of multiple vertically arranged hollow ring structures. Each basic unit includes a polytetrafluoroethylene (PTFE) film as the negative electrode friction material and copper foil as the positive electrode material and electrode. Multiple elastic Kapton films serve as elastic support layers, separating the copper foil and PTFE film layers to form a multi-layer stacked structure. When the transmission line vibration is transmitted to the triboelectric generator unit 7, the layers periodically contact and separate under the action of inertial force. Based on the principles of triboelectric charging and electrostatic induction, when the PTFE film contacts the copper foil, equal and opposite charges are generated on the surface; upon separation, the electrostatic balance is disrupted, generating a potential difference that drives electrons to flow through the external circuit, forming an alternating current. This current signal serves as the electrical energy output of the triboelectric generator unit 7, while its voltage / current waveform carries rich vibration information.

[0029] 3. The piezoelectric power generation unit (PEG) is symmetrically installed on both sides of the snap-fit ​​insulation structure, forming an overall "vibration damper-like" structure. This "vibration damper-like" structure refers to its mechanical appearance, which is similar to the vibration dampers commonly used in power transmission lines. It has a cantilever beam and end mass blocks, enabling it to generate a vibration phase opposite to that of the conductor under vibration excitation, thereby dissipating vibration energy. Furthermore, this invention integrates a piezoelectric element onto the elastic element, enabling energy recovery while reducing vibration.

[0030] Specifically, the device includes a spring steel rod with mass blocks 8 fixed to both ends. The spring steel rod passes through a fixed resin plate 4 at the bottom, forming a cantilever beam structure. Piezoelectric sheets 9, such as lead zirconate titanate (PZT) piezoelectric ceramics, are attached to the upper and lower surfaces of the spring steel rod. The inertia of the mass blocks 8 makes the piezoelectric power generation unit a dynamic vibration absorber. When the transmission line vibrates, the vibration phase of the piezoelectric power generation unit is opposite to that of the conductor, thereby generating an opposing force to counteract the conductor's vibration and achieve vibration suppression. Simultaneously, the periodic bending deformation of the spring steel rod forces the piezoelectric sheets 9 attached to it to deform. Based on the positive piezoelectric effect, polarization is generated inside the piezoelectric sheet 9, forming an alternating voltage between its upper and lower electrodes, achieving energy recovery. The piezoelectric power generation unit integrates vibration reduction and power generation.

[0031] Optionally, the elastic element can also be made of other metal materials with good elasticity, such as beryllium bronze, to adjust the stiffness and damping characteristics of the system. The piezoelectric element is not limited to PZT ceramic; it can also be a piezoelectric single-crystal PMN-PT or a piezoelectric polymer PVDF to adapt to different frequency responses and power generation requirements. The position and number of the mass blocks 8 can also be adjusted. For example, in addition to setting mass blocks at the ends, a mass block can be added in the middle of the spring steel rod to form a multi-degree-of-freedom vibration absorber, thereby widening the frequency range of vibration suppression and improving adaptability to complex wind vibrations. In some embodiments, the piezoelectric power generation unit can adopt a double cantilever beam symmetrical structure, that is, an independent cantilever beam-mass block-piezoelectric sheet assembly is set on each side of the snap-fit ​​insulation structure to enhance the overall stability and power generation capacity of the system.

[0032] 4. Energy management circuits, such as Figure 2 As shown, the energy management circuit is designed based on the LTC3588 chip and includes three rectifier channels and one energy storage capacitor. The output terminals of the electromagnetic generator (EMG) (EMG AC+ / EMG AC-), the triboelectric generator (TENG) (TENG), and the piezoelectric generator (PEG) (PZ1 / PZ2) are connected to the circuit. Since the TENG and PEG outputs are high-voltage AC, their output terminals are first rectified by external full-bridge rectifiers BR1 and BR2 before being connected to the circuit. The TENG channel has a thyristor SCR and a diode connected in series to stabilize the trigger voltage and improve energy transfer efficiency. The EMG output is low-voltage AC and is directly connected to the low-power full-bridge rectifier built into the LTC3588 chip. The rectified energy is input from the VIN pin of the LTC3588 and stored in the energy storage capacitor C1 (110μF). The circuit operates in an undervoltage lockout mode with ultra-low quiescent current. When the voltage on C1 accumulates to a preset threshold, the buck converter inside the chip starts up and converts the stored energy from the VOUT pin into a stable 3.3V DC voltage output to continuously power the vibration state recognition module in the subsequent stage.

[0033] To further improve energy harvesting efficiency, in some implementations, the EMG output can first pass through an external voltage doubler rectifier circuit before being connected to the LTC3588 chip, thereby enhancing energy extraction capabilities under low-amplitude vibrations. The energy storage capacitor C1 can be a supercapacitor instead of a conventional electrolytic capacitor to improve energy storage density and cycle life.

[0034] 5. Vibration state identification module, including a signal processing unit and an embedded microcontroller (MCU) (CC2340R5 chip). The raw time-domain voltage signal output by TENG first enters the signal processing unit. This unit consists of a high-pass filter, a zero-crossing comparator, and a passive attenuation network, used to filter out low-frequency noise, shape the AC signal into a square wave for frequency counting by the MCU, and adjust the high-voltage signal to the voltage range (0-3V) that the MCU's ADC can acquire.

[0035] The processed signal is sent to the MCU. A pre-trained convolutional neural network (CNN) model is deployed in the MCU. Its workflow is as follows: Figure 7 As shown: Step 1, Signal Acquisition and Preprocessing: The MCU acquires the TENG signal at a fixed sampling rate.

[0036] Step 2, Time-Frequency Conversion: Perform a Fast Fourier Transform (FFT) on the acquired time-domain signal to convert it into a frequency-amplitude distribution in the frequency domain, extract vibration frequency and intensity features, and reduce computational complexity.

[0037] Step 3: CNN Model Recognition: Input the preprocessed features, such as the FFT spectrum or the time-frequency graph obtained through short-time Fourier transform, into the CNN model. The CNN model structure used in this embodiment is as follows: Figure 8 As shown, the system mainly consists of two convolutional-pooling pairs and two fully connected layers. The first convolutional layer uses 16 3×3 convolutional kernels with ReLU activation, followed by 2×2 max pooling. The second convolutional layer uses 32 3×3 convolutional kernels with ReLU activation, followed by 2×2 max pooling as well. After two pooling operations, the resulting two-dimensional feature map is flattened into a one-dimensional feature vector. A batch normalization layer is introduced after each convolutional and fully connected layer. Finally, a softmax classifier is used to output the classification result of the vibration state.

[0038] Step 4, State Classification and Early Warning: The CNN model was trained to classify vibration states into nine categories. These nine categories are based on a combination of vibration frequency and amplitude: 5-20 Hz is classified as low frequency, 20-40 Hz as mid frequency, and above 40 Hz as high frequency; simultaneously, amplitudes less than 0.1 times the conductor diameter are defined as small amplitude, 0.1-0.2 times as medium amplitude, and greater than 0.2 times as large amplitude. This results in nine vibration states: low-frequency small amplitude, low-frequency medium amplitude, low-frequency large amplitude, mid-frequency small amplitude, mid-frequency medium amplitude, mid-frequency large amplitude, high-frequency small amplitude, high-frequency medium amplitude, and high-frequency large amplitude.

[0039] Step 5, Graded Early Warning: Based on the identified vibration state, the MCU executes a four-level early warning strategy: Normal state (low frequency, small amplitude), Level I warning (low frequency, medium amplitude / medium frequency, small amplitude), Level II warning (low frequency, large amplitude), Level III warning (medium frequency, medium amplitude), and Level IV warning (medium frequency, large amplitude). Warning information can be wirelessly transmitted to the monitoring platform via the MCU's integrated low-power Bluetooth module, such as... Figure 11 The host computer interface shown.

[0040] In other implementations, the input to the CNN model can also be the original time-domain waveform of the TENG signal. Feature extraction and classification can be performed directly through a one-dimensional convolutional neural network (1D-CNN), avoiding information loss caused by time-frequency conversion. The network structure can also be optimized, for example, by increasing the number of convolutional layers, using convolutional kernels of different sizes (such as 5×5), or introducing residual connections or attention mechanisms to pursue higher recognition accuracy or lower computational complexity. The granularity of vibration state classification is not limited to nine levels; it can be further refined or coarser depending on actual monitoring needs, for example, distinguishing only three levels: normal, warning, and danger.

[0041] The experimental results after material optimization are as follows: (1) Material optimization for triboelectric power generation unit: PTFE film thickness is 50 μm, and copper foil thickness is 30 μm. (See test results). Figure 3 With an amplitude of 4mm and an excitation frequency of 18Hz, the open-circuit voltage Voc can reach 275V.

[0042] (2) Material optimization for the electromagnetic power generation unit: The cylindrical magnet 5 uses neodymium iron boron (NdFeB) N52, and the electromagnetic induction coil 6 uses enameled wire with a diameter of 0.1mm and 1000 turns. (See test results.) Figure 4 Under 4mm amplitude and 18Hz excitation, the short-circuit current Isc can reach 25.7mA.

[0043] (3) Material optimization for the piezoelectric power generation unit: The piezoelectric element 9 is PZT-5H, with dimensions of 50mm × 20mm × 0.5mm. The spring steel rod is made of 65Mn spring steel with a thickness of 1mm. The mass block 8 has a mass of 50g. (See test results). Figure 5 With an amplitude of 4mm and an excitation frequency of 18Hz, the open-circuit voltage Voc can reach 62.5V.

[0044] (4) Vibration reduction effect test: such as Figure 6 As shown, in the 0-50Hz sweep frequency test, the amplitude of the conductor of the system equipped with the piezoelectric generator unit is attenuated throughout the entire frequency band, with the amplitude attenuation reaching the maximum of 92% at the 18Hz resonant point.

[0045] Furthermore, the CNN model was trained using a frequency range of 5-60 Hz and an amplitude range of 0-8 mm, with step sizes of 1 Hz and 0.5 mm. 100 vibration excitation experiments were conducted for each of the nine vibration states, and raw voltage signals were collected. The raw signals were then subjected to differentiation, normalization, and spectral subtraction for noise reduction. A time-frequency graph was generated using short-time Fourier transform, resulting in a dataset of 9000 samples, which was divided into training, validation, and test sets in a 7:2:1 ratio. The CNN model was built using the TensorFlow framework, with an initial learning rate of 0.001, 100 iterations, a batch size of 32, and optimized using the cross-entropy loss function. After training, the model achieved a recognition accuracy of 98.5% on the test set, and its confusion matrix is ​​shown below. Figure 9 As shown. Figure 10 The model demonstrates stable convergence of accuracy and loss values ​​during training and validation, proving its stability and generalization ability.

[0046] In summary, this invention integrates energy harvesting, vibration identification, and adaptive vibration reduction functions, solving the problem of functional separation in existing technologies. Through the collaborative work of the three modules, it achieves a full-chain solution from energy supply to condition monitoring and active protection.

[0047] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A power line oriented friction-electromagnetic-piezoelectric hybrid self-powered aeolian vibration identification and suppression system, characterized in that, include: Electromagnetic power generation unit, triboelectric power generation unit and piezoelectric power generation unit are integrated into a single package to convert the vibration energy of transmission lines into electrical energy. The piezoelectric power generation unit is constructed as a damper structure with a mass block and elastic elements, which is used to generate deformation under vibration excitation, thereby generating electrical energy and dissipating vibration energy, realizing the integration of energy recovery and vibration suppression. An energy management circuit is connected to the output terminals of the electromagnetic power generation unit, the triboelectric power generation unit, and the piezoelectric power generation unit, respectively, and is used to rectify, store, and manage the electrical energy generated by the three units, and to provide a stable operating voltage for subsequent circuits. The vibration state identification module is connected to the output terminal of the triboelectric generator unit and is used to collect the voltage signal output by the triboelectric generator unit and identify the vibration state of the transmission line based on the voltage signal.

2. The triboelectric-electromagnetic-piezoelectric hybrid self-powered wind vibration identification and suppression system according to claim 1, characterized in that, The piezoelectric power generation unit is a "vibration damper-like" structure, including at least one elastic beam, at least one piezoelectric element fixed on the elastic beam, and a mass block disposed at the end of the elastic beam; the elastic beam bends and deforms under vibration excitation, causing the piezoelectric element to deform, thereby generating electrical energy.

3. The triboelectric-electromagnetic-piezoelectric hybrid self-powered wind vibration identification and suppression system according to claim 2, characterized in that, The elastic beam is a spring steel rod, and the piezoelectric elements are symmetrically attached to the surface of the spring steel rod.

4. The triboelectric-electromagnetic-piezoelectric hybrid self-powered wind vibration identification and suppression system according to claim 1, characterized in that, The electromagnetic power generation unit includes a fixed part and a movable part. A magnet is provided on the fixed part, and a coil is provided on the movable part. The movable part reciprocates relative to the fixed part under vibration excitation, causing the coil to cut magnetic field lines to generate electrical energy.

5. The triboelectric-electromagnetic-piezoelectric hybrid self-powered wind vibration identification and suppression system according to claim 1, characterized in that, The triboelectric power generation unit is a vertical contact-separation triboelectric nanogenerator, comprising multiple alternating electrode layers and triboelectric material layers, used to generate electrical energy through contact-separation motion between layers under vibration excitation.

6. The triboelectric-electromagnetic-piezoelectric hybrid self-powered wind vibration identification and suppression system according to claim 1, characterized in that, The vibration state recognition module includes a microcontroller, which is equipped with a pre-trained convolutional neural network model for feature extraction and pattern recognition of the voltage signal output by the triboelectric power generation unit, so as to output the classification result of the vibration state.

7. The triboelectric-electromagnetic-piezoelectric hybrid self-powered wind vibration identification and suppression system according to claim 6, characterized in that, The vibration state identification module also includes a signal processing unit connected between the triboelectric generator and the microcontroller, which is used to filter and adjust the amplitude of the voltage signal; the microcontroller is also used to execute a preset graded early warning strategy based on the classification result of the vibration state.

8. The triboelectric-electromagnetic-piezoelectric hybrid self-powered wind vibration identification and suppression system according to claim 1, characterized in that, The energy management circuit includes rectifier circuits corresponding to the output terminals of the electromagnetic power generation unit, the triboelectric power generation unit, and the piezoelectric power generation unit, respectively, as well as a common energy storage element and voltage conversion circuit; wherein, the rectifier circuit corresponding to the triboelectric power generation unit is a full-bridge rectifier circuit, and its output terminal is connected in series with a thyristor and a diode to stabilize the trigger voltage.

9. The triboelectric-electromagnetic-piezoelectric hybrid self-powered wind vibration identification and suppression system according to claim 1, characterized in that, The electromagnetic power generation unit, the triboelectric power generation unit, and the piezoelectric power generation unit are stacked sequentially from top to bottom within the encapsulation housing.

10. The triboelectric-electromagnetic-piezoelectric hybrid self-powered wind vibration identification and suppression system according to claim 6, characterized in that, The system also includes a wireless communication module connected to the microcontroller, used to send vibration status identification results and early warning information to an external monitoring platform.

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