Snow removing and anti-freezing system and method for power transmission line

Abstract

The invention discloses a power transmission line snow removing and anti-freezing system, which comprises an intelligent monitoring subsystem, a data analysis and decision subsystem, a snow removing and anti-freezing execution subsystem and an energy supply subsystem, and is characterized in that the intelligent monitoring subsystem comprises a multi-parameter sensor network module which specifically comprises a temperature sensor, a humidity sensor, a weight sensor and an image sensor; the data acquisition and transmission module specifically adopts a microcontroller as a core and is responsible for collecting data acquired by each sensor; according to the scheme, the problems of low efficiency and complex operation of a traditional anti-freezing method are effectively solved, and all-weather intelligent protection of key infrastructures such as electric power and communication can be realized. The system not only improves the safety and reliability of facilities, but also has the advantages of energy conservation and environmental protection.

Description

Technical Field

[0001] The present invention relates to the technical field, and in particular to a snow removal and antifreeze system for a power transmission line and a method thereof. Background Art

[0002] In cold regions or in winter, power transmission lines often face the problem of snow and ice. When a large amount of snow or ice forms on the transmission lines, the weight of the lines will increase significantly, causing the lines to sag and increase sag, and may even cause serious accidents such as line disconnection and tower tilt, affecting the normal transmission of electricity.

[0003] Traditional methods of snow removal and antifreeze for power transmission lines mainly include manual snow removal and mechanical deicing. Manual snow removal is inefficient and poses a greater safety risk when working at heights; mechanical deicing often requires power outages, affecting the continuity of power supply, and mechanical devices are inconvenient to operate in complex line environments. In addition, some existing snow removal and antifreeze methods based on thermal melting have problems such as high energy consumption and uneven heating. Summary of the invention

[0004] The present invention provides a transmission line snow removal and antifreeze system and method thereof, aiming to improve the antifreeze and snow removal capabilities of the transmission line in an intelligent and automated manner, reduce accidents, and ensure the normal transmission of electricity.

[0005] 1. Intelligent monitoring subsystem

[0006] Multi-parameter sensor network

[0007] Temperature sensor: High-precision platinum resistance temperature sensor is used, and its measurement accuracy can be as high as ±0.1℃, which can accurately sense the slight changes in the ambient temperature around the transmission line. These temperature sensors are evenly distributed in key parts of the transmission line, such as the connection between the tower and the conductor, the suspension point of the conductor, etc., to fully cover the areas where snow and ice may accumulate on the line.

[0008] Humidity sensor: Capacitive humidity sensor is used, which has the characteristics of fast response speed and good stability. Humidity sensor works together with temperature sensor because humidity is one of the key factors affecting snow and ice formation.

[0009] Weight sensor: A special strain gauge weight sensor is installed at the suspension point of the tower, which can monitor the weight change of the transmission line in real time. When the weight increases due to snow or ice accumulation on the line, the weight sensor will immediately detect the change and transmit the data to the data processing center.

[0010] Image sensor: High-definition image sensors are installed at regular intervals on the towers. These image sensors have autofocus and optical zoom functions, and can clearly capture the surface conditions of the transmission lines. Image data is transmitted to the monitoring center via high-speed data transmission lines, and the staff can intuitively judge the degree of snow and ice on the lines through the images.

[0011] Data acquisition and transmission module

[0012] Data collector: It uses a low-power, high-performance microcontroller as the core and is responsible for collecting data collected by various sensors. It can process signals from multiple sensors at the same time and convert these signals into digital signals for subsequent data processing and transmission.

[0013] Wireless transmission unit: Using advanced 5G communication technology, the collected data is transmitted to the data processing center in real time. The 5G network has the characteristics of high speed and low latency, which can ensure the timely transmission of data and enable monitoring personnel to quickly grasp the real-time status of the transmission line.

[0014] (II) Data Analysis and Decision-making Subsystem

[0015] Data Processing Center

[0016] High-performance server: Equipped with a powerful central processor and large-capacity memory, it can quickly process the massive data transmitted from the intelligent monitoring subsystem. The server runs a data analysis algorithm based on deep learning to perform fusion analysis on multi-source data such as temperature, humidity, weight and images.

[0017] Database: Establish a special transmission line monitoring database to store historical data and real-time data. The database adopts a distributed storage architecture to ensure the security and reliability of data. Through the analysis of historical data, a model of snow and ice accumulation on transmission lines under different environmental conditions can be established to provide a reference for subsequent decision-making.

[0018] Intelligent decision-making module

[0019] Snow and ice prediction model based on deep learning: The convolutional neural network (CNN) is used to process image data to automatically identify snow and ice areas on the transmission line. At the same time, a dynamic prediction model for line snow and ice is established through a recurrent neural network (RNN) combined with temperature, humidity and weight data. The model can predict the trend of line snow and ice in the future based on the current environmental data and line status.

[0020] Decision rule engine: According to the results of the prediction model and the preset safety threshold, the corresponding decision rules are formulated. For example, when it is predicted that the thickness of snow on the line exceeds a certain value, the weight of ice reaches a critical value, or the ambient temperature drops sharply, the decision rule engine will automatically trigger snow removal and anti-freeze operations.

[0021] Snow and ice prediction model based on deep learning

[0022] 3.1 Model Architecture The snow and ice prediction model based on deep learning mainly consists of two parts: convolutional neural network (CNN) and recurrent neural network (RNN):

[0023] Convolutional Neural Network (CNN): Used to process image data and automatically identify snow and ice areas on power transmission lines and equipment. By extracting feature information from images through multiple convolutional layers, CNN can effectively analyze the morphology and distribution of snow and ice.

[0024] The formula means:

[0025] [f(x)=\sigma(W*x+b)]

[0026] Among them, (W) is the convolution kernel, (x) is the input image, (b) is the bias, (\sigma) is the activation function, and the commonly used one is the ReLU function.

[0027] Recurrent Neural Network (RNN): It is used to process time series data, such as temperature, humidity, weight and other sensor data, capture the time dependency of data, and predict the trend of snow and ice accumulation in a certain period of time in the future. RNN can infer future environmental changes based on historical data, and combine the image analysis results of CNN to provide a more accurate snow and ice risk assessment.

[0028] RNN expression:

[0029] [h_t=\s igma(W_h\cdot h_{t-1}+W_x\cdot x_t+b)]

[0030] Among them, (h_t) is the hidden state at the current moment, (x_t) is the input at the current moment, (W_h) and (W_x) are the weight matrices of the hidden state and input respectively, and (b) is the bias term.

[0031] 3.2 Data input and processing - Input data: The input data of the model includes real-time collected environmental data (temperature, humidity, wind speed, weight) and line image data.

[0032] Data processing: First, the image data is preprocessed, including image enhancement, noise removal, and scale normalization. Then, the environmental data is normalized to reduce the impact of data of different dimensions. The processed data is sent to the CNN and RNN networks for analysis and prediction.

[0033] 3.3 Output Results The model output includes two main results:

[0034] Snow and ice area detection: Analyze images through CNN and mark the snow and ice areas on the route.

[0035] Prediction of future snow and ice trends: By combining RNN with environmental data, the development trend of ice and snow accumulation in the future is predicted, and important parameters such as snow thickness and ice weight are predicted.

[0036] (III) Snow removal and antifreeze execution subsystem

[0037] Laser snow removal module

[0038] 1.1 High-power semiconductor laser - Laser type: A high-power semiconductor laser with a wavelength of 980nm is selected. This type of laser has been widely verified in industrial applications and has the advantages of small size, high efficiency and easy maintenance.

[0039] Output power: The power range of the laser can be continuously adjusted between 0-1000W, and the specific power value is dynamically adjusted according to the actual snow thickness, ice intensity and environmental conditions.

[0040] Installation method: The laser is installed on a special pan-tilt device, which has 360° rotation and vertical pitch adjustment functions. This ensures that the laser beam can be accurately aimed at different areas on the transmission line and the laser irradiation angle can be automatically adjusted without manual intervention.

[0041] 1.2 Optical focusing system - System composition: It is composed of high-precision convex lenses and reflectors to ensure that the laser beam can be effectively focused and improve energy utilization.

[0042] Spot diameter: Through optical focusing, the spot diameter of the laser beam can be adjusted to 1-5mm. A smaller spot can increase the energy density, thereby improving the effect of snow and ice removal. The focused laser can quickly vaporize the snow or ice.

[0043] Effect: The focused laser can effectively destroy the solid structure of snow and ice, thus removing it from the transmission line. An important advantage of laser snow removal is that it can precisely control the snow removal area to avoid unnecessary thermal damage to the line or surrounding equipment.

[0044] 1.3 Temperature Monitoring and Feedback System - Temperature Monitor: A real-time temperature sensor is installed to monitor the temperature changes in the laser irradiation area to ensure that the snow or ice is effectively melted without causing overheating damage to the transmission line itself.

[0045] Feedback control module: The laser is linked to the temperature monitoring system to adjust the laser output power through feedback control. When the temperature reaches the preset value, the laser automatically reduces the power or stops working to prevent overheating and damage to the circuit material.

[0046] 2. Ultrasonic deicing module The ultrasonic deicing module destroys the adhesion between the ice layer and the transmission line through high-frequency mechanical vibration, causing the ice layer to fall off the line. The module can work effectively in low-temperature environments and will not damage the line structure. Its main components include:

[0047] 2.1 High-power ultrasonic transducer - transducer material: Made of piezoelectric ceramic material, it can generate mechanical vibration after applying high-frequency electrical signals. Piezoelectric ceramic material has high energy conversion efficiency and long service life, suitable for long-term continuous work.

[0048] Working frequency: The working frequency range of ultrasonic transducer is 20-50kHz. In this frequency range, the vibration of ultrasonic wave can effectively penetrate the ice layer and destroy the bonding force between the ice layer and the line surface.

[0049] Transducer arrangement: Multiple transducers are evenly distributed around the transmission line to ensure that the vibration effect covers the entire line. The number and distribution density of transducers can be flexibly adjusted according to the length of the line and the ice accumulation.

[0050] 2.2 Ultrasonic generator - electrical signal generator: The ultrasonic generator provides a stable high-frequency electrical signal to the transducer. The power and frequency of its output can be adjusted in real time according to the thickness and hardness of the ice layer. By adjusting the frequency, the vibration effect can be optimized and the de-icing efficiency can be maximized.

[0051] Intelligent regulation function: The system automatically adjusts the ultrasonic power according to the ice thickness and ambient temperature fed back by the sensor. Thicker ice requires higher power, while thin ice or frost requires lower power to save energy and extend the life of the equipment.

[0052] 2.3 Vibration propagation mechanism - mechanical vibration propagation: The high-frequency vibration generated by the ultrasonic transducer is propagated through the line structure, causing the accumulated ice to vibrate on the conductor and produce tiny cracks, gradually destroying its adhesion and eventually causing the ice layer to detach from the line.

[0053] Ice breaking efficiency: The frequency and power of the ultrasonic wave determine the de-icing efficiency. Under ideal conditions, the ice layer can be effectively peeled off within seconds.

[0054] 3. Electromagnetic heating module The electromagnetic heating module uses high-frequency electromagnetic induction technology to heat the transmission line itself, thereby melting snow and ice. This module has the advantages of uniform heating, high energy efficiency, and no damage to the transmission line.

[0055] 3.1 High-frequency electromagnetic induction coil - coil design: The high-frequency electromagnetic induction coil is wound on the surface of the transmission line. When the high-frequency current passes through the coil, an alternating magnetic field is generated around the line. According to the principle of electromagnetic induction, an induced current is generated inside the wire, causing the wire itself to heat up.

[0056] Heating range: The heating range of electromagnetic induction can cover the entire transmission line, which is particularly suitable for the protection of longer lines. The heating effect can be flexibly controlled by adjusting the coil spacing density and current frequency.

[0057] 3.2 Temperature control and safety mechanism - power regulation device: The electromagnetic heating module is equipped with an intelligent power regulation device, which can dynamically adjust the current frequency and power according to the real-time temperature of the transmission line and external environmental conditions (such as wind speed, temperature, etc.), ensuring heating efficiency while avoiding overheating and damage to the line.

[0058] Overheat protection: When the temperature of the line approaches the preset upper limit, the system will automatically reduce or turn off the heating module to ensure that the line temperature does not exceed the safe range.

[0059] 4. Intelligent Control Unit The intelligent control unit is the core control system of the composite snow removal and antifreeze module, responsible for coordinating and controlling the work of the laser snow removal, ultrasonic deicing and electromagnetic heating modules. Its main components include:

[0060] 4.1 Programmable Logic Controller (PLC)

[0061] Control logic: PLC, as the control core, receives instructions from the deep learning prediction model and triggers corresponding snow removal and anti-icing measures according to the actual situation on the line. PLC has high reliability and flexible programming capabilities, and can formulate complex control strategies based on different input data (such as temperature, humidity, snow thickness, etc.).

[0062] Module coordination: The PLC is responsible for controlling the laser, ultrasonic and electromagnetic heating modules simultaneously to ensure that they work together. For example, when a large amount of snow is detected, the system can start laser snow removal first, and then cooperate with electromagnetic heating to ensure thorough de-icing.

[0063] 4.2 Human-computer interaction interface - operation interface: A human-computer interaction interface is set up at the bottom of the tower or in the remote monitoring center. The staff can view the system status through the touch screen or computer interface and understand the operation status of each module in real time.

[0064] Manual intervention: Operators can manually adjust parameters such as laser power, ultrasonic frequency, electromagnetic heating power, etc. through the interface to deal with special situations.

[0065] 5. Comprehensive Workflow 1. Sensors collect data: Temperature, humidity, weight and other sensors deployed in the system continuously collect the status information of the line and transmit the data to the intelligent control unit.

[0066] Deep learning prediction: Based on real-time data, the deep learning model predicts the possibility of snow or ice accumulation and sends operational instructions to the intelligent control unit.

[0067] Laser snow removal: When it is detected that the snow accumulation exceeds the threshold, the laser snow removal module is activated, using high-power laser to accurately melt or vaporize the snow.

[0068] Ultrasonic deicing: If the ice layer is thick, the ultrasonic deicing module is activated to destroy the adhesion of the ice layer through high-frequency mechanical vibration to ensure that the ice layer falls off the line.

[0069] Electromagnetic heating: In extremely low temperatures or large areas of snow accumulation, the electromagnetic heating module assists in snow and ice removal. Through induction heating, the wire itself generates heat energy to melt the snow.

[0070] Feedback and regulation: The system continuously monitors the temperature changes of the line and adjusts the working status and power output of each module according to real-time data to ensure safe operation of the line.

[0071] Summary The composite snow removal and antifreeze module integrates multiple advanced technologies such as laser snow removal, ultrasonic deicing and electromagnetic heating to achieve all-weather antifreeze and snow removal protection for transmission lines. The system can not only adapt to various extreme weather conditions and ensure the stable operation of the power grid, but also has the characteristics of intelligent control and adaptive adjustment, which improves the efficiency and energy efficiency of snow removal and anti-icing, and reduces the cost of manual intervention. It is an important technological innovation in the field of modern power transmission.

[0072] Electromagnetic heating: In extremely cold environments, the electromagnetic heating module continuously transmits high-frequency current to the conductors, causing the lines themselves to heat up and thus melt the snow and ice.

[0073] Real-time monitoring and adjustment: The intelligent control unit continuously monitors the temperature changes and working status of the line, and dynamically adjusts the operating parameters of each module to achieve the best snow removal and antifreeze effect

[0074] (IV) Energy supply subsystem

[0075] Hybrid Energy System

[0076] Solar photovoltaic panels: Install high-efficiency solar photovoltaic panels on the sunny side of the tower to convert solar energy into electrical energy. Photovoltaic panels are made of monocrystalline silicon material, which has the characteristics of high conversion efficiency and good stability. According to the height of the tower and the lighting conditions, the installation area and angle of the photovoltaic panels are reasonably designed to ensure the maximum reception of solar energy.

[0077] Small wind turbines: Install a small wind turbine on the top of the tower to generate electricity using wind energy. The blades of the wind turbine are made of lightweight and high-strength composite materials, which have good wind energy capture capabilities. By complementing with solar photovoltaic panels, it can ensure a stable power supply for the snow removal and antifreeze system under different weather conditions.

[0078] Energy storage battery: High-performance lithium-ion batteries are used as energy storage devices to store excess electricity generated by solar and wind power for use at night or in dark and windless conditions. The capacity of the energy storage battery is designed based on the power requirements of the snow removal and antifreeze system and the stability of energy supply.

[0079] Specific steps and methods:

[0080] (I) Data collection and transmission

[0081] The multi-parameter sensor network in the intelligent monitoring subsystem collects data such as temperature, humidity, weight and image of the transmission line in real time.

[0082] The data collector digitizes this data and transmits it to the data processing center in real time through a 5G wireless transmission unit.

[0083] 2. Data Analysis and Decision Making

[0084] After receiving the data, the high-performance server of the data processing center uses a deep learning-based snow and icing prediction model to analyze and process the data to predict the trend of snow and icing on the lines.

[0085] The decision rule engine determines whether it is necessary to start the snow removal and antifreeze operation based on the prediction results and the preset safety threshold. If necessary, the corresponding control instructions will be generated and sent to the snow removal and antifreeze execution subsystem.

[0086] (III) Snow removal and anti-freeze implementation

[0087] After receiving the control instructions, the intelligent control unit (PLC) of the snow removal and antifreeze execution subsystem controls the operation of the laser snow removal module, the ultrasonic deicing module and the electromagnetic heating module according to the instructions.

[0088] Laser snow removal module: According to the thickness and hardness of the snow on the line, the output power of the high-power semiconductor laser and the focal length of the optical focusing system are adjusted so that the laser beam can accurately irradiate the snow area and melt the snow quickly.

[0089] Ultrasonic deicing module: According to the thickness and hardness of the ice layer, the operating frequency of the high-power ultrasonic transducer and the output power of the ultrasonic generator are adjusted to generate high-frequency mechanical vibrations to make the ice fall off the wire.

[0090] Electromagnetic heating module: According to the temperature and environmental conditions of the line, adjust the input power of the high-frequency electromagnetic induction coil to keep the line within the appropriate temperature range to prevent snow and ice accumulation.

[0091] During the snow removal and antifreeze operation, the intelligent control unit monitors the operating status of each snow removal and antifreeze device in real time and feeds back the status information to the data processing center.

[0092] 4. Energy supply and management

[0093] The solar photovoltaic panels and small wind turbines of the energy supply subsystem convert solar and wind energy into electrical energy and store it in energy storage batteries.

[0094] The energy storage battery provides a stable power supply for the entire snow removal and antifreeze system. In the case of insufficient energy supply, the system will automatically adjust the operating parameters of the snow removal and antifreeze device to reduce power consumption and ensure the stable operation of the system.

[0095] The present invention provides a snow removal and antifreeze system for power transmission lines and a method thereof. By using a deep learning prediction model and decision-making control technology, the system effectively solves the problems of low efficiency and complex operation of traditional antifreeze methods, and can realize all-weather intelligent protection of key infrastructure such as electricity and communications. The system not only improves the safety and reliability of the facilities, but also has the advantages of energy saving and environmental protection, which is of great significance to improving the stability of social and economic operations. The model realizes efficient monitoring and prediction of snow accumulation and ice formation on power transmission lines. Through the automated decision-making control module, the system can take protective measures at the first time to ensure the safe operation of the facilities, reducing the cost of manual intervention and the impact on the environment. DETAILED DESCRIPTION

[0096] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0097] Those skilled in the art should understand that these implementations are only used to explain the technical principles of the present invention, and are not intended to limit the protection scope of the present invention.

[0098] 1. Intelligent monitoring subsystem

[0099] Multi-parameter sensor network

[0100] Temperature sensor: High-precision platinum resistance temperature sensor is used, and its measurement accuracy can be as high as ±0.1℃, which can accurately sense the slight changes in the ambient temperature around the transmission line. These temperature sensors are evenly distributed in key parts of the transmission line, such as the connection between the tower and the conductor, the suspension point of the conductor, etc., to fully cover the areas where snow and ice may accumulate on the line.

[0101] Humidity sensor: Capacitive humidity sensor is used, which has the characteristics of fast response speed and good stability. Humidity sensor works together with temperature sensor because humidity is one of the key factors affecting snow and ice formation.

[0102] Weight sensor: A special strain gauge weight sensor is installed at the suspension point of the tower, which can monitor the weight change of the transmission line in real time. When the weight increases due to snow or ice accumulation on the line, the weight sensor will immediately detect the change and transmit the data to the data processing center.

[0103] Image sensor: High-definition image sensors are installed at regular intervals on the towers. These image sensors have autofocus and optical zoom functions, and can clearly capture the surface conditions of the transmission lines. Image data is transmitted to the monitoring center via high-speed data transmission lines, and the staff can intuitively judge the degree of snow and ice on the lines through the images.

[0104] Data acquisition and transmission module

[0105] Data collector: It uses a low-power, high-performance microcontroller as the core and is responsible for collecting data collected by various sensors. It can process signals from multiple sensors at the same time and convert these signals into digital signals for subsequent data processing and transmission.

[0106] Wireless transmission unit: Using advanced 5G communication technology, the collected data is transmitted to the data processing center in real time. The 5G network has the characteristics of high speed and low latency, which can ensure the timely transmission of data and enable monitoring personnel to quickly grasp the real-time status of the transmission line.

[0107] (II) Data Analysis and Decision-making Subsystem

[0108] Data Processing Center

[0109] High-performance server: Equipped with a powerful central processor and large-capacity memory, it can quickly process the massive data transmitted from the intelligent monitoring subsystem. The server runs a data analysis algorithm based on deep learning to perform fusion analysis on multi-source data such as temperature, humidity, weight and images.

[0110] Database: Establish a special transmission line monitoring database to store historical data and real-time data. The database adopts a distributed storage architecture to ensure the security and reliability of data. Through the analysis of historical data, a model of snow and ice accumulation on transmission lines under different environmental conditions can be established to provide a reference for subsequent decision-making.

[0111] Intelligent decision-making module

[0112] Snow and ice prediction model based on deep learning: The convolutional neural network (CNN) is used to process image data to automatically identify snow and ice areas on the transmission line. At the same time, a dynamic prediction model for line snow and ice is established through a recurrent neural network (RNN) combined with temperature, humidity and weight data. The model can predict the trend of line snow and ice in the future based on the current environmental data and line status.

[0113] Decision rule engine: According to the results of the prediction model and the preset safety threshold, the corresponding decision rules are formulated. For example, when it is predicted that the thickness of snow on the line exceeds a certain value, the weight of ice reaches a critical value, or the ambient temperature drops sharply, the decision rule engine will automatically trigger snow removal and anti-freeze operations.

[0114] Snow and ice prediction model based on deep learning

[0115] 3.1 Model Architecture The snow and ice prediction model based on deep learning mainly consists of two parts: convolutional neural network (CNN) and recurrent neural network (RNN):

[0116] Convolutional Neural Network (CNN): Used to process image data and automatically identify snow and ice areas on power transmission lines and equipment. By extracting feature information from images through multiple convolutional layers, CNN can effectively analyze the morphology and distribution of snow and ice.

[0117] The formula means:

[0118] [f(x)=\sigma(W*x+b)]

[0119] Among them, (W) is the convolution kernel, (x) is the input image, (b) is the bias, (\sigma) is the activation function, and the commonly used one is the ReLU function.

[0120] Recurrent Neural Network (RNN): It is used to process time series data, such as temperature, humidity, weight and other sensor data, capture the time dependency of data, and predict the trend of snow and ice accumulation in a certain period of time in the future. RNN can infer future environmental changes based on historical data, and combine the image analysis results of CNN to provide a more accurate snow and ice risk assessment.

[0121] RNN expression:

[0122] [h_t=\s igma(W_h\cdot h_{t-1}+W_x\cdot x_t+b)]

[0123] Among them, (h_t) is the hidden state at the current moment, (x_t) is the input at the current moment, (W_h) and (W_x) are the weight matrices of the hidden state and input respectively, and (b) is the bias term.

[0124] 3.2 Data input and processing - Input data: The input data of the model includes real-time collected environmental data (temperature, humidity, wind speed, weight) and line image data.

[0125] Data processing: First, the image data is preprocessed, including image enhancement, noise removal, and scale normalization. Then, the environmental data is normalized to reduce the impact of data of different dimensions. The processed data is sent to the CNN and RNN networks for analysis and prediction.

[0126] 3.3 Output Results The model output includes two main results:

[0127] Snow and ice area detection: Analyze images through CNN and mark the snow and ice areas on the route.

[0128] Prediction of future snow and ice trends: By combining RNN with environmental data, the development trend of ice and snow accumulation in the future is predicted, and important parameters such as snow thickness and ice weight are predicted.

[0129] (III) Snow removal and antifreeze execution subsystem

[0130] Laser snow removal module

[0131] 1.1 High-power semiconductor laser - Laser type: A high-power semiconductor laser with a wavelength of 980nm is selected. This type of laser has been widely verified in industrial applications and has the advantages of small size, high efficiency and easy maintenance.

[0132] Output power: The power range of the laser can be continuously adjusted between 0-1000W, and the specific power value is dynamically adjusted according to the actual snow thickness, ice intensity and environmental conditions.

[0133] Installation method: The laser is installed on a special pan-tilt device, which has 360° rotation and vertical pitch adjustment functions. This ensures that the laser beam can be accurately aimed at different areas on the transmission line and the laser irradiation angle can be automatically adjusted without manual intervention.

[0134] 1.2 Optical focusing system - System composition: It is composed of high-precision convex lenses and reflectors to ensure that the laser beam can be effectively focused and improve energy utilization.

[0135] Spot diameter: Through optical focusing, the spot diameter of the laser beam can be adjusted to 1-5mm. A smaller spot can increase the energy density, thereby improving the effect of snow and ice removal. The focused laser can quickly vaporize the snow or ice.

[0136] Effect: The focused laser can effectively destroy the solid structure of snow and ice, thus removing it from the transmission line. An important advantage of laser snow removal is that it can precisely control the snow removal area to avoid unnecessary thermal damage to the line or surrounding equipment.

[0137] 1.3 Temperature Monitoring and Feedback System - Temperature Monitor: A real-time temperature sensor is installed to monitor the temperature changes in the laser irradiation area to ensure that the snow or ice is effectively melted without causing overheating damage to the transmission line itself.

[0138] Feedback control module: The laser is linked to the temperature monitoring system to adjust the laser output power through feedback control. When the temperature reaches the preset value, the laser automatically reduces the power or stops working to prevent overheating and damage to the circuit material.

[0139] 2. Ultrasonic deicing module The ultrasonic deicing module destroys the adhesion between the ice layer and the transmission line through high-frequency mechanical vibration, causing the ice layer to fall off the line. The module can work effectively in low-temperature environments and will not damage the line structure. Its main components include:

[0140] 2.1 High-power ultrasonic transducer - transducer material: Made of piezoelectric ceramic material, it can generate mechanical vibration after applying high-frequency electrical signals. Piezoelectric ceramic material has high energy conversion efficiency and long service life, suitable for long-term continuous work.

[0141] Working frequency: The working frequency range of ultrasonic transducer is 20-50kHz. In this frequency range, the vibration of ultrasonic wave can effectively penetrate the ice layer and destroy the bonding force between the ice layer and the line surface.

[0142] Transducer arrangement: Multiple transducers are evenly distributed around the transmission line to ensure that the vibration effect covers the entire line. The number and distribution density of transducers can be flexibly adjusted according to the length of the line and the ice accumulation.

[0143] 2.2 Ultrasonic generator - electrical signal generator: The ultrasonic generator provides a stable high-frequency electrical signal to the transducer, and its output power and frequency can be adjusted in real time according to the thickness and hardness of the ice layer. By adjusting the frequency, the vibration effect can be optimized and the de-icing efficiency can be maximized.

[0144] Intelligent regulation function: The system automatically adjusts the ultrasonic power according to the ice thickness and ambient temperature fed back by the sensor. Thicker ice requires higher power, while thin ice or frost requires lower power to save energy and extend the life of the equipment.

[0145] 2.3 Vibration propagation mechanism - mechanical vibration propagation: The high-frequency vibration generated by the ultrasonic transducer is propagated through the line structure, causing the accumulated ice to vibrate on the conductor and produce tiny cracks, gradually destroying its adhesion and eventually causing the ice layer to detach from the line.

[0146] Ice breaking efficiency: The frequency and power of the ultrasonic wave determine the de-icing efficiency. Under ideal conditions, the ice layer can be effectively peeled off within seconds.

[0147] 3. Electromagnetic heating module The electromagnetic heating module uses high-frequency electromagnetic induction technology to heat the transmission line itself, thereby melting snow and ice. This module has the advantages of uniform heating, high energy efficiency, and no damage to the transmission line.

[0148] 3.1 High-frequency electromagnetic induction coil - coil design: The high-frequency electromagnetic induction coil is wound on the surface of the transmission line. When the high-frequency current passes through the coil, an alternating magnetic field is generated around the line. According to the principle of electromagnetic induction, an induced current is generated inside the wire, causing the wire itself to heat up.

[0149] Heating range: The heating range of electromagnetic induction can cover the entire transmission line, which is particularly suitable for the protection of longer lines. The heating effect can be flexibly controlled by adjusting the coil spacing density and current frequency.

[0150] 3.2 Temperature control and safety mechanism - power regulation device: The electromagnetic heating module is equipped with an intelligent power regulation device, which can dynamically adjust the current frequency and power according to the real-time temperature of the transmission line and external environmental conditions (such as wind speed, temperature, etc.), ensuring heating efficiency while avoiding overheating and damage to the line.

[0151] Overheat protection: When the temperature of the line approaches the preset upper limit, the system will automatically reduce or turn off the heating module to ensure that the line temperature does not exceed the safe range.

[0152] 4. Intelligent Control Unit The intelligent control unit is the core control system of the composite snow removal and antifreeze module, responsible for coordinating and controlling the work of the laser snow removal, ultrasonic deicing and electromagnetic heating modules. Its main components include:

[0153] 4.1 Programmable Logic Controller (PLC)

[0154] Control logic: PLC, as the control core, receives instructions from the deep learning prediction model and triggers corresponding snow removal and anti-icing measures according to the actual situation on the line. PLC has high reliability and flexible programming capabilities, and can formulate complex control strategies based on different input data (such as temperature, humidity, snow thickness, etc.).

[0155] Module coordination: The PLC is responsible for controlling the laser, ultrasonic and electromagnetic heating modules simultaneously to ensure that they work together. For example, when a large amount of snow is detected, the system can start laser snow removal first, and then cooperate with electromagnetic heating to ensure thorough de-icing.

[0156] 4.2 Human-computer interaction interface - operation interface: A human-computer interaction interface is set up at the bottom of the tower or in the remote monitoring center. The staff can view the system status through the touch screen or computer interface and understand the operation status of each module in real time.

[0157] Manual intervention: Operators can manually adjust parameters such as laser power, ultrasonic frequency, electromagnetic heating power, etc. through the interface to deal with special situations.

[0158] 5. Comprehensive Workflow 1. Sensors collect data: Temperature, humidity, weight and other sensors deployed in the system continuously collect the status information of the line and transmit the data to the intelligent control unit.

[0159] Deep learning prediction: Based on real-time data, the deep learning model predicts the possibility of snow or ice accumulation and sends operational instructions to the intelligent control unit.

[0160] Laser snow removal: When it is detected that the snow accumulation exceeds the threshold, the laser snow removal module is activated, using high-power laser to accurately melt or vaporize the snow.

[0161] Ultrasonic deicing: If the ice layer is thick, the ultrasonic deicing module is activated to destroy the adhesion of the ice layer through high-frequency mechanical vibration to ensure that the ice layer falls off the line.

[0162] Electromagnetic heating: In extremely low temperatures or large areas of snow accumulation, the electromagnetic heating module assists in snow and ice removal. Through induction heating, the wire itself generates heat energy to melt the snow.

[0163] Feedback and regulation: The system continuously monitors the temperature changes of the line and adjusts the working status and power output of each module according to real-time data to ensure safe operation of the line.

[0164] Summary The composite snow removal and antifreeze module integrates multiple advanced technologies such as laser snow removal, ultrasonic deicing and electromagnetic heating to achieve all-weather antifreeze and snow removal protection for transmission lines. The system can not only adapt to various extreme weather conditions and ensure the stable operation of the power grid, but also has the characteristics of intelligent control and adaptive adjustment, which improves the efficiency and energy efficiency of snow removal and anti-icing, and reduces the cost of manual intervention. It is an important technological innovation in the field of modern power transmission.

[0165] Electromagnetic heating: In extremely cold environments, the electromagnetic heating module continuously transmits high-frequency current to the conductors, causing the lines themselves to heat up and thus melt the snow and ice.

[0166] Real-time monitoring and adjustment: The intelligent control unit continuously monitors the temperature changes and working status of the line, and dynamically adjusts the operating parameters of each module to achieve the best snow removal and antifreeze effect

[0167] (IV) Energy supply subsystem

[0168] Hybrid Energy System

[0169] Solar photovoltaic panels: Install high-efficiency solar photovoltaic panels on the sunny side of the tower to convert solar energy into electrical energy. Photovoltaic panels are made of monocrystalline silicon material, which has the characteristics of high conversion efficiency and good stability. According to the height of the tower and the lighting conditions, the installation area and angle of the photovoltaic panels are reasonably designed to ensure the maximum reception of solar energy.

[0170] Small wind turbines: Install a small wind turbine on the top of the tower to generate electricity using wind energy. The blades of the wind turbine are made of lightweight and high-strength composite materials, which have good wind energy capture capabilities. By complementing with solar photovoltaic panels, it can ensure a stable power supply for the snow removal and antifreeze system under different weather conditions.

[0171] Energy storage battery: High-performance lithium-ion batteries are used as energy storage devices to store excess electricity generated by solar and wind power for use at night or in dark and windless conditions. The capacity of the energy storage battery is designed based on the power requirements of the snow removal and antifreeze system and the stability of energy supply.

[0172] Specific steps and methods:

[0173] (I) Data collection and transmission

[0174] The multi-parameter sensor network in the intelligent monitoring subsystem collects data such as temperature, humidity, weight and image of the transmission line in real time.

[0175] The data collector digitizes this data and transmits it to the data processing center in real time through a 5G wireless transmission unit.

[0176] 2. Data Analysis and Decision Making

[0177] After receiving the data, the high-performance server of the data processing center uses a deep learning-based snow and icing prediction model to analyze and process the data to predict the trend of snow and icing on the lines.

[0178] The decision rule engine determines whether it is necessary to start the snow removal and antifreeze operation based on the prediction results and the preset safety threshold. If necessary, the corresponding control instructions will be generated and sent to the snow removal and antifreeze execution subsystem.

[0179] (III) Snow removal and anti-freeze implementation

[0180] After receiving the control instructions, the intelligent control unit (PLC) of the snow removal and antifreeze execution subsystem controls the operation of the laser snow removal module, the ultrasonic deicing module and the electromagnetic heating module according to the instructions.

[0181] Laser snow removal module: According to the thickness and hardness of the snow on the line, the output power of the high-power semiconductor laser and the focal length of the optical focusing system are adjusted so that the laser beam can accurately irradiate the snow area and melt the snow quickly.

[0182] Ultrasonic deicing module: According to the thickness and hardness of the ice layer, the operating frequency of the high-power ultrasonic transducer and the output power of the ultrasonic generator are adjusted to generate high-frequency mechanical vibrations to make the ice fall off the wire.

[0183] Electromagnetic heating module: According to the temperature and environmental conditions of the line, adjust the input power of the high-frequency electromagnetic induction coil to keep the line within the appropriate temperature range to prevent snow and ice accumulation.

[0184] During the snow removal and antifreeze operation, the intelligent control unit monitors the operating status of each snow removal and antifreeze device in real time and feeds back the status information to the data processing center.

[0185] 4. Energy supply and management

[0186] The solar photovoltaic panels and small wind turbines of the energy supply subsystem convert solar and wind energy into electrical energy and store it in energy storage batteries.

[0187] The energy storage battery provides a stable power supply for the entire snow removal and antifreeze system. In the case of insufficient energy supply, the system will automatically adjust the operating parameters of the snow removal and antifreeze device to reduce power consumption and ensure the stable operation of the system.

[0188] The present invention provides a snow removal and antifreeze system for power transmission lines and a method thereof. By using a deep learning prediction model and decision-making control technology, the system effectively solves the problems of low efficiency and complex operation of traditional antifreeze methods, and can realize all-weather intelligent protection of key infrastructure such as electricity and communications. The system not only improves the safety and reliability of the facilities, but also has the advantages of energy saving and environmental protection, which is of great significance to improving the stability of social and economic operations. The model realizes efficient monitoring and prediction of snow accumulation and ice formation on power transmission lines. Through the automated decision-making control module, the system can take protective measures at the first time to ensure the safe operation of the facilities, reducing the cost of manual intervention and the impact on the environment.

[0189] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments, but it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A snow removal and antifreeze system for power transmission lines, characterized in that: It includes intelligent monitoring subsystem, data analysis and decision-making subsystem, snow removal and antifreeze execution subsystem and energy supply subsystem. The intelligent monitoring subsystem includes: Multi-parameter sensor network module, including temperature sensor, humidity sensor, weight sensor, and image sensor; The data acquisition and transmission module uses a microcontroller as the core and is responsible for collecting the data collected by each sensor; Wireless transmission unit: Through 5G communication transmission, the collected data is transmitted to the data processing center in real time; The data analysis and decision-making subsystem includes: A data processing center, specifically including a high-performance server, for processing the data transmitted by the intelligent monitoring subsystem and performing fusion analysis on multi-source data such as temperature, humidity, weight and image; Database, specifically used to store historical data and real-time data; Intelligent decision-making module, including: Snow and ice prediction model based on deep learning: The convolutional neural network (CNN) module is used to process image data to automatically identify snow and ice areas on the transmission line. At the same time, combined with temperature, humidity and weight data, a dynamic prediction model for line snow and ice is established through the recurrent neural network (RNN) module. The model architecture of the snow and ice prediction model based on deep learning mainly includes two parts: the convolutional neural network (CNN) module and the recurrent neural network (RNN) module; Decision rule engine: formulates corresponding decision rules based on the results of the prediction model and the preset safety threshold; The snow removal and antifreeze execution subsystem includes: Laser snow removal module, which uses high-power semiconductor lasers to ensure that the laser beam can be accurately aimed at different areas on the transmission line; Optical focusing system, including high-precision convex lenses and reflectors, to ensure that the laser beam can be effectively focused; Temperature monitoring and feedback system, specifically using a temperature monitor; Feedback control module: The laser is linked with the temperature monitoring system to adjust the laser output power through feedback control; Ultrasonic deicing module The ultrasonic deicing module destroys the adhesion between the ice layer and the transmission line through high-frequency mechanical vibration, causing the ice layer to fall off the line. Electromagnetic heating module The electromagnetic heating module uses high-frequency electromagnetic induction technology to heat the transmission line itself, thereby melting snow and ice; Intelligent control unit: The intelligent control unit is the core control system of the composite snow removal and antifreeze module, responsible for coordinating and controlling the work of laser snow removal, ultrasonic deicing and electromagnetic heating modules; The energy supply subsystem comprises: A hybrid energy system consisting of photovoltaic solar panels, small wind turbines and batteries for energy storage.

2. A snow removal and antifreeze system for power transmission lines according to claim 1, characterized in that: The temperature sensor adopts a platinum resistance temperature sensor, and its measurement accuracy can be as high as ±0.1°C; The humidity sensor is a capacitive humidity sensor and works in conjunction with the temperature sensor; The weight sensor is used to monitor the weight change of the power transmission line in real time and transmit the data to the data processing center. The image sensor is used to capture the surface condition of the power transmission line and transmit the image data to the monitoring center via a high-speed data transmission line.

3. A snow removal and antifreeze system for power transmission lines according to claim 1, characterized in that: The convolutional neural network (CNN) module is used to process image data and automatically identify snow and ice areas on power transmission lines and equipment; the specific expression is: [f(x)=\sigma(W*x+b)] Among them, (W) is the convolution kernel, (x) is the input image, (b) is the bias, (\sigma) is the activation function, and the commonly used one is the ReLU function; Recurrent Neural Network (RNN): It is used to process time series data, such as temperature, humidity, weight and other sensor data, capture the time dependency of data, and predict the trend of snow and ice accumulation in the future. The specific expression is: [h_t=\sigma(W_h\cdot h_{t-1}+W_x\cdot x_t+b)] Among them, (h_t) is the hidden state at the current moment, (x_t) is the input at the current moment, (W_h) and (W_x) are the weight matrices of the hidden state and input respectively, and (b) is the bias term; Data input and processing - input data: The input data of the model includes real-time collected environmental data and line image data. Data processing: First, the image data is preprocessed, including image enhancement, noise removal, and scale normalization. Then, the environmental data is normalized to reduce the impact of data of different dimensions. The processed data is sent to the CNN and RNN networks for analysis and prediction. Output Results The model output includes two main results: Snow and ice area detection: CNN is used to analyze images and mark snow and ice areas on the route. Future snow and ice trend prediction: RNN is used to combine environmental data to predict the development trend of ice and snow in the future, and provide predictions of important parameters such as snow thickness and ice weight.

4. A snow removal and antifreeze system for power transmission lines according to claim 1, characterized in that: The ultrasonic deicing module, the ultrasonic deicing module, its main components include: High-power ultrasonic transducer, made of piezoelectric ceramic material, can generate mechanical vibration after applying high-frequency electrical signal; Working frequency: The working frequency range of ultrasonic transducer is 20-50kHz; Transducer arrangement: Multiple transducers are evenly distributed around the transmission line to ensure that the vibration effect covers the entire line; Ultrasonic generator, the ultrasonic generator provides a stable high-frequency electrical signal for the transducer.

5. The snow removal and antifreeze system for power transmission lines according to claim 1, characterized in that: The main components of the intelligent control unit include: A programmable logic controller that receives instructions from the deep learning prediction model and triggers corresponding snow removal and anti-icing measures based on the actual situation on the line; Human-computer interaction interface: A human-computer interaction interface is set up at the bottom of the tower or in the remote monitoring center, and the staff can view the system status through the touch screen or computer interface; Manual intervention: The operator can manually adjust the laser power, ultrasonic frequency, and electromagnetic heating power parameters through the interface.

6. A method for snow removal and antifreezing of power transmission lines, characterized in that: The specific steps are as follows:

1. Data collection and transmission The multi-parameter sensor network in the intelligent monitoring subsystem collects data such as temperature, humidity, weight and image of the transmission line in real time; The data collector digitizes the data and transmits it to the data processing center in real time through the 5G wireless transmission unit; 2. Data Analysis and Decision Making After receiving the data, the high-performance server of the data processing center uses the snow and icing prediction model based on deep learning to analyze and process the data and predict the trend of snow and icing on the line; The decision rule engine determines whether it is necessary to start the snow removal and antifreeze operation based on the prediction results and the preset safety threshold. If necessary, the corresponding control instructions will be generated and sent to the snow removal and antifreeze execution subsystem; (III) Snow removal and anti-freeze implementation After receiving the control instruction, the intelligent control unit (PLC) of the snow removal and antifreeze execution subsystem controls the operation of the laser snow removal module, the ultrasonic deicing module and the electromagnetic heating module according to the instruction; Laser snow removal module: According to the thickness and hardness of the snow on the line, the output power of the high-power semiconductor laser and the focal length of the optical focusing system are adjusted so that the laser beam can accurately irradiate the snow area and melt the snow quickly; Ultrasonic deicing module: According to the thickness and hardness of the ice layer, the operating frequency of the high-power ultrasonic transducer and the output power of the ultrasonic generator are adjusted to generate high-frequency mechanical vibrations to make the ice layer fall off the wire; Electromagnetic heating module: adjust the input power of the high-frequency electromagnetic induction coil according to the line temperature and environmental conditions to keep the line within a suitable temperature range to prevent snow and ice accumulation; During the snow removal and antifreeze operation, the intelligent control unit monitors the operating status of each snow removal and antifreeze device in real time and feeds back the status information to the data processing center; 4. Energy supply and management The solar photovoltaic panels and small wind turbines of the energy supply subsystem convert solar and wind energy into electrical energy and store it in energy storage batteries; The energy storage battery provides a stable power supply for the entire snow removal and antifreeze system.