Solar self-powered transmission line galloping feature acquisition and cloud early warning integrated system

Abstract

The application discloses a solar self-powered transmission line galloping feature collection and cloud early warning integrated system, relates to the technical field of transmission line monitoring and solar power supply, and comprises a solar power generation module, a multi-source sensor module, an edge calculation module, an energy storage and power management module, a wireless communication module, a cloud early warning platform and a self-adaptive energy efficiency management module; an electric energy output end of the solar power generation module is connected with the energy storage and power management module through a charge-discharge management circuit, and solar energy is converted into electric energy for storage; the application establishes a two-dimensional coupling decision matrix of energy supply and demand balance index and galloping active coefficient, synchronously incorporates energy abundance and monitoring demand intensity into energy efficiency management decision, actively reserves energy when energy is abundant and galloping risk is low, maintains necessary monitoring capability when energy is tight and galloping risk is high, and realizes overall optimal balance of monitoring quality and system endurance.

Description

Technical Field

[0001] This invention relates to the field of power transmission line monitoring and solar power supply technology, specifically to an integrated system for collecting and cloud-based early warning of galloping characteristics of solar-powered transmission lines. Background Technology

[0002] Conductor galloping is a large-amplitude, low-frequency vibration phenomenon that occurs in high-voltage transmission lines under the combined effects of icing and wind. In severe cases, it can lead to accidents such as phase-to-phase short circuits, hardware breakage, and even tower collapse, posing a significant threat to the safe operation of the power grid. Real-time monitoring and early warning of conductor galloping are important technical means to ensure the safe operation of the power grid.

[0003] Existing transmission line galloping monitoring systems are typically deployed in the field, far from power infrastructure, making power supply a core challenge in system design. Traditional solutions rely on current transformers for power or large-capacity battery packs that are periodically replaced. Current transformer power supply cannot provide stable power under light or no-load conditions, while large-capacity battery pack solutions face issues such as high maintenance costs and difficulties in disposing of used batteries. Solar power, due to its cleanliness and ease of deployment, is gradually becoming the mainstream power supply solution for field monitoring terminals.

[0004] However, solar power supply is constrained by weather conditions, exhibiting significant intermittency and fluctuation. During prolonged periods of cloudy and rainy weather, solar power generation drops sharply, potentially causing monitoring terminals to run out of power and resulting in data interruption. Existing solar-powered monitoring terminals generally employ fixed sampling periods and fixed communication strategies, failing to dynamically adjust power consumption based on weather conditions and battery status. This inefficient management leads to two problems: first, insufficient battery life during prolonged cloudy and rainy weather results in data loss after system crashes; second, the long-term adoption of conservative sampling strategies to ensure equipment endurance leads to the loss of crucial monitoring data during periods of high humidity.

[0005] The existing solution has a deeper problem: the level of motion activity and the energy supply status are two independent dimensions, but the current system only adjusts the power consumption strategy unidirectionally based on the battery status, ignoring the differentiated impact of motion activity on monitoring needs. When the battery has sufficient energy but the motion activity is low, the system still maintains high-power sampling, wasting energy; when the battery is low but the motion activity is high, the system reduces the sampling frequency due to insufficient energy, losing data precisely at the moment when monitoring is most needed. The coupling relationship between these two dimensions is not effectively utilized, causing the system to fail to achieve a globally optimal balance between monitoring quality and energy consumption.

[0006] Existing solutions lack the ability to coordinate the perception and prediction of meteorological conditions and battery state of charge, and cannot achieve the optimal balance between monitoring quality and system reliability under limited energy conditions. Therefore, this application proposes an integrated system for collecting the galloping characteristics of solar-powered transmission lines and cloud-based early warning to overcome the above-mentioned deficiencies. Summary of the Invention

[0007] To address the aforementioned technical issues, an integrated system for collecting and cloud-based early warning of the galloping characteristics of solar-powered transmission lines is provided. This technical solution solves the problem of lacking the ability to coordinate the perception and prediction of meteorological conditions and battery state of charge, thus failing to achieve the optimal balance between monitoring quality and system reliability under limited energy conditions.

[0008] To achieve the above objectives, the technical solution adopted by this invention is: an integrated system for collecting and cloud-based early warning of the galloping characteristics of solar-powered transmission lines, comprising a solar power generation module, a multi-source sensor module, an edge computing module, an energy storage and power management module, a wireless communication module, a cloud-based early warning platform, and an adaptive energy efficiency management module; The power output terminal of the solar power generation module is connected to the energy storage and power management module through a charge and discharge management circuit to convert solar energy into electrical energy for storage. The multi-source sensor module is installed on the power transmission line or tower to collect the conductor vibration acceleration signal, angular velocity signal and micro-meteorological environment data. Its data output terminal is connected to the input terminal of the edge computing module. The edge computing module performs attitude calculation and dance feature parameter extraction on the collected raw data, and its data output end is connected to the wireless communication module. The power output terminal of the energy storage and power management module is connected to the power input terminal of each module to provide the operating voltage for the system. The wireless communication module interacts with the cloud-based early warning platform via the NB-IoT network; The adaptive energy efficiency management module is connected to each module and is used to calculate the dancing activity coefficient and the energy supply and demand balance index. Using these two as two-dimensional inputs, the module determines the sampling frequency level, computing load level and communication upload strategy based on the two-dimensional decision matrix, and sends them to each module for execution.

[0009] Preferably, the solar power generation module includes a solar photovoltaic panel, a maximum power point tracking controller, and a voltage and current sensor; The maximum power point tracking controller adopts a Boost topology and achieves maximum power point tracking through the perturbation-observation method. The voltage and current sensors collect the output voltage and output current, and transmit them to the adaptive energy efficiency management module for power generation status monitoring.

[0010] Preferably, the multi-source sensor module includes a triaxial accelerometer, a triaxial gyroscope, a triaxial magnetometer, and a micro-meteorological sensor; The triaxial accelerometer is installed on the power transmission line, and the sampling rate is dynamically adjusted by the adaptive energy efficiency management module. The three-axis gyroscope sensor acquires the angular velocity signal of the wire twisting; A triaxial magnetometer sensor assists in attitude calculation to eliminate accumulated drift error from the gyroscope; The wind speed, ambient temperature, and humidity data collected by the micro-meteorological sensor are transmitted to the adaptive energy efficiency management module for calculating the dancing activity coefficient.

[0011] Preferably, the energy storage and power management module includes a lithium battery pack and a charge / discharge management circuit; The charge and discharge management circuit adopts a Buck-Boost bidirectional DC / DC topology, controls bidirectional energy conversion of charge and discharge through PWM signal, collects battery voltage, current and temperature data in real time, estimates battery state of charge by combining ampere-hour integration method and open circuit voltage method, and calibrates the accumulated error by open circuit voltage method each time the system is powered on. The current state of charge is transmitted in real time to the adaptive energy efficiency management module as an input parameter for calculating the energy supply and demand balance index.

[0012] Preferably, the adaptive energy efficiency management module includes a meteorological forecasting submodule, an energy supply and demand analysis submodule, and a hierarchical sampling communication control submodule, which together form a serial decision-making link; The weather forecasting submodule obtains hourly light intensity forecasts for the next N hours, where N ranges from 24 to 72. The energy supply and demand analysis submodule calculates the total power generation forecast for the next N hours based on the predicted light intensity. The ratio of the current available energy of the lithium battery pack to the sum of the total power generation forecast to the total energy consumption demand is used as the energy supply and demand balance index K. At the same time, the galloping activity coefficient β is calculated by weighted normalization combination based on wind speed, icing thickness and ambient temperature. The hierarchical sampling communication control submodule takes K and β as two-dimensional inputs, determines the sampling frequency level, computing load level and communication upload strategy according to the two-dimensional decision matrix, and sends them to the multi-source sensor module, the edge computing module and the wireless communication module for execution.

[0013] Preferably, the energy supply and demand balance index K is divided into three levels: K not less than 1.2 is the energy surplus level, K not less than 0.8 and less than 1.2 is the energy balance level, and K less than 0.8 is the energy shortage level; the dancing activity coefficient Set low activity threshold and high activity threshold They are divided into low-activity, medium-activity, and high-activity levels. The value range is from 0.2 to 0.3. The value range is from 0.5 to 0.7; The hierarchical sampling communication control submodule determines the sampling frequency, the computational load factor λ, and the ratio of the communication upload frequency to the reference upload frequency according to the following rules: When in the energy-sufficient range, the sampling frequency follows... From low activity level to high activity level, the sampling frequency increases from normal frequency to maximum sampling frequency, λ increases from 0.5 to 1.0, and the communication ratio increases from 40% to 100%. When in energy balance mode, the sampling frequency follows From low activity level to high activity level, the sampling frequency increases from 50% of the normal sampling frequency to 80% of the maximum sampling frequency, λ increases from 0.2 to 1.0, and the communication ratio increases from 50% to 100%. When in energy-stress mode When the activity level is low or medium, the sampling frequency is the lowest sampling frequency, λ is 0.2, and the communication ratio is 30%. For high-activity levels, the sampling frequency is set to the lowest sampling frequency, λ is increased to 0.5, and the communication ratio is increased to 50%. The maximum sampling frequency is 500Hz to 1000Hz, the normal sampling frequency is 100Hz to 500Hz, and the minimum sampling frequency is 10Hz to 100Hz. When λ is 1.0, all algorithms are executed; when λ is 0.5, only FFT spectrum analysis is executed; and when λ is 0.2, only the threshold comparison function is retained.

[0014] Preferably, the dancing activity coefficient It is composed of a linear combination of three weighted components: wind speed, ice thickness, and temperature. The wind speed component is the ratio of real-time wind speed to a wind speed reference value, where the wind speed reference value is 10 m / s to 15 m / s. The ice thickness component is the ratio of the estimated ice thickness to the critical ice thickness, where the critical ice thickness is 5 mm to 10 mm. The temperature component is the ratio of the difference between the ice melting temperature reference value and the ambient temperature to the ice melting temperature reference value, which is 5°C to 15°C. The sum of the weighting coefficients of the three components is 1.

[0015] Preferably, the emergency trigger threshold is dynamically adjusted according to the activity coefficient β: when β is at a low activity level, the emergency trigger threshold is 80% of the preset safety threshold; when β is at a medium activity level, it is 70% of the preset safety threshold; and when β is at a high activity level, it is 60% of the preset safety threshold. When the edge computing module detects that the amplitude of the conductor galloping exceeds the current emergency trigger threshold, regardless of the current energy efficiency mode of the system, the multi-source sensor module is forced to sample at the maximum sampling frequency, the edge computing module operates at full load, the alarm data is immediately uploaded through the highest priority channel, and at the same time, the linkage device is triggered to perform video recording or photo taking through RFID broadcast alarm signal.

[0016] Preferably, the wireless communication module adopts a hybrid communication architecture of NB-IoT and RFID: Remote data interaction is performed with the cloud-based early warning platform via the NB-IoT network. PSM and eDRX low-power modes are supported. The DRX cycle length is dynamically adjusted by the hierarchical sampling communication control submodule according to the dancing activity coefficient β. The higher the β, the shorter the DRX cycle and the higher the upload frequency. Near-field communication is established between the 2.4G active RFID and the linkage device near the power transmission line. When abnormal galloping is detected, an alarm signal is broadcast, triggering the visual terminal of the linkage device to record video or take pictures.

[0017] Preferably, the cloud-based early warning platform receives and stores the galloping monitoring data and micro-meteorological data uploaded by each monitoring terminal, performs galloping early warning analysis, performs spatial correlation analysis of galloping trends based on data from multiple monitoring terminals, and generates a galloping risk assessment report by combining line parameters such as conductor type, span, elevation difference, and tension. The richness of key feature parameters in the uploaded data depends on the energy efficiency level determined by the current energy supply and demand balance index K and the dancing activity coefficient β.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention establishes an energy supply and demand balance index. With dance activity coefficient The two-dimensional coupled decision matrix integrates energy adequacy and monitoring demand intensity into energy efficiency management decisions, solving the problem of disconnect between energy management and monitoring needs in existing technologies. The system proactively stores energy when energy is abundant but the risk of energy fluctuation is low, and maintains necessary monitoring capabilities when energy is scarce but the risk of energy fluctuation is high, achieving a globally optimal balance between monitoring quality and system endurance.

[0019] Introducing a dance activity coefficient As the second dimension of input for energy efficiency management decisions, the activity level of wind eclipse is quantified by comprehensively considering three meteorological parameters: wind speed, icing thickness, and ambient temperature. It is directly related to the sampling frequency, computing load, and communication strategy, so that the system power consumption strategy is dynamically adjusted according to the risk of turbulence, ensuring that critical data is not lost during the high incidence of turbulence and effectively reducing energy consumption during the stable weather period.

[0020] Emergency mode trigger threshold and dance activity coefficient Related, The higher the threshold, the lower the emergency trigger threshold, allowing for earlier entry into emergency monitoring status under high-risk weather conditions, thus improving the timeliness of emergency response.

[0021] Adopting a hybrid communication architecture of NB-IoT and RFID, near-field alarms can be directly triggered by RFID to link devices without relying on remote networks, thus solving the problem of insufficient network coverage in remote mountainous areas. At the same time, it avoids high-power uplink transmission via NB-IoT for every alarm, reducing communication energy consumption.

[0022] Figure 1 This is a schematic diagram of the overall system architecture of the present invention; Figure 2 This is a flowchart of the adaptive energy efficiency management process of the present invention; Figure 3 This is a schematic diagram of meteorological load coupling and hierarchical control strategy; Figure 4 This is a structural diagram of a solar power generation module; Figure 5 This is a structural diagram of the energy storage and power management module; Figure 6 This is a diagram showing the switching between a two-dimensional decision matrix and an energy efficiency management mode.

[0023] In the diagram: 100, Solar power generation module; 110, Solar photovoltaic panel; 120, Maximum power point tracking controller; 130, Voltage and current sensor; 200, Multi-source sensor module; 210, Triaxial accelerometer sensor; 220, Triaxial gyroscope sensor; 230, Triaxial magnetometer sensor; 240, Micro-meteorological sensor; 300, Edge computing module; 400, Energy storage and power management module; 410, Lithium battery pack; 420, Charge and discharge management circuit; 500, Wireless communication module; 600, Cloud-based early warning platform; 700, Adaptive energy efficiency management module; 710, Weather forecasting submodule; 720, Energy supply and demand analysis submodule; 730, Hierarchical sampling communication control submodule.

[0024] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0025] Example 1, in this example, refers to Figure 1 As shown, the integrated system for collecting and monitoring the galloping characteristics of solar-powered transmission lines and providing cloud-based early warning includes a solar power generation module 100, a multi-source sensor module 200, an edge computing module 300, an energy storage and power management module 400, a wireless communication module 500, a cloud-based early warning platform 600, and an adaptive energy efficiency management module 700.

[0026] The power output terminal of the solar power generation module 100 is connected to the lithium battery pack 410 of the energy storage and power management module 400 via the charge / discharge management circuit 420, converting solar energy into electrical energy for storage. The voltage and current sensor 130 collects the output voltage and current of the solar photovoltaic panel 110, transmitting the data to the adaptive energy efficiency management module 700 for power generation status monitoring. The data output terminal of the multi-source sensor module 200 is connected to the input terminal of the edge computing module 300, while the data output terminal of the micro-meteorological sensor 240 is connected to the weather prediction submodule 710 of the adaptive energy efficiency management module 700, providing wind speed, temperature, and humidity data for calculating the activity coefficient and forecasting weather. The data output terminal of the edge computing module 300 is connected to the wireless communication module 500, and the calculation results are uploaded to the cloud-based early warning platform 600 via the wireless communication module 500. The wireless communication module 500 interacts with the cloud-based early warning platform 600 via the NB-IoT network and also communicates with nearby linkage devices via the RFID communication submodule. The power output terminal of the energy storage and power management module 400 is connected to the power input terminals of the multi-source sensor module 200, the edge computing module 300, and the wireless communication module 500, respectively, to provide operating voltage for each module; at the same time, the charge and discharge management circuit 420 transmits the battery state of charge (SOC) to the energy supply and demand analysis submodule 720 of the adaptive energy efficiency management module 700 in real time.

[0027] The first control output of the adaptive energy efficiency management module 700 is connected to the control input of the solar power generation module 100, and is used to adjust the operating parameters of the maximum power point tracking controller. The second control output of the adaptive energy efficiency management module 700 is connected to the control input of the multi-source sensor module 200, and is used to adjust the sensor sampling frequency. The third control output of the adaptive energy efficiency management module 700 is connected to the control input of the wireless communication module 500, and is used to adjust the operating mode and upload strategy of the communication module. The fourth control output of the adaptive energy efficiency management module 700 is connected to the control input of the edge computing module 300, and is used to instruct the computational load level.

[0028] The solar power generation module 100 includes a solar photovoltaic panel 110, a maximum power point tracking controller 120, and a voltage and current sensor 130.

[0029] The solar photovoltaic panel 110 is made of monocrystalline or polycrystalline silicon, with a peak power range of 5W to 20W, selected based on the power consumption requirements of the monitoring terminal and the sunlight conditions of the installation location. The maximum power point tracking controller 120 employs a perturbation observation method, periodically fine-tuning the operating voltage and observing the direction of output power changes to determine and adjust the operating point, achieving maximum power point tracking and improving energy conversion efficiency. The voltage and current sensor 130 collects the output voltage and current of the solar photovoltaic panel 110, providing real-time data to the maximum power point tracking controller 120 and the adaptive energy efficiency management module 700.

[0030] The multi-source sensor module 200 includes a three-axis accelerometer 210, a three-axis gyroscope 220, a three-axis magnetometer 230, and a micro-weather sensor 240.

[0031] The triaxial accelerometer 210 is mounted on the power transmission line to collect the acceleration signal of the line vibration, with a measurement range of [missing information]. The sampling rate is dynamically controlled by the adaptive energy efficiency management module 700, and can be configured from 10Hz to 1000Hz. The three-axis gyroscope sensor 220 is used to acquire the angular velocity signal of the conductor's torsion, with a measurement range of... The triaxial magnetometer sensor 230 is used to assist in attitude calculation and eliminate the cumulative drift error caused by gyroscope integration. The micro-weather sensor 240 collects environmental wind speed, wind direction, temperature, and humidity data; the wind speed it collects... Ambient temperature data Input to the adaptive energy efficiency management module 700 for the dance activity coefficient. The calculation; the collected humidity data is used for icing thickness. The estimate, The estimated value is also passed to the adaptive energy efficiency management module 700 for use. calculate.

[0032] The edge computing module 300 is based on an ARM Cortex-M series microcontroller and performs the following processing: (1) Kalman filter attitude calculation: The data from the three-axis gyroscope sensor 220 and the three-axis accelerometer sensor 210 are fused to perform real-time attitude calculation and obtain the three-dimensional motion trajectory of the conductor. The Kalman filter is executed alternately in two stages: state prediction and observation update, to eliminate the cumulative drift error of the gyroscope and the dynamic noise of the accelerometer.

[0033] (2) FFT spectrum analysis: Perform fast Fourier transform on the acceleration signal to extract the characteristic parameters of conductor galloping, including galloping amplitude, dominant frequency and half wave number.

[0034] (3) Dynamic load adjustment: Receives the computing load level command issued by the adaptive energy efficiency management module 700, and adjusts the load according to the computing load coefficient. Dynamically adjust computational tasks. Computational load factor. The hierarchical sampling communication control submodule 730 issues the data based on the lookup results of the two-dimensional decision matrix. When the value is 1.0, all algorithms (including Kalman filtering, FFT spectrum analysis, and feature parameter extraction) are executed. When the value is 0.5, only FFT spectrum analysis is performed. When the value is 0.2, only the threshold comparison function is retained.

[0035] The energy storage and power management module 400 includes a lithium battery pack 410 and a charge / discharge management circuit 420. The lithium battery pack 410 has a capacity ranging from 3000mAh to 10000mAh. The charge / discharge management circuit 420 collects voltage, current, and temperature data of the lithium battery pack 410 in real time, and estimates the battery's state of charge (SOC) using a combination of the ampere-hour integral method and the open-circuit voltage method.

[0036] in, The initial state of charge. The rated capacity of the lithium battery pack 410, The Coulomb efficiency coefficient ranges from 0.98 to 1.00. For a moment The charging and discharging current, during charging Take a positive value, during discharge Take the negative value. The charge / discharge management circuit 420 calibrates the cumulative result of the ampere-hour integration method using the open-circuit voltage method each time the system is powered on, eliminating the cumulative error caused by long-term integration.

[0037] The energy storage and power management module 400 provides a stable operating voltage to the multi-source sensor module 200, edge computing module 300, and wireless communication module 500 via a DC-DC step-down circuit. Simultaneously, the charge / discharge management circuit 420 displays the current state of charge. The energy supply and demand analysis data is transmitted in real time to the adaptive energy efficiency management module 700 and then to the energy supply and demand balance submodule 720 for calculating the energy supply and demand balance index. Input parameters.

[0038] See Figure 2 The adaptive energy efficiency management module 700 includes a weather forecasting submodule 710, an energy supply and demand analysis submodule 720, and a hierarchical sampling communication control submodule 730. These three modules form a serial decision-making link: the output of the weather forecasting submodule 710 (predicted light intensity) serves as the input to the energy supply and demand analysis submodule 720, and the output of the energy supply and demand analysis submodule 720 (energy supply and demand balance index)... and dance activity coefficient The input of the hierarchical sampling communication control submodule 730 is used as the input, and the output of the hierarchical sampling communication control submodule 730 is used as the control command for the multi-source sensor module 200, the edge computing module 300, and the wireless communication module 500. The workflow is as follows: Step S101: The weather forecasting submodule 710 acquires the hourly light intensity forecast values ​​for the target monitoring terminal over the next N hours. The predicted light intensity value is obtained by calling the weather forecast API or by statistically analyzing historical light intensity data locally on the terminal. The value of N ranges from 24 to 72.

[0039] Step S102: The energy supply and demand analysis submodule 720 calculates the predicted total power generation of the solar power generation module 100 in the next N hours based on the predicted light intensity. :

[0040] in, The effective area of ​​the solar photovoltaic panel is 110. The photoelectric conversion efficiency of the solar photovoltaic panel 110. For the first Hourly light intensity prediction The conversion efficiency of the maximum power point tracking controller 120.

[0041] Step S103: The energy supply and demand analysis submodule 720 obtains the predicted load current value of the target transmission line in the next N hours and calculates the total energy consumption demand of the monitoring terminal under the current energy efficiency level. :

[0042] in, The sampling power of the multi-source sensor module 200, For the sensor sampling duty cycle, This represents the computing power of the edge computing module 300 under full load. This is the computational load factor. To calculate the task duty cycle, This refers to the power of the wireless communication module 500 in uplink transmission mode. This refers to the uplink transmission duty cycle. The power of the wireless communication module 500 in receive mode. For the duty cycle of the receive mode, This refers to the power of the wireless communication module 500 in standby mode. This represents the duty cycle in standby mode.

[0043] Step S104: The energy supply and demand analysis submodule 720 analyzes the current state of charge of the lithium battery pack 410. Total power generation forecast Total energy demand Calculate the energy supply and demand balance index :

[0044] in, This represents the current available energy of the lithium battery pack 410. Energy supply and demand balance index. It reflects the energy sufficiency of the system in the next N hours.

[0045] Step S105: Energy supply and demand analysis submodule 720 calculates the dancing activity coefficient. (The calculation method is explained in "Calculation of Dance Activity Coefficient" below), and will and Simultaneously, the hierarchical sampling communication control submodule 730 is input.

[0046] Step S106: The hierarchical sampling communication control submodule 730 uses the energy supply and demand balance index and dance activity coefficient As a two-dimensional input, the sampling frequency level, computational load level, and communication upload strategy are determined based on the two-dimensional decision matrix and then sent to the multi-source sensor module 200, edge computing module 300, and wireless communication module 500 for execution, respectively.

[0047] Among them, the above-mentioned dance activity coefficient The energy supply and demand analysis submodule 720 uses wind speed data collected in real time by the micro-meteorological sensor 240. Ambient temperature and ice thickness Comprehensive calculation of estimated values:

[0048] In the formula, This is the real-time wind speed collected by the micro-weather sensor 240. These are wind speed reference values, ranging from 10 m / s to 15 m / s. This is an estimate of the current icing thickness (estimated jointly by humidity sensor data and temperature data). This is the critical value for icing thickness, ranging from 5mm to 10mm. For ambient temperature, This is a reference value for the icing melting temperature, ranging from 5°C to 15°C. , , The weighting coefficients for the three components are respectively, satisfying... .

[0049] Icing increases the conductor's windward surface area, making galloping more intense; wind speeds approaching or exceeding the aerodynamic stability threshold can trigger galloping; ambient temperatures below [a certain threshold] can also cause galloping. The risk of icing increases. Dancing activity coefficient. To comprehensively reflect the impact of the above meteorological factors on the activity level of the cyclone, the weighting coefficients can be configured according to the micro-meteorological statistical characteristics of different regions.

[0050] The probability and severity of conductor galloping under current meteorological conditions were quantified. The larger the value, the more favorable the current meteorological conditions are for the occurrence of a gallop, and the stronger the system's demand for monitoring data. As the second dimension input of the hierarchical sampling communication control submodule 730, it directly affects the selection of sampling frequency level and calculation load level, enabling the system to actively maintain a high monitoring capability during high-risk periods and reduce energy consumption during low-risk periods.

[0051] like Figure 6 As shown, whether or not to actively maintain a high detection capacity depends on... The threshold range is determined by the hierarchical sampling communication control submodule 730 pairs. Set two thresholds: low activity threshold and high activity threshold The level of dance activity is divided into three levels: when At that time, the activity level of the agitation was low, and current meteorological conditions were unfavorable for its occurrence, resulting in low monitoring demand. The default value range is 0.2 to 0.3.

[0052] when At that time, the activity level of the agitation was moderate, meteorological conditions were conducive to its occurrence, and the monitoring need was generally low. The default value range is 0.5 to 0.7.

[0053] At that time, the activity level of the gossamer was high, the meteorological conditions were highly favorable for the gossamer to occur, the demand for monitoring was strong, and monitoring capabilities needed to be prioritized.

[0054] and The specific value can be determined based on the historical galloping statistics of the area where the monitoring line is located.

[0055] Example 2, in this example, see Figure 3 The system uses the energy supply and demand balance index The first dimension, the coefficient of dance activity. As the second dimension, a two-dimensional decision matrix is ​​established. According to... The value range is divided into three energy levels, and within each energy level, according to... The range of values ​​is selected to choose the corresponding parameter combination to achieve synchronous matching between energy adequacy and monitoring demand intensity.

[0056] when At that time, the system is in the energy-amplified mode: When energy is plentiful, the system has sufficient energy reserves, which can be used according to... Adjust monitoring intensity flexibly.

[0057] when (Low activity) mode: Multi-source sensor module 200 sampling frequency set to Edge computing module 300 computing load factor Set to 0.5, only perform FFT spectrum analysis. Wireless communication module 500 uploads data at the fourth upload frequency, which is 40% of the first upload frequency. This level has sufficient energy but low risk of vibration; therefore, the sampling and computation load is reduced to conserve energy for potential periods of high vibration in the future.

[0058] when (When active): The sampling frequency of the multi-source sensor module 200 is set to... The edge computing module's 300 computing load factor is set. At version 1.0, all algorithms are executed. The wireless communication module 500 periodically uploads monitoring data at the first upload frequency. At this level, energy is sufficient and the risk of vibration is moderate; therefore, the sampling frequency is increased and full-load calculations are performed to capture potential vibration events.

[0059] when (High activity) mode: Multi-source sensor module 200 sampling frequency set to Edge computing module 300 computing load factor Set to 1.0 and execute all algorithms. The wireless communication module 500 periodically uploads monitoring data at the first upload frequency. At this level, energy is sufficient and the risk of vibration is high, so it enters a high-density monitoring state to fully utilize energy reserves to acquire complete vibration data.

[0060] when At this time, the system is in energy balance mode: At energy balance, the system has limited energy reserves and needs to be adjusted according to... A delicate balance must be struck between monitoring capabilities and energy consumption.

[0061] when (Low activity) mode: Multi-source sensor module 200 sampling frequency set to Edge computing module 300 computing load factor Set to 0.2, retaining only the threshold comparison function. The wireless communication module 500 uploads data at the second upload frequency, which is 50% of the first upload frequency. This setting has limited energy and a low risk of vibration; therefore, the monitoring intensity is reduced to extend system battery life.

[0062] when (When active): The sampling frequency of the multi-source sensor module 200 is set to... Edge computing module 300 computing load factor Set to 0.5 and perform FFT spectrum analysis. Wireless communication module 500 uploads data at the second upload frequency, uploading only alarm data and key feature parameters. This level has limited energy but a moderate risk of vibration; maintaining basic computing power ensures uninterrupted feature parameter extraction.

[0063] when (High activity) mode: Multi-source sensor module 200 sampling frequency set to Edge computing module 300 computing load factor Set to 1.0 and execute all algorithms. The wireless communication module 500 uploads data at the first upload frequency. Although energy is limited at this level, the risk of galloping is high, and a high monitoring capability must be maintained to ensure that critical galloping data is not lost. This is one of the core design principles of this system: the value of monitoring data during high-risk galloping periods far exceeds the value of energy reserves.

[0064] when At that time, the system was in a low-energy state: When energy is scarce, the system's energy reserves are critical and need to be addressed according to... Determine whether basic monitoring capabilities still need to be maintained.

[0065] when (Low activity) mode: Multi-source sensor module 200 sampling frequency set to Edge computing module 300 computing load factor Set to 0.2, retaining only the threshold comparison function. The wireless communication module 500 uploads data at the third upload frequency, which is 30% of the first upload frequency, and only uploads alarm data. At this level, the energy is critical and the risk of disruption is low; maintain system survival with minimal power consumption while awaiting energy recovery.

[0066] when (When active): The sampling frequency of the multi-source sensor module 200 is set to... Edge computing module 300 computing load factor Set to 0.2, retaining only the threshold comparison function. The wireless communication module 500 uploads data at the third upload frequency, uploading only alarm data. At this level, the energy is critical, and the risk of energy fluctuation is moderate. The sampling frequency is reduced to the minimum but still retains the threshold comparison function to ensure that an alarm is triggered even if the energy fluctuation exceeds the safety threshold.

[0067] when (High activity) mode: Multi-source sensor module 200 sampling frequency set to Edge computing module 300 computing load factor Set to 0.5 and perform FFT spectrum analysis. Wireless communication module 500 uploads data at the second upload frequency, which is 50% of the first upload frequency, uploading alarm data and key characteristic parameters. Although energy is critical at this level, the risk of galloping is extremely high. It is essential to maintain FFT computing power and a high communication frequency to ensure that key characteristic parameters can be extracted and uploaded during galloping events, providing effective data support for cloud-based early warning. This is the bottom-line guarantee of this system design: even with energy shortages, basic data acquisition and upload capabilities must be guaranteed during high-risk galloping periods.

[0068] The thresholds and proportional coefficients mentioned above can be adjusted based on actual engineering experience. The system re-executes the above energy efficiency management process every T hours to update the energy supply and demand balance index. and dance activity coefficient Re-check the table to determine the energy efficiency level and parameter combination. The value of T ranges from 1 to 4 hours.

[0069] Emergency mode trigger conditions and dance activity coefficient Relatedly, when the galloping activity level is high, the probability of the conductor galloping significantly increases, and the emergency trigger threshold is lowered accordingly, so as to enter the emergency monitoring state earlier.

[0070] The system is based on the current Dynamically set emergency trigger threshold : when hour, ,in This is a preset safety threshold.

[0071] when hour, .

[0072] when hour, .

[0073] Regardless of the current energy efficiency management mode of the system, when the edge computing module 300 detects that the amplitude of conductor galloping exceeds the current emergency trigger threshold... Upon activation, the system immediately enters emergency mode. In emergency mode, the multi-source sensor module 200 is forced to... Sampling was performed, and the edge computing module 300 was running at full capacity. Alarm data is immediately uploaded through the highest priority communication channel, and simultaneously, an RFID broadcast alarm signal triggers the linkage device to perform recording or photographing operations. When the amplitude of the dance returns to the current... If the energy efficiency drops below 80% and remains below the preset time, the system will revert to the energy efficiency management mode determined by the two-dimensional decision matrix.

[0074] Regarding the sampling frequency, the distinctions are as follows for each level and activity level: Ample power supply, low activity level:

[0075] Ample tiers, medium or high activity levels: ,in The value ranges from 0.8 to 1.0. Balanced setting, low activity level: ,in The value ranges from 0.4 to 0.6. Balanced setting, medium-active mode:

[0076] Balanced setting, high activity level:

[0077] When in a tense, low-activity, or medium-activity state:

[0078] During periods of high tension and high activity: ,in The value range is from 1.0 to 2.0. in, This is the maximum sampling frequency supported by the sensor, ranging from 500Hz to 1000Hz. The normal sampling frequency ranges from 100Hz to 500Hz. This is the minimum sampling frequency, ranging from 10Hz to 100Hz. Each coefficient can be configured according to actual monitoring needs and must meet [the following requirements]: .

[0079] The wireless communication module 500 adopts a hybrid communication architecture of NB-IoT and RFID: NB-IoT Remote Communication: Interacts with the cloud-based early warning platform 600 via the NB-IoT network, supporting PSM mode and eDRX low-power mode.

[0080] RFID Near-Field Communication: The wireless communication module 500 also includes a 2.4G active RFID communication submodule for short-range communication with linkage devices installed near power transmission lines. When the edge computing module 300 detects abnormal conductor galloping, it broadcasts an alarm signal through the RFID communication submodule. Upon receiving the alarm signal, the linkage device triggers the visualization terminal to perform video recording or photo taking. Near-field alarms do not rely on NB-IoT networks and can still function normally in remote mountainous areas with insufficient network coverage.

[0081] The cloud-based early warning platform 600 receives and stores galloping monitoring data and micro-meteorological data uploaded from various monitoring terminals, executes galloping early warning analysis algorithms, and provides data visualization display functions. The cloud-based early warning platform 600 also performs spatial correlation analysis of galloping trends based on data uploaded from multiple monitoring terminals, and generates a galloping risk assessment report by combining line parameters (including conductor type, span, elevation difference, and tension).

[0082] The richness of key characteristic parameters (galloping amplitude, dominant frequency, half-wave number) in the monitoring data received by the cloud-based early warning platform 600 depends on the current situation. and The determined energy efficiency level. high, At higher settings, the cloud can obtain a complete sequence of dance feature parameters, supporting refined early warning analysis; high, Even at lower settings, the cloud can still obtain basic characteristic parameters and alarm information, supporting a preliminary assessment of the risk of vibration; Low, At the lower settings, the cloud primarily receives alarm data for monitoring system liveness.

[0083] Example: Taking a 220kV transmission line section as an example, this section is located in a mountainous area in southern China, where icing and galloping are prone to occur in winter. The monitoring terminal is equipped with a 10W solar photovoltaic panel and a 7000mAh lithium battery pack (410 type). Take 12 m / s, Take 8mm, Take 10°C, , , , , .

[0084] Scenario 1: Low risk of dancing during continuous rainy days. The weather forecasting submodule 710 obtains the predicted light intensity values ​​for the next 48 hours, all under low light conditions (below 50W / ㎡). The energy supply and demand analysis submodule 720 calculates... far below , When the wind speed drops below 0.6, the system enters energy-critical mode. Simultaneously, the micro-weather sensor 240 collects data showing a wind speed of 3 m / s, no icing, and an ambient temperature of 8°C, and calculates... , The dancing activity level is low. The result of the two-dimensional decision matrix lookup is: sampling frequency. Operational load factor Only the threshold comparison function is retained, and the communication frequency is the third upload frequency. The system maintains its survival with minimal power consumption, waiting for the weather to improve.

[0085] Scenario 2: Continuous rainy days, icy conditions, and strong winds pose a high risk of swaying. The predicted light intensity still indicates low light conditions. The wind speed was still below 0.8, indicating the system was in a state of energy shortage. However, at this time, the micro-weather sensor 240 collected data showing a wind speed of 15 m / s, an ice thickness of 10 mm, and an ambient temperature of -2°C. Calculations... , The level of activity is high. The result of the two-dimensional decision matrix lookup is: sampling frequency. However, the computational load factor (Perform FFT spectrum analysis) The communication frequency is increased to the second upload frequency. Although the system is under energy constraints, the risk of galloping is extremely high. Maintaining FFT computing power ensures that galloping feature parameters can be extracted and uploaded, providing effective data for cloud-based early warning.

[0086] Scene 3: The weather clears up and energy is restored. The predicted light intensity is gradually recovering to a relatively high level (above 200W / ㎡). Once the temperature rises above 1.2, the system enters its energy-sufficient mode. At this point, the micro-weather sensor 240 collects data showing a wind speed of 8 m / s, an icing thickness of 3 mm, and an ambient temperature of 5°C, and calculates... , The activity level is moderately active. The two-dimensional decision matrix lookup result is: sampling frequency. Operational load factor The system executes all algorithms at the primary upload frequency. It fully utilizes its ample energy reserves to maintain a high monitoring intensity, ensuring complete data acquisition of galloping patterns during the ice melting period.

[0087] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. An integrated system for collecting and cloud-based early warning of galloping characteristics of solar-powered self-powered transmission lines, characterized in that: include: Solar power generation module (100), multi-source sensor module (200), edge computing module (300), energy storage and power management module (400), wireless communication module (500), cloud early warning platform (600) and adaptive energy efficiency management module (700). The power output terminal of the solar power generation module (100) is connected to the energy storage and power management module (400) through the charge and discharge management circuit (420) to convert solar energy into electrical energy for storage; The multi-source sensor module (200) is installed on the power transmission line or tower to collect the vibration acceleration signal, angular velocity signal and micro-meteorological environment data of the line. Its data output terminal is connected to the input terminal of the edge computing module (300). The edge computing module (300) performs attitude calculation and dance feature parameter extraction on the collected raw data, and its data output end is connected to the wireless communication module (500). The power output terminal of the energy storage and power management module (400) is connected to the power input terminal of each module to provide the operating voltage for the system. The wireless communication module (500) interacts with the cloud-based early warning platform (600) via the NB-IoT network; The adaptive energy efficiency management module (700) is connected to each module and is used to calculate the dancing activity coefficient and the energy supply and demand balance index. Using the two as two-dimensional inputs, the module determines the sampling frequency level, computing load level and communication upload strategy according to the two-dimensional decision matrix and sends them to each module for execution.

2. The integrated system for collecting and cloud-based early warning of galloping characteristics of solar-powered self-powered transmission lines according to claim 1, characterized in that: The solar power generation module (100) includes a solar photovoltaic panel (110), a maximum power point tracking controller (120), and a voltage and current sensor (130). The maximum power point tracking controller (120) adopts a Boost topology and achieves maximum power point tracking through the perturbation-observation method; The voltage and current sensor (130) collects the output voltage and output current and transmits them to the adaptive energy efficiency management module (700) for power generation status monitoring.

3. The integrated system for collecting and cloud-based early warning of galloping characteristics of solar-powered self-powered transmission lines according to claim 1, characterized in that: The multi-source sensor module (200) includes a triaxial accelerometer (210), a triaxial gyroscope (220), a triaxial magnetometer (230), and a micro-weather sensor (240). The triaxial accelerometer (210) is installed on the power transmission line, and the sampling rate is dynamically adjusted by the adaptive energy efficiency management module (700); The three-axis gyroscope sensor (220) acquires the angular velocity signal of the torsion of the conductor; A triaxial magnetometer sensor (230) assists in attitude calculation to eliminate cumulative drift error of the gyroscope; The wind speed, ambient temperature and humidity data collected by the micro-meteorological sensor (240) are transmitted to the adaptive energy efficiency management module (700) for calculating the dancing activity coefficient.

4. The integrated system for collecting and cloud-based early warning of galloping characteristics of solar-powered self-powered transmission lines according to claim 1, characterized in that: The energy storage and power management module (400) includes a lithium battery pack (410) and a charge and discharge management circuit (420). The charge and discharge management circuit (420) adopts a Buck-Boost bidirectional DC / DC topology, controls bidirectional energy conversion of charge and discharge through PWM signal, collects battery voltage, current and temperature data in real time, uses ampere-hour integration method and open circuit voltage method to estimate battery state of charge, and calibrates the accumulated error by open circuit voltage method each time the system is powered on. The current state of charge is transmitted in real time to the adaptive energy efficiency management module (700) as an input parameter for calculating the energy supply and demand balance index.

5. The integrated system for collecting and cloud-based early warning of galloping characteristics of solar-powered transmission lines according to claim 3, characterized in that: The adaptive energy efficiency management module (700) includes a meteorological forecasting submodule (710), an energy supply and demand analysis submodule (720), and a hierarchical sampling communication control submodule (730), which together form a serial decision-making link; The meteorological forecasting submodule (710) obtains the hourly light intensity forecast value for the next N hours, where N ranges from 24 to 72. The energy supply and demand analysis submodule (720) calculates the total power generation prediction value in the next N hours based on the light intensity prediction value, and uses the ratio of the current available energy of the lithium battery pack (410) to the sum of the total power generation prediction value to the total energy consumption demand as the energy supply and demand balance index K. At the same time, it calculates the dancing activity coefficient β by weighted normalization combination based on wind speed, ice thickness and ambient temperature. The hierarchical sampling communication control submodule (730) uses K and β as two-dimensional inputs, determines the sampling frequency level, computing load level and communication upload strategy according to the two-dimensional decision matrix, and sends them to the multi-source sensor module (200), the edge computing module (300) and the wireless communication module (500) for execution.

6. The integrated system for collecting and cloud-based early warning of galloping characteristics of solar-powered self-powered transmission lines according to claim 5, characterized in that: The energy supply and demand balance index K is divided into three levels: K not less than 1.2 indicates an energy surplus level, K not less than 0.8 and less than 1.2 indicates an energy balance level, and K less than 0.8 indicates an energy shortage level; the activity coefficient... Set low activity threshold and high activity threshold They are divided into low-activity, medium-activity, and high-activity levels. The value range is from 0.2 to 0.

3. The value range is from 0.5 to 0.7; The hierarchical sampling communication control submodule (730) determines the sampling frequency, the computational load coefficient λ, and the ratio of the communication upload frequency to the reference upload frequency according to the following rules: When in the energy-sufficient range, the sampling frequency follows... From low activity level to high activity level, the sampling frequency increases from normal frequency to maximum sampling frequency, λ increases from 0.5 to 1.0, and the communication ratio increases from 40% to 100%. When in energy balance mode, the sampling frequency follows From low activity level to high activity level, the sampling frequency increases from 50% of the normal sampling frequency to 80% of the maximum sampling frequency, λ increases from 0.2 to 1.0, and the communication ratio increases from 50% to 100%. When in the energy-stress mode When the activity level is low or medium, the sampling frequency is the lowest sampling frequency, λ is 0.2, and the communication ratio is 30%. For high-activity levels, the sampling frequency is set to the lowest sampling frequency, λ is increased to 0.5, and the communication ratio is increased to 50%. The maximum sampling frequency is 500Hz to 1000Hz, the normal sampling frequency is 100Hz to 500Hz, and the minimum sampling frequency is 10Hz to 100Hz. When λ is 1.0, all algorithms are executed; when λ is 0.5, only FFT spectrum analysis is executed; and when λ is 0.2, only the threshold comparison function is retained.

7. The integrated system for collecting and cloud-based early warning of galloping characteristics of solar-powered self-powered transmission lines according to claim 5, characterized in that: The dancing activity coefficient It is composed of a linear combination of three weighted components: wind speed, ice thickness, and temperature. The wind speed component is the ratio of real-time wind speed to a wind speed reference value, where the wind speed reference value is 10 m / s to 15 m / s. The ice thickness component is the ratio of the estimated ice thickness to the critical ice thickness, where the critical ice thickness is 5 mm to 10 mm. The temperature component is the ratio of the difference between the ice melting temperature reference value and the ambient temperature to the ice melting temperature reference value, which is 5°C to 15°C. The sum of the weighting coefficients of the three components is 1.

8. The integrated system for collecting and cloud-based early warning of galloping characteristics of solar-powered transmission lines according to claim 7, characterized in that: Emergency trigger threshold follows the dance activity coefficient Dynamic adjustment: When the activity level is low, the emergency trigger threshold is 80% of the preset safety threshold; When the activity level is medium, the threshold is 70% of the preset security threshold. When the activity level is high, the threshold is 60% of the preset security threshold. When the edge computing module (300) detects that the amplitude of the conductor galloping exceeds the current emergency trigger threshold, regardless of the current energy efficiency mode of the system, the multi-source sensor module (200) forces sampling at the maximum sampling frequency, the edge computing module (300) operates at full load, the alarm data is immediately uploaded through the highest priority channel, and at the same time, the linkage device is triggered to perform video recording or photo taking operation through RFID broadcast alarm signal.

9. The integrated system for collecting and cloud-based early warning of galloping characteristics of solar-powered self-powered transmission lines according to claim 8, characterized in that: The wireless communication module (500) adopts a hybrid communication architecture of NB-IoT and RFID: Remote data interaction is performed with the cloud-based early warning platform (600) via the NB-IoT network, supporting PSM and eDRX low-power modes. The DRX cycle length is determined by the hierarchical sampling communication control submodule (730) based on the dancing activity coefficient. Dynamic adjustment: the higher the β, the shorter the DRX cycle and the higher the upload frequency. Near-field communication is established between the 2.4G active RFID and the linkage device near the power transmission line. When abnormal galloping is detected, an alarm signal is broadcast, triggering the visual terminal of the linkage device to record video or take pictures.

10. The integrated system for collecting and cloud-based early warning of galloping characteristics of solar-powered self-powered transmission lines according to claim 1, characterized in that: The cloud-based early warning platform (600) receives and stores galloping monitoring data and micro-meteorological data uploaded by each monitoring terminal, performs galloping early warning analysis, performs spatial correlation analysis of galloping trends based on data from multiple monitoring terminals, and generates a galloping risk assessment report by combining line parameters such as conductor type, span, elevation difference, and tension. The richness of key feature parameters in the uploaded data depends on the current energy supply and demand balance index K and the helical activity coefficient. The determined energy efficiency level.

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