A cable health on-line monitoring system for collecting wind power

Abstract

The present application relates to a kind of cable health on-line monitoring system of wind energy harvesting, belong to self-powered cable condition monitoring field.It includes the acquisition layer, back end layer and front end layer connected in turn;Acquisition layer includes several acquisition points, each acquisition point includes communication module, MCU microcontroller, temperature and humidity sensor and wind speed and direction sensor, MCU microcontroller controls communication module to transmit the information of the environment where cable is located collected by temperature and humidity sensor and wind speed and direction sensor to back end layer;Each acquisition node also includes a wind energy harvesting module, which powers the load of acquisition layer;Back end layer forwards the received data to front end layer by wireless communication mode;The present application can effectively solve the problem of energy limitation, reduce the laying and operation cost of sensor network, monitor the environmental change of each node in real time, and provide important guarantee for the safe operation of cable health and power network.

Description

Technical Field

[0001] This invention belongs to the field of self-powered cable health online monitoring, and relates to a cable health online monitoring system that collects wind power. Background Technology

[0002] Meteorological monitoring data is of great significance for precision agriculture, flash flood warning, ecological protection, and scientific research. However, real-time collection and remote transmission of meteorological data in field environments, especially in mountainous areas, forests, and farmland where public mobile communication networks are lacking and power lines are difficult to lay, presents significant challenges.

[0003] The existing technology has the following main shortcomings: First, traditional wired weather stations require the laying of dedicated communication and power cables, which are difficult to construct, costly, and difficult to maintain in the field, and are also susceptible to damage from lightning strikes and animal gnawing. Second, although wireless weather stations based on 4G DTUs can solve the wiring problem, their power consumption is high and they are not suitable for long-term unattended scenarios powered by batteries; at the same time, they cannot work at all in remote areas without 4G signals. Third, while short-range wireless solutions based on low-power Bluetooth ZigBee or BLE have low power consumption, their communication range is typically only tens to hundreds of meters, which cannot meet the needs of large-scale, long-distance monitoring. Solutions such as LiDAR are too expensive and not suitable for large-scale deployment. Fourth, existing meteorological monitoring systems based on the long-distance, low-power LoRa radio frequency technology typically use direct point-to-point transmission. However, in terms of transmitter design, this approach suffers from complex address configuration and a high risk of bus conflicts when simultaneously connecting multiple interface sensors, especially multiple sensors with RS485 bus and UART interface sensors. On the receiver side, dedicated gateway hardware or complex network servers are usually required, making it impossible to achieve a low-barrier connection with ordinary computers and increasing the user's operating costs. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide an online temperature monitoring system for power equipment with multiple power sources, thereby solving the technical problems existing in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a cable health monitoring system for collecting wind power, comprising a collection layer, a back-end layer, and a front-end layer connected in sequence; The acquisition layer includes several acquisition points. Each acquisition point includes a communication module, an MCU microcontroller, a temperature and humidity sensor, and a wind speed and direction sensor. The MCU microcontroller controls the communication module to transmit the information about the environment where the cable is located, collected by the temperature and humidity sensor and the wind speed and direction sensor, to the back-end layer. Each acquisition node also includes a wind power harvesting module, which supplies power to the communication module, the MCU microcontroller, the temperature and humidity sensor, and the wind speed and direction sensor through an energy regulation module.

[0006] The backend layer includes an anomaly detection module, a weighted risk scoring module, and a trend analysis module. The anomaly detection module processes and detects environmental parameters using formulas, and triggers a warning if any anomaly is detected. The weighted risk scoring module calculates a comprehensive risk index using formulas from various data models. The trend analysis module calculates the deviation of a sampling point from the mean of several nearby points to derive the trend.

[0007] The front-end layer includes a visual interface, a database, and a cloud server. The cloud server receives the processing results from the back-end layer and stores them in the database. The environmental index information of each collection node is displayed in real time through the visual interface.

[0008] The wind-driven energy harvesting module includes: a vibration energy acquisition module, an energy adapter module, an energy storage module, and an energy adapter module. The vibration energy acquisition module collects wind energy and converts it into electrical energy. The energy adapter module performs voltage and current stabilization processing and charges the energy storage module. The energy storage module is connected to the energy adapter module, and the energy adapter module is connected to the back-end load to supply power to the load.

[0009] Furthermore, the wind-driven energy harvesting module includes: a planar coil, a vibrating magnetic pole, a flat plate, an upper magnetic yoke, a lower magnetic yoke, and a lithium battery pack, which are connected to the energy regulation module respectively.

[0010] The flat plate has through holes and magnetic yoke through holes. Vibrating magnetic poles are set at the through holes, and planar coils are fixed at the magnetic yoke through holes. The upper magnetic yoke is fixed to the upper surface of the flat plate, and the lower magnetic yoke is fixed to the lower surface of the flat plate. The vibrating magnetic poles can move up and down. The up and down movement of the vibrating magnetic poles realizes the change in the magnitude and direction of the magnetic flux of the planar coil, thereby increasing the rate of change of magnetic flux. At the same time, it realizes bistable switching under the magnetic force of the upper and lower magnetic yokes, which makes it easy to superimpose the polarity reversal and bistable switching effects. While increasing the working bandwidth, it significantly improves the output power density to power the lithium battery pack.

[0011] Furthermore, the energy adapter module is a DC-DC step-down module.

[0012] Furthermore, the energy storage module is a lithium battery pack.

[0013] The backend layer includes a three-factor weighted risk scoring submodule, a Z-score statistical anomaly detection submodule, a proximal trend analysis submodule, and a status management module; The three-factor weighted risk scoring submodule calculates sub-risk components based on three physical quantities: temperature, humidity, and wind speed. These components are then weighted and summed according to preset weights to obtain the comprehensive icing risk index R. The calculation formula is: R = 0.4×f_temp(T) +0.3×f_hum(H) + 0.3×f_wind(W); where f_temp, f_hum, and f_wind are the temperature sub-risk function, humidity sub-risk function, and wind speed sub-risk function, respectively. The output of each function is normalized to the [0,1] interval. The temperature risk function f_temp(T) is defined as follows: output 0 when T>2℃; output 0.5 when T<-10℃; and output the linear interpolation result of (2-T) / 12 in other cases. The humidity risk function f_hum(H) is defined as follows: output 0 when H≤70%; output 1 when H>100%; and output the linear interpolation result of (H-70) / 30 in other cases. The wind speed risk function f_wind(W) is defined as follows: output 0 when W < 2 m / s; output 1 when W > 20 m / s; and output the linear interpolation result of (W-2) / 18 in other cases. The Z-score statistical anomaly detection submodule is based on a sliding historical window with no less than 10 sampling points. It calculates the mean and standard deviation of each sensor channel, performs a standardization test on the current sampled value, and independently tests the three signals of wind speed, temperature, and humidity. If the absolute value of the Z-score of any channel exceeds the set threshold (the default threshold is 2.5), an immediate anomaly signal is output. The near-end trend analysis submodule calculates the latest sampled value of each parameter relative to the near-end mean, and selects the deviation of the most recent 5 sampled points to reflect the direction and magnitude of the parameter's change. Positive values ​​indicate an upward trend, and negative values ​​indicate a downward trend. The state management module maintains a continuous risk state machine with a counter, which accumulates the number of periods in which the risk index continuously exceeds the threshold. The state machine only outputs an alarm command when the accumulated number of periods reaches the set duration threshold (default 5 sampling periods) or when the anomaly detection submodule outputs an immediate anomaly signal. When the alarm state is cleared, the state machine synchronously outputs a recovery command. The default risk index threshold is 0.7, and both the duration threshold and the risk index threshold are dynamically configured through an external configuration file. The front-end layer includes a visual interface, a database, and a cloud server. The cloud server receives the processing results from the back-end layer and stores them in the database. The visual interface displays environmental index information, comprehensive risk instruments, trend directions, and system logs of each data collection node in real time.

[0014] The beneficial effects of this invention are as follows: This invention proposes an online cable health monitoring system that collects wind energy for self-generation. This system can effectively solve the problem of energy constraints on site, monitor the health status of cables in real time, and reduce the laying and maintenance costs of sensor networks. It is of great significance for ensuring the safe operation of power systems.

[0015] The present invention provides an online cable health monitoring system for collecting wind power, which can effectively solve the problems of small communication range and low data transmission reliability in the environment, and effectively ensure the communication quality during the cable health monitoring process.

[0016] After receiving the data, the back-end layer in this invention can monitor the health status of the cable in real time, so as to detect abnormalities in a timely manner and take corresponding measures to minimize losses and ensure safety.

[0017] The wind energy harvesting and self-powering technology in this invention enables sensors to be self-powered, thereby eliminating the need for on-site power wiring and battery replacement. It can effectively solve the problems of energy limitation and deployment and maintenance of sensors, and greatly reduce the cost of laying and maintaining sensor networks.

[0018] The Z-score statistical anomaly detection mechanism in this invention can adapt to the historical distribution of sensor data and has a high sensitivity and rapid response capability to sudden sensor anomalies or extreme weather changes, thus making up for the shortcomings of risk models that are sensitive to slowly accumulating risks but have a lagging response to sudden risks.

[0019] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0020] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein: Figure 1 This is a diagram showing the overall architecture of an online cable health monitoring system. Figure 2 This is a structural diagram of the transmitting end of an online cable health monitoring system. Figure 3 This is a structural diagram of the receiver end of an online cable health monitoring system. Figure 4 This is a schematic diagram of the three-factor weighted risk scoring submodule. Figure 5 This is a schematic diagram of the state management module. Detailed Implementation

[0021] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0022] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0023] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0024] Please see Figure 1 This is an online health monitoring system for cables that collect wind energy for self-generation, comprising a data acquisition layer, a back-end layer, and a front-end layer connected in sequence; The acquisition layer includes several acquisition points. Each acquisition point includes a communication module, an MCU microcontroller, a temperature and humidity sensor, and a wind speed and direction sensor. The MCU microcontroller controls the communication module to transmit the information about the environment where the cable is located, collected by the temperature and humidity sensor and the wind speed and direction sensor, to the back-end layer. Each acquisition node also includes a wind power harvesting module, which supplies power to the communication module, the MCU microcontroller, the temperature and humidity sensor, and the wind speed and direction sensor through an energy regulation module. Transmitter circuit connection as follows Figure 2 As shown, the specific connection relationships are as follows: Main controller: ESP32-DevKitC-V4 development board.

[0025] SHT30 (UART interface): Its TX pin is connected to GPIO16 (UART2 RX) of ESP32, and its RX pin is connected to GPIO17 (UART2 TX).

[0026] Wind speed sensor (RS485 interface): Its A / B line is connected to the A / B terminal of the first MAX485 module. The RO pin of this MAX485 module is connected to GPIO13 (UART1 RX) of ESP32, the DI pin is connected to GPIO14 (UART1 TX) of ESP32, and the DE / RE pin (merged control) is connected to the independent GPIO4 of ESP32.

[0027] Wind direction sensor (RS485 interface): Its A / B lines are connected to the A / B terminals of the second MAX485 module. The RO pin of this MAX485 module is also connected to GPIO13 of the ESP32 (sharing UART1 RX with wind speed), and its DI pin is also connected to GPIO14 of the ESP32 (sharing UART1 TX with wind speed). Its DE / RE pins are connected to another independent GPIO5 of the ESP32.

[0028] LoRa transmitter module (E22-400T22S): Its RX pin is connected to GPIO1 (UART0 TX) of ESP32, and its TX pin is connected to GPIO3 (UART0 RX).

[0029] Power Supply: Two 18650 capacitors connected in series (approximately 7.4V) are connected to the Vin pin of the MP1584EN step-down module. The MP1584EN output is adjusted to 3.3V and connected to the VCC pins of the ESP32, SHT30, two MAX485 chips, and the LoRa module. The GND pins of all modules are connected to the GND pins of the power supply via a star connection. The wind speed and direction sensors are powered by separate 12V lithium batteries according to their nominal voltages; the GND pin of this 12V battery must be connected to the GND pin of the 3.3V system.

[0030] Receiver circuit connection as follows Figure 3 As shown, the TX pin of a separate LoRa receiver module with the same model and frequency parameters as the transmitter is connected to the RX pin of a USB-to-TTL module, such as the CH340; the RX pin of the LoRa receiver module is connected to the TX pin of the USB-to-TTL module; the VCC and GND of both are interconnected. The USB male connector of the USB-to-TTL module is directly plugged into the USB port of a computer running Windows or Linux operating system.

[0031] The backend layer runs on the monitoring center computer. In receive mode, it connects to a separate LoRa receiver module via a USB-to-TTL adapter to receive wireless data from the acquisition layer in real time. The core analysis engine of the backend layer consists of the following four sub-modules: (I) Three-Factor Weighted Risk Scoring Submodule Figure 4 ) After receiving the current sampled data, the system calculates the comprehensive icing risk index R. The value of R ranges from [0,1], and the larger the value, the higher the icing risk. Temperature risk function f_temp(T): Returns 0 if T>2℃; Returns 0.5 if T<-10℃; Otherwise returns (2-T) / 12.

[0032] Humidity risk function f_hum(H): Returns 0 if H≤70%; Returns 1 if H>100%; Otherwise, Returns (H-70) / 30.

[0033] The wind speed risk function f_wind(W) returns 0 if W < 2 m / s; 1 if W > 20 m / s; otherwise, it returns (W-2) / 18.

[0034] The overall risk index R = 0.4×f_temp + 0.3×f_hum + 0.3×f_wind.

[0035] (ii) Z-score statistical anomaly detection submodule The system performs Z-score detection on three signals: wind speed, temperature, and humidity. A historical sliding window of at least 10 sampling points is required. The mean μ and standard deviation σ of the historical sequence are calculated. If σ is not 0, z = |(value-μ) / σ| is calculated. If z > 2.5 (the default threshold), the channel is marked as abnormal. Any abnormality in any channel triggers an immediate abnormality signal, bypassing the state machine's continuous counter and directly entering the alarm state.

[0036] (III) Near-end trend analysis submodule The system calculates the near-end average for wind speed, temperature, and humidity by taking the five most recent sampling points. The latest sampled value is then subtracted from the average to obtain the trend value: a positive value indicates an upward trend, and a negative value indicates a downward trend, which helps maintenance personnel judge the trend of the parameters.

[0037] (iv) Status Management Module The status management module maintains the alarm status of the previous cycle (last_alert) and the high-risk cycle counter (high_risk_count). The following logic is executed in each sampling cycle ( Figure 5 ): If the current risk index R > risk_threshold (default 0.7), then high_risk_count is incremented by 1; otherwise, it is reset to zero.

[0038] If high_risk_count ≥ duration_threshold, the default is 5 periods or an immediate abnormal signal is true, then the alarm status for the current period is True; otherwise, it is False.

[0039] If the current alarm status differs from the previous cycle and the alarm status is activated, the "ALERT" command is output; if the alarm status is deactivated, the "OK" command is output. If the status remains unchanged, no command is output to avoid redundant communication.

[0040] The backend layer can use the LoRa module to send "ALERT" or "OK" commands to the acquisition layer, triggering local linkages such as audible and visual alarms and LED indicators in the acquisition layer.

[0041] The front-end layer includes a cloud server, a database, and a visualization interface. The cloud server receives the processing results from the back-end layer, including raw sensor data, comprehensive risk index, alarm status, trend data, etc., and stores them in the database. The visualization interface provides two formats: Desktop graphical interface (based on Tkinter): Real-time refresh of wind speed, temperature, humidity, and wind direction values ​​for each monitoring node; displays trend arrows and a comprehensive risk dashboard; provides data acquisition start / stop control and data export functions.

[0042] Web dashboard (HTML / JavaScript): Uses Chart.js to draw a real-time wind speed line chart (last 50 points), and Canvas to draw a semi-circular risk dashboard (green / yellow / red levels). The top card displays the current value of each parameter and trend arrows, and the bottom scrolls to display system event logs. The control panel supports sliding adjustment of risk thresholds and switching between simulation scenarios.

[0043] The backend main program executes a complete data processing flow once every second: The current sampling data dictionary is obtained through the LoRa receiver module; if there is no new data, the current round is skipped. Extract the three components of wind speed, temperature and humidity, and append them to the corresponding historical cache list. The Z-score window is up to 30 and the trend window is up to 50. Use the three-factor weighted risk module to calculate the comprehensive risk index R; The Z-score anomaly detection module is called to independently detect the three signals and obtain the immediate anomaly flag from the logic. The trend analysis module is invoked to calculate the trend values ​​of the three signals; Call the status management module to update the alarm status based on R, the real-time anomaly flag, and the configured threshold, and output the command. If there is a new command (ALERT / OK) and reverse communication is configured, it will be sent to the acquisition layer via the LoRa module; Write the data from this round, including timestamps, wind speed, temperature, humidity, and risk index, into a CSV log file; Update the front-end display interface, choosing between desktop GUI or web page push; Sleep until the next sampling period.

[0044] The above process is encapsulated in an exception handling structure, where any runtime exceptions are recorded and automatically recovered, ensuring long-term stable operation of the system.

[0045] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An online health monitoring system for cables that harvest wind power, characterized in that: It includes a data acquisition layer, a backend layer, and a frontend layer connected in sequence; The acquisition layer includes several acquisition points. Each acquisition point includes a communication module, an MCU microcontroller, a temperature and humidity sensor, and a wind speed and direction sensor. The MCU microcontroller controls the communication module to transmit the information about the environment where the cable is located, collected by the temperature and humidity sensor and the wind speed and direction sensor, to the back-end layer. Each acquisition node also includes a wind-driven energy harvesting module, which supplies power to the communication module, the MCU microcontroller, the temperature and humidity sensor, and the wind speed and direction sensor through an energy regulation module. The backend layer includes an anomaly detection module, a weighted risk scoring module, and a trend analysis module; the anomaly detection module processes and detects environmental parameters, and triggers a warning if any anomaly is detected; the weighted risk scoring module calculates a comprehensive risk index through various data models; The trend analysis module calculates the mean deviation of the sampling point relative to several nearby points to obtain the trend. The front-end layer includes a visual interface, a database, and a cloud server. The cloud server receives the processing results from the back-end layer and stores them in the database. The environmental index information of each collection node is displayed in real time through the visual interface.

2. The online health monitoring system for cables that collect wind power according to claim 1, characterized in that: The wind-powered energy harvesting module includes a planar coil, a vibrating magnetic pole, a flat plate, an upper magnetic yoke, a lower magnetic yoke, and a lithium battery pack, which are respectively connected to the energy regulation module. The plate has a through hole and a magnetic yoke through hole, the vibrating magnetic pole is set at the through hole, the planar coil is fixed at the magnetic yoke through hole, the upper magnetic yoke is fixed on the upper surface of the plate, and the lower magnetic yoke is fixed on the lower surface of the plate; the vibrating magnetic pole can move up and down.

3. The cable health online monitoring system for collecting wind kinetic energy for power generation according to claim 2, characterized in that: The wind-powered energy harvesting module inputs the collected electrical energy into the energy regulation module for signal conditioning. Specifically, the energy regulation module converts the wind energy into electrical energy and performs voltage stabilization and rectification.

4. The cable health online monitoring system for collecting wind kinetic energy for power generation according to claim 1, characterized in that: The communication module is a LoRa communication module, the temperature and humidity sensor is an SHT30 temperature and humidity sensor, and the wind speed and direction sensor is a mechanical wind speed and direction sensor.

5. The cable health online monitoring system for collecting wind kinetic energy for power generation according to claim 1, characterized in that: The energy regulation module is an MP1584EN module.

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