CN114719909A - Big data-based power transmission line iron tower attitude online monitoring system and method - Google Patents
Big data-based power transmission line iron tower attitude online monitoring system and method Download PDFInfo
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- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
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Abstract
The invention discloses a big data-based on-line monitoring system and method for the attitude of a power transmission line iron tower, wherein the on-line monitoring system comprises a power supply, an inclination angle sensor, a wind speed measuring instrument, a control circuit board, a cloud platform database and an upper computer, the power supply is respectively connected with the inclination angle sensor, the wind speed measuring instrument and the control circuit board, the power supply provides power for the inclination angle sensor, the wind speed measuring instrument and the control circuit board, and the inclination angle sensor and the wind speed measuring instrument are both connected with the control circuit board, and the method comprises the following steps: firstly, carrying out finite element analysis on a mechanical structure of an iron tower; secondly, extracting and processing features of the original data through a convolutional neural network; thirdly, a cloud platform database is constructed and an upper computer is developed; fourthly, laying a hardware system on the main material of the iron tower; has the advantages that: the problem of icing load can't direct measurement is solved, simple structure, easily installation, maintenance and dismantlement, the cost is lower, has higher economic benefits, can promote on a large scale.
Description
Technical Field
The invention relates to a power transmission line iron tower attitude online monitoring system and method, in particular to a power transmission line iron tower attitude online monitoring system and method based on big data.
Background
At present, perfect monitoring and early warning systems are essential for power systems, especially for long-distance transmission lines across regions, often crossing mountains, ravines or extreme temperature zones. These zones not only have a harsh weather environment, but are inconvenient for routing inspection work. Under the action of various adverse factors, the power transmission towers are not only easy to incline, subside and even collapse, but also cannot transmit fault information to the operation and maintenance center at the first time, and further aggravate property loss.
The existing iron tower monitoring system mainly comprises three types of position and attitude monitoring, stress sensor monitoring and tilt sensor monitoring by different sensors. The position and attitude detection is mainly to detect the displacement generated by the key node of the iron tower and the reference coordinate through high-precision satellite positioning, and the method depends on the precision of the positioning technology. Typical examples of the method include a method for comparing a reference origin variable quantity provided by an invention patent "a method and a system for monitoring iron tower deformation (application number: CN 201910621261.8)", and a method for comparing an iron tower coordinate provided by an invention patent "an iron tower safety monitoring and early warning system (application number: CN 202021994179.4)" with a nearby reference point coordinate. The stress sensor is mainly characterized in that a grating optical fiber sensor is attached to the surface of a measured rod piece, and the change of stress is judged by measuring the reflection wavelength or the transmission wavelength of a grating. The invention discloses a distributed grating optical fiber detection method provided by the invention patent of an on-line monitoring device and method (application number: CN201610157393.6) for the deformation of a power transmission line iron tower based on an optical fiber grating.
The tilt sensor monitoring is that an acceleration sensor containing a gyroscope is placed on an iron tower cross arm or a main material with a certain height, and when an iron tower body inclines, the sensor sends a signal to remotely transmit information. However, for the iron tower, no matter what kind of load acts on the tower body, the direct cause of the safety accident of the iron tower is that the stress of the tower body structure changes. The traditional inclination angle sensor does not directly measure the stress, particularly does not reflect the relation between the inclination angle and the tower body stress, and is easy to cause larger errors.
Disclosure of Invention
The invention aims to solve the problems of the existing monitoring system and method for a power transmission tower in the using process, and provides a big data-based on-line monitoring system and method for the attitude of the power transmission tower.
The invention provides a big data-based on-line monitoring system for the attitude of a power transmission line iron tower, which comprises a power supply, an inclination angle sensor, a wind speed measuring instrument, a control circuit board, a cloud platform database and an upper computer, wherein the power respectively with inclination sensor, anemometry appearance and control circuit board are connected, the power is for inclination sensor, anemometry appearance and control circuit board provide electric power, inclination sensor and anemometry appearance all are connected with control circuit board, inclination sensor and anemometry appearance can give control circuit board to the data real-time transmission of gathering, can carry out wireless communication between control circuit board and the cloud platform database, control circuit board can be to cloud platform database received data transmission, also can carry out wireless communication between cloud platform database and the host computer, the host computer can follow and carry out data download in the cloud platform database, show and analysis.
The power supply comprises a solar power generation device and a battery, wherein the solar power generation device is connected with the battery, the solar power generation device charges the battery, the battery is respectively connected with the inclination angle sensor, the wind speed measuring instrument and the control circuit board, the battery provides power for the inclination angle sensor, the wind speed measuring instrument and the control circuit board, the output voltage of the solar power generation device is 6V, the output power is 5W, and the specification of the battery is a 4000mAh capacity lithium ion battery.
The four tilt angle sensors are all assembled at positions of nine meters away from the main material of the iron tower, and are arranged in a square equilateral mode.
The wind speed measuring instrument is assembled at a wide and flat position within a ten-meter range of an angle of the tower of the iron tower, the wind speed measuring instrument comprises a wind sensor and a wind direction sensor, a remote communication module is integrated in the wind speed measuring instrument, and the remote communication module is communicated with the control circuit board through a Lora wireless network.
Control circuit board's model is STM32F, control circuit board is including control module, storage module and wireless communication module, wherein control module is connected with inclination sensor, control module is used for driving inclination sensor and receives the inclination change signal that inclination sensor returned, control module is connected with storage module, storage module can be input to the inclination change signal of control module output, control module still is connected with wireless communication module, control module can send the data in the storage module to the cloud platform database through wireless communication module, wireless communication module adopts and carries out data interchange between Lora wireless network communication and the cloud platform database.
The cloud platform database adopts an Ali cloud server ECS, the Ali cloud server ECS carries a windows server 201964 bit operating system, the Ali cloud server ECS has a 100G solid state disk space, the Ali cloud server ECS communicates with the outside by using a proprietary network, the cloud platform database stores and receives and transmits data in a cloud platform by using an Oracle database, the storage process of the cloud platform database is developed by using an Oracle apex tool, the programming language of the cloud platform database is developed by using pl-sql, the communication and data middle stations in the cloud platform database are developed by using a pycharm tool, and the programming language is developed by using python.
The display interface in the upper computer is displayed by adopting a webpage, the front end and the rear end of the upper computer are developed by using an intellj ideal tool, and the programming language of the upper computer is java.
The power supply, the tilt angle sensor, the wind speed measuring instrument and the upper computer are all assembled on the existing equipment, and therefore specific models and specifications are not described repeatedly.
The invention provides a big data-based power transmission line iron tower posture on-line monitoring method, which comprises the following steps:
firstly, carrying out finite element analysis on a mechanical structure of the iron tower, namely decomposing continuous and irregular objects into a finite number of units, establishing a mechanical structure model of the iron tower in ANSYS software, and simulating a combined working condition of applying wind load and icing load;
the wind load comprises a line wind load, a tower wind load and an insulator string wind load;
the line wind load is calculated as follows:
Wx＝α·Wo·μz·μsc·βc·d·Lp·B1·sin2θ
Wo＝V2/1600
wherein, WxThe standard value of the horizontal wind load is vertical to the direction of the lead and the ground wire;
alpha is the uneven coefficient of wind pressure;
μzis the wind pressure height variation coefficient;
μscbody system of conducting wire or ground wireCounting;
βcadjusting the wind load adjustment coefficient of the wire and the ground wire of 500kV and 750kV lines;
d is the outer diameter of the wire or the ground wire or the calculated outer diameter during ice coating;
Lpthe horizontal span of the tower;
B1increasing the coefficient of the ice wind load of the lead wire, the ground wire and the insulator string;
theta is an included angle between the wind direction and the direction of the lead or the ground wire;
Wothe standard value of the reference wind pressure is used;
v is the wind speed with the reference height of 10 m.
The calculation formula of tower wind load is as follows:
Ws＝Wo·μz·μs·B2·As·βz
wherein, WsThe standard value of the tower wind load is obtained;
μsis the body shape factor of the member;
B2increasing the coefficient of the tower component icing wind load;
Ascalculating the projection area of the windward side component;
βzand adjusting the coefficient for the tower wind load.
The calculation formula of the wind load of the insulator string is as follows:
W1＝Wo·μz·B1·A1
wherein, W1Standard value of wind load of the insulator string;
A1and calculating the area of the insulator string subjected to the wind pressure.
An icing load of
Wherein: wfIs the icing load standard value;
k1the safety factor of the wire;
k2the safety factor of the ground wire;
inputting the obtained maximum wind load and the maximum icing load into finite element analysis software;
stress data and key rod piece displacement data of the iron tower under a load of a specific numerical value are obtained through finite element analysis, and the relationship between the change size of displacement and the change size of an angle when a certain point is deformed under a stress can be obtained through formula deduction as follows:
wherein: theta is the angle change size;
s is the displacement generated by the point;
h is the height of the point;
secondly, designing a judging method, adopting machine learning, carrying out feature extraction and processing on original data through a convolutional neural network to obtain the corresponding relation between the load size and the inclination angle of the iron tower under different working conditions, and judging the safety state of the mechanical structure of the iron tower through the measuring result of an inclination angle sensor, wherein the specific method comprises the following steps:
combining the stress, displacement and angle variation obtained in the first step with the data of temperature, humidity, wind direction, wind power, rainfall and snow amount, water accumulation amount and ice coating amount of the iron tower in extreme weather to form a training set and a testing set, and labeling corresponding normal samples and fault samples in the training set and the testing set.
And constructing a first layer of convolution layer, and performing feature extraction on the original data.
Using the ReLU function as an activation function, the ReLU function expression is: f (x) max (0, x)
And constructing a pooling layer, compressing the input characteristic quantity, simplifying the network calculation complexity and extracting main characteristics.
A second and third convolutional layer are constructed using the same activation function and pooling layer as the first layer.
And constructing a full connection layer, connecting all the characteristics, sending the data into a classifier, and carrying out fault classification on the reconstructed characteristic data after characteristic extraction and learning by the classifier.
Setting a training sample, verifying the sample and the proportion of a test sample, and verifying the model result to obtain the corresponding relation between the angle variation and the temperature, humidity, wind direction, wind power, rainfall and snow amount, water accumulation amount and ice coating amount of the iron tower in extreme weather.
Step three, constructing a cloud platform database and developing an upper computer, wherein the specific method comprises the following steps:
an Ariyun server ECS is used, carries a windows server 201964-bit operating system, has a 100G solid state disk space, and communicates with the outside by using a special network;
storing and receiving data in a cloud platform database by using an Oracle database, developing the storage process of the cloud platform database by using an Oracle apex tool, developing a programming language by using pl-sql, developing a communication and data middle platform in the cloud platform database by using a pycharm tool, and developing the programming language by using python;
the display interface of the upper computer is displayed by a webpage, the front end and the rear end of the upper computer are developed by an intellij idea tool, and the programming language is java;
fourthly, laying a hardware system on a main material of the iron tower by taking the displacement and stress corresponding model obtained in the second step as a basis, placing an inclination angle sensor, a control circuit board and a power supply on each monitoring point in a matched manner, packaging the control circuit board by using an F-shaped waterproof junction box, and installing a magnetic steel material on the back of the waterproof junction box to ensure that the F-shaped waterproof junction box is directly adsorbed to the surface of the main material of the iron tower, wherein the control circuit board comprises an STM32F103C8T6 core, a BC26 wireless communication module, a 24C512 storage chip and a power supply access port with the maximum voltage of 5V; the control circuit board integrates an AD module, the battery voltage can be monitored in real time, early warning is carried out in time at low voltage, the BC26 communication module can be inserted into an Internet of things card, a special channel of the Internet of things is used for data transmission, a distributed MQTT communication protocol is adopted, the information transmission quality can be ensured in extreme environments with high concurrency and poor network signals, a 24C512 storage chip has 512Kbit capacity, sensor data can be temporarily stored under the condition of unstable network, data loss is prevented, and a power supply interface provides the maximum 5V direct current voltage for the circuit board;
the inclination angle sensor adopts an MPU6050, is connected with an STM32F103C8T6 core, contains a three-axis gyroscope inside, and can detect the angle change of more than 0.1 degrees in three directions of X, Y, Z;
after the hardware system is installed, the solar power generation device charges the battery;
after the system is powered on, a power supply supplies power to the control circuit board, the control module drives the inclination angle sensor to work after being powered on, data obtained by the inclination angle sensor is transmitted to the control module, the control module puts the data into the storage module for temporary storage, the control module provides the data in the storage module for the wireless communication module, and then the wireless communication module remotely transmits the data to the cloud platform database through the Lora gateway;
after the system is powered on, the power supply supplies power to the wind speed measuring instrument, the wind speed measuring instrument comprises a wind sensor and a wind direction sensor, an Lora communication module is integrated in the wind speed measuring instrument, and the wind speed measuring instrument can directly communicate information to a cloud platform database;
and a display system in the upper computer downloads data from the cloud platform database, analyzes the data and gives an alarm when abnormal data are detected.
The invention has the beneficial effects that:
compared with the existing system and method for monitoring the state of the iron tower based on the tilt angle sensor, the system and method for monitoring the attitude of the iron tower of the power transmission line based on the big data solve the problem that the icing load cannot be directly measured, decouple the load quantity which causes the iron tower to tilt in many ways, and specifically classify the reasons of the iron tower failure. The method converts the inclination angle of a certain point measured by the inclination angle sensor into the stress of the point of the iron tower, and is more reliable compared with a method for obtaining the safety state of the iron tower by directly using the inclination angle sensor. The invention has simple structure, easy installation, maintenance and disassembly, lower cost, perfect functions, capability of being additionally installed at the later stage, certain expandable space, higher economic benefit and large-scale popularization.
Drawings
Fig. 1 is a block diagram of the overall structure of the online monitoring system according to the present invention.
Fig. 2 is a schematic diagram of the output result of the mechanical structure load analysis according to the present invention.
FIG. 3 is a diagram illustrating actual output and predicted output values according to the present invention.
FIG. 4 is a diagram illustrating a mean square error reduction process of the fitting process according to the present invention.
The labels in the above figures are as follows:
1. the wind power generation system comprises a power supply 2, an inclination angle sensor 3, a wind speed measuring instrument 4, a control circuit board 5, a cloud platform database 6, an upper computer 7, a control module 8, a storage module 9 and a wireless communication module.
Detailed Description
Please refer to fig. 1 to 4:
the invention provides a big data-based online monitoring system for the attitude of an iron tower of a power transmission line, which comprises a power supply 1, an inclination angle sensor 2, a wind speed measuring instrument 3, a control circuit board 4, a cloud platform database 5 and an upper computer 6, wherein the power supply 1 is respectively connected with the inclination angle sensor 2, the wind speed measuring instrument 3 and the control circuit board 4, the power supply 1 supplies power to the inclination angle sensor 2, the wind speed measuring instrument 3 and the control circuit board 4, the inclination angle sensor 2 and the wind speed measuring instrument 3 are both connected with the control circuit board 4, the inclination angle sensor 2 and the wind speed measuring instrument 3 can transmit collected data to the control circuit board 4 in real time, the control circuit board 4 can be in wireless communication with the cloud platform database 5, the control circuit board 4 can transmit the received data to the cloud platform database 5, and the cloud platform database 5 can also be in wireless communication with the upper computer 6, the upper computer 6 can download, display and analyze data from the cloud platform database 5.
The power supply 1 comprises a solar power generation device and a battery, wherein the solar power generation device is connected with the battery, the solar power generation device charges the battery, the battery is respectively connected with the inclination angle sensor 2, the wind speed measuring instrument 3 and the control circuit board 4, the battery provides power for the inclination angle sensor 2, the wind speed measuring instrument 3 and the control circuit board 4, the output voltage of the solar power generation device is 6V, the output power is 5W, and the specification of the battery is a lithium ion battery with the capacity of 4000 mAh.
The four tilt angle sensors 2 are assembled, the four tilt angle sensors 2 are all assembled at positions nine meters away from the main material of the iron tower, and the four tilt angle sensors 2 are arranged in a square equilateral mode.
The wind speed measuring instrument 3 is assembled at a wide and flat position within a ten-meter range of an angle of the tower of the iron tower, the wind speed measuring instrument 3 comprises a wind sensor and a wind direction sensor, a remote communication module is integrated in the wind speed measuring instrument 3, and the remote communication module is communicated with the control circuit board 4 through a Lora wireless network.
The cloud platform database 5 adopts an Ali cloud server ECS, the Ali cloud server ECS carries a windows server 201964 bit operating system, the Ali cloud server ECS has a 100G solid state disk space, the Ali cloud server ECS communicates with the outside by using a proprietary network, the cloud platform database 5 uses an Oracle database to store and transmit and receive data in a cloud platform, the storage process of the cloud platform database 5 is developed by using an Oracle apex tool, the programming language of the cloud platform database 5 uses pl-sql, the communication and data center stations in the cloud platform database 5 are developed by using a pycharm tool, and the programming language uses python.
The display interface in the upper computer 6 is displayed by adopting a webpage, the front end and the rear end of the upper computer 6 are developed by using an intellijiade tool, and the programming language of the upper computer 6 is java.
The power supply 1, the tilt angle sensor 2, the wind speed measuring instrument 3 and the upper computer 6 are all assembled by existing equipment, and therefore specific models and specifications are not described in detail.
The invention provides a big data-based power transmission line iron tower posture on-line monitoring method, which comprises the following steps:
firstly, carrying out finite element analysis on a mechanical structure of the iron tower, namely decomposing continuous and irregular objects into a finite number of units, establishing a mechanical structure model of the iron tower in ANSYS software, and simulating a combined working condition of applying wind load and icing load;
the wind load comprises a line wind load, a tower wind load and an insulator string wind load;
the line wind load is calculated as follows:
Wx＝α·Wo·μz·μsc·βc·d·Lp·B1·sin2θ
Wo＝V2/1600
wherein, WxThe standard value of the horizontal wind load is vertical to the direction of the lead and the ground wire;
alpha is the uneven coefficient of wind pressure;
μzis the wind pressure height variation coefficient;
μscthe body form factor of the lead or the ground wire;
βcadjusting the wind load adjustment coefficient of the wire and the ground wire of 500kV and 750kV lines;
d is the outer diameter of the wire or the ground wire or the calculated outer diameter during ice coating;
Lpthe horizontal span of the tower;
B1to guide and groundThe ice wind load increase coefficient of the wire and the insulator string is increased;
theta is an included angle between the wind direction and the direction of the lead or the ground wire;
Wois a standard value of reference wind pressure;
v is the wind speed with the reference height of 10 m.
The calculation formula of tower wind load is as follows:
Ws＝Wo·μz·μs·B2·As·βz
wherein, WsThe standard value of the tower wind load is obtained;
μsis the body shape factor of the member;
B2increasing the coefficient of the tower component icing wind load;
Ascalculating the projection area of the windward side component;
βzand adjusting the coefficient for the tower wind load.
The calculation formula of the insulator wind load is as follows:
W1＝Wo·μz·B1·A1
wherein, W1Standard value of wind load of the insulator string;
A1and calculating the area of the insulator string subjected to the wind pressure.
An icing load of
Wherein: wfIs the icing load standard value;
k1the safety factor of the lead is;
k2the safety factor of the ground wire;
inputting the obtained maximum wind load and the maximum icing load into finite element analysis software;
stress data and key rod piece displacement data of the iron tower under the load of a specific numerical value are obtained through finite element analysis, and the relationship between the change size of displacement and the change size of an angle when a certain point is stressed and deformed can be obtained through formula deduction as follows:
wherein: theta is the angle change size;
s is the displacement generated by the point;
h is the height of the point;
secondly, designing a judging method, adopting machine learning, carrying out feature extraction and processing on original data through a convolutional neural network to obtain the corresponding relation between the load size and the inclination angle of the iron tower under different working conditions, and judging the safety state of the mechanical structure of the iron tower through the measuring result of an inclination angle sensor, wherein the specific method comprises the following steps:
combining the stress, displacement and angle variation obtained in the first step with the data of temperature, humidity, wind direction, wind power, rainfall and snow amount, water accumulation amount and ice coating amount of the iron tower in extreme weather to form a training set and a testing set, and labeling corresponding normal samples and fault samples in the training set and the testing set.
And constructing a first layer convolution layer, and performing feature extraction on the original data.
Using the ReLU function as the activation function, the ReLU function expression is: f (x) max (0, x)
And constructing a pooling layer, compressing the input characteristic quantity, simplifying the network calculation complexity and extracting main characteristics.
A second and third convolutional layer are constructed using the same activation function and pooling layer as the first layer.
And constructing a full connection layer, connecting all the characteristics, sending the data into a classifier, and carrying out fault classification on the reconstructed characteristic data after the characteristics are extracted and learned by the classifier.
Setting a training sample, verifying the sample and the proportion of a test sample, and verifying the model result to obtain the corresponding relation between the angle variation and the temperature, humidity, wind direction, wind power, rainfall and snow amount, water accumulation amount and ice coating amount of the iron tower in extreme weather.
Thirdly, a cloud platform database 5 is constructed and an upper computer 6 is developed, and the specific method comprises the following steps:
an Ariyun server ECS is used, carries a windows server 201964-bit operating system, has a 100G solid state disk space, and communicates with the outside by using a special network;
storing and transmitting and receiving data in a cloud platform database 5 by using an Oracle database, developing the storage process of the cloud platform database 5 by using an Oracle apex tool, developing a programming language by using pl-sql, developing a communication and data middle platform in the cloud platform database 5 by using a pycharm tool, and developing the programming language by using python;
the display interface of the upper computer 6 is displayed by a webpage, the front end and the rear end of the upper computer 6 are developed by an intellij idea tool, and the programming language is java;
fourthly, a hardware system is laid on a main material of the iron tower by taking the displacement and stress corresponding model obtained in the second step as a basis, each monitoring point is provided with an inclination angle sensor 2, a control circuit board 4 and a power supply 1 in a matched mode, the control circuit board is packaged by an F-shaped waterproof junction box, the back of the waterproof junction box is provided with a magnetic steel material, the F-shaped waterproof junction box is directly adsorbed to the surface of the main material of the iron tower, the control circuit board 4 comprises an STM32F103C8T6 core, a BC26 wireless communication module 9 and a 24C512 storage chip, and a power supply 1 with the maximum voltage of 5V is used as an access port; the control circuit board 4 integrates an AD module, can monitor the battery voltage in real time, can give an early warning in time at low voltage, the BC26 communication module can be inserted into an Internet of things card, and uses an Internet of things dedicated channel for data transmission, a distributed MQTT communication protocol is adopted, so that the information transmission quality can be ensured in extreme environments with high concurrency and poor network signals, a 24C512 storage chip has 512Kbit capacity, and can temporarily store sensor data under the condition of unstable network, so that data loss is prevented, and a power supply interface provides the maximum 5V direct current voltage for the circuit board;
the tilt sensor 2 adopts an MPU6050, the tilt sensor 2 is connected with an STM32F103C8T6 core, a three-axis gyroscope is contained in the tilt sensor 2, and the tilt sensor 2 can detect the angle change of more than 0.1 degrees in three directions of X, Y, Z;
after the hardware system is installed, the solar power generation device charges the battery;
after the system is powered on, the power supply 1 supplies power to the control circuit board 4, the control module 7 drives the inclination angle sensor 2 to work after being powered on, data obtained by the inclination angle sensor 2 is transmitted to the control module 7, the control module 7 puts the data into the storage module 8 for temporary storage, the control module 7 provides the data in the storage module 8 for the wireless communication module 9, and then the wireless communication module 9 remotely transmits the data to the cloud platform database 5 through the Lora gateway;
after the system is powered on, the power supply 1 supplies power to the wind speed measuring instrument 3, the wind speed measuring instrument 3 comprises a wind sensor and a wind direction sensor, an Lora communication module is integrated in the wind speed measuring instrument 3, and the wind speed measuring instrument 3 can directly communicate information to a cloud platform database 5;
and a display system in the upper computer 6 downloads data from the cloud platform database 5, analyzes the data and gives an alarm when abnormal data are detected.
Claims (8)
1. The utility model provides a transmission line iron tower gesture on-line monitoring system based on big data which characterized in that: including the power, inclination sensor, anemometry appearance, control circuit board, cloud platform database and host computer, wherein the power respectively with inclination sensor, anemometry appearance and control circuit board are connected, the power is for inclination sensor, anemometry appearance and control circuit board provide electric power, inclination sensor and anemometry appearance all are connected with control circuit board, inclination sensor and anemometry appearance can give control circuit board to the data real-time transmission of gathering, can carry out wireless communication between control circuit board and the cloud platform database, control circuit board can be to cloud platform database to received data transmission, also can carry out wireless communication between cloud platform database and the host computer, the host computer can carry out data download from the cloud platform database, show and the analysis.
2. The big-data-based power transmission line iron tower attitude online monitoring system according to claim 1, characterized in that: the power supply comprises a solar power generation device and a battery, wherein the solar power generation device is connected with the battery, the solar power generation device charges the battery, the battery is respectively connected with the inclination angle sensor, the wind speed measuring instrument and the control circuit board, the battery provides power for the inclination angle sensor, the wind speed measuring instrument and the control circuit board, the output voltage of the solar power generation device is 6V, the output power is 5W, and the specification of the battery is a lithium ion battery with the capacity of 4000 mAh.
3. The big-data-based power transmission line iron tower attitude online monitoring system according to claim 1, characterized in that: the four tilt angle sensors are all assembled at positions of nine meters away from the ground of the main material of the iron tower, and are arranged in a square equilateral mode.
4. The big-data-based power transmission line iron tower attitude online monitoring system according to claim 1, characterized in that: the wind speed measuring instrument is assembled at a wide and flat position within a ten-meter range of an angle of the tower of the iron tower, the wind speed measuring instrument comprises a wind sensor and a wind direction sensor, a remote communication module is integrated in the wind speed measuring instrument, and the remote communication module is communicated with the control circuit board through a Lora wireless network.
5. The big-data-based power transmission line iron tower attitude online monitoring system according to claim 1, characterized in that: control circuit board's model be STM32F, control circuit board is including control module, storage module and wireless communication module, wherein control module is connected with inclination sensor, control module is used for driving inclination sensor and receives the inclination change signal that inclination sensor returned, control module is connected with storage module, storage module can be input to the inclination change signal of control module output, control module still is connected with wireless communication module, control module can send the data in the storage module to the cloud platform database through wireless communication module, wireless communication module adopts and carries out data interchange between Lora wireless network communication and the cloud platform database.
6. The big-data-based power transmission line iron tower attitude online monitoring system according to claim 1, characterized in that: the cloud platform database adopts an Ali cloud server ECS, the Ali cloud server ECS carries a windows server 201964 bit operating system, the Ali cloud server ECS has a 100G solid state disk space, the Ali cloud server ECS communicates with the outside by using a proprietary network, the cloud platform database stores and receives data in a cloud platform by using an Oracle database, the storage process of the cloud platform database is developed by using an Oracle apex tool, the programming language of the cloud platform database is developed by using pl-sql, the communication and data center platforms in the cloud platform database are developed by using a pycharm tool, and the programming language is developed by using python.
7. The big-data-based power transmission line iron tower attitude online monitoring system according to claim 1, characterized in that: the display interface in the upper computer is displayed by adopting a webpage, the front end and the rear end of the upper computer are developed by using an intellj idea tool, and the programming language of the upper computer is java.
8. A big data-based power transmission line iron tower attitude online monitoring method is characterized by comprising the following steps: the method comprises the following steps:
firstly, finite element analysis is carried out on a mechanical structure of the iron tower, namely, continuous and irregular objects are decomposed into a limited number of units, an iron tower mechanical structure model is established in ANSYS software, and the combined working condition of applying wind load and icing load is simulated;
the wind load comprises a line wind load, a tower wind load and an insulator string wind load; the line wind load is calculated as follows:
Wx＝α·Wo·μz·μsc·βc·d·Lp·B1·sin2θ
Wo＝V2/1600
wherein, WxThe standard value of the horizontal wind load is vertical to the direction of the lead and the ground wire;
alpha is the uneven coefficient of wind pressure;
μzis the wind pressure height variation coefficient;
μscthe form factor of the wire or the ground wire;
βcadjusting the wind load adjustment coefficient of the wire and the ground wire of 500kV and 750kV lines;
d is the outer diameter of the wire or the ground wire or the calculated outer diameter during ice coating;
Lpthe horizontal span of the tower;
B1increasing the coefficient of the ice wind load of the lead wire, the ground wire and the insulator string;
theta is an included angle between the wind direction and the direction of the lead or the ground wire;
Wothe standard value of the reference wind pressure is used;
v is the wind speed with the reference height of 10 m;
the calculation formula of tower wind load is as follows:
Ws＝Wo·μz·μs·B2·As·βz
wherein, WsThe standard value of the tower wind load is obtained;
μsis the body shape factor of the member;
B2increasing the coefficient of the tower component icing wind load;
Ascalculating the projection area of the windward side component;
βzadjusting the coefficient for the tower wind load;
the calculation formula of the insulator wind load is as follows:
W1＝Wo·μz·B1·A1
wherein, W1Standard value of wind load of the insulator string;
A1calculating the area of the insulator string bearing wind pressure;
an icing load of
Wherein: w is a group offIs the icing load standard value;
k1the safety factor of the wire;
k2the safety factor of the ground wire;
inputting the obtained maximum wind load and the maximum icing load into finite element analysis software;
stress data and key rod piece displacement data of the iron tower under the load of a specific numerical value are obtained through finite element analysis, and the relationship between the change size of displacement and the change size of an angle when a certain point is stressed and deformed can be obtained through formula deduction as follows:
wherein: theta is the angle change size;
s is the displacement generated by the point;
h is the height of the point;
secondly, designing a judging method, adopting machine learning, carrying out feature extraction and processing on original data through a convolutional neural network to obtain the corresponding relation between the load size and the inclination angle of the iron tower under different working conditions, and judging the safety state of the mechanical structure of the iron tower through the measuring result of an inclination angle sensor, wherein the specific method comprises the following steps:
combining the stress, displacement and angle variation obtained in the first step with the data of temperature, humidity, wind direction, wind power, rainfall and snow amount, water accumulation amount and ice coating amount of the iron tower under extreme weather to form a training set and a testing set, and labeling normal samples and fault samples corresponding to the training set and the testing set;
constructing a first layer of convolution layer, and extracting the characteristics of the original data;
using the ReLU function as an activation function, the ReLU function expression is: f (x) max (0, x)
Constructing a pooling layer, compressing the input characteristic quantity, simplifying the network calculation complexity and extracting main characteristics;
constructing a second convolutional layer and a third convolutional layer, and using the same activation function and pooling layer as the first layer;
constructing a full connection layer, connecting all the characteristics, sending the data into a classifier, and carrying out fault classification on the reconstructed characteristic data after characteristic extraction and learning by the classifier;
setting a training sample, a verification sample and a test sample proportion, verifying a model result, and obtaining a corresponding relation between the angle variation and the temperature, humidity, wind direction, wind power, rainfall and snow amount, water accumulation amount and ice coating amount of the iron tower under extreme weather;
step three, constructing a cloud platform database and developing an upper computer, wherein the specific method comprises the following steps:
an Ariyun server ECS is used, carries a windows server 201964-bit operating system, has a 100G solid state disk space, and communicates with the outside by using a special network;
storing and receiving data in a cloud platform database by using an Oracle database, developing the storage process of the cloud platform database by using an Oracle apex tool, developing a programming language by using pl-sql, developing a communication and data middle platform in the cloud platform database by using a pycharm tool, and developing the programming language by using python;
the display interface of the upper computer is displayed by a webpage, the front end and the rear end of the upper computer are developed by an intellij idea tool, and the programming language is java;
fourthly, laying a hardware system on a main material of the iron tower by taking the displacement and stress corresponding model obtained in the second step as a basis, placing an inclination angle sensor, a control circuit board and a power supply on each monitoring point in a matched manner, packaging the control circuit board by using an F-shaped waterproof junction box, and installing a magnetic steel material on the back of the waterproof junction box to ensure that the F-shaped waterproof junction box is directly adsorbed to the surface of the main material of the iron tower, wherein the control circuit board comprises an STM32F103C8T6 core, a BC26 wireless communication module, a 24C512 storage chip and a power supply access port with the maximum voltage of 5V; the control circuit board integrates an AD module, the battery voltage can be monitored in real time, early warning is carried out in time at low voltage, the BC26 communication module can be inserted into an Internet of things card, a special channel of the Internet of things is used for data transmission, a distributed MQTT communication protocol is adopted, the information transmission quality can be ensured in extreme environments with high concurrency and poor network signals, a 24C512 storage chip has 512Kbit capacity, sensor data can be temporarily stored under the condition of unstable network, data loss is prevented, and a power supply interface provides the maximum 5V direct current voltage for the circuit board;
the inclination angle sensor adopts an MPU6050, is connected with an STM32F103C8T6 core, contains a three-axis gyroscope inside, and can detect the angle change of more than 0.1 degrees in three directions of X, Y, Z;
after the hardware system is installed, the solar power generation device charges the battery;
after the system is powered on, a power supply supplies power to the control circuit board, the control module drives the inclination angle sensor to work after being powered on, data obtained by the inclination angle sensor is transmitted to the control module, the control module puts the data into the storage module for temporary storage, the control module provides the data in the storage module for the wireless communication module, and then the wireless communication module remotely transmits the data to the cloud platform database through the Lora gateway;
after the system is powered on, the power supply supplies power to the wind speed measuring instrument, the wind speed measuring instrument comprises a wind sensor and a wind direction sensor, an Lora communication module is integrated in the wind speed measuring instrument, and the wind speed measuring instrument can directly communicate information to a cloud platform database;
and a display system in the upper computer downloads data from the cloud platform database, analyzes the data and gives an alarm when abnormal data are detected.
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| CN115615493A (en) * | 2022-12-16 | 2023-01-17 | 深圳市瑞芬科技有限公司 | Sensor for measuring inclination and swing of wind generating set |
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