CN114719909B - Transmission line iron tower attitude online monitoring system and method based on big data - Google Patents
Transmission line iron tower attitude online monitoring system and method based on big data Download PDFInfo
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Abstract
The invention discloses a big data-based on-line monitoring system and a big data-based on-line monitoring method for the posture 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, wherein 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: step one, finite element analysis is carried out on a mechanical structure of an iron tower; secondly, extracting and processing the characteristics of the original data through a convolutional neural network; thirdly, constructing a cloud platform database and developing an upper computer; fourthly, paving a hardware system on the main material of the iron tower; the beneficial effects are that: the problem that the icing load cannot be directly measured is solved, the structure is simple, the installation, the maintenance and the disassembly are easy, the cost is low, the economic benefit is high, and the device can be widely popularized.
Description
Technical Field
The invention relates to an online monitoring system and an online monitoring method for the posture of a power transmission line iron tower, in particular to an online monitoring system and an online monitoring method for the posture of the power transmission line iron tower based on big data.
Background
At present, a perfect monitoring and early warning system is indispensable to a power system, especially a trans-regional long-distance power transmission line, which often passes through mountains, ravines or extreme temperature zones. These zones are not only subjected to severe weather conditions, but also inconvenient to patrol. Under the action of a plurality of adverse factors, the power transmission towers are easy to incline, sink and even collapse, and can not transmit fault information to an operation and maintenance center at the first time, so that property loss is further aggravated.
The existing iron tower monitoring system mainly comprises three types of position and posture monitoring, stress sensor monitoring and inclination sensor monitoring by different sensors. The position and posture detection mainly detects displacement generated by key nodes of the iron tower and reference coordinates through high-precision satellite positioning, and the method depends on the precision of a positioning technology. The invention relates to a method for monitoring the deformation of an iron tower and a method for monitoring the change of a reference origin provided by a system (application number: CN 201910621261.8) and a method for comparing the coordinates of the iron tower provided by a system (application number: CN 202021994179.4) for monitoring and early warning safety of the iron tower with the coordinates of nearby reference points. The stress sensor mainly adopts a grating fiber sensor to be attached to the surface of a measured rod, and the stress change is judged by measuring the reflection wavelength or the transmission wavelength of the grating, so that the method has higher precision, but has low economic benefit and is not beneficial to large-scale popularization. The invention relates to a distributed grating optical fiber detection method, and more particularly relates to an on-line monitoring device and method (application number: CN 201610157393.6) for deformation of a transmission line tower based on an optical fiber grating.
The inclination angle sensor monitors that an acceleration sensor containing a gyroscope is placed on a cross arm of an iron tower or a main material with a certain height, and when the tower body of the iron tower is inclined, the sensor sends out a signal to remotely transmit information. However, for the iron tower, no matter which load is applied to the tower body, the direct cause of the safety accident of the iron tower is that the stress of the tower body structure is changed. The traditional inclination angle sensor does not directly measure stress, particularly does not reflect the relation between the inclination angle and the tower body stress, and is easy to cause larger error.
Disclosure of Invention
The invention aims to solve a plurality of problems existing in the use process of the existing power transmission tower monitoring system and method, and provides a power transmission line tower posture online monitoring system and method based on big data.
The invention provides a big data-based power transmission line iron tower attitude online monitoring system which comprises a power supply, an inclination sensor, a wind speed measuring instrument, a control circuit board, a cloud platform database and an upper computer, wherein the power supply is respectively connected with the inclination sensor, the wind speed measuring instrument and the control circuit board, the power supply provides power for the inclination sensor, the wind speed measuring instrument and the control circuit board, the inclination sensor and the wind speed measuring instrument are both connected with the control circuit board, the collected data can be transmitted to the control circuit board in real time by the inclination sensor and the wind speed measuring instrument, wireless communication can be carried out between the control circuit board and the cloud platform database, the control circuit board can transmit the received data to the cloud platform database, wireless communication can also be carried out between the cloud platform database and the upper computer, and the upper computer can download, display and analyze the data from the cloud platform database.
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 sensor, the wind speed measuring instrument and the control circuit board, the battery provides power for the inclination 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 4000mAh capacity lithium ion battery.
The four inclination angle sensors are assembled at the position of the main material of the iron tower, which is nine meters away from the ground, and are distributed in square equilateral shapes.
The wind speed measuring instrument is assembled at the open and flat position of the tower angle of the iron tower within the range of ten meters, 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.
The model of control circuit board 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 receiving the inclination change signal that inclination sensor returned, control module is connected with storage module, the inclination change signal of control module output can be input to storage module, control module still is connected with wireless communication module, control module can pass through wireless communication module with the data in the storage module and send cloud platform database, wireless communication module adopts the data exchange between Lora wireless network communication and the cloud platform database.
The cloud platform database adopts an Ariy server ECS, the Ariy server ECS is provided with a windows server 2019 64 bit operating system, the Ariy server ECS is provided with a 100G solid state disk space, the Ariy server ECS uses a proprietary network for external communication, the cloud platform database uses an Oracle database for storing and receiving data in the cloud platform, the cloud platform database storage process uses an Oracle apex tool for development, the cloud platform database programming language uses pl-sql, the communication and data center in the cloud platform database uses a pycharm tool for development, and the programming language uses 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 intellij idea tool, and the programming language of the upper computer is java.
The power supply, the inclination angle sensor, the wind speed measuring instrument and the upper computer are all assembled by the existing equipment, so that specific models and specifications are not repeated.
The invention provides a big data-based on-line monitoring method for the attitude of a power transmission line iron tower, which comprises the following steps:
first, finite element analysis is carried out on a mechanical structure of the iron tower, namely, a continuous irregular object is decomposed into a finite number of units, a mechanical structure model of the iron tower is built in ANSYS software, and a combined working condition of applying wind load and icing load is simulated;
the wind load is divided into line wind load, tower wind load and insulator string wind load;
the calculation formula of the line wind load is as follows:
W x ＝α·W o ·μ z ·μ sc ·β c ·d·L p ·B 1 ·sin 2 θ
W o ＝V 2 /1600
wherein W is x Is a horizontal wind load standard value perpendicular to the direction of the lead and the ground wire;
alpha is the uneven wind pressure coefficient;
μ z is the wind pressure height change coefficient;
μ sc the body form factor of the wire or the ground wire;
β c the wind load adjustment coefficients of the line wires and the ground wires are 500kV and 750 kV;
d is the outer diameter of the wire or the ground wire or the calculated outer diameter during icing;
L p the horizontal span of the tower;
B 1 the coefficient is increased for the ice-covered wind load of the lead, the ground wire and the insulator string;
θ is the angle between the wind direction and the direction of the wire or ground;
W o the standard value of the reference wind pressure;
v is the wind speed with reference height of 10 m.
The wind load of the tower is calculated as follows:
W s ＝W o ·μ z ·μ s ·B 2 ·A s ·β z
wherein W is s The wind load standard value of the tower;
μ s is the body form factor of the component;
B 2 the ice-covered wind load of the tower component is increased by a coefficient;
A s calculating a value for the projected area of the windward component;
β z and adjusting the coefficient for the wind load of the tower.
The calculation formula of the wind load of the insulator string is as follows:
W 1 ＝W o ·μ z ·B 1 ·A 1
wherein W is 1 The insulator string wind load standard value;
A 1 and calculating a wind pressure bearing area of the insulator string.
The ice coating load is as follows
Wherein: w (W) f Is the standard value of ice coating load;
calculating the breaking force of the wire;
k 1 is the safety factor of the lead;
calculating breaking force for the ground wire;
k 2 is the safety coefficient of the ground wire;
inputting the obtained maximum wind load and maximum icing load into finite element analysis software;
the stress data and the key rod piece displacement data of the iron tower under the load of a specific value are obtained through finite element analysis, and when the stress of a certain point is deformed through formula deduction, the change of the displacement and the change of the angle are related as follows:
wherein: m is the angle change;
s is the magnitude of the displacement occurring at that 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 measurement result of the inclination sensor, wherein the method comprises the following steps:
and (3) combining the stress, displacement and angle variation obtained in the first step with data of temperature, humidity, wind direction, wind power, rainfall, snow amount, water accumulation amount and ice covering amount of the iron tower in 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.
And constructing a first convolution layer, and extracting the characteristics of the original data.
Using a 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 network calculation complexity and extracting main characteristics.
The second and third convolution layers are constructed using the same activation function and pooling layers as the first layer.
And constructing a full connection layer, connecting all the features, sending the data into a classifier, and performing fault classification on the reconstructed feature data after feature extraction and learning by the classifier.
Setting a training sample, verifying the specific gravity of the sample and testing the sample, and verifying the model result to obtain the corresponding relation between the angle change quantity and the temperature, humidity, wind direction, wind power, rainfall and snow quantity, water accumulation quantity and ice covering quantity of the iron tower in extreme weather.
Thirdly, constructing a cloud platform database and developing an upper computer, wherein the method specifically comprises the following steps:
an ECS (electronic control system) of an Ali cloud server is used, the ECS of the Ali cloud server is provided with a windows server 2019 64-bit operating system, a 100G solid state disk space is possessed, and a proprietary network is used for external communication;
storing and receiving data in a cloud platform database by using an Oracle database, wherein the storage process of the cloud platform database is developed by using an Oracle apex tool, a programming language is developed by using pl-sql, a communication and data center in the cloud platform database is developed by using a pyrm tool, and a programming language is developed by using python;
the display interface of the upper computer adopts webpage display, the front end and the rear end of the upper computer are developed by using an intellij idea tool, and a programming language is java;
fourthly, paving a hardware system on the main material of the iron tower, wherein each monitoring point is provided with an inclination angle sensor, a control circuit board and a power supply in a matching way, packaging the main material by using an F-shaped waterproof junction box, mounting a magnetic steel material on the back of the waterproof junction box, directly adsorbing the F-shaped waterproof junction box 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 and a 24C512 storage chip, and a maximum 5V power supply access port; the control circuit board integrates an AD module, can monitor battery voltage in real time, early warn in time when the voltage is low, the BC26 communication module can be inserted into an Internet of things card, data transmission is carried out by using a special channel of the Internet of things, the distributed MQTT communication protocol is adopted, the information transmission quality can be ensured under the extreme environment of high concurrency and poor network signals, the 24C512 storage chip has 512Kbit capacity, sensor data can be temporarily stored under the condition of unstable network, data loss is prevented, and the power supply interface provides maximum 5V direct current voltage for the circuit board;
the inclination angle sensor adopts an MPU6050, the inclination angle sensor is connected with an STM32F103C8T6 core, a triaxial gyroscope is arranged in the inclination angle sensor, and the inclination angle sensor can detect the change of an angle of more than 0.1 DEG 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 electrified, the power supply supplies power to the control circuit board, the control module drives the inclination sensor to work after being electrified, data obtained by the inclination sensor is transmitted to the control module, the control module stores the data in the storage module temporarily, the control module provides the data in the storage module for the wireless communication module, and the wireless communication module remotely transmits the data to the cloud platform database through the Lora gateway;
after the system is electrified, 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, a Lora communication module is integrated in the wind speed measuring instrument, and the wind speed measuring instrument can directly communicate information to the cloud platform database;
and the display system in the upper computer downloads data from the cloud platform database, analyzes the data, and gives an alarm prompt 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 inclination angle sensor, the system and method for online monitoring the attitude of the iron tower of the power transmission line based on big data solve the problem that the icing load cannot be directly measured, decouple the load quantity which causes the inclination of the iron tower in many aspects, and specifically classify the reasons of the faults of the iron tower. The invention converts the inclination angle of a certain point measured by the inclination angle sensor into the stress of the certain point of the iron tower, and the method is more reliable than a method for directly using the inclination angle sensor to obtain the safety state of the iron tower. The invention has simple structure, easy installation, maintenance and disassembly, lower cost, perfect function, capability of being additionally installed at a later stage, certain expandable space, higher economic benefit and wide popularization.
Drawings
Fig. 1 is a block diagram of the overall structure of the on-line 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 schematic diagram of actual output and predicted output values according to the present invention.
Fig. 4 is a schematic diagram of a mean square error decreasing process in the fitting process according to the present invention.
The labels in the above figures are as follows:
1. power supply 2, inclination angle sensor 3, wind speed measuring instrument 4 and control circuit board
5. Cloud platform database 6, upper computer 7, control module 8 and storage module
9. And a wireless communication module.
Detailed Description
Please refer to fig. 1 to 4:
the invention provides a big data-based transmission line iron tower attitude online monitoring system which comprises a power supply 1, an inclination 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 sensor 2, the wind speed measuring instrument 3 and the control circuit board 4, the power supply 1 provides power for the inclination sensor 2, the wind speed measuring instrument 3 and the control circuit board 4, the inclination sensor 2 and the wind speed measuring instrument 3 are both connected with the control circuit board 4, the collected data can be transmitted to the control circuit board 4 in real time by the inclination sensor 2 and the wind speed measuring instrument 3, wireless communication can be carried out between the control circuit board 4 and the cloud platform database 5, the received data can be transmitted to the cloud platform database 5 and the upper computer 6, and the upper computer 6 can also carry out wireless communication, and carry out data downloading, displaying and analyzing 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 sensor 2, the wind speed measuring instrument 3 and the control circuit board 4, the battery provides power for the inclination 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 4000mAh capacity lithium ion battery.
The four inclination angle sensors 2 are assembled, the four inclination angle sensors 2 are all assembled at the position of the main material of the iron tower, which is nine meters away from the ground, and the four inclination angle sensors 2 are distributed in a square equilateral manner.
The wind speed measuring instrument 3 is assembled at the wide and flat position of the angle of the tower in the range of ten meters, the wind speed measuring instrument 3 comprises a wind sensor and a wind direction sensor, a remote communication module is integrated inside 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 model of control circuit board 4 is STM32F, control circuit board 4 is including control module 7, storage module 8 and wireless communication module 9, wherein control module 7 is connected with inclination sensor 2, control module 7 is used for driving inclination sensor 2 and receiving the inclination change signal that inclination sensor 2 returned, control module 7 is connected with storage module 8, the inclination change signal of control module 7 output can be input to storage module 8, control module 7 still is connected with wireless communication module 9, control module 7 can pass through wireless communication module 9 with the data in the storage module 8 and send cloud platform database 5, wireless communication module 9 adopts the communication of Lora wireless network to carry out data exchange with cloud platform database 5 between.
The cloud platform database 5 adopts an ali cloud server ECS, the ali cloud server ECS is provided with a windows server 2019 64 bit operating system, the ali cloud server ECS is provided with a 100G solid state disk space, the ali cloud server ECS uses a proprietary network for external communication, the cloud platform database 5 uses an Oracle database for storing and receiving data in a cloud platform, the storage process of the cloud platform database 5 uses an Oracle apex tool for development, the programming language of the cloud platform database 5 uses pl-sql, the communication and data center in the cloud platform database 5 uses a pycharm tool for development, 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 intellijidad tool, and the programming language of the upper computer 6 is java.
The power supply 1, the inclination angle sensor 2, the wind speed measuring instrument 3 and the upper computer 6 are all assembled by the existing equipment, so specific models and specifications are not repeated.
The invention provides a big data-based on-line monitoring method for the attitude of a power transmission line iron tower, which comprises the following steps:
first, finite element analysis is carried out on a mechanical structure of the iron tower, namely, a continuous irregular object is decomposed into a finite number of units, a mechanical structure model of the iron tower is built in ANSYS software, and a combined working condition of applying wind load and icing load is simulated;
the wind load is divided into line wind load, tower wind load and insulator string wind load;
the calculation formula of the line wind load is as follows:
W x ＝α·W o ·μ z ·μ sc ·β c ·d·L p ·B 1 ·sin 2 θ
W o ＝V 2 /1600
wherein W is x Is a horizontal wind load standard value perpendicular to the direction of the lead and the ground wire;
alpha is the uneven wind pressure coefficient;
μ z is the wind pressure height change coefficient;
μ sc the body form factor of the wire or the ground wire;
β c the wind load adjustment coefficients of the line wires and the ground wires are 500kV and 750 kV;
d is the outer diameter of the wire or the ground wire or the calculated outer diameter during icing;
L p the horizontal span of the tower;
B 1 the coefficient is increased for the ice-covered wind load of the lead, the ground wire and the insulator string;
θ is the angle between the wind direction and the direction of the wire or ground;
W o the standard value of the reference wind pressure;
v is the wind speed with reference height of 10 m.
The wind load of the tower is calculated as follows:
W s ＝W o ·μ z ·μ s ·B 2 ·A s ·β z
wherein W is s The wind load standard value of the tower;
μ s is the body form factor of the component;
B 2 the ice-covered wind load of the tower component is increased by a coefficient;
A s calculating a value for the projected area of the windward component;
β z and adjusting the coefficient for the wind load of the tower.
The calculation formula of the wind load of the insulator is as follows:
W 1 ＝W o ·μ z ·B 1 ·A 1
wherein W is 1 The insulator string wind load standard value;
A 1 and calculating a wind pressure bearing area of the insulator string.
The ice coating load is as follows
Wherein: w (W) f Is the standard value of ice coating load;
calculating the breaking force of the wire;
k 1 is the safety factor of the lead;
calculating breaking force for the ground wire;
k 2 is the safety coefficient of the ground wire;
inputting the obtained maximum wind load and maximum icing load into finite element analysis software;
the stress data and the key rod piece displacement data of the iron tower under the load of a specific value are obtained through finite element analysis, and when the stress of a certain point is deformed through formula deduction, the change of the displacement and the change of the angle are related as follows:
wherein: m is the angle change;
s is the magnitude of the displacement occurring at that 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 measurement result of the inclination sensor, wherein the method comprises the following steps:
and (3) combining the stress, displacement and angle variation obtained in the first step with data of temperature, humidity, wind direction, wind power, rainfall, snow amount, water accumulation amount and ice covering amount of the iron tower in 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.
And constructing a first convolution layer, and extracting the characteristics of the original data.
Using a 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 network calculation complexity and extracting main characteristics.
The second and third convolution layers are constructed using the same activation function and pooling layers as the first layer.
And constructing a full connection layer, connecting all the features, sending the data into a classifier, and performing fault classification on the reconstructed feature data after feature extraction and learning by the classifier.
Setting a training sample, verifying the specific gravity of the sample and testing the sample, and verifying the model result to obtain the corresponding relation between the angle change quantity and the temperature, humidity, wind direction, wind power, rainfall and snow quantity, water accumulation quantity and ice covering quantity of the iron tower in extreme weather.
Thirdly, constructing a cloud platform database 5 and developing an upper computer 6, wherein the specific method is as follows:
an ECS (electronic control system) of an Ali cloud server is used, the ECS of the Ali cloud server is provided with a windows server 2019 64-bit operating system, a 100G solid state disk space is possessed, and a proprietary network is used for external communication;
storing and receiving data in a cloud platform database 5 by using an Oracle database, developing a 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 center in the cloud platform database 5 by using a pyrm tool, and developing a programming language by using python;
the display interface of the upper computer 6 adopts webpage display, the front end and the rear end of the upper computer 6 are developed by using intellij idea tools, and the programming language is java;
fourthly, a hardware system is paved on the main material of the iron tower, each monitoring point is provided with an inclination sensor 2, a control circuit board 4 and a power supply 1, an F-shaped waterproof junction box is used for packaging, a magnetic steel material is arranged on the back of the waterproof junction box, 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 maximum 5V power supply 1 supplies an access port; the control circuit board 4 integrates an AD module, can monitor the battery voltage in real time, early warn in time when the voltage is low, the BC26 communication module can insert an Internet of things card, uses a special channel of the Internet of things for data transmission, adopts a distributed MQTT communication protocol, can ensure the information transmission quality under the extreme environment of high concurrency and poor network signals, the 24C512 storage chip has 512Kbit capacity, can temporarily store sensor data under the unstable condition of the network, prevents data loss, and the power supply interface provides the maximum 5V direct current voltage for the circuit board;
the dip angle sensor 2 adopts an MPU6050, the dip angle sensor 2 is connected with an STM32F103C8T6 core, a triaxial gyroscope is arranged in the dip angle sensor 2, and the dip angle sensor 2 can detect the change of an angle of more than 0.1 DEG 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 electrified, the power supply 1 supplies power to the control circuit board 4, the control module 7 drives the tilt sensor 2 to work after being electrified, data obtained by the tilt sensor 2 is transmitted to the control module 7, the control module 7 stores the data in the storage module 8 temporarily, the control module 7 provides the data in the storage module 8 for the wireless communication module 9, and the wireless communication module 9 remotely transmits the data to the cloud platform database 5 through the Lora gateway;
after the system is electrified, 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, a Lora communication module is integrated inside the wind speed measuring instrument 3, and the wind speed measuring instrument 3 can directly communicate information to the cloud platform database 5;
the display system in the upper computer 6 downloads data from the cloud platform database 5, analyzes the data, and gives an alarm prompt when abnormal data is detected.
Claims (1)
1. An online monitoring method for the attitude of a power transmission line iron tower based on big data is characterized by comprising the following steps: the system comprises a power supply, an inclination sensor, a wind speed measuring instrument, a control circuit board, a cloud platform database and an upper computer, wherein the power supply is respectively connected with the inclination sensor, the wind speed measuring instrument and the control circuit board, the power supply provides power for the inclination sensor, the wind speed measuring instrument and the control circuit board, the inclination sensor and the wind speed measuring instrument are both connected with the control circuit board, the inclination sensor and the wind speed measuring instrument can transmit acquired data to the control circuit board in real time, wireless communication can be carried out between the control circuit board and the cloud platform database, the control circuit board can transmit received data to the cloud platform database, wireless communication can also be carried out between the cloud platform database and the upper computer, and the upper computer can download, display and analyze the data from the cloud platform database;
the online monitoring method comprises the following steps:
first, finite element analysis is carried out on a mechanical structure of the iron tower, namely, a continuous irregular object is decomposed into a finite number of units, a mechanical structure model of the iron tower is built in ANSYS software, and a combined working condition of applying wind load and icing load is simulated;
the wind load is divided into line wind load, tower wind load and insulator string wind load;
the calculation formula of the line wind load is as follows:
W x ＝α·W o ·μ z ·μ sc ·β c ·d·L p ·B 1 ·sin 2 θ
W o ＝V 2 /1600
wherein W is x Is a horizontal wind load standard value perpendicular to the direction of the lead and the ground wire;
alpha is the uneven wind pressure coefficient;
μ z is the wind pressure height change coefficient;
μ sc the body form factor of the wire or the ground wire;
β c the wind load adjustment coefficients of the line wires and the ground wires are 500kV and 750 kV;
d is the outer diameter of the wire or the ground wire or the calculated outer diameter during icing;
L p the horizontal span of the tower;
B 1 the coefficient is increased for the ice-covered wind load of the lead, the ground wire and the insulator string;
θ is the angle between the wind direction and the direction of the wire or ground;
W o the standard value of the reference wind pressure;
v is the wind speed with reference height of 10 m;
the wind load of the tower is calculated as follows:
W s ＝W o ·μ z ·μ s ·B 2 ·A s ·β z
wherein W is s The wind load standard value of the tower;
μ s is the body form factor of the component;
B 2 the ice-covered wind load of the tower component is increased by a coefficient;
A s calculating a value for the projected area of the windward component;
β z the wind load of the tower is adjusted by a coefficient;
the calculation formula of the wind load of the insulator is as follows:
W 1 ＝W o ·μ z ·B 1 ·A 1
wherein W is 1 The insulator string wind load standard value;
A 1 calculating a wind pressure bearing area of the insulator string;
the ice coating load is as follows
Wherein: w (W) f Is the standard value of ice coating load;
calculating the breaking force of the wire;
k 1 is the safety factor of the lead;
calculating breaking force for the ground wire;
k 2 is the safety coefficient of the ground wire;
inputting the obtained maximum wind load and maximum icing load into finite element analysis software;
the stress data and the key rod piece displacement data of the iron tower under the load of a specific value are obtained through finite element analysis, and when the stress of a certain point is deformed through formula deduction, the change of the displacement and the change of the angle are related as follows:
wherein: m is the angle change;
s is the magnitude of the displacement occurring at that 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 measurement result of the inclination sensor, wherein the method comprises the following steps:
combining the stress, displacement and angle variation obtained in the first step with data of temperature, humidity, wind direction, wind power, rainfall, snow amount, water accumulation amount and ice covering amount of the iron tower in 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 convolution layer, and extracting features of the original data;
using a 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 network calculation complexity and extracting main characteristics;
constructing a second layer of convolution layer and a third layer of convolution layer, and using the same activation function and pooling layer as the first layer;
constructing a full connection layer, connecting all the features, sending the data into a classifier, and performing fault classification on the reconstructed feature data after feature extraction and learning by the classifier;
setting a training sample, a verification sample and a test sample specific gravity, and verifying a model result to obtain the corresponding relation between the angle change quantity and the temperature, humidity, wind direction, wind power, rainfall and snow quantity, water accumulation quantity and ice covering quantity of the iron tower in extreme weather;
thirdly, constructing a cloud platform database and developing an upper computer, wherein the method specifically comprises the following steps:
using a cloud server and using a private network for external communication;
storing and receiving data in a cloud platform database by using an Oracle database, wherein the storage process of the cloud platform database is developed by using an Oracle apex tool, a programming language is developed by using pl-sql, a communication and data center in the cloud platform database is developed by using a pyrm tool, and a programming language is developed by using python;
the display interface of the upper computer adopts webpage display, the front end and the rear end of the upper computer are developed by using an intellijidade tool, and a programming language is java;
fourthly, paving a hardware system on the main material of the iron tower, wherein each monitoring point is provided with an inclination angle sensor, a control circuit board and a power supply in a matching way, packaging the main material by using an F-shaped waterproof junction box, mounting a magnetic steel material on the back of the waterproof junction box, directly adsorbing the F-shaped waterproof junction box 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 and a 24C512 storage chip, and a maximum 5V power supply access port; the control circuit board integrates an AD module, can monitor battery voltage in real time, early warn in time when the voltage is low, the BC26 communication module can be inserted into an Internet of things card, data transmission is carried out by using a special channel of the Internet of things, the distributed MQTT communication protocol is adopted, the information transmission quality can be ensured under the extreme environment of high concurrency and poor network signals, the 24C512 storage chip has 512Kbit capacity, sensor data can be temporarily stored under the condition of unstable network, data loss is prevented, and the power supply interface provides maximum 5V direct current voltage for the circuit board;
the inclination angle sensor adopts an MPU6050, the inclination angle sensor is connected with an STM32F103C8T6 core, a triaxial gyroscope is arranged in the inclination angle sensor, and the inclination angle sensor can detect the change of an angle of more than 0.1 DEG 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 electrified, the power supply supplies power to the control circuit board, the control module drives the inclination sensor to work after being electrified, data obtained by the inclination sensor is transmitted to the control module, the control module stores the data in the storage module temporarily, the control module provides the data in the storage module for the wireless communication module, and the wireless communication module remotely transmits the data to the cloud platform database through the Lora gateway;
after the system is electrified, 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, a Lora communication module is integrated in the wind speed measuring instrument, and the wind speed measuring instrument can directly communicate information to the cloud platform database;
and the display system in the upper computer downloads data from the cloud platform database, analyzes the data, and gives an alarm prompt when abnormal data are detected.
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