CN111601270A - Internet of things power monitoring system and method of wireless sensor network topological structure - Google Patents
Internet of things power monitoring system and method of wireless sensor network topological structure Download PDFInfo
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Abstract
The invention discloses an Internet of things power monitoring system and method of a wireless sensor network topological structure, and relates to the technical field of power monitoring. The invention constructs an internet of things data architecture comprising an information sensing layer, an internet of things network layer, a monitoring layer and a data application layer, and constructs a wireless sensor network topological structure, thereby realizing the fusion of sensing data of various sensors and facilitating the centralized processing of the data; monitoring personnel can detect the data by using conventional monitoring equipment without on site, remote, online and real-time monitoring of bottom sensing data can be realized, and the monitoring capability is improved. The invention adopts the traveling wave positioning method to realize the positioning of the monitoring fault node, can acquire the monitoring of a specific position, simultaneously realizes the fault diagnosis, realizes the system of data acquisition, position positioning, fault diagnosis and Internet of things architecture and realizes the remote, on-line and real-time monitoring of various fault nodes.
Description
Technical Field
The invention relates to the technical field of power monitoring, in particular to an Internet of things power monitoring system and method of a wireless sensor network topological structure.
Background
With the accelerated development of power grid technology and application, the scale and the user quantity of a power grid information system increase day by day, and the power grid structure is more and more complex, which puts higher requirements on the fault diagnosis of the power grid. As is well known, in large-scale power grid system applications, fault phenomena are very common and are difficult to avoid, so that fault detection and diagnosis levels directly affect the health and benign operation of a power grid, which is related to the benefit of users, and how to effectively improve the power grid information fault detection technology is a technical problem to be solved urgently at present. At present, a common fault detection and diagnosis method is only to detect a single component (such as an excitation system and a regulator thereof, a prime motor and a regulator thereof, a synchronous transmitter and a power load) in power grid information, a detection technology is only limited to a primary level, detection data only reflects fault characteristics of the single component, global measurement cannot be carried out, fault detection and diagnosis results are too comprehensive, reliability of fault diagnosis is difficult to improve, and capability of controlling the power grid information is reduced.
With the rapid development of power technology, a Wireless Sensor Network (WSN, hereinafter referred to as WSN) gradually enters the realization of people, the Network is a monitoring system composed of a large number of Sensor nodes in a self-organizing manner, and how to apply the Network to realize the monitoring of the power monitoring system is a technical problem which is urgently needed to be solved at present.
Disclosure of Invention
Aiming at the defects of the prior art, the invention discloses an Internet of things power monitoring system and method of a wireless sensor network topological structure.
The invention adopts the following technical scheme:
an internet of things power monitoring system of a wireless sensor network topology, wherein the system comprises:
the information sensing layer is internally provided with a wireless sensor network, the wireless sensor network organizes and combines tens of thousands of sensor nodes in a free manner in a wireless communication manner to form a network form, and the wireless sensor network at least comprises a physical layer, a data link, a network layer, a transmission layer and an application layer in architecture; wherein the physical layer comprises at least radio, infrared and light waves; the data link layer at least comprises a topological structure generation module, a topological management module and a network management module; the network layer at least comprises a router, a network transmission interface and a transceiver module; the transmission layer at least comprises a transmission control module, a transmission network unit and a transmission energy management module; the application layer at least comprises a data positioning module, a data time synchronization module and an application management module; the wireless sensor network is provided with sensors in a staggered manner, the sensors sense the working state of each power device and transmit the sensed data information of the power grid device; the sensors at least comprise a photoelectric sensor, an infrared sensor, a speed sensor, an acceleration sensor, a GIS sensor, a vibration sensor, a ripple wave sensor, a temperature and humidity sensor, an angle sensor, a magnetic field sensor, a rotating speed sensor, an RFID label, GPS equipment, a ray radiation sensor, a heat-sensitive sensor, an energy consumption sensor or an M2M terminal;
the network layer of the Internet of things is internally provided with a wired communication module or a wireless communication module and is used for receiving and transmitting the data information of the electrical equipment sensed by the information sensing layer; the wired communication module at least comprises an RS485 communication module or an RS232 communication module, and the wireless communication module at least comprises a TCP/IP network system, a ZigBee wireless network, a GPRS communication module or CDMA wireless communication, 3G network communication, 4G network communication, WLAN communication, LTE communication, a cloud server or a Bluetooth communication module; the cloud server at least comprises a distributed storage module, a data transmission interface, a CPU, an internal memory, a disk, a bandwidth and a cloud network interface, wherein the cloud server is formed by constructing a cloud resource pool by intensively and virtualizing a scale-level bottom server and allocating computing resources from the resource pool, wherein the CPU, the internal memory, the disk or the bandwidth exist in a free combination mode;
the monitoring layer is internally provided with a data fusion algorithm module and a traveling wave positioning module, wherein the data fusion algorithm module comprises a signal conditioning circuit, an A/D conversion module, a microprocessor and a power supply module which are connected with the wireless sensor network node and various sensors, and a data fusion value with a smaller mean square value error expected value is obtained through different data sensed by the various sensors; the traveling wave positioning module is used for positioning the position of the power equipment and comprises a fault information acquisition unit and a wireless signal receiving module;
the data application layer is internally provided with a computer service system, an oscilloscope and a display which are connected with the computer service system, and the computer service system receives the data transmitted by the monitoring layer through the data interface; wherein:
the output end of the information perception layer is connected with the input end of the internet of things network layer, the output end of the internet of things network layer is connected with the input end of the monitoring layer, and the output end of the monitoring layer is connected with the input end of the data application layer.
As a further technical scheme of the invention, the sensor also comprises an RFID (radio frequency identification) label, a camera, a reader-writer or a GPS (global positioning system) positioning device.
As a further technical scheme of the invention, the sensor node is a single-chip programmable UHF transceiver chip embedded with an 8051 single chip microcomputer, the type of the transceiver chip is a CC2510 chip, and a 32kB Flash memory, a 4kB SRAM module, an 8-channel 8-14bit A/D converter, a 16-bit timer, an 8-bit timer, a UART/SPI module, an RTC module, a watchdog circuit, a DES coding module and a general I/O module are embedded in the CC2510 chip.
The invention also adopts the following technical scheme:
an Internet of things power monitoring method of a wireless sensor network topological structure comprises the following steps:
(S1) acquiring data information of various sensors arranged in the wireless sensor network node through the information sensing layer;
(S2) transmitting data information of various sensors in the information perception layer through the Internet of things network layer;
(S3) carrying out data fusion, traveling wave positioning calculation and fault diagnosis on the received data information of various sensors in the wireless sensor network node through a monitoring layer;
(S4) applying the calculated data information in the data application layer.
As a further technical solution of the present invention, the data fusion method in the step (S3) is an adaptive weighted fusion algorithm.
As a further technical scheme of the invention, the method of the self-adaptive weighting fusion algorithm comprises the following steps: under the condition that the total mean square error of various different data is minimum, the optimal weighting operators corresponding to different sensors are searched out in a self-adaptive mode according to the data measurement values sensed by the sensors, so that the data search result reaches the optimal solution.
As a further technical scheme of the invention, the mathematical model construction method of the self-adaptive weighting fusion algorithm comprises the following steps:
the variance of the data sensed by the ith sensor is recorded as sigmaiThe output data obtained by fusing the data is recorded as XiThe weighting operator is denoted as WiAnd the value of i is 1-n, and after the data is set, the calculation relationship between the fused X numerical value and the weighting operator can satisfy the following conditions:
then when calculating the total variance, then there are:
wherein
Expressed as total variance, E is expressed as variance, since X1、X2,…,XnIndependent of each other, and the data is an unbiased estimate of X, so by variance calculation, one canError of data fusion calculation can be reduced by
E[(X-Xp)(X-Xq)]＝0 (4)
Wherein (p ≠ q, p ≠ 1,2, 3.. n; q ≠ 1,2, 3.. n);
the weighting operator for each different sensor can be expressed as:
and further calculating and outputting an optimal weighting operator of each sensor.
As a further technical solution of the present invention, the traveling wave positioning calculation method comprises:
when the fault of the power equipment is detected, fault traveling waves are emitted to the outside due to sudden increase of voltage due to existence of a fault point, the traveling waves are reflected and refracted due to resistance of various factors in the traveling wave propagation process, the traveling waves are generally refracted and reflected at the positions of a bus, a power supply, the fault point and the like in the transmission process, the traveling waves are calculated by detecting the position time of the fault point and testing at a plurality of time points, and the time point of the fault traveling wave detected for the 1 st time is assumed to be t1The time point of the fault traveling wave detected at the 2 nd time is t2Then, the fault traveling wave is transmitted for 2 times between 2 time points on the bus l and different fault points in the power distribution network, and then the position calculation formula of the fault point is as follows:
where L is the distance between the fault point and the bus L, v represents the propagation velocity of the traveling wave, t1And t1The traveling waves detected at different time points are obtained according to the formula (6), and the method for measuring the fault distance is realized based on the measurement of the propagation speed and the propagation time of the fault traveling wave between the fault point and the bus at any end, and depends on the accuracy of the measurement of the traveling wave speed and the traveling wave propagation time.
As a further aspect of the present invention, the fault diagnosis method in the step (S3) is an EMD empirical mode decomposition method.
As a further technical scheme of the invention, the traveling wave positioning method is a single-ended traveling wave method or a double-ended traveling wave method.
Positive effect
The invention constructs a wireless sensor network topological structure, realizes the fusion of multiple sensor sensing data and is convenient for centralized processing of the data;
the invention adopts an Internet of things architecture system, monitoring personnel can detect the sensing data by using conventional monitoring equipment without on-site, remote, on-line and real-time monitoring of bottom sensing data can be realized, and the monitoring capability is improved;
the invention adopts the traveling wave positioning method to realize the positioning of the monitoring fault node, can acquire the monitoring of the specific position and improve the data monitoring capability;
the invention realizes the traveling wave positioning method, the fault diagnosis, the data acquisition, the position positioning, the fault diagnosis and the system of the Internet of things architecture through the traveling wave method, and realizes the remote, on-line and real-time monitoring of various fault nodes.
Drawings
Fig. 1 is a schematic diagram of an architecture of an internet of things power monitoring system of a wireless sensor network topology structure according to the present invention;
FIG. 2 is a schematic diagram of a wireless sensor network topology structure in an Internet of things power monitoring system of the wireless sensor network topology structure of the present invention;
FIG. 3 is a schematic diagram of a hardware structure of a traveling wave positioning module in an Internet of things power monitoring system of a wireless sensor network topology structure according to the invention;
FIG. 4 is a schematic flow chart of an Internet of things power monitoring method of a wireless sensor network topology structure according to the invention;
FIG. 5 is a schematic diagram of a mathematical model construction method of a self-adaptive weighting fusion algorithm in the power monitoring method of the Internet of things of the wireless sensor network topology structure;
FIG. 6 is a schematic diagram of decomposition of VMD signals in the Internet of things power monitoring method of the wireless sensor network topology structure of the invention
Fig. 7 is a schematic diagram of a change of a Hilbert transform function in the power monitoring method for the internet of things of the wireless sensor network topology structure.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1 System
As shown in fig. 1 to 3, an internet of things power monitoring system with a wireless sensor network topology structure, wherein the system includes:
the information sensing layer is internally provided with a wireless sensor network, the wireless sensor network organizes and combines tens of thousands of sensor nodes in a free manner in a wireless communication manner to form a network form, and the wireless sensor network at least comprises a physical layer, a data link, a network layer, a transmission layer and an application layer in architecture; wherein the physical layer comprises at least radio, infrared and light waves; the data link layer at least comprises a topological structure generation module, a topological management module and a network management module; the network layer at least comprises a router, a network transmission interface and a transceiver module; the transmission layer at least comprises a transmission control module, a transmission network unit and a transmission energy management module; the application layer at least comprises a data positioning module, a data time synchronization module and an application management module; the wireless sensor network is provided with sensors in a staggered manner, the sensors sense the working state of each power device and transmit the sensed data information of the power grid device; the sensors at least comprise a photoelectric sensor, an infrared sensor, a speed sensor, an acceleration sensor, a GIS sensor, a vibration sensor, a ripple wave sensor, a temperature and humidity sensor, an angle sensor, a magnetic field sensor, a rotating speed sensor, an RFID label, GPS equipment, a ray radiation sensor, a heat-sensitive sensor, an energy consumption sensor or an M2M terminal;
the network layer of the Internet of things is internally provided with a wired communication module or a wireless communication module and is used for receiving and transmitting the data information of the electrical equipment sensed by the information sensing layer; the wired communication module at least comprises an RS485 communication module or an RS232 communication module, and the wireless communication module at least comprises a TCP/IP network system, a ZigBee wireless network, a GPRS communication module or CDMA wireless communication, 3G network communication, 4G network communication, WLAN communication, LTE communication, a cloud server or a Bluetooth communication module; the cloud server at least comprises a distributed storage module, a data transmission interface, a CPU, an internal memory, a disk, a bandwidth and a cloud network interface, wherein the cloud server is formed by constructing a cloud resource pool by intensively and virtualizing a scale-level bottom server and allocating computing resources from the resource pool, wherein the CPU, the internal memory, the disk or the bandwidth exist in a free combination mode;
the monitoring layer is internally provided with a data fusion algorithm module and a traveling wave positioning module, wherein the data fusion algorithm module comprises a signal conditioning circuit, an A/D conversion module, a microprocessor and a power supply module which are connected with the wireless sensor network node and various sensors, and a data fusion value with a smaller mean square value error expected value is obtained through different data sensed by the various sensors; the traveling wave positioning module is used for positioning the position of the power equipment and comprises a fault information acquisition unit and a wireless signal receiving module;
the data application layer is internally provided with a computer service system, an oscilloscope and a display which are connected with the computer service system, and the computer service system receives the data transmitted by the monitoring layer through the data interface; wherein:
the output end of the information perception layer is connected with the input end of the internet of things network layer, the output end of the internet of things network layer is connected with the input end of the monitoring layer, and the output end of the monitoring layer is connected with the input end of the data application layer.
In an embodiment of the present invention, the sensor further includes an RFID tag, a camera, a reader/writer, or a GPS positioning device.
In the embodiment of the invention, the sensor node is a single-chip programmable UHF transceiver chip embedded with an 8051 single chip microcomputer, the model of the transceiver chip is a CC2510 chip, and a 32kB Flash memory, a 4kB SRAM module, an 8-channel 8-14bit A/D converter, a 16-bit timer, an 8-bit timer, a UART/SPI module, an RTC module, a watchdog circuit, a DES coding module and a general I/O module are embedded in the CC2510 chip.
In the above embodiments, the Wireless Sensor Network (WSN) is a Wireless Network formed by a large number of stationary or mobile sensors in a self-organizing and multi-hop manner, so as to cooperatively sense, collect, process and transmit information of sensed objects in a geographic area covered by the Network, and finally transmit the information to an owner of the Network. Wireless sensor networks are made up of a large number of small, low-cost, wireless communication, sensing, and data processing capable sensor nodes dispersed throughout a work area. Each node may have different perception forms, such as sonar, shock wave, infrared ray and the like, but the node can complete the collection, transmission, decision making and implementation of target information, and realize tasks such as area monitoring, target tracking, positioning and prediction. Each node has the ability to store, process, and transfer data. Through the wireless network, the sensor nodes can exchange information with each other and can also transmit the information to a remote end.
EXAMPLE 2 method
As shown in fig. 4 to 7, an internet of things power monitoring method of a wireless sensor network topology structure includes the following steps:
(S1) acquiring data information of various sensors arranged in the wireless sensor network node through the information sensing layer;
(S2) transmitting data information of various sensors in the information perception layer through the Internet of things network layer;
(S3) carrying out data fusion, traveling wave positioning calculation and fault diagnosis on the received data information of various sensors in the wireless sensor network node through a monitoring layer;
(S4) applying the calculated data information in the data application layer.
In the above embodiment, the data fusion method in the step (S3) is an adaptive weighted fusion algorithm.
As a further technical scheme of the invention, the method of the self-adaptive weighting fusion algorithm comprises the following steps: under the condition that the total mean square error of various different data is minimum, the optimal weighting operators corresponding to different sensors are searched out in a self-adaptive mode according to the data measurement values sensed by the sensors, so that the data search result reaches the optimal solution.
In the above embodiment, the mathematical model construction method of the adaptive weighted fusion algorithm includes:
the variance of the data sensed by the ith sensor is recorded as sigmaiThe output data obtained by fusing the data is recorded as XiThe weighting operator is denoted as WiAnd the value of i is 1-n, and after the data is set, the calculation relationship between the fused X numerical value and the weighting operator can satisfy the following conditions:
then when calculating the total variance, then there are:
wherein
Expressed as total variance, E is expressed as variance, since X1、X2,…,XnIndependent of each other, and the data is the unbiased estimation of X, so the error of data fusion calculation can be reduced by variance calculation
E[(X-Xp)(X-Xq)]＝0 (4)
Wherein (p ≠ q, p ≠ 1,2, 3.. n; q ≠ 1,2, 3.. n);
the weighting operator for each different sensor can be expressed as:
and further calculating and outputting an optimal weighting operator of each sensor.
In the above embodiment, the traveling wave positioning calculation method includes:
when the fault of the power equipment is detected, fault traveling waves are emitted to the outside due to sudden increase of voltage due to existence of a fault point, the traveling waves are reflected and refracted due to resistance of various factors in the traveling wave propagation process, the traveling waves are generally refracted and reflected at the positions of a bus, a power supply, the fault point and the like in the transmission process, the traveling waves are calculated by detecting the position time of the fault point and testing at a plurality of time points, and the time point of the fault traveling wave detected for the 1 st time is assumed to be t1The time point of the fault traveling wave detected at the 2 nd time is t2Then, the fault traveling wave is transmitted for 2 times between 2 time points on the bus l and different fault points in the power distribution network, and then the position calculation formula of the fault point is as follows:
where L is the distance between the fault point and the bus L, v represents the propagation velocity of the traveling wave, t1And t1The traveling waves detected at different time points are obtained according to the formula (6), and the method for measuring the fault distance is realized by measuring the propagation speed and the propagation time of the fault traveling wave between the fault point and the bus at any endDepending on the accuracy of the traveling wave velocity and traveling wave travel time measurements.
In the above embodiment, the fault diagnosis method in the step (S3) is an EMD empirical mode decomposition method, and the traveling wave positioning method is a single-ended traveling wave method or a double-ended traveling wave method, which is described as an example of the single-ended traveling wave method. When fault diagnosis is carried out, the method comprises the following steps:
(1) firstly, after fault signals in the power equipment are acquired through various sensors, transient voltage and transient current data of various detection nodes on a power transmission line of the power equipment can be acquired in real time. In the specific application process, different line numbers are set for each line, and each detection node is provided with a detection node number.
(2) And decoupling and transforming the fault signals in the line to separate the component signals of the fault traveling wave. By using the modal aliasing phenomenon and the endpoint effect in the EMD algorithm, the signal-to-noise ratio of the traveling wave signal is effectively improved. The EMD (Empirical Mode Decomposition, EMD for short) carries out signal Decomposition according to the time scale characteristics of the fault data of the power distribution network, and when the EMD is used, any basis function does not need to be preset. When EMD calculation is carried out, the acquired transient voltage and transient current data are converted into detection point waveforms, and the detection point waveforms comprise waveforms formed by all detection points on the same line and waveforms formed by the same detection point at different times.
(3) VMD decomposition is carried out on the obtained fault signal, as shown in FIG. 6, the decomposed modal component information is output, a self-adaptive wiener filter set is carried out through a VMD algorithm, pseudo components in the fault signal of the power distribution network can be reduced, modal aliasing is not obvious, signal noise is effectively removed, then the time sequence of the fault waveform of each line is determined by adjusting time of waveforms formed at different time stages at the same detection point, and the line which detects the fault waveform at the earliest is determined.
(4) The instantaneous frequency of the modal component of the power distribution network fault signal is obtained by using Hilbert transform function processing, as shown in fig. 7, according to the instantaneous frequency of the modal component signal extracted by the Hilbert transform function, the arrival time calibration of the initial wave head is realized, then combines with VMD conversion and Hilbert conversion to realize the detection of the traveling wave head, further effectively improves the detection precision of the traveling wave head, can locate the position of a fault point by the detected initial traveling wave signal of the power distribution network fault, the arrival time of the component signal of the initial travelling wave of the power distribution network fault signal is then determined on the basis of the extracted instantaneous frequency, in the line which detects fault waveforms according to different time, waveforms formed by all detection points on the same line are compared with pre-stored standard waveforms, and two detection nodes which detect faults at the earliest are determined, so that the positioning accuracy and reliability of fault points can be improved.
(5) The instantaneous frequency of the modal component of the power distribution network fault signal is obtained by using Hilbert transform function processing, as shown in fig. 6, according to the instantaneous frequency of the modal component signal extracted by the Hilbert transform function, the arrival time calibration of the initial wave head is realized, then combines with VMD conversion and Hilbert conversion to realize the detection of the traveling wave head, further effectively improves the detection precision of the traveling wave head, can locate the position of a fault point by the detected initial traveling wave signal of the power distribution network fault, the arrival time of the component signal of the initial travelling wave of the power distribution network fault signal is then determined on the basis of the extracted instantaneous frequency, in the line which detects fault waveforms according to different time, waveforms formed by all detection points on the same line are compared with pre-stored standard waveforms, and two detection nodes which detect faults at the earliest are determined, so that the positioning accuracy and reliability of fault points can be improved.
Although specific embodiments of the present invention have been described above, it will be understood by those skilled in the art that these specific embodiments are merely illustrative and that various omissions, substitutions and changes in the form of the detail of the methods and systems described above may be made by those skilled in the art without departing from the spirit and scope of the invention. For example, it is within the scope of the present invention to combine the steps of the above-described methods to perform substantially the same function in substantially the same way to achieve substantially the same result. Accordingly, the scope of the invention is to be limited only by the following claims.
Claims (10)
1. The utility model provides a wireless sensor network topology's thing networking power monitoring system which characterized in that: the system comprises:
the information sensing layer is internally provided with a wireless sensor network, the wireless sensor network organizes and combines tens of thousands of sensor nodes in a free manner in a wireless communication manner to form a network form, and the wireless sensor network at least comprises a physical layer, a data link, a network layer, a transmission layer and an application layer in architecture; wherein the physical layer comprises at least radio, infrared and light waves; the data link layer at least comprises a topological structure generation module, a topological management module and a network management module; the network layer at least comprises a router, a network transmission interface and a transceiver module; the transmission layer at least comprises a transmission control module, a transmission network unit and a transmission energy management module; the application layer at least comprises a data positioning module, a data time synchronization module and an application management module; the wireless sensor network is provided with sensors in a staggered manner, the sensors sense the working state of each power device and transmit the sensed data information of the power grid device; the sensors at least comprise a photoelectric sensor, an infrared sensor, a speed sensor, an acceleration sensor, a GIS sensor, a vibration sensor, a ripple wave sensor, a temperature and humidity sensor, an angle sensor, a magnetic field sensor, a rotating speed sensor, an RFID label, GPS equipment, a ray radiation sensor, a heat-sensitive sensor, an energy consumption sensor or an M2M terminal;
the network layer of the Internet of things is internally provided with a wired communication module or a wireless communication module and is used for receiving and transmitting the data information of the electrical equipment sensed by the information sensing layer; the wired communication module at least comprises an RS485 communication module or an RS232 communication module, and the wireless communication module at least comprises a TCP/IP network system, a ZigBee wireless network, a GPRS communication module or CDMA wireless communication, 3G network communication, 4G network communication, WLAN communication, LTE communication, a cloud server or a Bluetooth communication module; the cloud server at least comprises a distributed storage module, a data transmission interface, a CPU, an internal memory, a disk, a bandwidth and a cloud network interface, wherein the cloud server is formed by constructing a cloud resource pool by intensively and virtualizing a scale-level bottom server and allocating computing resources from the resource pool, wherein the CPU, the internal memory, the disk or the bandwidth exist in a free combination mode;
the monitoring layer is internally provided with a data fusion algorithm module and a traveling wave positioning module, wherein the data fusion algorithm module comprises a signal conditioning circuit, an A/D conversion module, a microprocessor and a power supply module which are connected with the wireless sensor network node and various sensors, and a data fusion value with a smaller mean square value error expected value is obtained through different data sensed by the various sensors; the traveling wave positioning module is used for positioning the position of the power equipment and comprises a fault information acquisition unit and a wireless signal receiving module;
the data application layer is internally provided with a computer service system, an oscilloscope and a display which are connected with the computer service system, and the computer service system receives the data transmitted by the monitoring layer through the data interface; wherein:
the output end of the information perception layer is connected with the input end of the internet of things network layer, the output end of the internet of things network layer is connected with the input end of the monitoring layer, and the output end of the monitoring layer is connected with the input end of the data application layer.
2. The internet of things power monitoring system with the wireless sensor network topology structure as claimed in claim 1, wherein: the sensor also comprises an RFID label, a camera, a reader-writer or a GPS positioning device.
3. The internet of things power monitoring system with the wireless sensor network topology structure as claimed in claim 1, wherein: the sensor node is a single-chip programmable UHF transceiver chip embedded with an 8051 single chip microcomputer, the type of the transceiver chip is a CC2510 chip, and a 32kB Flash memory, a 4kB SRAM module, an 8-channel 8-14bit A/D converter, a 16-bit timer, an 8-bit timer, a UART/SPI module, an RTC module, a watchdog circuit, a DES coding module and a general I/O module are embedded in the CC2510 chip.
4. An Internet of things power monitoring method of a wireless sensor network topological structure is characterized by comprising the following steps: the method comprises the following steps:
(S1) acquiring data information of various sensors arranged in the wireless sensor network node through the information sensing layer;
(S2) transmitting data information of various sensors in the information perception layer through the Internet of things network layer;
(S3) carrying out data fusion, traveling wave positioning calculation and fault diagnosis on the received data information of various sensors in the wireless sensor network node through a monitoring layer;
(S4) applying the calculated data information in the data application layer.
5. The method for monitoring the power of the internet of things of a wireless sensor network topology structure according to claim 4, wherein the method comprises the following steps: the data fusion method in the step (S3) is an adaptive weighted fusion algorithm.
6. The method for monitoring the power of the internet of things of a wireless sensor network topology structure according to claim 5, wherein the method comprises the following steps: the method of the self-adaptive weighting fusion algorithm comprises the following steps: under the condition that the total mean square error of various different data is minimum, the optimal weighting operators corresponding to different sensors are searched out in a self-adaptive mode according to the data measurement values sensed by the sensors, so that the data search result reaches the optimal solution.
7. The method for monitoring the power of the internet of things of a wireless sensor network topology structure according to claim 6, characterized in that: the mathematical model construction method of the self-adaptive weighting fusion algorithm comprises the following steps:
the variance of the data sensed by the ith sensor is recorded as sigmaiThe output data obtained by fusing the data is recorded as XiThe weighting operator is denoted as WiAnd the value of i is 1-n, and after the data is set, the calculation relationship between the fused X numerical value and the weighting operator can satisfy the following conditions:
then when calculating the total variance, then there are:
wherein
Expressed as total variance, E is expressed as variance, since X1、X2，…，XnIndependent of each other, and the data is the unbiased estimation of X, so the error of data fusion calculation can be reduced by variance calculation
E[(X-Xp)(X-Xq)]＝0 (4)
Wherein (p ≠ q, p ≠ 1,2, 3.. n; q ≠ 1,2, 3.. n);
the weighting operator for each different sensor can be expressed as:
and further calculating and outputting an optimal weighting operator of each sensor.
8. The method for monitoring the power of the internet of things of a wireless sensor network topology structure according to claim 4, wherein the method comprises the following steps: the traveling wave positioning calculation method comprises the following steps:
when a fault of the electrical equipment is detected, due to the presence of the fault point, it will be due to the sudden change of voltageThe method comprises the steps of increasing fault traveling waves emitted to the outside, emitting reflection and refraction of the traveling waves due to resistance of various factors in the traveling wave propagation process, emitting refraction and reflection of the traveling waves at positions such as a bus, a power supply and a fault point in the transmission process, detecting the position time of the fault point, testing at a plurality of time points to calculate the traveling waves, and assuming that the time point of the fault traveling waves detected for the 1 st time is t1The time point of the fault traveling wave detected at the 2 nd time is t2Then, the fault traveling wave is transmitted for 2 times between 2 time points on the bus l and different fault points in the power distribution network, and then the position calculation formula of the fault point is as follows:
where L is the distance between the fault point and the bus L, v represents the propagation velocity of the traveling wave, t1And t1The traveling waves detected at different time points are obtained according to the formula (6), and the method for measuring the fault distance is realized based on the measurement of the propagation speed and the propagation time of the fault traveling wave between the fault point and the bus at any end, and depends on the accuracy of the measurement of the traveling wave speed and the traveling wave propagation time.
9. The method for monitoring the power of the internet of things of a wireless sensor network topology structure according to claim 4, wherein the method comprises the following steps: the fault diagnosis method in the step (S3) is an EMD empirical mode decomposition method.
10. The method for monitoring the power of the internet of things of a wireless sensor network topology structure according to claim 4, wherein the method comprises the following steps: the traveling wave positioning method is a single-ended traveling wave method or a double-ended traveling wave method.
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