The on-site environmental monitoring of transmission lines faces difficulties such as complex environment, difficult communication, and difficult to determine alarm strategies. An on-site monitoring and early warning scheme for transmission lines based on the Internet of Things (IOT) is proposed. Taking advantage of the low power consumption, low cost and multi-sensor characteristics of the Internet of Things, the selection of the hardware platform of the system and the early warning algorithm are proposed.
1 Introduction
On-site environmental monitoring of transmission lines relies on sensors that can be directly installed on the transmission line to record the running status of the equipment in real time to realize on-line measurement, diagnosis and overhaul of transmission lines, which is very important for the operation safety of high-voltage and ultra-high-voltage power grids. With the rapid development of MEMS, system-on-chip, wireless communication and low-power embedded technology, the IOT has provided all-weather, low-cost, high-reliability and high-reliability solutions for on-site environmental monitoring of transmission lines. Program. Based on the existing transmission line monitoring system, the IOT-based on-site environmental monitoring program is designed by analyzing the key technologies of IOT.
2 Problems with existing monitoring and early warning systems
Domestic and foreign power workers have conducted extensive research on the environmental monitoring of transmission lines. In the early days, manual inspections were used to monitor the icing of power facilities. With the development of computer network and communication technology, the literature developed a computer monitoring system for power facilities using power communication network; a company introduced GPRS (GSM/CDMA) technology and video technology into transmission facility monitoring, and developed real-time monitoring of overhead transmission line ice coating. The system introduces the transmission line disaster monitoring system and the ice coating online monitoring system. These devices have achieved certain effects on the actual site, but there are still the following problems: 1 Manual monitoring means consumes a lot of manpower and material resources, and can not achieve real-time observation in 24 h. At the same time, due to the wide wiring range, some wiring area geography It is impossible to achieve full-scale monitoring; 2 ice and snow disasters cause power line collapse and disconnection, and a large number of communication cables are broken. Public communication networks and power communication networks are interrupted to varying degrees, and monitoring data cannot be reliably sent to monitoring. Center; 3 affected by regional, climatic, topographic and other factors, specific monitoring areas require specific alarm strategies, and need to carry out targeted monitoring data accumulation and strategy improvement.
3 Overview of the Internet of Things
Figure 1 shows a block diagram of an IOT system. Among them, the sensor node has the functions of sensing, computing and communication, each node can collect environmental data (such as temperature, humidity, wind speed, vibration frequency and amplitude, etc.), communicate with each other using wireless multi-hop mode, and according to applications and systems The collected data is processed in-network. The aggregation node collects the information collected and processed by the sensor network and delivers it to the user via the Internet or satellite. The user is the recipient and application of the perceived information, either a person or a computer or other device.
As a component of the IOT, the sensor node is generally composed of four basic components, as shown in Figure 2.
The sensing unit is a perceptual environment that produces perceptual data and is typically composed of a set of miniaturized sensor components. The processing unit (usually built-in memory) processes the sensor data and controls the nodes to work with other nodes to accomplish the perceived tasks. Low-power microprocessors, such as the Mica2 Mote system, use a 7.37 MHz 8-bit ATMega12 8L microprocessor with 128 kB of program flash, 4kB of SRAM, and consume 16.5 mW, usually running on TInyOS, MANTIS, etc. A miniaturized operating system customized for IOT. The transceiver unit ensures that the nodes communicate with each other. IOT generally considers that short-range wireless low-power communication technology is more suitable. At present, with the popularity of ZigBee (IEEE802.15.4) technology, IOT has widely adopted ZigBee devices. The energy unit provides the energy needed for the node to function properly. Since IOT usually works in an unattended state, the network lifetime depends on the energy of the node, so saving energy is an important factor in IOT design.
4 Hardware selection of the monitoring system
At present, there are many hardware platforms for IOT nodes at home and abroad. Typical nodes include the Mica series, Sensoria WINS, Toles, μAMPS series, XYZnode, Zabranet, and others. In fact, the main difference between the platforms is the use of different processors, wireless communication protocols and different sensors related to the application. In this Mica series of nodes are more mature and widely used.
The microprocessor chip of the Micaz node uses Atmega128. The Micaz51 pin expansion interface can be connected to analog input, digital I/O, I2C, SPI interface and UART interface. The communication module uses the CC2420 chip. The chip is the first communication chip to support Zigbee communication technology. The carrier frequency is 2.4 GHz, the data transmission rate is up to 250 kbps, and the communication distance is 60-150 m, which is more suitable for indoor applications. The data acquisition module uses the ADXL202JE accelerometer to simultaneously acquire the acceleration of two axes.
The IRIS node platform is an IOT node based on the ATmega128l micro-processing chip and the RF230 radio chip. It is a small wireless measurement system specially designed for embedded sensor networks. It is a Mote module that works at 2.4 GHz and supports IEEE802.15.4 protocol. For low power IOT.
Several new features added to the IRIS platform have improved node performance overall. Its characteristics are as follows: 1 relative to the MICA series, it has 3 times the working distance, 2 times the storage space; 2 outdoor test without the amplifier, the line of sight of the node can reach 500 m; 3 based on IEEE802.15.4/ RF transmitter for ZigBee protocol; 42.4 to 2.48 GHz. Global compatible ISM band; 5 direct sequence spread spectrum technology, anti-RF interference, good data shielding; 6250 kbps data transmission rate; 7 support reliable multi-hop Mesh network; 8 plug and play, can connect sensor board, data acquisition Board, gateway and software. In addition, IRIS's 51-pin expansion interface connects analog inputs, digital I/O, I2C, SPI and UART interfaces, making them easy to interface with other peripherals. In view of the advantages of the IRIS platform, it is chosen here as the hardware node of the monitoring system.
5 linear discriminant classification algorithm
The physical quantities required for on-site environmental monitoring of transmission lines include local temperature, amplitude and frequency of the line, and wind speed. Taking the ice warning as an example, according to the specific climate physical environment of each region, an expert system with different parameters based on the data needs to be established. The linear discriminant classification algorithm (LDA) as a multi-source early warning decision scheme has the characteristics of simple and efficient algorithm and high confidence.
Discriminant analysis is a commonly used statistical analysis method. It judges which type of method the research object belongs to by observing or measuring several variable values. For discriminant analysis, the classification of the observed object and a number of variable values ​​indicating the characteristics of the observed object must be known. Discriminant analysis is to select the variables that can provide more information, and establish a discriminant function, so that the discriminant rate of the discriminant function is the smallest when using the derived discriminant function to discriminate the category.
There are two types of D-dimensional training samples xk (k = 1, 2, ..., n), where n1 samples are from the wi type, n2 samples are from the wi type, n = n1 + n2. Two types of training samples form a subset X1 and X2 of the training samples, respectively. make
, yk is the scalar obtained by transforming w from vector xk, which is one-dimensional. In fact, for a given w, yk is the value of the decision function. The sample mean vector of each type in the D-dimensional feature space is:
After mapping, the larger the distance between the average values ​​of the two classes, the better, and the smaller the dispersion within the sample class of each class, the better. Therefore, define the Fisher criterion function as:
The largest solution for JF is w*, which is the optimal solution vector, which is Fisher's linear discriminant.
6 LDA-based line monitoring solution
The wireless communication IOT is arranged on the transmission line to collect the transmission line temperature, the amplitude and frequency of the line, the wind speed, and the line tension. Collecting data during the winter will require a numerical storage of the physical quantities of both the de-icing and the de-icing-free states to establish a training set.
7 Conclusion
An on-site monitoring and early warning scheme for transmission lines based on the Internet of Things is proposed. Taking advantage of the low-power, low-cost, multi-sensor and wireless communication of the Internet of Things, combined with the specific problems faced by the current transmission line monitoring, the selection of the hardware platform of the system and the early warning algorithm are proposed. The monitoring and early warning system program can establish a training set according to the specific local environmental characteristics, thereby establishing a discriminant function with high reliability and conducting effective monitoring and early warning.
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