Key Incorporation Technologies Research On Transmission Line Tower Status Monitoring Based On RFSN

The tower is a key component of the transmission line system and its stability and reliability is the guarantee for the operation safety of the entire transmission line.Compared with the traditional inspection and wired monitoring technologies,the transmission line state monitoring technology based on wireless sensor network(WSN)has the advantages of easy installation and maintenance,low cost and strong real time.It is the development trend of the future smart grid monitoring technology.Radio frequency identification(RFID)and WSN technology are two key technologies in the front-end of the Internet of things.Compared with the existing WSN-based monitoring technologies,the status monitoring technology for transmission line tower based on RFID and WSN fusion network(RFSN)shows the advantages of fast fault location,equipment life cycle management and passive long-term monitoring.Starting from the physical layer and the network layer of the incorporated network respectively,this paper focuses on the research on the new issues after incorporating the RFID technology into the wireless monitoring network of the transmission line tower,which including the three topics below:RFID sensor tag is the key of RFSN network integration,and low power integrated sensor design is the key of RFID sensor tag design.A phase-locked loop(PLL)based sensor interface circuit structure is proposed,which transfers the sensor signal to frequency domain for processing.It has the advantages of simple structure,low working voltage and easy integration.This work proposes a humidity sensor manufacturing method based on the top metal layer to fabricate the interdigital electrode.The fabrication process is fully compatible to the standard complementary metal oxide semiconductor(CMOS)process without any post-processing technology,and the manufacturing cost is low.An integrated temperature sensor based on PLL interface circuit is proposed,which transfers the temperature sensor information to the frequency domain for processing.It has the characteristics of small occupation area and low power consumption.This paper proposes a low power passive RFID sensor tag structure,which embedding the sensor information into the label ID information of the tag to effectively avoid the high power consumption and time delay caused by the sensor data written into non-volatile memory.The power management section of the tag employs the scheme of dual-antenna energy harvesting,which can enhance the energy harvesting capability of passive sensor tags.The rectifier circuit adopts the architecture of a single stage AC-DC connected with the low voltage DC-DC charge pump,which can achieve a lower sensitivity compared to the traditional multistage AC-DC rectifier.For long-distance applications,a novel structure of solar energy-harvesting RFID sensor tag is also proposed.The test results show that the proposed RFID sensor tag has longer working distance and stronger sensor carrying capacity compared with the state of the art designs,and can effectively complete the status monitoring of tower foundation concrete and tower vibration.This paper proposes an online monitoring network architecture for transmission line tower based on RFSN,which includes four communication layers:RFID sensor tag to reader and ZigBee fusion node,sensor node to relay node,relay node to substation(or base station)and substation(or base station)to control center.The relay node setting is the key to the research of monitoring network performance.Therefore,the transmission delay and cost model of relay nodes are established,which can help the network to obtain the balance of delay performance and cost under different conditions.For the redundant data of RFID,a data cleaning algorithm suitable for transmission line tower state monitoring RFSN network is proposed,which can effectively reduce the redundant data collected from the reader end.The algorithm holds the advantages of simple and high real-time.This work presents a scheme of vibration recognition for transmission tower breaking based on deep learning.The convolution neural network(CNN)of deep learning algorithm is used to extract the deep features of transmission tower vibration signals,and the relevant vector machine(RVM)classifier is trained to obtain the vibration pattern recognition model of transmission tower,which not only can reduce the amount of data transmitted by the fusion network,but also can achieve high accuracy and efficiency of vibration identification.