scholarly journals A New Robust Identification Method for Transmission Line Parameters Based on ADALINE and IGG Method

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 132960-132969
Author(s):  
Ancheng Xue ◽  
He Kong ◽  
Yongzhao Lao ◽  
Quan Xu ◽  
Yuehuan Lin ◽  
...  
Keyword(s):  
Transmission Line ◽  
Robust Identification ◽  
Identification Method

scholarly journals Robust Identification Method for Transmission Line Parameters That Considers PMU Phase Angle Error

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 86962-86971 ◽  
Author(s):  
Ancheng Xue ◽  
Feiyang Xu ◽  
Kenneth E. Martin ◽  
Hongyu You ◽  
Jingsong Xu ◽  
...  
Keyword(s):  
Transmission Line ◽  
Phase Angle ◽  
Angle Error ◽  
Robust Identification ◽  
Identification Method

scholarly journals External Breaking Vibration Identification Method of Transmission Line Tower Based on Solar-Powered RFID Sensor and CNN

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 519
Author(s):  
Fangming Deng ◽  
Kaiyun Wen ◽  
Zhongxin Xie ◽  
Huafeng Liu ◽  
Jin Tong
Keyword(s):  
Transmission Line ◽  
Radio Frequency Identification ◽  
Descent Method ◽  
Identification Accuracy ◽  
Batch Size ◽  
Optimum Number ◽  
Gradient Descent Method ◽  
Super Capacitor ◽  
Identification Method ◽  
Solar Powered

This paper proposes an external breaking vibration identification method of transmission line tower based on a radio frequency identification (RFID) sensor and deep learning. The RFID sensor is designed to obtain the vibration signal of the transmission line tower. In order to achieve long-time monitoring and longer working distance, the proposed RFID sensor tag employs a photovoltaic cell combined with a super capacitor as the power management module. convolution neural network (CNN) is adopted to extract the characteristics of vibration signals and relevance vector machine (RVM) is then employed to achieve vibration pattern identification. Furthermore, the Softmax classifier and gradient descent method are used to adjust the weights and thresholds of CNN, so as to obtain a high-precision identification structure. The experiment results show that the minimum sensitivity of the proposed solar-powered RFID sensor tag is −29 dBm and the discharge duration of the super capacitor is 63.35 h when the query frequencies are 5/min. The optimum batch size of CNN is 5, and the optimum number of convolution cores in the first layer and the second layer are 2 and 4, respectively. The maximum number of iterations is 10 times. The vibration identification accuracy of the proposed method is over 99% under three different conditions.

A Earth Fault Section Identification Method Based on ANN

2014 ◽  
Vol 1006-1007 ◽  
pp. 913-918
Author(s):  
Jia Wei Xie ◽  
Gui Hong Bi ◽  
Shi Long Chen ◽  
Jie Zhang
Keyword(s):  
Transmission Line ◽  
High Frequency ◽  
Low Frequency ◽  
Earth Fault ◽  
Identification Method ◽  
Fault Resistance ◽  
Internal Fault ◽  
The Neural Network ◽  
Transient Voltage ◽  
Fault Section Identification

The method to discriminate the fault within or beyond the protective zone according to the signals with high frequency detected in protection has problem with low reliability when the resistance of earth fault is high. The transient voltage with low frequency detected in protection can reflect the size of fault intensity approximately, and the transient voltage with high frequency detected in protection in internal fault is bigger than that in external fault when the size of fault intensity is same. The high frequency transient voltage and low frequency transient voltage detected in protection are chosen as inputs and fault section as outputs for training and testing the neural network for fault identification in the UHVDC transmission line, fault section can be identified when input fault characteristic data to the trained model. A variety of transmission line earth fault situation are simulated by PSCAD, and this earth fault section identification method based ANN shows satisfactory performance, even the fault resistance is high.

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