Simulation of Insulator Damage Identification in DC Transmission Line Using Deep Learning
Due to being installed in the wild,insulators on transmission lines are affected by weather factors,en-vironmental pollution,and other factors,leading to damage and interruption of power supply.To avoid failure and en-sure safe operation of the power grid,a method for identifying damage on insulators of DC transmission line was pro-posed based on deep learning.Firstly,the noise source in aerial images was analyzed,and the median filtering algo-rithm was used to determine the pixel median values.Then,non-linear smoothing was applied to remove noise.Sec-ondly,common types of damage,damage characteristics,and causes of insulator were analyzed.Meanwhile,a random forest decision tree was established.Using deep learning algorithm,obvious damage features were selected as the basis for recognition.Next,a convolutional neural network model based on Alex Net was constructed,and the loss function was calculated to determine the optimal learning rate.Finally,the recognition results were outputted based on the learning and training.Te experimental results show that the proposed method can enhance image details and achieve an accuracy rate of over 0.8 in identifying damage on insulators of DC transmission lines with fast convergence speed.
Deep learningDC transmission lineInsulatorDamage identificationConvolutional neural network
NETL
NSTL
万方数据
维普
