A New Convolutional Network Structure for Power Quality Disturbance Identification and Classification in Micro-Grids

Aiming at the problems of a low convergence speed, low accuracy and poor generalization ability of traditional power disturbance identification and classification methods, a new deep convolutional network structure is presented, and a power quality disturbance identification and classification method for microgrids based on the new network structure is proposed. The network consists of a five-layer one-dimensional modified Inception-residual network (ResNet) (1D-MIR) and a three-layer full-connection tier, which is a deep convolutional network. The idea of the method can be described as follows: First, power disturbance signals are expressed by an n-dimensional unit vector, and a database of these power disturbance signals is established. Second, the disturbance signals in the database are randomly sampled, and the power quality disturbances are calibrated with the n-dimensional unit vector to form both data and test sets. Finally, the gradient descent method and the adaptive moment estimation method (Adam) are adopted to train and optimize the network, respectively, and the trained and optimized network is applied to power quality disturbance identification and classification. A large number of experiments has been conducted, and the obtained results show that the constructed network can quickly extract the characteristics of the various disturbance signals, including single and composite disturbances, and identify and classify them. A comparison of the results obtained by the proposed method with those obtained by several other methods reveals that the proposed method attains a higher accuracy, higher convergence speed and stronger generalization ability.