Fault Location with High Precision of Flexible DC Distribution System Using Wavelet Transform and Convolution Neural Network - Wang, Dafei; Wang, Baohua; Zhang, Wenhui; Zhang, Chi; Yu, Jiacheng (Frontiers in Energy Research, 2021)
Though flexible DC distribution system (FDCDS) is becoming a new hotspot in power system lately because of the rapid devel-opment of power electronic device and massive use of renewable energy, the failure to realize accurate fault location with high pre-cision restricts its further application. Thus, a novel precise pole-to-ground fault location method of FDCDS based on con-volution neural network (CNN) and wavelet transform (WT) is proposed in this paper for the limitation on the number of meas-uring points and high difficulty in extracting characteristics of FDCDS. The fault voltage signal is decomposed with mul-ti-resolution by discrete wavelet transform (DWT), and then the transient energy function is constructed to select the frequency bands containing rich fault characteristics for signal reconstruction. The reconstructed signal forms two-dimensional time-frequency images through continuous wavelet transform (CWT), which are used as the input of CNN classifier after image enhancement to form the mapping relation between the fault feature and fault position, so as to complete fault location with high precision. The sample data on PSCAD/EMTDC verifies the accuracy and relia-bility of proposed method, which can achieve fault location with positioning precision of 30m. The proposed method overcomes the influence of the control strategy of converter and the number of input capacitor of bridge arm in the time-domain analysis, and still has strong robustness in the case that FDCDS is connected with many DGs with output fluctuation. Furthermore, four other methods for fault location as comparisons are given to reflect the validity and anti-interference ability of proposed method in various noises.