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 systems lately because of the rapid development of power electronic devices and massive use of renewable energy, the failure to realize accurate fault location with high precision restricts its further application. Thus, a novel precise pole-to-ground fault location method of FDCDS based on wavelet transform (WT) and convolution neural network (CNN) is proposed in this paper for the limitation on the number of measuring points and high difficulty in extracting characteristics of FDCDS. The fault voltage signal is decomposed with multi-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 using the powerful generalization ability of CNN, so as to complete fault location with high precision. The sample data on PSCAD/EMTDC verifies the accuracy and reliability of the proposed method, which can achieve fault location with positioning precision of 30 m. The proposed method overcomes the influence of the control strategy of the converter and the number of input capacitors of the bridge arm in the time-domain analysis, and still has strong robustness in the case that FDCDS is connected with many distributed generations (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 methods in various noises.