DC fault detection technology for AC/DC distribution network based on DBN

Abstract

With the development of multi-source AC/DC distribution networks, DC fault detection technology has become the key to DC protection. Aiming at the characteristics of large fault current with a rapid rise and difficulty to extract fault features when distribution line faults occur in the DC part, a fault detection technology of AC/DC distribution network based on deep belief network (DBN) is proposed combining feature extraction in the time domain and frequency domain. By analyzing the characteristics of the fault equivalent circuit, Fourier transform and phase-mode transform are used to extract the frequency domain and time domain characteristics of the fault current and voltage signals as the input of DBN, and the Softmax classifier is used to output the fault pole selection and fault area identification results. An AC/DC distribution network model is built on PSCAD to test the algorithm. The simulation result shows that the proposed detection method still has high accuracy and the strong ability to tolerate noise under the influence of line-distributed capacitance and control strategy. The further algorithm comparison experiment shows that the necessary combination of fault feature extraction and effective deep learning model training can complete the complex fault detection.

Keywords

• ac/dc distribution network
• deep belief network (dbn)
• feature extraction
• fault selection
• fault identification
• phase-mode transform