Intelligent Fault Detection Scheme for Microgrids With Wavelet-Based Deep Neural Networks

TLDR

Fault detection is essential in microgrids, but inverter‑interfaced distributed generation renders traditional current‑dependent schemes ineffective. The paper proposes an intelligent fault detection scheme using wavelet transform and deep neural networks to quickly identify fault type, phase, and location for microgrid protection and recovery. The scheme extracts statistical features from branch current samples via discrete wavelet transform and feeds them into deep neural networks, and its performance is evaluated on the CERTS microgrid and IEEE 34‑bus system. The scheme achieves higher fault‑type classification accuracy, can locate faults, and shows superior detection accuracy, faster computation, and robustness to measurement uncertainty in simulations.

Abstract

Fault detection is essential in microgrid control and operation, as it enables the system to perform fast fault isolation and recovery. The adoption of inverter-interfaced distributed generation in microgrids makes traditional fault detection schemes inappropriate due to their dependence on significant fault currents. In this paper, we devise an intelligent fault detection scheme for microgrid based on wavelet transform and deep neural networks. The proposed scheme aims to provide fast fault type, phase, and location information for microgrid protection and service recovery. In the scheme, branch current measurements sampled by protective relays are pre-processed by discrete wavelet transform to extract statistical features. Then all available data is input into deep neural networks to develop fault information. Compared with previous work, the proposed scheme can provide significantly better fault type classification accuracy. Moreover, the scheme can also detect the locations of faults, which are unavailable in previous work. To evaluate the performance of the proposed fault detection scheme, we conduct a comprehensive evaluation study on the CERTS microgrid and IEEE 34-bus system. The simulation results demonstrate the efficacy of the proposed scheme in terms of detection accuracy, computation time, and robustness against measurement uncertainty.