Intelligent Fault Detection Scheme for Microgrids With Wavelet-Based Deep Neural Networks
By James Jian Qiao Yu, Yunhe Hou, Albert Y. S. Lam, Victor O. K. Li. IEEE Transactions on Smart Grid, 2019
393 citations recorded by the source corpus.
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.
Subfields: Control and Systems Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/4fce253ac11420b478d34d5d396ddcea3ca0314f
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In Control and Systems Engineering
In Electrical and Electronic Engineering
Citations in the corpus
- Online False Data Injection Attack Detection With Wavelet Transform and Deep Neural Networks (2018, background): “As an alternative, they are typically selected strategically according to the data properties [40], [41].”