DC Fault Detection and Pulsed Load Monitoring Using Wavelet Transform-Fed LSTM Autoencoders
Yue Ma, Damian Oslebo, Atif Maqsood, Keith Corzine
Abstract
The extensive deployment of power electronics loads in naval ship power systems indicates the ship electrification is inevitable in future trends. Next-generation warships require high-power density weapons drawing pulse power from the dc grid. A particularly concerning issue is that these pulse loads draw large currents in short periods of time, similar to fault behavior, and maybe indiscernible from a fault. This article proposed a novel machine learning-based algorithm—long short-term memory (LSTM) recurrent neural network (RNN)-based autoencoder (AE) networks to detect dc faults and monitor load conditions applied to naval pulse loads. The novel load monitoring solution presented herein can be applied to any load profile that exhibits repetitive transients during normal operation. The frequency-domain features of the load current are extracted under wavelet transform for the network training to set the network weights and biases. Once the network training is completed, the LSTM RNN-based AE will produce both signal classification and signal reconstruction of the pulse load based on wavelet features of input current. Any faults should yield large reconstruction errors for protective action. Finally, the method is demonstrated in experimental results.