Convolutional Wavelet Neural Network Based Non-Intrusive Load Monitoring for Next Generation Shipboard Power Systems

In this study, a non-intrusive load monitoring (NILM) framework is developed for next generation shipboard power systems (SPS) based on a discrete wavelet transform (DWT) and a convolutional neural network (CNN). We have applied the developed NILM method to a four-zone medium voltage direct current (MVDC) SPS to evaluate the effectiveness of the proposed method. Each zone of the MVDC SPS consists of multiple components, such as propulsion load, pulsed load, high ramp rate load, cooling load, and hotel load. The current signals from the main generators are the main inputs to the proposed NILM model. The current signals are first processed with a discrete wavelet transform to create a coefficient matrix that reflects the status of all the components in each zone. Then, a multi-class classification problem is formulated and solved using a CNN model to monitor the load statuses in real time. The results of case studies show that the developed wavelet-CNN based NILM model can (i) accurately monitor the status of all components with a total accuracy of over 98%, (ii) identify unique pulsed loads with a total accuracy of over 99%, and (iii) sustain the functionality of load monitoring under extreme events such as cyber/physical attacks.