A Greedy Layer-Wise Learning Algorithm for Open-Circuit Fault Diagnosis of Grid-Connected Inverters

Abstract

This paper introduces a greedy layer-wise learning algorithm to diagnose open-circuit faults of grid-connected inverters. Inverters play important roles in energy conversion, especially when converting direct current to alternating current. The accurate functioning of inverters is essential for successful energy conversion. The diagnosis of inverter faults is the primary requirement to guarantee the reliability of the entire energy conversion operation. In this work, a multilayer learning algorithm based on a restricted Boltzmann machine (RBM) is presented for fault diagnosis of an inverter topology. It uses both supervised and unsupervised layer-wise learning and hierarchically extracts the features from a given data. A three-phase two-level grid-connected PV inverter test model has been operated for twenty-two conditions to assess the effectiveness of the proposed algorithm. The investigation results in diagnostic accuracy of 99.786% for twenty-two operating conditions of the inverter.