A Fast and Intelligent Open-Circuit Fault Diagnosis Method for a Five-Level NNPP Converter Based on an Improved Feature Extraction and Selection Model

Abstract

The open-circuit faults of power semiconductor devices in multilevel converters are generally diagnosed by analyzing circuit signals. For converters with five or more levels, the difficulty of fault detection increases with increasing topological complexity, the number of switching devices and the number of candidate signal parameters. In this paper, a complete solution for open-circuit fault detection for a five-level nested neutral-point piloted (NNPP) converter is proposed based on improved unsupervised feature learning algorithms. Feature engineering and machine learning algorithms are applied for feature extraction and selection and the construction of classification models. Circuit signals are monitored, and their time-domain characteristics are extracted for fault recognition. An unsupervised feature learning selector, which combines a dependence-guided unsupervised feature selection (DGUFS) filter and a random forest feature selection (RFFS) wrapper to automatically select parameters and generate the optimal feature subset for fault detection, is proposed. The random forest (RF) algorithm is used to build a classifier. The experimental results show that the solution framework presented has the advantages of high efficiency, high flexibility, a superior fault recognition rate and good generalization ability. INDEX TERMS Fault diagnosis, feature extraction and selection, filter-wrapper method, DGUFS-RFFS, open-circuit fault, five-level NNPP converter.

A Hybrid Fault Detection and Diagnosis of Grid-Tied PV Systems: Enhanced Random Forest Classifier Using Data Reduction and Interval-Valued Representation

“…It aims to detect and classify the faults of PV arrays by combining multiple learning algorithms to achieve a superior diagnostic performance. In order to guarantee good detection and diagnosis performances, the application of the RF classifier algorithm should be preceded by the preparation of data inputs, where the feature extraction and selection (FES) are the two most important steps [18], [19]. The goal of the feature extraction is to extract the parameters that correctly describe the system operating conditions, while the feature selection aims to select a small feature subset using a certain criterion.…”