Fault Detection and Classification with Optimization Techniques for a Three-Phase Single-Inverter Circuit

Abstract

Fault detection and isolation are related to system monitoring, identifying when a fault has occurred, and determining the type of fault and its location. Fault detection is utilized to determine whether a problem has occurred within a certain channel or area of operation. Fault detection and diagnosis have become increasingly important for many technical processes in the development of safe and efficient advanced systems for supervision. This paper presents an integrated technique for fault diagnosis and classification for open-and short-circuit faults in three-phase inverter circuits. Discrete wavelet transform and principal component analysis are utilized to detect the discontinuity in currents caused by a fault. The features of fault diagnosis are then extracted. A fault dictionary is used to acquire details about transistor faults and the corresponding fault identification. Fault classification is performed with a fuzzy logic system and relevance vector machine (RVM). The proposed model is incorporated with a set of optimization techniques, namely, evolutionary particle swarm optimization (EPSO) and cuckoo search optimization (CSO), to improve fault detection. The combination of optimization techniques with classification techniques is analyzed. Experimental results confirm that the combination of CSO with RVM yields better results than the combinations of CSO with fuzzy logic system, EPSO with RVM, and EPSO with fuzzy logic system.

Machine learning based multi class fault diagnosis tool for voltage source inverter driven induction motor

“…Fault feature was extracted through fast fourier transform (FFT) from sampled stator currents to train the model using multilayer perceptron, support vector machine (SVM) [1], [7], [8]. Rule based approach like fuzzy logic technique was employed for fault detection in the Induction motors [2]- [6]. Discrete wavelet transform (DWT) [8], [9] and principal component analysis (PCA) were utilized to identify the discontinuity in the feature caused by the faults.…”

Frequency Response Analysis Technique of Short Circuit Faults Detection in Photovoltaic Single-Phase Inverter Experimental Study

“…In this work, short-circuit faults are explored, it occurs when one of the two switches remain continuously under-voltage, or when the second switch is in turn controlled to close. This fault is due to a malfunction of the transistor driver (a control board fault, a connection problem between the control board and the driver) or a physical fault of the silicon chip resulting from exceeding the operating temperature [12][13][14]. Short circuit faults in power switches are difficult to handle due to the damaged a component being subjected to high current, a high voltage and excessive local temperatures [15].…”

Fault Detection and Classification in MMC-HVDC Systems Using Learning Methods

“…To build the aforementioned artificial intelligence machine, feature extraction techniques such as Fourier analysis [20,21], wavelet transform [14,15], Clarke transform [12] or feature subset selection techniques, such as principal component analysis (PCA) [10,22] and multidimensional scaling (MDS), plays an important role. Sometimes to select suitable sub-features, the genetic algorithm (GA) [10,22,23] or particle swarm optimization (PSO) [24] are employed. It is well known that feature extraction has always been a bottleneck in the field of fault diagnosis.…”