Faults Classification Of Power Electronic Circuits Based On A Support Vector Data Description Method

Abstract

Power electronic circuits (PECs) are prone to various failures, whose classification is of paramount importance. This paper presents a data-driven based fault diagnosis technique, which employs a support vector data description (SVDD) method to perform fault classification of PECs. In the presented method, fault signals (e.g. currents, voltages, etc.) are collected from accessible nodes of circuits, and then signal processing techniques (e.g. Fourier analysis, wavelet transform, etc.) are adopted to extract feature samples, which are subsequently used to perform offline machine learning. Finally, the SVDD classifier is used to implement fault classification task. However, in some cases, the conventional SVDD cannot achieve good classification performance, because this classifier may generate some so-called refusal areas (RAs), and in our design these RAs are resolved with the one-against-one support vector machine (SVM) classifier. The obtained experiment results from simulated and actual circuits demonstrate that the improved SVDD has a classification performance close to the conventional one-against-one SVM, and can be applied to fault classification of PECs in practice.

Data Mining Applications to Fault Diagnosis in Power Electronic Systems: A Systematic Review

“…In [39], Discrete Wavelet Transform (DWT) and Fuzzy Inference Logic methods have been selected to detect the various faults include DC SC to ground, OC of insulated gate bipolar transistor (IGBT), SC damage of IGBT, DC link capacitor, and single line to ground fault of a machine terminal is used in a 3-phase inverter. In another study [40], the OC faults of thyristors used in a 3-phase full-bridge rectifier in 21 types have been investigated via the support vector data description and SVM techniques. A multi-layer ANN based on multi-valued neuron with a complex QR-decomposition has been designed and utilized in [41] to identify capacitor faults in a Class-E DC-AC inverter.…”

“…Improper performance in the face of time-series data is considered as one of the problems of this method. [32] Faults in a single thyristor and the faults happening in two thyristors at the same time Three-phase full-bridge controlled rectifier [34] valve SC, valve pulse loss, single-phase grounding, two-phase grounding, three-phase grounding, DC line grounding HVDC converter [35] SC incipient fault Three-phase squirrel-cage induction motor fed by a sinusoidal PWM converter [37] Switch OC Cascaded H-bridge multi-level (5-level) inverter [40] thyristors OC fault Three-phase full-bridge rectifier [162] Switch OC Three-level NPC inverter [152] Diode OC Three-phase full-bridge rectifier [155] Structural and functional faults Analog to digital converter [7] OC, SC, component degradation of power MOSFET, inductor, diode, and capacitor DC-DC Converter (closed-loop single-ended primary inductance converter) [56] Eight kinds of faults related to DC bus capacitor and energy storage inductor Dual-buck bidirectional DC-AC converter [59] Switch OC and SC MMC [60] Switch OC MMC [61] IGBT OC Traction inverters [153] Multiple OC switch fault A back-to-back converter in doubly-fed induction generatorbased wind turbine systems ELM [33] Intern-turn SC Three-phase converter-fed induction motor…”

Data-Driven Fault Localization in Distribution Systems with Distributed Energy Resources

“…And in [22], A hybrid method combing model-based diagnosis and SVDD-based anomaly detector is proposed to identify unknown faults and also classify multiple-faults in an internal combustion engine using only single-fault training data. In [23], a method for power electronic circuits fault classification is proposed based on the SVDD classifier.…”

Open Switch Fault Diagnosis Method for PWM Voltage Source Rectifier Based on Deep Learning Approach

“…In addition, some new algorithms and technologies have been employed in this field. A diagnosis method based on support vector Data Description is proposed in [25]. Wang et.al propose a novel diagnosis method with the information fusion technique [26].…”