A Model-Data-Hybrid-Driven Diagnosis Method for Open-Switch Faults in Power Converters

To combine the advantages of both model-driven and data-driven methods, this paper proposes a model-data-hybrid-driven (MDHD) method to diagnose open-switch faults in power converters. This idea is based on the explicit analytical model of converters and the learning capability of artificial neural network (ANN). The process of the method is divided into two parts: offline model analysis and learning, and online fault diagnosis. For both parts, model-driven and data-driven are combined. With the model information and data-based learning capability, a fast diagnosis for various operating conditions can be achieved without high computation burden, tricky threshold selection and complex rulemaking. This can greatly contribute to the practical application. The open-switch fault diagnosis in a two-level three-phase converter is studied for method validation. For this converter, an ANN is trained with two input elements, seven output elements, and two neurons in the hidden layer. Experimental results are given to demonstrate good performance

A Complete Equivalent Circuit for Linear Induction Motors With Laterally Asymmetric Secondary for Urban Railway Transit

Since the linear induction motor commonly work with a laterally asymmetric secondary as it is applied to pull rail vehicles, this paper presents a complete equivalent circuit model considering the asymmetry to predict thrust, vertical and transversal forces. First, six correction factors are presented to quantify the variations in the air-gap magnetic flux and secondary induced current as the linear induction motor operating with a laterally asymmetric secondary. Second, it develops a circuit model based on the existing T-model for the rotary induction motor and two correction factors for the magnetizing branch, which is used to indicate the electromagnetic variations in the air-gap flux and secondary plate due to the asymmetry. Third, the mathematical expressions for the thrust, vertical and transversal forces are derived by applying the equivalent circuit model. Then, the six correction factors are calculated with a prototype motor, and the results of them are comprehensively analyzed. Finally, the characteristics in the prototype motor are calculated with the mathematical expressions in a range of rated speed，and validated by the experimental measurements carried out on a test rig and line for linear motors

A Fast Diagnosis Method for Both IGBT Faults and Current Sensor Faults in Grid-Tied Three-Phase Inverters With Two Current Sensors

© 1986-2012 IEEE. This article considers fault detection in the case of a three-phase three-wire (3P3W) inverter, when only two current sensors are used to save cost or due to a faulty current sensor. With two current sensors, there is no current method addressing the diagnosis of both IGBT open-circuit (OC) faults and current sensor faults. In order to solve this problem, this article proposes a method which innovatively combines two kinds of diagnosis variables, line voltage deviations and phase voltage deviations. The unique faulty characteristics of diagnosis variables for each fault are extracted and utilized to distinguish the fault. Using an average model, the method only needs the signals already available in the controller. Both IGBT OC faults and current sensor faults can be detected quickly in inverter mode and rectifier mode, so that the converter can be protected in a timely way to avoid further damages. In addition, error-adaptive thresholds are adopted to make the method robust. Effects such as system unbalance are analyzed to ensure that the method is robust and feasible. Simulation and experimental results are used to verify and validate the effectiveness of the method