Comparative Study of Three Fault Diagnostic Methods for Three Phase Inverter with Induction Motor

Abstract

In recent times, inverters are considered as the basic building block in an electrical drive system used widely in many industrial drive applications. However, the reliability of these inverters is mainly affected by the failure of power electronic switches. Various faults in inverter may influence the system operation by unexpected maintenance, which increases the cost factor and reduce overall efficiency. In this paper, comparative study of three different fault detection and diagnosis systems for three phase inverter is presented. The basic purpose of these fault detection and diagnosis systems is to detect single or multiple faults efficiently. These techniques rely on the neural network for fault detection and diagnosis by using Clarke transformed two-dimensional features extraction, three-dimensional features extraction and features extraction using discrete wavelet transform (DWT) with a different number of features in each technique. Several features are extracted using different mechanisms and used in the neural network as input for fault detection and diagnosis. Furthermore, a simulation study is carried out to analyze the fault detection and diagnosis response of these techniques. Also, a comparative study has been performed by considering fault detection time and accuracy. Comparison results prove the supremacy of three-dimensional feature extraction technique over other two techniques as it can detect and diagnose single, double and triple faults in a single cycle with high accuracy as compared to other two techniques multi-cycles detection.

A Robust Technique for Detection, Diagnosis, and Localization of Switching Faults in Electric Drives Using Discrete Wavelet Transform

“…The discrepancy between the predicted and the estimated fluxes are used for detecting SO faults [9]. Artificial intelligence-based techniques are also widely used for fault detection, such as neural networks [10], support vector machines [11], fuzzy logic [12], hybrid techniques such as wavelet neural networks [13], and multi-sensory control with wavelet analysis [14].…”

Condition Monitoring and Feature Extraction of Stator Current Phasors for Enhanced Fault Diagnosis in AC Drive

“…For Fault diagnosis in three phase induction motors, MCSA technique was employed. Techniques involved for fault detection were Fast Fourier Transform, ruled based approaches like Fuzzy Logic [2][3][4][5][6], AI techniques like Multilayer Perceptron, Support Vector Machine [7,8], Discrete Wavelet Transform(DWT) [9] were applied for rotor bar fault detection in squirrel cage rotors. Fuzzy logic and DWT techniques involve large computational time.…”

Neural Network Based Fault Diagnosis of Three Phase Inverter Fed Vector Control Induction Motor

“…The position of this trajectory in the (d, q) frame makes it possible to calculate the intervals of the angles of the fault to localize the faulty IGBT. Other researchers [5][6][7][8] have proposed the Park average current (i dmean , i qmean ) technique to calculate the exact open-circuit fault angle in order to identify the faulty IGBT switch. Authors [5,9,10] have proposed the technique based on the spectral analysis of the stator currents.…”