Regression Based Performance Analysis and Fault Detection in Induction Motors by Using Deep Learning Technique

The recent improvements related to the area of electric locomotive, power electronics, assembly processes and manufacturing of machines have increased the robustness and reliability of induction motors. Regardless of the increased availability, the application of induction motors in many fields alleges the need for operating state supervision and condition monitoring. In other words, fault identification at the initial stage helps make appropriate control decisions, influencing product quality as well as providing safety. Inspired by these demands, this work proposes a regression based modeling for the analysis of performance in induction motors. In this approach, the feature extraction process is combined with classification for efficient fault detection. Deep Belief Network (DBN) stacked with multiple Restricted Boltzmann Machine (RBM) is exploited for the robust diagnosis of faults with the adoption of training process. The influences of harmonics over induction motors are identified and the losses are mitigated. The simulation of the suggested approach and its comparison with traditional approaches are executed. An overall accuracy of 99.5% is obtained which in turn proves the efficiency of DBN in detecting faults

Modelling and simulation of sinusoidal pulse width modulation controller for solar photovoltaic inverter to minimize the switching losses and improving the system efficiency

With the extinction of fossil fuels and high increase in power demand, the necessity for renewable energy power generation has increased globally. Solar PV is one such renewable energy power generation, widely used these days in the power sector. The inverters used for power conversion suffer from power losses in the switching elements. This paper aims at the detailed analysis on switching losses in these inverters and also aims at increasing the efficiency of the inverter by reducing losses. Losses in these power electronic switches vary with their types. In this analysis the most widely used semiconductor switches like the insulated gate bipolar transistor (IGBT) and metal oxide semiconductor field effect transistor (MOSFET) are compared. Also using the sinusoidal pulse width modulation (SPWM) technique, improves the system efficiency considerably. Two SPWM-based singlephase inverters with the IGBT and MOSFET are designed and simulated in a MATLAB Simulink environment. The voltage drop and, thereby, the power loss across the switches are compared and analysed. The proposed technique shows that the SPWM inverter with the IGBT has lower power loss than the SPWM inverter with the MOSFET