In this paper, a dc-dc buck converter with digital PI-controlled is analyzed and designed considering all design parameters such as inductance current variation, output voltage ripple etc. The designed dc-dc buck converter provides stable output voltage against to load changes and output voltage variations. Buck converter control method relies on voltage mode controlled PWM (Pulse width Modulation) with digital PI (Proportional Integral) controller. The design criteria, operating mode selection, suitable material selection, etc. of low cost and high-performance buck converter are explained in detail. Finally, the designed converter is carried out experimentally and the experimental results shows the effectiveness of designed converter under different load profiles.

Detection of Stator Winding Inter-Turn Short Circuit Faults in Permanent Magnet Synchronous Motors and Automatic Classification of Fault Severity via a Pattern Recognition System Condition monitoring;Digital signal processing;Fault detection;Fault severity classification;Pattern recognition system;Permanent magnet synchronous motors; In this study, automatic detection of stator winding inter-turn short circuit fault (SWISCFs) in surface-mounted permanent magnet synchronous motors (SPMSMs) and automatic classification of fault severity via a pattern recognition system (PRS) are presented. In the case of a stator short circuit fault, performance losses become an important issue for SPMSMs. To detect stator winding short circuit faults automatically and to estimate the severity of the fault, an artificial neural network (ANN)-based PRS was used. It was found that the amplitude of the third harmonic of the current was the most distinctive characteristic for detecting the short circuit fault ratio of the SPMSM. To validate the proposed method, both simulation results and experimental results are presented.

This paper presents a separation method to discern broken rotor bar fault from low frequency load torque oscillation thorough radial leakage flux spectrums in induction motors (IMs). Broken rotor bar fault can usually be detected using classical motor current based analysis (MCSA), but it may not provide reliable results since its performance depend on motor topology, stator winding and load type. Particularly, if a motor is subjected to load fluctuation, then oscillation related signatures exhibit similar behavior that of broken bar which leads misleading signatures. In this paper, radial leakage flux spectrum is exhaustively analyzed thorough a fluxgate sensor to discern these two effects in IMs. It is shown that there are some additional characteristics broken bar signatures such as 3sf _s and (f _s -f _r )±2sf _s in radial leakage flux which do not appear in low frequency load torque oscillation case. A 2-Dimensional (2D) finite element analysis and experiments are carried to show that using leakage flux can provide a separation method and more reliable results than classical MCSA in IMs.

This paper proposes a method for separation of broken rotor bar failures from low-frequency load torque oscillation in direct torque control (DTC) induction motor drives by using v_q voltage and i_q current components’ spectra. The effect of load torque oscillation should be considered in induction motor drives for reliable broken bar fault detection. Induction machine drivers are run in DTC mode to control its torque and speed. In practice, the presence of load torque fluctuation may sometimes cause false positive alarms on stator current spectrum. However, discerning of broken rotor bar failure from low-frequency load variation for DTC drives remains unexplored. Experimental results show that by using the proposed method broken rotor bar failure can be reliably detected in the presence of low-frequency load torque oscillation in DTC induction motor drives.

Vibration analysis is considered the most common and effective means of detecting mechanical faults such as imbalance, misalignment, and looseness in induction motor driven systems. Most mechanical problems result in an increase in vibration at multiples of the rotor speed frequency (1x) making it difficult to discern the source of vibration. In case of a fault alarm, the maintenance engineer usually performs a walk-around test to identify the source of vibration for planning maintenance, and therefore, is exposed to safety risks. In this paper, a new remote and automated test method for identifying the source of mechanical vibration during the starting transient of induction motors is proposed. The level and speed-dependency of vibration during rotor acceleration are used for identifying imbalance from other mechanical defects that produce 1x vibration. Test results on a 380 V, 5.5 kW induction motor under mechanical defects are given for verification. It is shown that the proposed method can provide automated identification of the source of vibration enabling maintenance to be performed in a safe, low cost, and efficient manner. The data acquired and analyzed for the testing are described and shared through this paper.