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This paper demonstrates the application of the Selective Harmonic Elimination Pulse Width Modulation control of a single-phase H-bridge inverter. Its primary purpose is to reduce the harmonic content of the output waveform to ensure power quality. The N-R technique was applied to find the roots "firing angles" of the control circuit of the H-bridge inverter by using a set of nonlinear algebraic equations obtained by analysis of the Fourier series of the voltage waveform to eliminate certain harmonic orders. Different cases were analyzed using this technique to selectively eliminate the 3rd, 5th, 7th, 9th, and 11th harmonics. It was also demonstrated that the suggested method's efficacy is confirmed by the obtained results using MATLAB/Simulink programming, and almost perfect harmonic elimination is verified, so filters of smaller size can be used, which means lower cost filters. [ABSTRACT FROM AUTHOR]

In the last few years, Brushless DC Motor (BLDC) was performed in different implementations, essentially sensitive applications; motors often run in hard loading conditions, especially after increasing production with complicated operating conditions. Therefore, overheating, overload, and other complex operating states are responsible for motor bearing faults. Bearing faults are essentially popular because of the details of drive operation. The monitoring and personifying bearing conditions are comparatively rare in many kinds of motor applications. Early diagnosis of bearing faults is meaningful in ensuring the seamless operation of BLDC motors in the industrial environment. The detection and classification of faults help to execute maintenance operations in an effective manner. This work shows an approach for identifying rolling elements imperfections in the BLDC motor in unsteady operating states. By using a Discrete Wavelet Transform (DWT), vibration and Stator current signal measurements represent the fault indexes to extract significant features; these features are highly reduced through the applications of Principle Components Analysis (PCA) to remove the redundant, irrelevant, or noisy data. A Recurrent Neural Network (RNN) and Feed Forward Neural Network (FFN) are used to expose and classify the existence of bearing faults and compare them from training time and accuracy; these networks were trained on the selected signal and can effectually recognize between healthy and fault signals, the accuracy of the faults diagnosis system equal to 99. 44% to recognize the kind of fault. The contribution of current work can be summarized as follows: due to the dynamic behavior of electrical machines, recurrent Neural Networks have been implemented instead of static Neural Networks that have been used in almost all previous work. Furthermore, the proposed technique can diagnose faults under stationary and non-stationary operating conditions. [ABSTRACT FROM AUTHOR]