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The paper presents methodologies to detect and locate short-circuit faults on the stator winding of VSI-fed PM brushless dc motors. Normal performance characteristics of the motor are obtained through a discrete-time lumped-parameter network model. The model is modified to accommodate shortcircuit faults in order to simulate faulty operation. Fault signatures are extracted from the waveforms of electromagnetic torque and phase-voltage summation using wavelet transform. Three independent detection techniques are introduced. Experimental measurements agree acceptably with simulation results, and validate the proposed methods. This work sets forth the fundamentals of an automatic fault detector and locator, which can be used in a fault-tolerant drive.Index Terms-Brushless dc motor, fault detection, wavelet transform.

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Please cite this article in press as: Salem, F., Awadallah, M.A., Detection and assessment of partial shading in photovoltaic arrays. J. Electr. Syst. Inform. Technol. (2016), http://dx. AbstractThe paper presents a methodology for detection and assessment of partial shading conditions in photovoltaic (PV) arrays based on artificial neural networks (ANN) as a preliminary step toward automatic supervision and monitoring. The PV array is modeled under normal and partial shading conditions for performance comparison. ANN is designed, trained, and tested for full identification of the partial shading condition. One ANN detects the presence of partial shading and distinguishes it from the uniform change in environmental conditions. If the first ANN detects partial shading on the PV array, other two ANN agents determine the shading factor and infers the number of shaded modules of the array, consequently. Results show excellent performance of ANN on the detection and assessment of partial shading.