Spread-spectrum (SS)-based pulsewidth modulation (PWM) techniques play an important role when tackling the electromagnetic interference (EMI) problem in the switch-mode power supply (SMPS) domain. They achieve the best EMI noise reduction and emerge as a promising and very effective solution to comply with electromagnetic standards. The current work provides an insight into the SS techniques: its history, its types, and mainly its ability to mitigate the EMI problem in the SMPS domain. Moreover, it gives a short review of the different SS-based PWM techniques going from the most basic and simple, i.e., the periodic modulation, to the most developed and complex, i.e., the random and chaotic modulations. Besides, a short review of the different implementation methods was addressed and they were categorized into analog and digital implementation techniques where its pros and cons were provided. Finally, a new fast digital implementation method based on a dsPIC33F microcontroller is presented. Its effectiveness was proved and its advantages over the reviewed implementation techniques were recognized. It reached a 20 dBuV EMI level reduction when the digital chaotic and periodic profiles were embedded into a boost converter prototype.

This paper deals with the implementation and analysis of a new maximum power point tracking (MPPT) control method, which is tested under variable climatic conditions. This new MPPT strategy has been created for photovoltaic systems based on Particle Swarm Optimization (PSO). The novel Improved Particle Swarm Optimization (IPSO) algorithm is tested in several simulations which have been implemented in view of the various system responses such as: voltage, current, and power. The performances of the proposed IPSO algorithm have been completed and compared with results of well-established methods adopted in the literature showing a higher accuracy.

The induction motor (IM) is considered to be one of the most important types of motors used in industries. A sudden failure in this machine can lead to unwanted downtime, with consequences in costs, product quality, and safety. Over the last decade, several methods and techniques have been proposed to diagnose and detect faults in induction machines. In this paper, we present the development of a new algorithm based on the combination of both the Park’s vector approach (PVA) and the extended Park’s vector approach (EPVA) for broken rotor bars (BRBs) fault detection and identification. This fault can be detected using the PVA by monitoring the thickness and orientation of the park’s vector pattern and using EPVA by identifying specific spectral components related to the fault. For ability evaluation of our suggested algorithm, simulations and experiments are conducted and presented. The obtained results demonstrate that the proposed algorithm is accurate and effective and can be extensively used in IM fault detections and identifications.

This paper proposes a Low-Voltage Ride-Through control strategy for a three-phase grid-connected photovoltaic (PV) system. At two stages, the topology is considered for the grid-tied system fed by a photovoltaic generator with a boost converter followed by a three-phase voltage source inverter. A flexible control strategy is built for the proposed system. It accomplishes the PV converter operations under the normal operating mode and under grid faults (symmetrical and asymmetrical grid voltage sag). The boost converter is controlled via an incremental conductance maximum power point tracking technique to maximize the PV generator power extraction. In the case of voltage sag, the implemented control strategy provides a switch between MPPT mode and non-MPPT mode to ensure the protection of the power converters. Theoretical modeling and simulation studies were performed, and significant results are extracted and presented to prove the effectiveness of the proposed control algorithm.

This paper handles with the artificial neural network-based fault detection and classification method for a photovoltaic (PV) system. In fact, the diagnosis of the PV plants is extremely required in order to maintain the optimum performance. Therefore, in this paper, we used the artificial neural network for the fault classification. The studied system composed of a PV array with the implementation of Perturb and Observe (P&O) maximum power point tracking (MPPT) using boost converter. The simulation has been accomplished in MATLAB/SIMULINK software. Five different faults have been implemented in the PV system. We used the neural network fitting tool to build and train the network and evaluate its performance using the mean square error (MSE) and regression analysis. The proposed technique has proved effective in terms of results.