This paper proposes two new open-circuit fault detection methods suitable for the diagnosis of power electronics converters. The faulty semiconductor devices are identified using conducted and radiated electromagnetic signatures of the dc bus through nonintrusive measurements. The first method uses a low-cost electromagnetic interference filter to collect the common-mode emissions signature. The second one utilizes an external antenna to collect the emitted near-field signature. Both methods are tested on a three-level neutral point clamped inverter (NPC) inverter with the aim to identify the clamping diodes open-circuit faults. Indeed, each open-circuit fault affects the common-mode emission signature in the time-domain, while the fast Fourier transform of the emitted near-field showed substantial reduction of spectrum amplitude at a specific radio frequency range and the appearance of a new spectral rail. The effectiveness of these two methods has been tested through numerical simulations and validated by experimental results which confirmed its high performance in detecting single as well as multiple open-circuit faults for three-level NPC inverter.

Short term load forecasting is one of the most important tasks for power suppliers, and it is getting more important with deregulation of electricity market and emergence of smart grids. This paper proposes a load prediction model of one day ahead with resolution of one hour, using regression random forests. With information about season, temperature, type of the day and hourly load, a training process is performed to build the adopted model. A real load data set from Tunisian Power Company is used for test, and special attention is paid to the load profile which is specific to warm countries with excessive and unstable demand in summer. The results reflect accuracy and effectiveness of the proposed method, keeping low prediction error for long test periods.

In this paper, we present a Web-based intelligent tutoring system, called Remote Laboratory (RL). It is a computer-based learning environment that allows students from anywhere to access and perform experiments on real laboratory equipment from a distance via the Internet. As such, RLs are excellent platforms for students to network and collaborate with students from other. The project creates a platform in higher education sector, to be used to support student collaborative activities in RLs in a structured way that will enable students to develop in technical skills. We discuss how to employ facial expression recognition (FER) to develop an intelligent system to guide the students and also help them to improve the teaching and learning process

The quasi-type-l phase-locked loop (QT1-PLL) is a grid synchronization technique that has become very popular in recent years thanks to its attractive performance such as easy implementation, fast dynamic response, and good accuracy in steady-state operation. However, it is still vulnerable to operation under harmonically distorted grid voltages with frequency drift. This paper proposes a novel QT1-PLL based synchronization algorithm that makes an appropriate combination of two filters’ types: an in-loop third-order moving average filter (MAF) with a reduced window width, and a simplified second-order fast delayed signal cancellation (FDSC) based prefiltering stage. The proposed PLL is named third-order MAF based QT1-PLL (TQT1-PLL). Though both TQT1-PLL's filters do not need any adaptive algorithm, it is able to reject non-triplen odd-harmonics and the fundamental frequency negative sequence (FFNS) even under grid frequency drift. Its correct operation is confirmed through numerical simulations and real-time implementation on a digital signal processor (DSP). Moreover, the obtained results confirm its ability to reduce the ripple in the estimated frequency and phase under distorted grid voltages and off-nominal frequency operation. Authors show also through an analytical development that the topology of the proposed TQT1-PLL can be extended to enable the rejection of the DC-offset.