Mostefa Brahami

This paper presents the design, implementation, and experimental validation of a 150 W two-stage light-emitting diode (LED) driver integrating a power factor correction (PFC) stage and a half-bridge LLC resonant converter for public lighting applications. The problem addressed is the insufficient power quality, limited efficiency, and poor harmonic performance of conventional LED drivers used in street lighting. The proposed method combines an advanced PFC front-end with an LLC resonant converter optimized using first harmonic approximation (FHA) to achieve high efficiency, stable output regulation, and soft-switching operation. Experimental results demonstrate a significant improvement in power quality, with the input current total harmonic distortion (THD) reduced from 134% to 17%, a near-unity power factor, a regulated LED output of 31.6 V/4.72 A, and a conversion efficiency exceeding 95%. The significance of this work lies in providing a high-performance, standards-compliant driver that supports reliable, energy-efficient, and grid-friendly public lighting with reduced operational costs.

The increasing demand for electricity, driven by sustained population growth, has placed considerable pressure on Algeria’s power generation infrastructure to expand its output. In parallel, the sharp decline in the cost of solar photovoltaic (PV) electricity and the rising tariffs on conventional energy sources have intensified interest in PV-based electricity generation. Accordingly, precise evaluation of the annual and monthly performance of solar PV plants has become essential for the optimal design and deployment of new facilities. This study presents a comprehensive performance assessment of a 12 MW grid-connected photovoltaic power plant located in Sidi Bel Abbès, Algeria. Real-time operational data were collected over a one-year period, from January 1st to December 31st, 2023. Key performance indicators analyzed include energy output, final yield (Yf), and performance ratio (PR), among others. The plant delivered a total of 20,780.67 MWh to the grid in 2023. The final yield ranged from 3.25 to 5.88 kWh/kWp, while the performance ratio varied between 79% and 89.71%. The annual capacity factor was calculated to be 19.44%. These empirical results are compared against simulation outputs obtained using PVsyst and the Solar GIS-PV Planner tools.

Transformers are fundamental and among the most expensive electrical devices in any power transmission and distribution system. Therefore, it is essential to implement powerful maintenance methods to monitor and predict their condition. Due to its many advantages—such as early detection, accurate diagnosis, cost reduction, and rapid response time—dissolved gas analysis (DGA) is regarded as one of the most effective ways to assess a transformer’s condition. In this contribution, we propose a new probabilistic hierarchical intelligent system consisting of five subnetworks of the radial basis functions (RBF) type. Indeed, hierarchical classification minimizes the complexity of the discrimination task by employing a divide-and-conquer strategy, effectively addressing the issue of unbalanced data (a significant disparity between the categories to be predicted). This approach contributes to a more precise and sophisticated diagnosis of transformers. The first subnetwork detects the presence or absence of defects, separating defective samples from healthy ones. The second subnetwork further classifies the defective samples into three categories: electrical, thermal, and cellulosic decomposition. The samples in these categories are then precisely assigned to their respective subcategories by the third, fourth, and fifth subnetworks. To optimize the hyperparameters of the five models, the Linde–Buzo–Gray algorithm is implemented to reduce the number of centers (radial functions) in each subnetwork. Subsequently, a single-layer perceptron is trained to determine the optimal synaptic weights, which connect the intermediate layer to the output layer. The results obtained with our proposed system surpass those achieved with another implemented alternative (a single RBF), with an average sensitivity percentage as high as 96.85%. This superiority is validated by a Student’s t-test, showing a significant difference greater than 5% (p-value < 0.001). These findings demonstrate and highlight the relevance of the proposed hierarchical configuration.

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This extensive study offers a thorough analysis of the complex technical and economical factors involved in incorporating photovoltaic (PV) systems into the buildings' electrical grid in Algeria. It focuses on a crucial element in the country's energy transition. This study aims to evaluate the viability and potential advantages of implementing solar panels on the rooftop of a university parking lot located at the prestigious Faculty of Electrical Engineering of the University of Sidi Bel Abbess. By utilising advanced modelling tools such as PVSOL and HOMER Pro, this study applies a comprehensive methodology to assess the feasibility of this facility. In the first step, a comprehensive three-dimensional model is carefully constructed using PVSOL software in order to assess the maximum achievable photovoltaic (PV) capacity for the neighbouring parking lot. This estimation considers several parameters, including potential shadows, size of furniture, direction, and slope. This thorough assessment establishes the foundation for subsequent examinations. Following this, a thorough technical-economic analysis is performed using HOMER Pro software to comprehensively evaluate the advantages and disadvantages of two separate scenarios: one that exclusively depends on the current electrical network and another that combines conventional grid power with solar-generated electricity. The study offers useful insights into the economic feasibility and sustainability of incorporating photovoltaic (PV) systems into the university's infrastructure, taking into account several aspects such as installation costs, maintenance requirements, electricity rates, and possible savings. The results highlight the considerable potential offered by integrating photovoltaic (PV) systems into the electrical grids of buildings, enabling the provision of electricity to both structures and perhaps facilitating the recycling of electric vehicles. Not only does this have the potential to reduce long-term energy costs, but it also plays a crucial role in promoting Algeria's shift towards more eco-friendly and sustainable energy options. This, in turn, contributes to global initiatives to address climate change and promote a more environmentally friendly future for future generations.

Many power transformers throughout the world are nearing or have gone beyond their theoretical design life. Since these important assets represent approximately 60% of the cost of the substation, monitoring their condition is necessary. Condition monitoring helps in the decision to perform timely maintenance, to replace equipment or extend its life after evaluating if it is degraded. The challenge is to prolong its residual life as much as possible. Dissolved Gas Analysis (DGA) is a well-established strategy to warn of fault onset and to monitor the transformer’s status. This paper proposes a new intelligent system based on DGA; the aim being, on the one hand, to overcome the conventional method weaknesses; and, on the other hand, to improve the transformer diagnosis efficiency by using a four-step powerful artificial intelligence method. (1) Six descriptor sets were built and then improved by the proposed feature reduction approach. Indeed, these six sets are combined and presented to a Kohonen map (KSOM), to cluster the similar descriptors. An averaging process was then applied to the grouped data, to reduce feature dimensionality and to preserve the complete information. (2) For the first time, four direct Multiclass Support Vector Machines (M-SVM) were introduced on the Generic Model basis; each one received the KSOM outputs. (3) Dempster–Shafer fusion was applied to the nine membership probabilities returned by the four M-SVM, to improve the accuracy and to support decision making. (4) An output post-processing approach was suggested to overcome the contradictory evidence problem. The achieved AUROC and sensitivity average percentages of 98.78–95.19% (p-value < 0.001), respectively, highlight the remarkable proposed system performance, bringing a new insight to DGA analysis.

Introduction. Static converters are among the most widely used equipment in several applications, for example, electric power transmission, motor speed variation, photovoltaic panels, which constitute the electronic components. The design of a power electronics device is done without any real means of predicting electromagnetic disturbances during the product development phase. This case-by-case development process is repeated until a solution is found that best respects all the electromagnetic compatibility constraints. The purpose is the development of a boost-inverter converter under electromagnetic compatibility constraints. The improvements made to the inverter are mainly in the control, the choice of power switches and the electromagnetic compatibility solutions brought to the device. The quality of the wave is improved by acting on the type of control and the choice of switches. Methods. In the first time, we have highlighted a comparison between two most frequently used power components (MOSFET and IGBT) in the inverter and the boost by simulation using ISIS and LT-spice softwares. The sinusoidal voltage with modulation circuit is greatly simplified by the use of the PIC16F876A microcontroller. In a second step, we validate the obtained results with experimental measurements. We start with the boost, then the inverter. In addition, the circuits made are housed in boxes to avoid accidental contact for people. The equipment is designed to isolate the load from the power supply in case of: over voltages, under voltages, high and low battery level and short circuits. Results. All the simulations were performed using the ISIS and LT-spice softwares. The obtained results are validated by experimental measurements performed in the ICEPS Laboratory at the University of Sidi Bel-Abbes in Algeria. The realization of a single-phase inverter with a pulse width modulation control, associated with a boost chopper and the waveforms of the current and voltage across each static converter its opening are presented. The sources of disturbances in power devices are at the origin of the temporal and frequency characteristics of the signals coming from the hot spots of the power switches and the resonances created during the switching of these elements.

This paper describes a new strategy for optimizing the switching angles of a three-phase inverter in a photovoltaic system. It presents non-traditional solutions to the problem of selective harmonic elimination (SHE) in three-phase inverter (VSI)-fed induction motor drives. The aforementioned problem was solved independently by using hybrid genetic algorithms (HGAs) and a modified Newton–Raphson method. GAs can obtain the correct solution even if the first generation is arbitrary. The solution then converges rapidly. The modified Newton–Raphson method is used to solve transcendental equations of the SHE pulses width modulation (SHEPWM) technique, which is a unique method that produces all possible solutions without assuming the initial angles. This modified technique is not complex and ensures rapid convergence to the solution. A real-time experimental verification of the SHEPWM technique was performed in the OP5600 RT-Lab simulator. The results obtained show that the proposed SHEPWM algorithm controls the fundamental voltage and effectively eliminates the desired harmonics, and that the evolution of the signal quality increases according to the modulation index. For M=1.1 the SHE-PWM gives the best result: a current THD of 5% for a switching frequency of 1150 Hz.