Publications Scientifiques
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Item Retraction notice to "Feasibility study of a grid-connected PV/wind hybrid energy system for an urban dairy farm" [Heliyon 10 (2024) e40650](Cell Press, 2025) Bouregba, Hicham; Hachemi, Madjid; Samatar, Abdullahi Mohamed; Mekhilef, Saad; Stojcevski, Alex; Seyedmahmoudian, Mehdi; Hamidat, AbderrahmaneItem SVPWM-Based Control of a Three-Phase Five-Level NPC Inverter for Grid-Connected Solar Power System(Institute of Electrical and Electronics, 2025) Elamri, Oumaymah; Toubal Maamar, Alla Eddine; Oukassi, Abdellah; El Kharki, Abdellah; Hammoudi, Abderazek; Mekhilef, SaadThis study focuses on analyzing a photovoltaic system for energy production and its integration into the grid. Take into account the key grid parameters, including frequency, three-phase system symmetry, and voltage waveforms. Non-sinusoidal voltages can cause interference that affects the operation of networked equipment. To address this issue, a three-phase five-level neutral-point-clamped inverter is incorporated into the system, utilizing the space vector pulse width modulation technique for control. The control strategy of the converter is presented in detail. The study was carried out utilizing Matlab/Simulink, and the simulation outcomes demonstrate the efficiency of this control approach for renewable energy applicationsItem A New Fast and Efficient MPPT Algorithm for Partially Shaded PV Systems Using a Hyperbolic Slime Mould Algorithm(Wiley-Hindawi, 2024) Belmadani, Hamza; Bradai, Rafik; Kheldoun, Aissa; Mohammed, Karam Khairullah; Mekhilef, Saad; Belkhier, Youcef; Oubelaid, AdelThe design of new efficient maximum power point tracking (MPPT) techniques has become extremely important due to the rapid expansion of photovoltaic (PV) systems. Because under shading conditions the characteristics of PV devices become multimodal having several power peaks, traditional MPPT techniques provide crappy performance. In turn, metaheuristic algorithms have become massively employed as a typical substitute in maximum power point tracking. In this work, a new optimizer, which was named the hyperbolic slime mould algorithm (HSMA), is designed to be employed as an efficient MPPT algorithm. The hyperbolic tangent function is incorporated into the optimizer framework equations to scale down large perturbations in the tracking stage and boost its convergence trend. Moreover, to provide a strong exploration capability, a new mechanism has been developed in such a way the search process is carried out inside the best two power peak regions along the initial iterations. This region inspection mechanism is the prime hallmark of the designed optimizer in avoiding local power peaks and excessive global search operations. The developed algorithm was examined through diverse complicated partial shading conditions to challenge its global and local search abilities. A comparative analysis was carried out against the well-regarded PSO, GWO, and the standard slime mould algorithm. In overall, the designed optimizer defeated its contenders in all aspects offering higher efficiency, superior robustness, faster convergence, and fewer fluctuations to the operating point. An experimental setup that consists of the DSpace microcontroller and a PV emulator was employed to validate the algorithm overall performance. The recorded outcomes outline that the developed optimizer can achieve a tracking time of 0.6 seconds and 0.86 seconds on average, with 99.85% average efficiency under complex partial shading conditions.Item An enhanced battery model using a hybrid genetic algorithm and particle swarm optimization(Springer Nature, 2023) Mammeri, Elhachemi; Ahriche, Aimad; Necaibia, Ammar; Bouraiou, Ahmed; Mekhilef, Saad; Dabou, Rachid; Ziane, AbderrezzaqBatteries are widely used for energy storage in stand-alone PV systems. However, both PV modules and batteries exhibit nonlinear behavior. Therefore, battery modeling is an essential step toward appropriate battery control and overall PV system management. Empirical models remain reliable for lead-acid batteries, especially the Copetti model, which describes many inner and outer battery phenomena, including temperature dependency. However, the parameters of the Copetti model require further adjustment to increase its ability to accurately represent battery behavior. Recently, metaheuristic algorithms have been employed for parameter identification, especially hybrid algorithms that combine the advantages of two or more algorithms. This paper proposes an enhanced battery model based on the Copetti model. The parameter identification of the enhanced model has been carried out using a novel hybrid PSO-GA algorithm (HPGA). The hybrid algorithm combines GA and PSO in a cascade configuration, with GA as the master algorithm. The HPGA algorithm has been compared with other algorithms, namely GA, PSO, ABC, COA, and a hybrid GWO-COA, to reveal its advantages and disadvantages. The NRMSE is used to evaluate algorithms in terms of tracking speed and efficiency. HPGA shows an improvement in tracking efficiency compared to GA and PSO. The proposed model is validated on several charging-discharging data and exhibits a 15% lower mean error compared to the Copetti model with original parameters. Additionally, the proposed model demonstrates a lower mean error of 0.16% compared to other models in the literature with a 0.36% mean error at least.Item Stability and accuracy improvement of motor current estimator in low-speed operating based on sliding mode takagi-sugeno algorithm(Publishing House of the Romanian Academy, 2022) Ahriche, Aimad; Abdelhakim, Idir; Doghmane, Mohamed Zinlabidine; Kidouche, Madjid; Mekhilef, SaadThis paper is devoted to presenting a new mathematical development and hardware implementation of an accurate and stable technique for the current estimation-based sliding mode observer in high-performance speed-sensorless ac-drive. The proposed algorithm is built by using induction motor (IM) flux equations in two referential frames to enhance the robustness of the observer. Indeed, all equations are given in both stator-flux and rotor-flux rotating frames. On the other hand, to eliminate the necessity of rotor-speed adaptation, a fully speed-sensorless scheme is adopted. Furthermore, to minimize chattering and improve accuracy, a new fuzzy sliding surface is introduced instead of the conventional correction vector. The observer stability is guaranteed by means of Lyapunov’s second method. The feasibility and the effectiveness of the proposed algorithm are verified by using a hardware setup based on the DS1104 controller board. Experimental results are shown and discussedItem Performance evaluation of metaheuristic techniques for optimal sizing of a stand-alone hybrid PV/wind/battery system(Elsevier, 2021) Fares, Dalila; Fathi, Mohamed; Mekhilef, SaadThis study presents a performance evaluation of ten metaheuristics optimization techniques that are applied to solve the sizing problem for a stand-alone hybrid renewable energy system including a photovoltaic module, wind turbine, and a battery (PV/WT/Battery). The algorithms include genetic algorithm (GA), cuckoo search (CS), simulated annealing (SA), harmony search (HS), Jaya algorithm, firefly optimization algorithm (FA), flower pollination algorithm (FPA), moth flame optimization (MFO), brainstorm optimization in objective space (BSO-OS), and the simplified squirrel search algorithm (S-SSA). The optimization process aims to minimize the total net present cost (TNPC) of the system while maintaining the acceptable deficiency of power supply probability (DPSP). The levelized energy cost and the relative excess power generated criteria are also considered. The studied algorithms have been simulated for four DPSP values (0%, 0.3%, 1%, and 5%), each for 50 independent runs. Based on the simulation results, FPA and SA demonstrated high robustness and accuracy with zero standard deviation and a 0% increase in the TNPC values compared to the optimal solutions. The FAO showed the best performance in terms of execution time with an average of 6.32 s, followed by BSO-OS (6.36 s) and SA (7.84 s). The SA has the best compromise between robustness, accuracy, and rapidity, and is found to be the best option to solve the sizing problem. The FPA is the most advantageous in case the execution time is not crucial for the optimization. Our findings will be a good reference for researchers to select the best technique for the sizing problemItem Shading fault detection in a grid-connected PV system using vertices principal component analysis(Elsevier, 2021) Rouani, Lahcene; Harkat, Mohamed Faouzi; Kouadri, Abdelmalek; Mekhilef, SaadPartial shading severely impacts the performance of the photovoltaic (PV) system by causing power losses and creating hotspots across the shaded cells or modules. Proper detection of shading faults serves not only in harvesting the desired power from the PV system, which helps to make solar power a reliable renewable source, but also helps promote solar versus other fossil fuel electricity-generation options that prevent making climate change targets (e.g. 2015’s Paris Agreement) achievable. This work focuses primarily on detecting partial shading faults using the vertices principal component analysis (VPCA), a data-driven method that combines the simplicity of its linear model and the ability to consider the uncertainties of the different measurements of a PV system in an interval format. Data from a gridconnected monocrystalline PV array, installed on the rooftop of the Power Electronics and Renewable Energy Research Laboratory (PEARL), University of Malaya, Malaysia, have been used to train the VPCA model. To prove the effectiveness of this VPCA method, four partial shading patterns have been created. The obtained performance has, then, been tested against a regular PCA. In addition to its ability to acknowledge the uncertainty of a PV system, the VPCA method has shown an enhanced performance of detecting partial shading fault in comparison with the standard PCA. Also, included in the article is an extension of the contribution plot diagnosis-based method, of the Q-statistic, to the interval-valued case aiming to pinpoint the out-of-control variables.Item State feedback control for stabilization of PMSM-based servo-drive with parametric uncertainty using interval analysis(Wiley, 2021) Khelouat, Lila; Ahriche, Aimad; Mekhilef, SaadFor a class of multivariable uncertain dynamic systems, the parametric uncertainties are belonging to a closed interval with lower and upper boundaries a priori known. Thereby, these systems can be analyzed based on interval structures and interval matrices. In this article, a fully interval analysis–based method is developed and applied to the state feedback control of permanent magnet synchronous motor (PMSM) in order to design a stabilized servo-drive. Indeed, the parametric uncertainties are investigated in state space representation, which represents a simplified and effective way to analyze the robust stability for the interval system. Firstly, a robust state feedback control technique, dedicated to an uncertain system is introduced. For that, the robust controllability test is performed for the interval system by using the linear independency condition of column interval vectors. It is proven that there is a direct correlation between the controllability test and the existence of a robust modal P-regulator for the correction of the uncertain system. It is also shown that it invariably relies on each input's controllability indices and thus their effect on the uncertain system's state variables. In order to ensure stability in a closed loop, the modal P-regulator is designed with possibility of incorporation of an integral action. The modified modal PI regulator has the ability to reject disturbance and guarantee zero-steady-state error for step inputs. In fact, the stability is achieved by placing all coefficients of the system characteristic polynomial within assigned intervals based on Kharitonov's Theorem. The technique provides a matrix gain with interval coefficients for the stabilizing regulator. Finally, the developed approach is applied to position control of linearized model of the PMSM-based servo-drive, presenting parametrical uncertainties. To demonstrate the efficiency of the proposed method, a numerical and graphical comparison of conventional LQR and pole placement, state feedback controllers for the PMSM servo-drive with the robust interval controller is provided. In order to verify the feasibility of the whole proposed technique, calculations and simulations are performed by using Matlab/Intlab toolbox. Real-time simulation is also investigated using Lab-View Compact-RIOItem Novel technique for transmission line parameters estimation using synchronised sampled data(IET Digital Library, 2020) Bendjabeur, Abdelhamid; Kouadri, Abdelmalek; Mekhilef, SaadAccurate transmission line parameters values are of high importance in setting protection relays and rigorous locating faults that may occur along the transmission network. For this purpose, the work reported in this study presents a new technique for the delicate determination of transmission lines parameters that are uniformly distributed along the line length. The developed technique is able to approximate the steady-state profiles for the transmission line voltage and current as a function of time and line length by given sets of polynomials that, in turn, are substituted in model equations. Synchronised time-domain data, recorded from both line terminals, are utilised as boundary conditions for the distributed-parameter transmission line model. The well-known Galerkin method is adopted to transform the line model into a system of non-linear algebraic equations to be solved. This system of algebraic equations is converted to residuals that are consequently regrouped in a cost function to be optimised. Thereby, the series resistance, series inductance and the shunt capacitance per line length are the parameters minimising the cost function. Both simulations and calculation are performed with MATLAB software. The obtained results show the effectiveness and accuracy of the new approach.Item Nonparametric Kullback-divergence-PCA for intelligent mismatch detection and power quality monitoring in grid-connected rooftop PV(Elsevier, 2019) Bakdi, Azzeddine; Bounoua, Wahiba; Mekhilef, Saad; Halabi, Laith M.In parallel to sustainable growth in solar fraction, continuous reductions in Photovoltaic (PV) module and installation costs fuelled a profound adoption of residential Rooftop Mounted PV (RMPV) installations already reaching grid parity. RMPVs are promoted for economic, social, and environmental factors, energy performance, reduced greenhouse effects and bill savings. RMPV modules and energy conversion units are subject to anomalies which compromise power quality and promote fire risk and safety hazards for which reliable protection is crucial. This article analyses historical data and presents a novel design that easily integrates with data storage units of RMPV systems to automatically process real-time data streams for reliable supervision. Dominant Transformed Components (TCs) are online extracted through multiblock Principal Component Analysis (PCA), most sensitive components are selected and their time-varying characteristics are recursively estimated in a moving window using smooth Kernel Density Estimation (KDE). Novel monitoring indices are developed as preventive alarms using Kullback-Leibler Divergence (KLD). This work exploits data records during 2015–2017 from thin-film, monocrystalline, and polycrystalline RMPV energy conversion systems. Fourteen test scenarios include array faults (line-to-line, line-to-ground, transient arc faults); DC-side mismatches (shadings, open circuits); grid-side anomalies (voltage sags, frequency variations); in addition to inverter anomalies and sensor faults