Publications Scientifiques

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    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, Abderrahmane
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    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, Adel
    The 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.
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    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, Abderrezzaq
    Batteries 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.
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    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, Saad
    This 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 discussed
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    Shading fault detection in a grid-connected PV system using vertices principal component analysis
    (Elsevier, 2021) Rouani, Lahcene; Harkat, Mohamed Faouzi; Kouadri, Abdelmalek; Mekhilef, Saad
    Partial 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.
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    Novel technique for transmission line parameters estimation using synchronised sampled data
    (IET Digital Library, 2020) Bendjabeur, Abdelhamid; Kouadri, Abdelmalek; Mekhilef, Saad
    Accurate 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.
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    Comparison of Two Hybrid Global Maximum Power Point Algorithms for Photovoltaic Module under Both Uniform and Partial Shading Condition
    (IEEE, 2020) Fares, Dalila; Fathi, Mohamed; Mekhilef, Saad
    Power vs. Voltage (P-V) characteristics of a photovoltaic module (PV) show multiple peaks under partial shading conditions (PSCs). Most conventional maximum power point tracking (MPPT) techniques can accurately locate the single point under uniform conditions but fail under PSCs. Intelligent algorithms can locate the global point (GMPP) among the local ones (LP) but incur more computational cost. Combining both types as hybrid GMPPT provides more effective performance under different environmental conditions. This paper aims to analyze and compare the performance of two hybrid GMPP techniques under both uniform conditions and partial shading. In the proposed approach, the genetic algorithm (GA) and particle swarm optimization (PSO) are integrated with the perturb and observe algorithm (P&O). The simulation results in Matlab/Simulink confirm that both hybrid algorithms can track the GMPP. Furthermore, they show the ability to differentiate between different environment changes occurrences
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    Real-time fault detection in PV systems under MPPT using PMU and high-frequency multi-sensor data through online PCA-KDE-based multivariate KL divergence
    (Elsevier, 2021) Bakdi, Azzeddine; Bounoua, Wahiba; Guichi, Amar; Mekhilef, Saad
    This paper considers data-based real-time adaptive Fault Detection (FD) in Grid-connected PV (GPV) systems under Power Point Tracking (PPT) modes during large variations. Faults under PPT modes remain undetected for longer periods introducing new protection challenges and threats to the system. An intelligent FD algorithm is developed through real-time multi-sensor measurements and virtual estimations from Micro Phasor Measurement Unit (Micro-PMU). The high-dimensional high-frequency multivariate characteristics are nonlinear time-varying where computational efficiency becomes crucial to realize online adaptive FD. The adaptive assumption-free method is developed through Principal Component Analysis (PCA) for dimension reduction and feature extraction with reduced complexity. Novel fault indicators and discrimination index are developed using Kullback–Leibler Divergence (KLD) for an accurate evaluation of Transformed Components (TCs) through recursive Smooth Kernel Density Estimation (KDE). The algorithm is developed through extensive data with measurements from a GPV system under Maximum PPT (MPPT) and Intermediate PPT (IPPT) switching modes. The validation scenarios include seven faults: open circuit, voltage sags, partial shading, inverter, current feedback sensor, and MPPT/IPPT controller in boost converter faults. The adaptive algorithm is proved computationally efficient and very accurate for successful FD under large temperature and irradiance variations with noisy measurements
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    Transmission line fault location by solving line differential equations
    (ELSEVIER, 2020) Bendjabeur, Abdelhamid; Kouadri, Abdelmalek; Mekhilef, Saad
    The present paper describes a new algorithm for reliably locating the faults that frequently take place on electrical transmission lines. The proposed fault location technique considers a distributed-parameter line model that is given in the form of Partial Differential Equations (PDEs) whose boundary conditions are taken as synchronized time-domain data recorded from both sending and receiving terminals. The Adomian Decomposition Method is employed to spatially solve the sampled-time line model. The obtained solution provides phase voltages and currents profiles at each sample time as a function of line length. This function is expressed as a polynomial whose coefficients are simply determined by an easily-applied recursive procedure. One of the main interesting features of the developed scheme is that it can handle the case of unsymmetrical transmission lines without the need of modal decomposition that decouples the original three-phase system to an equivalent three independent single-phase systems. Simulations and calculations are all proceeded with MATLAB. The obtained results through different simulations show that the new methodology is operational, applicable, and accurate.
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