Browsing by Author "Mekhilef, Saad"

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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
A data-driven algorithm for online detection of component and system faults in modern wind turbines at different operating zones
(Elsevier, 2019) Bakdi, Azzeddine; Kouadri, Abdelmalek; Mekhilef, Saad
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.
Fault detection in a grid-connected photovoltaic system using adaptive thresholding method
(Elsevier, 2018) Ammiche, Mustapha; Kouadri, Abdelmalek; Halabi, Laith M.; Guichi, Amar; Mekhilef, Saad
In this paper, an adaptive monitoring scheme with Fuzzy Logic Filter (FLF) is developed and applied to monitor a Grid-Connected Photovoltaic System (GCPVS). This method is based on Principal Component Analysis (PCA) and Moving Window Principal Component Analysis (MWPCA). It is designed to generate adaptive thresholds for its monitoring indices. The FLF filters the monitoring indices to reduce the number of False Alarms (FA) and increase the Fault Detection Rate (FDR). The application is carried out on the GCPVS of the Power Electronics and Renewable Energy Research Laboratory (PEARL) of Malaya University. The proposed technique is compared against PCA method in terms of FAR reduction. The detection ability of the adaptive thresholding with FLF monitoring scheme is tested first on simulated faults then it is applied to detect a real abnormal behaviour. The results show that the proposed method is effective in reducing the number of false alarms and in detecting different types of faults with high accuracy
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.
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
A novel global MPPT technique based on squirrel search algorithm for PV module under partial shading conditions
(Elsevier, 2021) Fares, Dalila; Fathi, Mohamed; Shams, Immad; Mekhilef, Saad
The partial shading condition (PSC) makes it challenging for the PV system to harvest maximum power via maximum power point tracking (MPPT). Various MPPT algorithms based on bio-inspired optimization methods were proposed in the literature. The mechanism employed by these algorithms varies from one to another, making them perform differently when tracking the GMPP. This paper introduces a novel MPPT technique based on the improved squirrel search algorithm (ISSA). The performance of the proposed ISSA improved the tracking time by 50% in comparison with the conventional SSA algorithm. Similarly, the proposed method has also been compared with popular Genetic algorithm (GA), and particle swarm optimization (PSO). The results proved the ability of the proposed algorithm in tracking the GMPP with faster convergence and fewer power oscillations in comparison. The feasibility and effectiveness of the proposed ISSA based MPPT have been validated experimentally, and the results clearly demonstrate its capability in tracking the GMPP with an average efficiency of 99.48% and average tracking time of 0.66 s.
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.
Performance evaluation of metaheuristic techniques for optimal sizing of a stand-alone hybrid PV/wind/battery system
(Elsevier, 2021) Fares, Dalila; Fathi, Mohamed; Mekhilef, Saad
This 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 problem
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
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
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.
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
State feedback control for stabilization of PMSM-based servo-drive with parametric uncertainty using interval analysis
(Wiley, 2021) Khelouat, Lila; Ahriche, Aimad; Mekhilef, Saad
For 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-RIO
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, Saad
This 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 applications
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.