Publications Scientifiques
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Item Optimized fractional order Takagi-Sugeno Fuzzy-PID power system stabilizer: An enhanced dung beetle optimization approach(Elsevier, 2025) Hattabi, Intissar; Kheldoun, Aissa; Bradai, Rafik; Belmadani, HamzaThis paper introduces a novel Fractional Order Takagi-Sugeno Fuzzy-PID (FO-TSF-PID) controller, optimized using an enhanced Dung Beetle Optimization (EDBO) algorithm, to improve the damping of low-frequency oscillations in power systems. The controller's design involves simultaneous optimization of membership functions (MFs) and gains, enhancing performance, particularly under three-phase fault conditions. The optimization process, executed through the EDBO algorithm, is both flexible and straightforward to implement. The FO-TSF-PID controller was tested on a two-area power system subjected to three symmetrical faults. Performance evaluations demonstrated the controller's superiority over the standard Fractional Order PID (FOFPID) controller, achieving significant improvements in inter-area and local-area eigenvalues. Specifically, inter-area improvements were 87.08 % with PSO, 83.86 % with EO, 81.29 % with DBO, and 78.89 % with EDBO, while local-area improvements were 71.01 % with PSO, 70.52 % with EO, 65.73 % with DBO, and 64.32 % with EDBO. Comparative analysis against traditional controllers such as Lead-Lag Power System Stabilizer (PSS), Proportional-Integral-Derivative (PID), and Fractional Order PID (FOPID) consistently showed the FO-TSF-PID controller's enhanced stability and robustness. Further comparisons revealed that the EDBO-optimized FO-TSF-PID controller achieved 99.94 %, 99.93 %, and 99.95 % enhancements compared to those optimized using PSO, EO, and DBO, respectively. The results indicate that the EDBO-optimized FO-TSF-PID controller excels in reducing settling time, minimizing overshoot, and improving steady-state error, thus proving its efficacy in stabilizing power systemsItem Seasonal Forecasting of Global Horizontal Irradiance for Grid-Connected PV Plants: A Combined CNN-BiGRU Approach(Institute of Electrical and Electronics Engineers, 2024) Ait Mouloud, Louiza; Kheldoun, Aissa; Merabet, Oussama; Belmadani, Hamza; Bisht, Singh Vimal; Oubelaid, Adel; Bajaj, MohitThe quest for environmental sustainability in power systems necessitates the incorporation of renewable energy sources into the grid infrastructure. Among these renewable sources, solar energy has risen to prominence due to its widespread availability. However, the variable nature of solar irradiance poses challenges in operational and control aspects of its integration. A potential solution lies in predictions of global horizontal irradiance (GHI). This study introduces an ensemble deep learning-based forecasting approach, leveraging a Convolutional Neural Network and Bidirectional Gated Recurrent Unit (CNN-BiGRU). The efficacy of this approach is evaluated against three ensemble models: The Convolutional Neural Network Bidirectional Long Short Term Memory (CNN-BiLSTM), Convolutional Neural Network Gated Recurrent Unit (CNN-GRU), the Convolutional Neural Network Long Short Term Memory (CNN-LSTM). The comparative analysis is centered on seasonal GHI forecasting in Alice Springs, Australia, with a 1-hour time horizon. Four metrics are employed to gauge the accuracy of the models: coefficient of determination (R2), mean absolute error (MAE), normalised root mean square error (nRMSE), and root mean square error (RMSE). The findings reveal that the proposed ensemble bidirectional model outperforms its counterparts in all seasons. Specifically, in terms of seasonal forecasting, the CNN-BiGRU model achieves a maximum nRMSE of 0.0955, indicating its superior performance.Item Guided Seagull Optimization for Improved PV MPPT in Partial Shading(Institute of Electrical and Electronics Engineers Inc, 2023) Belmadani, Hamza; Merabet, Oussama; Obelaid, Adel; Kheldoun, Aissa; Mohit, Bajaj; Ansari, Md Fahim; Bradai, RafikBased on the Seagull Optimization approach, this paper proposes a completely new, rapid Maximum Power Point tracking method. After adding opposition learning and adjusting the convergence factor to the initial version, the intended algorithm - dubbed The Guided Seagull Optimizer (GSO) - was produced. Essentially, the goal of the new technique is to increase convergence speed while maintaining a reasonable global search capability. The GSO algorithm was tested on a stand-alone photovoltaic system subjected to complex multi-peak partial shadowing patterns. Overall, the findings reveal that the technique outperforms typical SOA and PSO algorithms when it comes to of convergence time, efficiency, and adaptability.Item An optimal coordination of directional overcurrent relays using a Gorilla troops optimizer(IEEE, 2023) Merabet, Oussama; Bouchahdane, Mohamed; Belmadani, Hamza; Kheldoun, Aissa; Eltom, Ahmed; Bradai, RafikThe optimization of coordination of directional overcurrent relays in an interconnected power system is given in this work. The goal of protective relay coordination is to achieve selectivity while maintaining sensitivity and a fast fault clearing time. The coordination research revolves around calculating the relays’ time dial setting (TDS) and plug setting (PS). DOCR coordination is a difficult and fascinating problem in nonlinear optimization. To avoid too much breakdown and interference, the overall working duration of all necessary relays must be kept to a minimum. To solve the coordination issue at the DOCR, coordination is carried out using the Gorilla troops optimizer (GTO). IEEE 3-bus and 8-bus test systems are among the test systems to which the suggested method has been implemented. The Results collected demonstrate the suggested GTO efficiency in reducing the relay operation time for the DOCRs’ optimum cooperationItem A twofold hunting trip African vultures algorithm for the optimal extraction of photovoltaic generator model parameters(Taylor et francis, 2022) Belmadani, Hamza; Kheldoun, Aissa; Bradai, Rafik; Bradai, Rafik; Daula Siddique, MarifThe development of reliable simulators that finely imitate the behavior of PV devices is vitally important for the design and optimization of efficient and stable photovoltaic systems. In this work, an improved variant of the African Vultures Optimization Algorithm named IAVOA is designed to serve as a powerful tool for extracting the unknown parameters of photovoltaic models. The introduced scheme incorporates a twofold strategy in such a way that allows a portion of the search agents to conduct a global search while the remaining portion performs a local search. The embedded mechanism is based on two equations added to the standard version, and by which the exploration and exploitation capabilities of the algorithm have significantly been fostered. To testify the performance of the IAVOA, a comparative study based on the Root Mean Square Error (RMSE), was conducted on six distinct benchmark PV models, and the obtained results were, in most cases, remarkably superior to the ones achieved by its competitors. The algorithm was able to produce values for the ideality factors that have not been previously found by any existing work to the best of our knowledge. In turn, the Double Diode and Triple Diode models’ accuracies were notably improved with RMSE scores of 6.9096×10−4 and 7.4011×10−4 respectively for the RTC France cell, and 1.4251×10−2 for the STP6-120/36 module, outperforming the existing techniques. In light of that, it can be reliably presumed that the IAVOA is indeed a promising algorithm for the electrical characterization of PV devices.Item Fuzzy logic enhanced control for a single-stage grid-tied photovoltaic system with shunt active filtering capability(Wiley, 2021) Ayachi Amor, Yacine; Hamoudi, Farid; Kheldoun, Aissa; Didier, Gaëtan; Rabiai, ZakariaIn this paper, a three-phase single-stage grid-connected photovoltaic (PV) system with active power filtering capability by means of a three-level T-type inverter is presented. The system is intended to fulfill many functions: harmonic mitigation, unity power factor operation, maximum power extraction from PV source, and so on. For the proposed system to achieve these tasks with a good dynamic performance, a new control strategy based on the fuzzy logic controller is developed. Fuzzy control has three main stages and each one requires many settings or selection of parameters. A new approach of setting the scaling factors which considerably affect the system's response is proposed. Furthermore, a methodology to properly set the fuzzy rules is suggested. The electrical power chain of the system comprises a farm of a PV source, three-level T-type inverter space vector pulse width modulation controlled, inductor filter, non-linear load, and the utility grid. To evaluate the performance of the proposed control, a processor-in-the-loop is performed as a hardware verification of the inverter control algorithm using a low-cost STM32F4 discovery board, while the power circuit plant is modeled in the host computer using Matlab/Simulink. The obtained results are very satisfactory and confirm the role of each component, especially in terms of maximum power tracking, power quality, unity power factor operation, and control robustnessItem An experimental assessment of direct torque control and model predictive control methods for induction machine drive(IEEE, 2018) Ammar, Abdelkarim; Kheldoun, Aissa; Metidji, Brahim; Talbi, Billel; Ameid, Tarek; Azzoug, YounesFinite-State Model Predictive Control methods (FS-MPC) have been presented recently in the field of electrical drive and power electronics as an alternative to the conventional strategies. This paper presents a comparative evaluation between Direct Torque Control (DTC) and two finite-state model predictive control strategies applied to induction motor drive. Both DTC and MPC are nonlinear control techniques which dispense with the use of modulation unit (i.e. pulse width modulator (PWM) or space vector modulator (SVM)). DTC can provide good decoupled flux and torque control using pair of hysteresis comparators and look-up switching table for voltage vectors selection. In contrast with the model predictive control which includes the inverter model in control design. The optimal selection of inverter switching states minimizes the error between references and the predicted values of control variables by the optimization of a cost function. The effectiveness of applied algorithms is investigated by an experimental implementation using real-time interface (RTI) based on dSpace 1104Item Design and implementation of three-level T-type inverter based on simplified SVPWM using cost-effective STM32F4 board(Inderscience Online Journals, 2021) Amor, Yacine Ayachi; Kheldoun, Aissa; Metidji, Brahim; Hamoudi, Farid; Merazka, Abdeslam; Lazoueche, YoussoufThis paper investigates the design and validation of simplified space vector pulse width modulation (SVPWM) as a switching control for a three-phase three-level T-type inverter using STM32F4 board interfaced with MATLAB/Simulink environment. Usually, the SVPWM algorithm implemented using either DSP card or Dspace platform, which affects the cost of the system. On the contrary, the proposed algorithm offers a great reduction of computations compared with the conventional one, which grants an easy digital implementation. Thanks to the geometrical symmetry of six sectors, in which exists a close relationship between on-time calculations and on-time arrangement of the switching states. This can be exploited for the remaining sectors based on a single computation that relies on the first sector only. The proposed algorithm has been validated in both simulation and experimental tests. The results show the ability and the flexibility of using the STM32F4 board to drive a three-level T-type inverter