Publications Scientifiques

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Author: search.filters.author.Kheldoun, Aissa

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    Enhanced power system stabilizer tuning using marine predator algorithm with comparative analysis and real time validation
    (Nature Portfolio, 2024) Hattabi, Intissar; Kheldoun, Aissa; Bradai, Rafik; Khettab, Soufian; Sabo, Aliyu; Belkhier, Youcef; Khosravi, Nima; Oubelaid, Adel
    This study concentrates on the implementation of Marine Predator Algorithm (MPA) scheme for tuning of a power system stabilizer’s (PSS’s) parameters to damp the low-frequency oscillations in a power system. To this, the single machine infinite bus system (SMIB), the Western System Coordinating Council (WSCC) and the New England 10 machine 39-bus power system are utilized for testing and comparing different metaheuristic algorithms using different fitness functions. Optimal PSS parameters of SMIB test system are validated using CU-SLRT Std, a real-time digital simulator. The comparative studies demonstrate that the MPA optimized PSS yields improvements of up to 98.62% in the Particle Swarm Optimization (PSO) at 69.42%, Whale Optimization Algorithm (WOA) at 71.79%, Flower Pollination Algorithm (FPA) at 72.39%, African vulture optimization algorithm (AVOA) at 78.04%, Wild Horse Optimization (WHO) algorithm at 68.57% under various operating scenarios. The superiority of the MPA optimized PSS has been validated using Hardware-in-the-loop implementation for the SMIB test system.
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    Performance evaluation of PUC7‐based multifunction single‐phase solar active filter in real outdoor environments: Experimental insights
    (John Wiley and Sons Inc, 2024) Khettab, Soufiane; Kheldoun, Aissa; Bradai, Rafik; Oubelaid, Adel; Kumar, Sandeep; Khosravi, Nima
    This paper presents a novel architecture to enhance the performance of grid-connected photovoltaic (PV) systems through the introduction of several key novelties. Firstly, a packed U-cell seven-level (PUC7)-based single-phase solar active filter is implemented, offering a comprehensive solution for harmonics mitigation, reactive power compensation, and efficient power extraction from the PV source, while facilitating the injection of real power into the grid. Secondly, the p-q power injection algorithm is modified to accommodate the extraction of solar power from the PV generator to the grid, simultaneously addressing the need for harmonic current injection to improve power quality. This modification ensures dynamic performance by extracting reference current with harmonic content and solar power information, thereby enhancing the system's overall efficiency. Lastly, the proposed architecture undergoes real outdoor testing, validating its performance in various key aspects including maximum power tracking, reduction of total harmonic distortion in comparison with previous work, operation at unity power factor, and testing the effective operation of the multifunction feature. These contributions collectively demonstrate the effectiveness of the proposed system in enhancing power injection quality and reactive power compensation under real outdoor conditions of PV systems connected to the grid.
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    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, Mohit
    The 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.
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    A Taguchi method-based optimization algorithm for the analysis of the wind driven-self-excited induction generator
    (Institute of Advanced Engineering and Science (IAES), 2024) Boukenoui, Rachid; Bradai, Rafik; Kheldoun, Aissa
    This paper investigates the use of a new global optimization algorithm that is based on Taguchi method to determine the performance parameters of self-excited induction generator being driven by variable speed wind. This analysis is based on solving equations obtained from the per-phase equivalent circuit of the induction generator. The equations have two unknowns namely the frequency and the magnetizing reactance. Both unknown are strongly dependent on the wind turbine speed, the capacity of the excitation, the load being connected at the terminals of the stator and eventually the per-phase equivalent circuit parameters. The resulting equations are nonlinear and subsequently to solve them one can employ either gradient-based algorithms or heuristic algorithms. This paper uses a new heuristic algorithm based on the Taguchi method which, in addition to its global research capability, offers superior characteristics in terms of accuracy and ease of implementation. A comparison with recently published optimization methods is carried out to show its performances in terms of accuracy and ease of implementation. The MATLAB software will be used to perform this analysis on a machine of 0.75 kW while some will be validated experimentally to confirm the aforementioned benefits.
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    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, Rafik
    Based 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.
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    Optimal coordination of directional overcurrent relays in complex networks using the Elite marine predators algorithm
    (Elsevier, 2023) Merabet, Oussama; Bouchahdane, Mohamed; Belmadani, Hamza; Kheldoun, Aissa; Eltom, Ahmed
    The integration of renewable energy sources in the distribution network (DN) has had a significant influence by reducing power loss and enhancing network dependability. Aside from that, the protection system has met coordination issues as a result of bidirectional power flow and variations in fault levels. Therefore, an optimal coordination strategy is required to deal with relays coordination problem. The coordination problem of the directional overcurrent relays (DOCRs) is a restricted optimization issue that involves determining appropriate time dial settings (TDS) and plug setting (PS) to reduce relays operating time. Currently, a various nontraditional optimization strategies have been presented to overcome this challenge. In this paper, a modified version of the marine predators algorithm (MPA) referred to as Elite marine predator (EMPA) is developed for the optimal coordination of DOCRs. Therefore, the EMPA method is used to find out the optimal settings for the DOCRs problem. The suggested algorithm's performance is evaluated using standard test systems, including 3-bus, 8-bus, 9-bus, and 15-bus. The findings are compared with the traditional MPA and with other recent optimization methods presented in the literature to prove the efficiency and superiority of the proposed EMPA in reducing relay operation time for optimal DOCRs coordination.
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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 adaptive protection coordination for microgrids utilizing an improved optimization technique for user-defined DOCRs characteristics with different groups of settings considering N-1 contingency
    (Elsevier, 2024) Merabet, Oussama; Kheldoun, Aissa; Bouchahdane, Mohamed; Eltom, Ahmed H.; Kheldoun, Ahmed
    Due to the increased integration of distributed generation (DG) in electric power systems, optimal coordination of overcurrent relays has become a key problem in power distribution systems. However, there is a lack of expertise in the creation of optimum microgrid coordination that takes into account all N-1 scenarios through the nonstandard relay characteristics. This work presents a novel technique for optimal coordination of directional overcurrent relays (DOCRs) in terms of relay curve settings (A and B), time dial setting (TDS) and plug setting (PS) to achieve the shortest running time and attain optimal settings. The optimization is carried out using a modified version of the Hanger Games Search algorithm (MHGS). The performance of the proposed method is assessed using the 14 bus distribution system while considering all the N-1 contingencies. DIgSILENT software was utilized to perform the required power system analysis, such as power flow and short circuit analysis. The MHGS method is used to determine the best settings for the DOCRs problem. The results are compared to the traditional HGS as well as those obtained by other current optimization approaches provided in the literature in order to demonstrate the effectiveness and superiority of the proposed MHGS in lowering relay operation time for optimum DOCRs coordination.
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    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, Rafik
    The 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 cooperation
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    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, Marif
    The 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.