Browsing by Author "Belmadani, Hamza"

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An accelerated aquila optimizer for maximum power point tracking of PV systems under partial shading conditions
(EDP Sciences, 2024) Belmadani, Hamza; Merabet, Oussama; Khettab, Sofiane; Maindola, Meenakshi; Bajaj, Mohit; Oubelaid, Adel
In this work, an improved version of the recent Aquila Optimizer was designed for Maximum Power Point Tracking. The new algorithm was tested on a standalone PV system under several complex partial shading scenarios. A comparative study was conducted to evaluate efficiency, robustness, and convergence speed against the PSO, and the standard AO algorithms. The results indicate that the proposed Accelerated Aquila Optimizer (AAO) generally outperformed its competitors, particularly in terms of convergence time.
Development of new maximum power tracking techniques for stand-alone PV system under nonuniform irradiance conditions
(Université M’Hamed BOUGARA de Boumerdes : Institut de génie electrique et electronique (IGEE), 2020) Belmadani, Hamza; Mellal, Sohaib; Kheldoun, Aissa (supervisor)
The overwhelming need to decarbonize the energy sector to peter out climate changes, and catch up with the increasing demand of energy, have paved the way to an immense deployment of renewables around the globe. Solar systems are used to convert sunlight that hits their panels into electrical energy via the photovoltaic effect. However, photovoltaics have a very low efficiency, and the generated power depends almost entirely on the amount of collected solar irradiance, temperature, the electrical load and the ambient circumstances that surrounds them. Since it is not possible to have a fixed stream of solar radiation or temperature, it is crucial to come up with effective means to tackle these problems. In this regard, Maximum power trackers are integrated with PV systems to cope with the dynamically fluctuating operating conditions, and keep the generated power as high as possible. This thesis focuses on maximum power point tracking (MPPT) in PV systems using soft computing techniques. Equilibrium Optimizer, Seagull Optimization and Slime Mould Algorithm are three novel metaheuristic techniques proposed in this project. Matlab and Simulink are used to simulate a standalone PV system driven by an MPPT controller and assess the three stated optimizers. The recommended techniques demonstrated outstanding results, under distinct insolation levels and complex shading conditions. To confirm their effectiveness, a comparative study on the basis of robustness, convergence time and efficiency, is carried out along with other well-known techniques: Particle Swarm Optimization (PSO), Whale Optimization (WOA), Grey wolf Optimization (GWO), Wind Driven Optimization (WDO) and the Grasshopper Optimization algorithm (GOA). Obtained results revealed that the proposed algorithms are either superlative or competitive in terms of both convergence speed and tracking efficiency.
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.
Identification and control of asynchronous motor using meta-heuristic algorithms
(2023) Ghernaout, Rayane; Kheldoun, Aissa (Supervisor); Belmadani, Hamza
The present study is centered on the examination, regulation, and enhancement of induction motors (IMs) through the application of meta-heuristic algorithms. The aim of this study is to optimize the performance and efficiency of induction motors (IMs) in various applications. The study begins with the formulation of a mathe- matical model for induction motors (IMs). Subsequently, meta-heuristic algorithms, namely EO, RSBA, and JAYA, are employed to determine the parameters of the IM.The estimation of parameters is conducted by utilizing the inputs of measured stator voltages, currents, and rotor speed. This study focuses on the modeling of indirect rotor flux-oriented control(IRFOC )and the utilization of the resulting IM param- eters to identify the motor. Control gains are then optimized through the imple- mentation of RSBA and JAYA algorithms. The findings of the simulation indicate that the system’s performance has been enhanced in comparison to conventional manual tuning techniques. The project acknowledges the difficulties involved in the optimization process and emphasizes the significance of meticulous parameterse- lection. In summary, this study serves as a valuable contribution to the progression of IM technology, highlighting its potential to enhance performance and efficiency in industrial settings.
MPPT Algorithms for photovoltaic systems under partial shading conditions
(Université M’Hamed BOUGARA de Boumerdes : Institut de génie electrique et electronique (IGEE), 2023) Benrabah, Hamza; Chalah, Samira (Supervisor); Belmadani, Hamza
This master's thesis delves into the comprehensive study of Photovoltaic (PV) systems, focusing on various aspects critical to their efficient operation and optimization. The work is divided into five chapters, each addressing a distinct facet of PV technology and control techniques. Chapter 1: provides a foundational understanding of PV systems, including an introduction to solar energy, photovoltaic cell technology, PV modeling, and the characteristics of solar cell I-V curves. In Chapter 2: the focus shifts to DC-DC converters, exploring the principles and operation of Boost and Buck converters, as well as the versatile Buck-Boost inverter converter. The advantages of Boost converters are also highlighted. Chapter 3: delves into the critical topic of Maximum Power Point Tracking (MPPT), elucidating the principles behind MPPT operation, typical MPPT-based PV system configurations, and the classification of MPPT algorithms, including indirect and direct methods. Chapter 4: introduces soft computing algorithms and novel techniques for MPPT, including Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Seagull Optimization Algorithm (SOA), and the innovative Guided Seagull Optimizer. These algorithms play a pivotal role in enhancing the performance of PV systems. Finally, in Chapter 5, the thesis presents simulation and experimental results to validate the effectiveness of the discussed algorithms under varying irradiance conditions, offering insights into their real-world applicability and performance.This research contributes to the growing body of knowledge surrounding PV systems, offering valuable insights into their operation, optimization, and control, with a particular emphasis on the application of soft computing techniques to maximize energy extraction.
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.
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.
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
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, Hamza
This 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 systems
Optimum sizing of hybrid sustainable and renewable energy systems using a modified harris hawks optimizer
(Elsevier, 2025) Sifou, Djamel Eddine; Kheldoun, Aissa; Chaib, Ahmed; Belmadani, Hamza; Alharbi, Hisham; Alharbi, Saleh S.; Agajie, Takele Ferede; Ghoneim, Sherif S.M.
To boost the use of renewable energy sources while maintaining reliability and affordability, Multi-source renewable and sustainable energy systems must be optimally sized. This research introduces a stand-alone metaheuristic algorithm for designing a hybrid sustainable and renewable energy system combining Wind turbine, PV and battery system. The main goal is to lower the overall present-day system's cost at the same time considering the indicator of reliability, which is the loss of power supply probability (LPSP), as a constraint. The developed algorithm resulted from enhancing the recent Harris Hawks Optimizer (HHO). The modified version incorporates a vector that saves the best three solutions and opposition learning to enhance the population diversity and assist the algorithm in jumping out of local optima regions. Three scenarios are presented, the first is modeled by PV/Bat the second one is modeled by WT/Bat while the third one consists of PV/WT/Bat. The studied project is located in Sidi Khattab, Relizane province, Algeria. The results demonstrate that the MHHO outperforms a range of well-known algorithms, among which one can cite the original HHO, Krill Optimization Algorithm (KOA), Red Squirrel Algorithm (RSA), Modified Coati Optimization Algorithm (MCOA), and Generalized Oppositional-based Social Spider Algorithm (GOOSE). Compared to the other algorithms, MHHO demonstrated superior performance in all proposed configuration settings
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.
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.