Browsing by Author "Oubelaid, Adel"

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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.
Data aggregation point placement optimization in Smart Metering Networks
(JES, 2024) Grainat, Youcef; Recioui, Abdelmadjid; Oubelaid, Adel
This study explores the application of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) within the framework of smart grids (SG), specifically for the optimal placement of data aggregation points (DAPs) across a network of 150 Z-wave smart meters distributed within various smart cities. The investigation aims to identify which of the two- optimization strategies offers a more cost-efficient solution while evaluating their performance in terms of transmission average latency (AL) and execution time (ET) efficiency. The results indicate that although ACO slightly edges out PSO in reducing overall costs in networks with a higher complexity and more DAPs, PSO demonstrates superior performance in execution speed, lower AL, and total cost, underscoring its viability for swift integration in smart metering infrastructures.
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.
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.
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.
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.