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    Improving Power Quality and Dynamic Performance of Modular Multilevel Converter based Microgrid Solar System using Intelligent MPPT controller
    (2024) Yakhelef, Yassine; Herkat, Wiam
    while it provides perfectly clean electricity without any environmental effect, solar energy is the most abundant and free renewable energy source worldwide. However, because of its highly dependency on the weather condition variation particularly those of operating temperature and sunlight irradiance, the solar based electrical energy conversion system is characterized with lot of energy wastage and noticeable low efficiency, which requires an appropriate and effective design of its control part and components for enhancing its generation and delivery performance. The purpose in this work is to perform an investigate study and simulation under Matlab/Simulink environment of the behaviour and reaction of the artificial neural network (ANN) based MPPT controller under various and different climatic condition characterized particularly by the rate of the sunlight intensity variation during the day. Different weather situations regarding the sunlight intensity rate variation ranging from slow to harsh and severe changes are considered and applied in order to study the dynamic performance of the MPPT controller for improving the power quality generation as well as the dynamic performance of the grid tied solar conversion system when implemented around the modular multilevel converter as the newly power conversion emerged technology whatever these atmospheric situations. The simulation results have shown the superiority and outstanding of the ANN MPPT controller in terms of the output power quality and dynamic performance in reaching and retaining the stability at the higher power level regardless of the weather change.
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    A new methodology to predict the sequence of GFRP layers using machine learning and JAYA algorithm
    (Elsevier, 2023) Fahem, Noureddine; Belaidi, Idir; Oulad Brahim, Abdelmoumin; Capozucca, Roberto; Thanh, Cuong Le; Khatir, Samir; Abdel Wahab, Magd M.
    In this paper, the best stacking sequence using experimental tests of GFRP composites is investigated. The main objective of this work is to determine the main specification of GFRP composite material, which is represented by its physics-mechanical properties, weight, and cost, before performing a series of experimental tests based on various stacking sequences. Our methodology is divided into three stages. The first stage is characterized by extracting the bending data from mechanical tests of some GFRP composites. In the second stage, the validated numerical model is used to simulate numerous cases of stacking sequences. In the last stage, the extracted data is used to determine the parameters for different stacking sequences using an inverse technique based on ANN and JAYA algorithm. The results provide a good prediction of parameters as well as a good orientation to make decisions on the best GFRP stacking sequence to be used, according to the required specifications of the manufacturer.
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    Exploring tensile properties of bio composites reinforced date palm fibers using experimental and Modelling Approaches
    (Elsevier, 2024) Saada, Khalissa; Zaoui, Moussa; Amroune, Salah; Benyettou, Riyadh; Hechaichi, Amina; Jawaid, Mohammad; Hashem, Mohamed Ibrahim; Uddin, Imran
    The objective of this study was to assess the tensile strength of epoxy bio-composites reinforced with palm fibers, both untreated and treated with sodium carbonate NaHCO3 at a concentration of 10 % (w/v) for 24 and 96 h, with varying weight percentages of fibers (15 %, 20 %, 25 %, and 30 %). To predict the mechanical performance of the composites, two methods were employed: artificial neural network (ANN) and response surface methodology (RSM). A Box-Behnken RSM design was used to conduct experiments and establish a mathematical model of the bio-composite behavior as a function of the fiber percentage in the samples, specimen cross-section, and treatment time. The ANN forecasts showed consistent expected values for the bio-composite sample behavior, with a correlation coefficient (R2) greater than 0.98 for Young's modulus and 0.97 for stress. Similarly, the correlation coefficients obtained by RSM for the mechanical properties were also highly satisfactory, with an R2 of 0.89 for Young's modulus and 0.87 for stress. Finally, the errors generated by each method (Box-Behnken and ANN) were compared to the experimental results.
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    Choosing the adapted artificial intelligence method (ANN and ANFIS) based MPPT controller for thin layer PV array
    (Springer, 2023) Bouchetob, Elaid; Nadji, Bouchra
    Because of the many advantages that artificial intelligence technologies provide in comparison to more conventional methods, a rising number of solar power plants are beginning to use them in their monitoring of the MPP. When there is a sudden change in solar temperature and irradiance, it is possible that the MPP will not be tracked as accurately. As a consequence of this, these methods could make up for the deficiencies of those that are more well-established (P&O, IC, etc.). Aside from that, there is a wide range of methods to AI, each of which has a particular advantage. By making some minor adjustments to the architecture, an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) were used to monitor the MPP of Thin Layer panel technology at the Oued Nechou installation in Ghardaia. Each connection channel now has six panels rather than the previous maximum of 12 panels, and the junction box has 210 channels rather than the prior maximum of 105 channels. In the last step, a DC-DC boost converter is used to increase the power output voltages produced by the module
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    EEG signal feature extraction and classification for epilepsy detection
    (Slovene Society Informatika, 2022) Cherifi, Dalila; Falkoun, Noussaiba; Ouakouak, Ferial; Boubchir, Larbi; Nait-Ali, Amine
    Epilepsy is a neurological disorder of the central nervous system, characterized by sudden seizures caused by abnormal electrical discharges in the brain. Electroencephalogram (EEG) is the most common technique used for Epilepsy diagnosis. Generally, it is done by the manual inspection of the EEG recordings of active seizure periods (ictal). Several techniques have been proposed throughout the years to automate this process. In this study, we have developed three different approaches to extract features from the filtered EEG signals. The first approach was to extract eight statistical features directly from the time-domain signal. In the second approach, we have used only the frequency domain information by applying the Discrete Cosine Transform (DCT) to the EEG signals then extracting two statistical features from the lower coefficients. In the last approach, we have used a tool that combines both time and frequency domain information, which is the Discrete Wavelet Transform (DWT). Six different wavelet families have been tested with their different orders resulting in 37 wavelets. The first three decomposition levels were tested with every wavelet. Instead of feeding the coefficients directly to the classifier, we summarized them in 16 statistical features. The extracted features are then fed to three different classifiers k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Artificial Neural Network (ANN) to perform two binary classification scenarios: healthy versus epileptic (mainly from interictal activity), and seizure-free versus ictal. We have used a benchmark database, the Bonn database, which consists of five different sets. In the first scenario, we have taken six different combinations of the available data. While in the second scenario, we have taken five combinations. For Epilepsy detection (healthy vs epileptic), the first approach performed badly. Using the DCT improved the results, but the best accuracies were obtained with the DWT-based approach. For seizure detection, the three methods performed quite well. However, the third method had the best performance and was better than many state-of-the-art methods in terms of accuracy. After carrying out the experiments on the whole EEG signal, we separated the five rhythms and applied the DWT on them with the Daubechies7 (db7) wavelet for feature extraction. We have observed that close accuracies to those recorded before can be achieved with only the Delta rhythm in the first scenario (Epilepsy detection) and the Beta rhythm in the second scenario (seizure detection)
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    Damage detection in GFRP composite structures by improved artificial neural network using new optimization techniques
    (Elsevier, 2023) Zara, Abdeldjebar; Belaidi, Idir; Khatir, Samir; Oulad Brahim, Abdelmoumin; Boutchicha, Djilali; Abdel Wahab, Magd
    Structural damage identification has been researched for a long time and continues to be an active research topic. This paper proposes the use of the natural frequencies of a novel composite structures made of glass fibre reinforced polymer (GFRP). The proposed methodology consists of an improved Artificial Neural Network (ANN) using optimization algorithms to detect the exact crack length. In the first step, the characterization of fabricated material is provided to determine Young's modulus using an experimental static bending test, tensile test and modal analysis test. Next, numerical validation is performed using commercial software ABAQUS to extract more data for different crack locations in the structure. The comparison between experimental and numerical results shows a good agreement. ANN has been improved using recent optimization techniques such as Jaya, enhanced Jaya (E-Jaya), Whale Optimization Algorithm (WOA) and Arithmetic Optimization Algorithm (AOA) to calibrate the influential parameters during training. After considering several scenarios, the results show that the accuracy of E-Jaya is better than other optimization techniques. This study on crack identification using improved ANN can be used to investigate the safety and soundness of composite structures
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    Identification and modeling of a rotary kiln in cement plant based on ANN (MLP)
    (Springer, 2022) Doghmane, Mohamed Zinelabidine; Kidouche, Madjid; Eladj, S.; Ouali, A.
    The objective of this study is to identify and model a rotary cement kiln based on production history data by using an artificial neural network MLP algorithm. The usefulness of this algorithm is that it provides a reliable empirical relation between the inputs parameters (Flow, Temperature, and pressure) and the outputs, which indicate the cement quality. Where, the most critical process in a cement production facility is cooking the mixed raw material in a rotary kiln; its task is to gradually burn and bakes a suitable mixture of input material to produce clinker. Therefore, the rotary kiln is the most important part in a cement factory. From another side, the control of a cement kiln is a complex process due to many factors namely: The Non linearity of the system caused by the chemical reactions, its dynamic and high dimensionality. Therefore, identification, modelling, prediction and simulation of Kiln system is very crucial step in managing and optimizing the cement production. Since the ANN has demonstrated its effectiveness in identifying a large class of complex nonlinear systems, it has been proposed in this case study to model cement Kiln of plant based on Multi-Layer Perceptron (MLP) approach. The MLP algorithm has been trained by using history data of twenty four months, and it has been tested and validated through comparison with production data of the next six months after the training. The obtained results have demonstrated the superiority of the proposed ANN approach over the conventional modelling approaches
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    Application of a radial basis function artificial neural network to seismic data inversion
    (Elsevier, 2009) Baddari, Kamel; Aïfa, Tahar; Djarfour, Noureddine; Ferahtia, Jalal