AHMED FARIS Amiri
أحمد فارس عميري
ahmedfaris.amiri@univ-msila.dz
0658258117
- Departement of ELECTRONICS
- Faculty of Technology
- Grade PHd
About Me
Location
Msila, Msila
Msila, ALGERIA
Code RFIDE- 1996-06-16 00:00:00
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AHMED FARIS Amiri birthday
- 2024-01-24
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2024-01-24
Fault Detection and Diagnosis of a PhotovoltaicSystem Based on Deep Learning Using the Combination of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU)
The meticulous monitoring and diagnosis of faults in photovoltaic (PV) systems enhancee their reliability and facilitate a smooth transition to sustainable energy. This paper introduces a novel application of deep learning for fault detection and diagnosis in PV systems, employing a three-step approach. Firstly, a robust PV model is developed and fine-tuned using a heuristic optimization approach. Secondly, a comprehensive database is constructed, in-corporating PV model data alongside monitored module temperature and solar irradiance for both healthy and faulty operation conditions. Lastly, fault classification utilizes features extracted from a combination consisting ofaConvolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU). The amalgamation of parallel and sequential processing enables the neural network to leverage the strengths of both convolutional and recurrent layers concurrently, facilitating effective fault detection and diagnosis. The results affirm the proposed technique’s efficacy in detecting and classifying various PV fault types, such as open circuits, short circuits, and partial shading. Furthermore, this work underscores the significance of dividing fault detection and diagnosis into two distinct steps rather than employing deep learning neural networks to determine fault types directly.
Citation
Ahmed Faris amiri , Houcine OUDIRA , Aissa CHOUDER , , (2024-01-24), Fault Detection and Diagnosis of a PhotovoltaicSystem Based on Deep Learning Using the Combination of a Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (Bi-GRU), Sustainability 2024, Vol:16, Issue:3, pages:1-24, MDPI
- 2024-01-05
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2024-01-05
Faults detection and diagnosis of PV systems based on machine learning approach using random forest classifier
Accurate and reliable fault detection procedures are crucial for optimizing photovoltaic (PV) system performance. Establishing a trustworthy PV array model is the primary step and a vital tool for monitoring and diagnosing PV systems. This paper outlines a two-step approach for creating a reliable PV array model and implementing a fault detection procedure using Random Forest Classifiers (RFCs). Firstly, we extracted the five unknown parameters of the one-diode model (ODM) by combining the current– voltage translation method to predict the reference curve and employing the modified grey wolf optimization (MGWO) algorithm. In the second step, we simulated the PV array to obtain maximum power point (MPP) coordinates and construct operational databases through co-simulations in PSIM/MATLAB. We developed two RFCs: one for fault detection (a binary classifier) and another for fault diagnosis (a multiclass classifier). Our results confirmed the accuracy of the PV array modeling approach. We achieved a root mean square error (RMSE) value of 0.0122 for the ODM parameter extraction and RMSEs lower than 0.3 in dynamic PV array output current simulations under cloudy conditions. Regarding the fault detection procedure, our results demonstrate exceptional classification accuracy rates of 99.4% for both fault detection and diagnosis, surpassing other tested models like Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Neural Networks (MLP Classifier), Decision Trees (DT), and Stochastic Gradient Descent (SGDC).
Citation
Ahmed Faris amiri , Houcine OUDIRA , Aissa CHOUDER , , (2024-01-05), Faults detection and diagnosis of PV systems based on machine learning approach using random forest classifier, Energy Conversion and Management, Vol:301, Issue:1, pages:1-15, Elsevier
- 2023-10-28
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2023-10-28
Prediction Model of PV Module Based on Artificial Neural Networks for the Energy Production
The accurate characterization and prediction of current-voltage characteristics of photovoltaic (PV) modules under different operating conditions is the main step for energy prediction and an important tool for monitoring and supervision the system. However, one of the problems of this technology is that as yet there are no models in the literature to directly calculate the daily dynamic maximum power of these kinds of PV systems. The development of models is an important task to support the application of this technology because it allows the prediction of the energy yield. In this paper a model based on artificial neural networks has been developed to address this important issue. The model takes into account the main important parameters that influence the electrical output of these kinds of systems which are direct irradiance, and module temperature. Comparative study with The simulated dynamic MPP model using the single diode model is presented to demonstrate the effectiveness of the considered approach. The obtained results show that the proposed model can be used for estimating the maximum power of a grid connected system located in the Centre de Developpement des Energies Renouvelables (CDER) in Algiers with an adequate margin of error.
Citation
Ahmed Faris amiri , Houcine OUDIRA , Aissa CHOUDER , ,(2023-10-28), Prediction Model of PV Module Based on Artificial Neural Networks for the Energy Production,5th Novel Intelligent and Leading Emerging Sciences Conference (NILES),Egypt
- 2022-11-26
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2022-11-26
Faults Detection of PV Systems Based on Extracted Parameters using Modified Grey Wolf Algorithm
Accurate and reliable fault detection procedures are crucial to ensure normal operation of photovoltaic (PV) systems. To this end, the use of trusted model is the major step and an essential tool for monitoring and supervision the system under consideration. In his paper a suggested procedure based on three main steps is presented. Firstly, the unknown parameters of the one diode model (ODM) are accurately identified using modified grey wolf (MGW) algorithm. Subsequently, based on the extracted parameters, the evolution of maximum power point model was modeled and simulated versus measurements of a grid connected real MPP system . Finally, the PV array is simulated to take out the MPP coordinates by using a PSIMTM/MatlabTM co-simulation, as well as an efficient fault detection process based on simple approach is implemented. The obtain results show the effectiveness of this method in detecting and diagnosing faults for real time application.
Citation
Ahmed Faris amiri , Houcine OUDIRA , Aissa CHOUDER , ,(2022-11-26), Faults Detection of PV Systems Based on Extracted Parameters using Modified Grey Wolf Algorithm,the 2022 International Conference of advanced Technology in Electronic and Electrical Engineering (ICATEEE),M'sila University, Algeria,