• Energy Research

Tested by the manufacturer, the electrical characteristics of PV modules are rated at the Standard Test Conditions (STC; 1000 W/m2 and 25 °C). But under real working conditions where the temperature and the irradiance are different from those of STC, the electrical specifications are definitely affected. Which in turn, affect the conversion efficiency and the Fill factor (FF). Here, the conversion efficiency and the FF of three different PV modules technologies: Poly-Crystalline Silicon (Poly C-Si), Copper Indium Gallium Selenide (CIGS) and Cadmium Telluride (CdTe) under STC and real working conditions of solar irradiance and temperature are evaluated based on real data, then analyzed. To this, a test facility is employed to carry out the required tests for the aforementioned PV technologies. The investigation presented in this paper aims to seek which PV technology is preferable in a specific level of irradiance and temperature.

Algeria is a wealthy country with natural resources, namely, nuclear, renewable, and non-renewable sources. The non-renewable energy sources are considered the lion’s share for energy production (98%). Algeria’s efforts to ensure and strengthen its energy security will take an important step in the coming decades by commissioning new energy infrastructure based on intensive use of water, coal, nuclear, non-renewable, and renewable sources. The implementation of new power infrastructure is expected to be operational from 2030. The renewable power realization in Algeria is relatively less compared to other African countries, i.e., Morocco, Egypt, South Africa, etc. The total renewable power installed capacity in Algeria reached 686 MW in 2020, as part of its national energy portfolio, although the Algerian government has spent tremendous efforts on introducing new sustainable technologies to enable the transition towards a cleaner and sustainable energy system. Indeed, the country announced its plan to install around 22 GW of renewable energy capacity by 2030. It will include 1 GW bio-power from the waste, 13.5 GW from solar PV, 2 GW from CSP, 15 MW from geothermal, 400 MW cogeneration, and, finally, 5 GW from wind. The scope of the present research provides general information about the usage of energy resources such as fossil, nuclear, and renewable sources in Algeria and also covers the energy supply outlook. The present effort is the first of its kind which discusses the application of the coal and nuclear as clean energy sources as part of renewable energy transition. Additionally, it also includes the description of the existing Algerian energy sector and information about water and water desalination and their usage in other sectors.

L'irradiation horizontale globale prévue (GHI) peut aider à la conception, au dimensionnement et à l'analyse des performances des systèmes photovoltaïques (PV), y compris les systèmes de pompage PV à eau utilisés pour les applications d'irrigation. Dans cet article, divers modèles de réseaux neuronaux profonds (DNN) pour la prédiction à un jour du GHI à Hail city (Arabie saoudite) sont développés et étudiés. Les modèles DNN considérés comprennent la mémoire à long terme (LSTM), le LSTM bidirectionnel (BiLSTM), l'unité récurrente fermée (GRU), le GRU bidirectionnel (Bi-GRU), le réseau neuronal convolutionnel unidimensionnel (CNN 1D ) et d'autres configurations hybrides telles que CNN-LSTM et CNN-BiLSTM.A. L'ensemble de données des enregistrements quotidiens GHI collectés entre le 1er janvier 2000 et le 30 juin 2020 auprès de la National Aeronautics and Space Administration (NASA) à un endroit aride (Hail, Arabie Saoudite) est utilisé pour développer et comparer les modèles basés sur DNN ci-dessus. Les paramètres affectant la précision des modèles ont également été analysés en profondeur. Seules les valeurs historiques du GHI quotidien ont été utilisées pour construire les modèles basés sur DNN, tandis que des paramètres météorologiques supplémentaires tels que la température de l'air, la vitesse du vent, la direction du vent, la pression atmosphérique et l'humidité relative ne sont pas pris en compte dans ce travail. La bibliothèque Keras et le langage Python ont été utilisés utilisé pour développer et comparer les modèles de prévision GHI. Les métriques d'évaluation telles que le coefficient de corrélation (r), l'erreur absolue moyenne en pourcentage (MAPE), l'erreur absolue moyenne (MAE), la fonction de distribution cumulative (CDF) et l'écart type (σ ) sont choisies pour évaluer la performance des modèles de prévision. Les résultats obtenus ont montré que les modèles DNN ont fourni de bonnes performances à l'échelle mondiale avec une valeur maximale atteinte de r = 96 %, pour la prévision quotidienne GHI. La irradiación horizontal global pronosticada (GHI) puede ayudar a diseñar, dimensionar y analizar el rendimiento de los sistemas fotovoltaicos (PV), incluidos los sistemas de bombeo de agua PV utilizados para aplicaciones de riego. En este documento, se desarrollan e investigan varios modelos de redes neuronales profundas (DNN) para la predicción de un día de anticipación de GHI en la ciudad de Hail (Arabia Saudita). Los modelos DNN considerados incluyen memoria a largo plazo (LSTM), LSTM bidireccional (BiLSTM), unidad recurrente cerrada (GRU), GRU bidireccional (Bi-GRU), red neuronal convolucional unidimensional (CNN 1D ) y otras configuraciones híbridas como CNN-LSTM y CNN-BiLSTM. Un conjunto de datos de grabaciones diarias de GHI recopiladas durante el 1 de enero de 2000 al 30 de junio de 2020 de la Administración Nacional de Aeronáutica y del Espacio (NASA) en una ubicación árida (Hail, Arabia Saudita) se utiliza para desarrollar y comparar los modelos basados en DNN anteriores. Los parámetros que afectan la precisión de los modelos también se han analizado profundamente. Solo se han utilizado valores históricos de GHI diarios para construir los modelos basados en DNN, mientras que los parámetros climáticos adicionales como la temperatura del aire, la velocidad del viento, la dirección del viento, la presión atmosférica y la humedad relativa no se consideran en este trabajo. Biblioteca Keras y lenguaje Python han sido utilizado para desarrollar y comparar los modelos de pronóstico de GHI. Las métricas de evaluación como el coeficiente de correlación (r), el error porcentual absoluto medio (MAPE), el error absoluto medio (MAE), la función de distribución acumulativa (CDF) y la desviación estándar (σ ) se optan para evaluar el rendimiento de los modelos de predicción. Los resultados obtenidos mostraron que los modelos DNN han proporcionado un buen rendimiento a nivel mundial con un valor máximo alcanzado de r = 96%, para el pronóstico diario de GHI. Forecasted global horizontal irradiation (GHI) can help for designing, sizing and performances analysis of photovoltaic (PV) systems including water PV pumping systems used for irrigation applications.In this paper, various deep neural networks (DNN) models for one day-ahead prediction of GHI at Hail city (Saudi Arabia) are developed and investigated.The considered DNN models include long-shortterm memory (LSTM), bidirectional-LSTM (BiLSTM), gated recurrent unit (GRU), bidirectional-GRU (Bi-GRU), one-dimensional convolutional neural network (CNN 1D ) and other hybrid configurations such as CNN-LSTM and CNN-BiLSTM.A dataset of daily GHI recordings collected during January 1, 2000 to June 30, 2020 from National Aeronautics and Space Administration (NASA) at an arid location (Hail, Saudi Arabia) is used to develop and compare the above DNN-based models.The parameters affecting the accuracy of the models have been also deeply analyzed.Only historical values of daily GHI have been used to build the DNN-based models whereas additional weather parameters such as air temperature, wind speed, wind direction, atmospheric pressure and relative humidity are not considered in this work.Keras library and Python language have been used to develop and compare the GHI forecasting models.The evaluation metrics such as correlation coefficient (r), Mean Absolute Percent Error (MAPE), Mean Absolute Error (MAE), cumulative distribution function (CDF) and standard deviation (σ ) are opted to evaluate the performance of the prediction models.The obtained results showed that the DNN models have provided globally good performances with a maximum reached value of r = 96%, for daily GHI forecasting. يمكن أن يساعد الإشعاع الأفقي العالمي المتوقع (GHI) في تصميم وتحجيم وتحليل أداء الأنظمة الكهروضوئية (PV) بما في ذلك أنظمة ضخ المياه الكهروضوئية المستخدمة في تطبيقات الري. في هذه الورقة، يتم تطوير نماذج مختلفة للشبكات العصبية العميقة (DNN) للتنبؤ قبل يوم واحد من GHI في مدينة حائل (المملكة العربية السعودية) والتحقيق فيها. تشمل نماذج DNN التي يتم النظر فيها الذاكرة قصيرة المدى (LSTM)، ثنائية الاتجاه - LSTM (BiLSTM)، وحدة متكررة مسورة (GRU)، ثنائية الاتجاه - GRU (Bi - GRU)، شبكة عصبية التفافية أحادية البعد (CNN 1D ) والتكوينات الهجينة الأخرى مثل CNN - LSTM و CNN - BiLSTM. يتم استخدام مجموعة بيانات من تسجيلات GHI اليومية التي تم جمعها خلال الفترة من 1 يناير 2000 إلى 30 يونيو 2020 من الإدارة الوطنية للملاحة الجوية والفضاء (ناسا) في موقع قاحل (حائل، المملكة العربية السعودية) لتطوير ومقارنة النماذج القائمة على DNN المذكورة أعلاه. كما تم تحليل المعلمات التي تؤثر على دقة النماذج بعمق. تم استخدام القيم التاريخية فقط لـ GHI اليومية لبناء النماذج القائمة على DNN بينما لا يتم النظر في معلمات الطقس الإضافية مثل درجة حرارة الهواء وسرعة الرياح واتجاه الرياح والضغط الجوي والرطوبة النسبية في هذا العمل. تستخدم لتطوير ومقارنة نماذج التنبؤ بمؤشر GHI. يتم اختيار مقاييس التقييم مثل معامل الارتباط (r)، متوسط النسبة المئوية المطلقة (MAPE)، متوسط الخطأ المطلق (MAE)، وظيفة التوزيع التراكمي (CDF) والانحراف المعياري (σ ) لتقييم أداء نماذج التنبؤ. أظهرت النتائج التي تم الحصول عليها أن نماذج DNN قد قدمت أداءً جيدًا عالميًا بحد أقصى للقيمة التي تم الوصول إليها r = 96 ٪، للتنبؤ اليومي بمؤشر GHI.

Abstract In this paper, a Multiclass Adaptive Neuro-Fuzzy Classifier (MC-NFC) for fault detection and classification in photovoltaic (PV) array has been developed. Firstly, to show the generalization capability in the automatic faults classification of a PV array (PVA), Fuzzy Logic (FL) classifiers have been built based on experimental datasets. Subsequently, a novel classification system based on Adaptive Neuro-fuzzy Inference System (ANFIS) has been proposed to improve the generalization performance of the FL classifiers. The experiments have been conducted on the basis of collected data from a PVA to classify five kinds of faults. Results showed the advantages of using the fuzzy approach with reduced features over using the entire original chosen features. Then, the designed MC-NFC has been compared with an Artificial Neural Networks (ANN) classifier. Results demonstrated the superiority of the MC-NFC over the ANN-classifier and suggest that further improvements in terms of classification accuracy can be achieved by the proposed classification algorithm; furthermore faults can be also considered for discrimination.

Abstract In this paper, the power factor of a grid-connected photovoltaic inverter is controlled using the input output Feedback Linearization Control (FLC) technique. This technique transforms the nonlinear state model of the inverter in the d–q reference frame into two equivalent linear subsystems, and then applies a pole placement linear control loops on this subsystem in order to separately control the grid power factor and the dc link voltage of the inverter. Maximum Power Point Tracker (MPPT) that allows extraction of maximum available power from the photovoltaic (PV) array has been included. This MPPT is based on variable step size incremental conductance method. Compared with conventional fixed step size method, the variable step MPPT improves the speed and the accuracy of the tracking.

AbstractAn implementation of an intelligent photovoltaic module on reconfigurable Field Programmable Gate Array (FPGA) is described in this paper. An experimental database of meteorological data (irradiation and temperature) and output electrical generation data of a Photovoltaic (PV) module (current and voltage) under variable climate condition is used in this study. Initially, an Artificial Neural Network (ANN) is developed under Matlab/Similuk, environment for modeling the PV module. The inputs of the ANN–PV module are the global solar irradiation and temperature while the outputs are the current and voltage generated from the PV‐module. Subsequently, the optimal configuration of the ANN model (ANN–PV module) is written and simulated under the Very High Description Language (VHDL) and ModelSim. The synthesized architecture by ModelSim is then implemented on an FPGA device. The designed MLP‐photovoltaic module permits the evaluation of performance of the PV module using only environmental parameters and involves less computational effort. The device can also be used for predicting the output electrical energy from the PV module and for a real time simulation in specific climatic conditions. Copyright © 2010 John Wiley & Sons, Ltd.

Abstract Growing of PV for electricity generation is one of the highest in the field of the renewable energies and this tendency is expected to continue in the next years. Due to the various seasonal, hourly and daily changes in climate, it is relatively difficult to find a suitable analytic model for predicting the performance of a grid-connected photovoltaic (GCPV) plant. In this paper, an artificial neural network is used for modelling and predicting the power produced by a 20 kW p GCPV plant installed on the roof top of the municipality of Trieste (latitude 45°40′N, longitude 13°46′E), Italy. An experimental database of climate (irradiance and air temperature) and electrical (power delivered to the grid) data from January 29th to May 25th 2009 has been used. Two ANN models have been developed and implemented on experimental climate and electrical data. The first one is a multivariate model based on the solar irradiance and the air temperature, while the second one is an univariate model which uses as input parameter only the solar irradiance. A database of 3437 patterns has been divided into two sets: the first (2989 patterns) is used for training the different ANN models, while the second (459 patterns) is used for testing and validating the proposed ANN models. Prediction performance measures such as correlation coefficient ( r ) and mean bias error (MBE) are presented. The results show that good effectiveness is obtained between the measured and predicted power produced by the 20 kW p GCPV plant. In fact, the found correlation coefficient is in the range 98–99%, while the mean bias error varies between 3.1% and 5.4%.