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All Journal International Journal of Renewable Energy Development International Journal of Renewable Energy Development
Haddad, Salim
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Chemistry Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering

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Automatic and Online Detection of Rotor Fault State Ouanas, Ali; Medoued, Ammar; Haddad, Salim; Mordjaoui, Mourad; Sayad, D.
International Journal of Renewable Energy Development Vol 7, No 1 (2018): February 2018
Publisher : Center of Biomass & Renewable Energy, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/ijred.7.1.43-52

Abstract

In this work, we propose a new and simple method to insure an online and automatic detection of faults that affect induction motor rotors. Induction motors now occupy an important place in the industrial environment and cover an extremely wide range of applications. They require a system installation that monitors the motor state to suit the operating conditions for a given application. The proposed method is based on the consideration of the spectrum of the single-phase stator current envelope as input of the detection algorithm. The characteristics related to the broken bar fault in the frequency domain extracted from the Hilbert Transform is used to estimate the fault severity for different load levels through classification tools. The frequency analysis of the envelope gives the frequency component and the associated amplitude which define the existence of the fault. The clustering of the indicator is chosen in a two-dimensional space by the fuzzy c mean clustering to find the center of each class. The distance criterion, the K-Nearest Neighbor (KNN) algorithm and the neural networks are used to determine the fault type. This method is validated on a 5.5-kW induction motor test bench.Article History: Received July 16th 2017; Received: October 5th 2017; Accepted: Januari 6th 2018; Available onlineHow to Cite This Article: Ouanas, A., Medoued, A., Haddad, S., Mordjaoui, M., and Sayad, D. (2017) Automatic and online Detection of Rotor Fault State. International Journal of Renewable Energy Development, 7(1), 43-52.http://dx.doi.org/10.14710/ijred.7.1.43-52
Evaluating and analyzing the performance of PV power output forecasting using different models of machine-learning techniques considering prediction accuracy Bouakkaz, Abderraouf; Lahsasna, Adel; Gil Mena, Antonio; Haddad, Salim; Luigi Ferrari, Mario; Jiménez- Castaneda, Rafael
International Journal of Renewable Energy Development Vol 14, No 1 (2025): January 2025
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2025.60547

Abstract

Solar energy as a clean, renewable, and sustainable energy source has considerable potential to meet global energy needs. However, the intermittent and uncertain character of the solar energy source makes the power balance management a very challenging task. To overcome these shortcomings, providing accurate information about future energy production enables better planning, scheduling, and ensures effective strategies to meet energy demands. The present paper aims to assess the performance of PV power output forecasting in PV systems using various machine learning models, such as artificial neural networks (ANN), linear regression (LR), random forests (RF), and Support Vector Machines (SVM). These learning algorithms are trained on two different datasets with different time steps: in the first one, a historical weather forecast with a one hour time step, and in the second one, a dataset of on-site measurements with a 5-minute time step. To provide a reliable estimation of prediction accuracy for different learning algorithms, a k-fold cross-validation (CV) is applied. Through a comparison analysis, an assessment of the accuracy of these algorithms based on various metrics such as RMSE, MAE, and MRE is performed, providing a detailed evaluation of their performance. Results obtained from this study demonstrate that the random forest algorithm (RF) outperformed other algorithms in predicting PV output, achieving the smallest prediction error, where the best values for RMSE, MRE, MAE, and R² for the weather dataset were 0.856 W, 0.256%, 0.364 W, and 0.99999, respectively, while thevalues for RMSE, MRE, MAE, and R² for the on-site measurements dataset were 8.525 W, 11.163%, 3.922 W, and 0.99922, respectively.
Co-Authors Bouakkaz, Abderraouf Gil Mena, Antonio Jiménez- Castaneda, Rafael Lahsasna, Adel Luigi Ferrari, Mario Medoued, Ammar Mordjaoui, Mourad Ouanas, Ali Sayad, D.