Browsing by Author "Ait Mouloud, Louiza"

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Processor in the loop based rotor bars fault diagnosis of an induction machine
(Springer, 2022) Deboucha, Abdelhakim; Ait Mouloud, Louiza; Ounass, Badredine; Bayou, Nada
Induction machines are found in hazardous environments where they are exposed to harsh conditions resulting in failures that lead eventually to the machine downtimes, therefore, production shutdowns, financial losses, and waste of raw materials. Hence, to prevent such catastrophic consequences, online detection and diagnosis of such faults becomes of interest. In this study, the induction machine model has been developed along with faulty cases when the squirrel cage bars/end ring are cracked. Using MATLAB software, the model is simulated for healthy and faulty cases along with Fast Fourier Transform analysis. Three faulty cases were taken into consideration; where, two adjacent, two separated broken bars and the end ring cracking are simulated. Moreover, and as a preliminary to real time diagnosis, the FFT is implemented on the STM32 as a processor in the loop (PIL) to quickly detect the failure in the IM. Simulation results show that time domain analysis could only categorize whether the IM is healthy or faulty. Whereas, spectral analysis would give more insight on the failure by detecting the number of broken bars. On the other hand, the FFT implementation on the STM32 board raises promises to give initial real diagnosis failures that existed on the induction machine
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.
Seasonal quantile forecasting of solar photovoltaic power using Q-CNN-GRU
(Nature Research, 2025) Ait Mouloud, Louiza; Kheldoun, Aissa; Oussidhoum, Samira; Alharbi, Hisham; Alotaibi, Saud; Alzahrani, Thabet
Accurately predicting solar power is essential for ensuring electric grid reliability and integrating renewable energy sources. This paper presents a novel approach to probabilistic solar power forecasting by combining Convolutional Neural Networks (CNN) with Gated Recurrent Units (GRU) into a hybrid Quantile-CNN-GRU model. The proposed model generates intra-day probabilistic quantile forecasts and is rigorously evaluated using datasets from geographically and climatically diverse regions and hemispheres: the Netherlands (temperate maritime climate), Alice Springs (arid desert climate), and Hebei (humid subtropical climate). These datasets cover varied temporal horizons (1-hour, 6-hour, 12-hour, and 24-hour predictions) and seasonal conditions (summer, fall, spring, and winter), highlighting the model’s adaptability to different scenarios. The performance of the proposed Quantile-CNN-GRU model is benchmarked against state-of-the-art deep learning models, including standalone quantile-based architectures such as Quantile-GRU and Quantile-Long Short Term Memory (LSTM). A comprehensive evaluation framework is applied, employing probabilistic tools like the Continuous Ranked Probability Score (CRPS) for assessing forecast reliability, sharpness, and reliability diagrams with consistency bars to evaluate the calibration of the predictions. Results demonstrate that the proposed Quantile-CNN-GRU model consistently outperforms its counterparts in terms of CRPS, across varying forecast horizons and seasonal conditions. To further enhance performance, a multivariate case study incorporating exogenous inputs, specifically Numerical Weather Prediction (NWP) data, is conducted. Through sensitivity analysis, the influence of these additional inputs on forecast horizons and seasonal variability is systematically explored. The study reveals that integrating NWP data significantly improves the model’s predictive skill, particularly for longer forecast horizons and during transitional seasons like spring and fall, when solar variability is higher.