Résumé: Photovoltaic (PV) systems are highly influenced by environmental conditions, yet there is a lack of comprehensive seasonal performance assessments tailored to specific regional climates. This study addresses that gap by developing a realtime health monitoring and performance evaluation framework for a PV system located in Guelma, Algeria, a region with pronounced seasonal variability. The proposed method integrates SCADA data with key performance metrics in a MATLAB environment to analyze system behavior across four representative months: January (winter), April (spring), July (summer), and October (autumn). The framework examines the impact of solar irradiance, ambient temperature, and daylight duration on electrical parameters such as voltage, current, power output, and energy production. Results reveal distinct seasonal trends, with peak performance observed in summer and reduced efficiency in winter due to lower irradiance and temperature fluctuations. Despite these variations, the system maintained high operational efficiency (70.9%–74.6%), in compliance with IEC standards, indicating robust system health. This research demonstrates the effectiveness of real-time monitoring for optimizing PV performance under dynamic climatic conditions and provides actionable insights for improving system design, predictive maintenance, and energy yield in similar regional contexts.

Résumé: Power quality issues caused by current harmonics from nonlinear and unbalanced loads are a growing concern. This paper presents a novel control strategy for four-wire shunt active power filters (SAPF) that surpasses existing conventional methods in mitigating harmonics and power factor correction. The strategy employs an improved synchronous detection method (SDM) enhanced by an adaptive linear neural network (ADALINE) trained using the least mean square (LMS) algorithm. This approach accurately estimates harmonic frequencies, enabling the SAPF to generate precise compensation currents. The effectiveness of the proposed method is validated through MATLAB-Simulink simulations under balanced supply conditions, encompassing diverse load scenarios. These simulation results are compared with those obtained using instantaneous power theory (IPT). They demonstrate the ability of the proposed method to achieve excellent harmonic identification and elimination, to comply with IEEE 519 harmonic limits, to ensure sinusoidal and balanced line currents, and to compensate for reactive power and neutral current. Furthermore, its simple architecture and noise robustness make it a promising solution for enhancing power quality.

Résumé: Finding a precise method for improved fault detection and classification when dealing with non-stationary vibration signals is the main goal of this paper. For the detection and classification of induction motor failures, a wavelet packet decomposition (WPD) associated to an artificial neural network (ANN) technique is considered. The effectiveness of this approach depends on the characteristics that have been carefully chosen and prepared to enable the classifier support the healthy conditions of the monitored system with the aid of the measured signal. Different testing data sets of healthy and defective bearings under various rotating speeds are studied to train the ANN classifier in order to demonstrate the effectiveness of the proposed method. The results showed the high performance of this procedure as an efficient method for bearing fault diagnosis.

Résumé: Finding a precise method for improved fault detection and classification when dealing with non-stationary vibration signals is the main goal of this paper. For the detection and classification of induction motor failures, a wavelet packet decomposition (WPD) associated to an artificial neural network (ANN) technique is considered. The effectiveness of this approach depends on the characteristics that have been carefully chosen and prepared to enable the classifier support the healthy conditions of the monitored system with the aid of the measured signal. Different testing data sets of healthy and defective bearings under various rotating speeds are studied to train the ANN classifier in order to demonstrate the effectiveness of the proposed method. The results showed the high performance of this procedure as an efficient method for bearing fault diagnosis.

Résumé: The current research focuses on the study of two main causes of bearing defects: load unbalance and bearing improper lubrication using Dspace 1104 card for three stator current signals acquisition. This study suggests a straightforward and effective technique for identifying and categorizing two different kinds of defects. It consists of introducing the current space vector (CSV) analysis technique to avoid loss of information between the three stator current signals; the resulting signal is then processed by wavelet packet decomposition (WPD) to calculate the energy of the final level WPD nodes. The node containing the highest energy values will be selected to train the Multilayer Perceptron Neural Network (MLP-NN) classifier implemented by round-robin cross-validation technique. The results confirm the efficiency of the proposed procedure in bearing causes defects classification with an average accuracy of 100% for the tests and 99.88% for the training

Résumé: This study investigates the effectiveness of L-kurtosis as a robust alternative to traditional kurtosis for identifying and categorizing rolling bearing faults in vibration signals. By comparing L-kurtosis-energy and kurtosis-energy features derived from wavelet packet decomposition (WPD) coefficients; this research evaluates their Studies in Engineering and Exact Sciences, Curitiba, v.5, n.3, p.01-28, 2024 2 performance using a multi-layer perceptron neural network (MLP-NN). Experimental data encompassing various rotating speeds, fault types, and severities were utilized to train and test the MLP-NN on both healthy and defective bearing conditions. The results demonstrate that while kurtosis-energy achieved 95.63% accuracy in defect classification, replacing kurtosis with L-kurtosis significantly enhanced accuracy to 99.92%. This improvement underscores the resilience of L-kurtosis to outliers and its ability to handle non-normally distributed vibration signals effectively. The findings affirm the potential of L-kurtosis-energy features to improve fault detection methodologies, making them more reliable for industrial applications. This study highlights the importance of robust diagnostic tools for advancing predictive maintenance strategies and ensuring operational reliability.

Résumé: In this paper, an ADALINE neural network-based intelligent harmonic detection approach for controlling a four-leg shunt active power filter is presented. It successfully gets over the instantaneous power theory (pq0), a flaw in the traditional approach. The advantages of the suggested approach are its ease of computation, robustness, and adaptability to imbalances and/or disruptions in voltage and current. The traditional p-q-0 instantaneous power theory is contrasted with the intelligent control approach. MATLAB-Simulink simulation is used to test it. Both approaches are evaluated for their capacity to rebalance source currents and to reduce the total harmonic distortion current, individual harmonics THD, power factor, reactive power, and neutral current magnitude.

Résumé: This paper presents a simulation-based analysis of a four-wire, three-phase photovoltaic (PV) Shunt Active Power Filter (SAPF) designed to mitigate harmonic distortion and improve power quality. Implemented in MATLAB-Simulink, the system employs an adaptive harmonic cancellation algorithm based on the Widrow-Hoff ADALINE method for harmonic identification. Simulation results confirm its effectiveness in reducing Total Harmonic Distortion (THD) under fluctuating irradiance and unbalanced nonlinear loads, outperforming the conventional Adaptive LMS (Direct ADALINE) method. The proposed SAPF maintained sinusoidal, balanced source currents with minimal voltage overshoot and a near-unity power factor. Harmonic suppression was highly effective, keeping THD below 4% and reducing dominant 3rd, 5th, and 7th-order harmonics to under 1%, even under severe load imbalances and low irradiance. Additionally, stable DC link voltage ensured reliable compensation of reactive and neutral currents. These findings highlight the system’s potential to enhance grid stability, support renewable energy integration, and offer a scalable solution for modern power systems.