Direct components extraction of voltage in photovoltaic active filter connected in a perturbed electrical network (based on robust PLL algorithm)

The quality and performances of the compensation of harmonic currents depends strongly on the performances of the identification blocks of control side of the photovoltaic generators used as active filters. Then, the use of harmonics identification methods is not valid because the network voltage must be sinusoidal and balanced, which is not the case in practice. Hence, to make the application of the identification methods of harmonic currents versatile and for any voltage form, we use the detection system of the fundamental component of the direct voltage. In this paper, a comparison between the conventional method used for extracting the direct component of the network voltage which is based on the phase-locked loop (PLL) and the new approach based on a multivariable extraction filter. Finally, simulation results show that the proposed multivariable filter may better work even if the network voltage is (perturbed and unbalanced). Furthermore, this filters permits to generalize the use of identification methods for filtering the different perturbations of active and reactive current

Étude d'un système de compensation d'harmonique en utilisant un générateur photovoltaïque "GPV"

99 p. : ill. ; 30 cmL’utilisation des convertisseurs statiques dans les installations de conversion d’énergie électrique a considérablement contribué à améliorer les performances et l’efficacité de ces systèmes. En revanche, ils ont participé à détériorer la qualité du courant et de la tension des réseaux de distribution. En effet, ces systèmes consomment des courants non-sinusoidaux, même s’ils sont alimentés par une tension sinusoïdale : ils se comportent comme des générateurs de courants harmoniques. Plusieurs méthodes permettant l’identification des composantes harmoniques du courant à partir du calcul des puissances harmoniques. D’autres méthodes basées sur la soustraction de la partie active du courant fondamental du courant total peuvent être utilisées dans le cas où l’objectif du filtre actif est la compensation à la fois de l’énergie réactive et des harmoniques. Dans notre mémoire, on a choisi de mettre en application la méthode des puissances instantanées sous forme analogique vue sa rapidité. Les systèmes de filtrage actif classiques utilisent une capacité dont la charge est assurée par le réseau lui-même à travers un redresseur ou à travers la diode montée en antiparallèle aux bornes des transistors de l’onduleur en formant ainsi une source flottante. La tension de cette dernière n’est pas constante, à cause de sa sensibilité aux échanges de puissances actives entre la charge polluante et le réseau. Il est alors nécessaire de réguler la tension du bus continu ce qui se fait au détriment des performances du filtrage des harmoniques. C’est pourquoi nous proposons un dispositif de compensation des harmoniques composé d’un Générateur Photovoltaïque GPV pour répondre au mieux aux exigences du système de filtrag

APF Applied on PV Conversion Chain Network Using FLC

This paper focuses on regulation of the parallel active power filter (APF) Dc Voltage bus by judicious choice of rule bases and intervals for each selected fuzzy variable of suitable fuzzy logic controller. In addition, an algorithm describes the main steps for designing an FLC that has any number of rules with direct application to the APF capacitor voltage regulation. Where their simulation, by MATLAB, applied to PV conversion chain network will be represented in the booths cases, constant and variable non-linear loads after modeling, to show the effectiveness of this kind of regulators on electrical power quality and improve the reliability of the APF on PV system. The delivered voltage of PV plant has been regulated and controlled with MPPT using P&O technique and FLC regulator after modeling of each part of the conversion chain. PV plant supplies a nonlinear load from the rectifier installed on the output of the conversion chain via a controlled power inverter. A 3 × 3 rules fuzzy regulator is implanted in the control part of the APF to examine the influence of the FLC on the produced electrical power quality. Simulation results are represented and analyzed

Machine Learning-Based Forecasting of Temperature and Solar Irradiance for Photovoltaic Systems

The integration of photovoltaic (PV) systems into the global energy landscape has been boosted in recent years, driven by environmental concerns and research into renewable energy sources. The accurate prediction of temperature and solar irradiance is essential for optimizing the performance and grid integration of PV systems. Machine learning (ML) has become an effective tool for improving the accuracy of these predictions. This comprehensive review explores the pioneer techniques and methodologies employed in the field of ML-based forecasting of temperature and solar irradiance for PV systems. This article presents a comparative study between various algorithms and techniques commonly used for temperature and solar radiation forecasting. These include regression models such as decision trees, random forest, XGBoost, and support vector machines (SVM). The beginning of this article highlights the importance of accurate weather forecasts for the operation of PV systems and the challenges associated with traditional meteorological models. Next, fundamental concepts of machine learning are explored, highlighting the benefits of improved accuracy in estimating the PV power generation for grid integration

LSTM-Autoencoder Deep Learning Model for Anomaly Detection in Electric Motor

Anomaly detection is the process of detecting unusual or unforeseen patterns or events in data. Many factors, such as malfunctioning hardware, malevolent activities, or modifications to the data’s underlying distribution, might cause anomalies. One of the key factors in anomaly detection is balancing the trade-off between sensitivity and specificity. Balancing these trade-offs requires careful tuning of the anomaly detection algorithm and consideration of the specific domain and application. Deep learning techniques’ applications, such as LSTMs (long short-term memory algorithms), which are autoencoders for detecting an anomaly, have garnered increasing attention in recent years. The main goal of this work was to develop an anomaly detection solution for an electrical machine using an LSTM-autoencoder deep learning model. The work focused on detecting anomalies in an electrical motor’s variation vibrations in three axes: axial (X), radial (Y), and tangential (Z), which are indicative of potential faults or failures. The presented model is a combination of the two architectures; LSTM layers were added to the autoencoder in order to leverage the LSTM capacity for handling large amounts of temporal data. To prove the LSTM efficiency, we will create a regular autoencoder model using the Python programming language and the TensorFlow machine learning framework, and compare its performance with our main LSTM-based autoencoder model. The two models will be trained on the same database, and evaluated on three primary points: training time, loss function, and MSE anomalies. Based on the obtained results, it is clear that the LSTM-autoencoder shows significantly smaller loss values and MSE anomalies compared to the regular autoencoder. On the other hand, the regular autoencoder performs better than the LSTM, comparing the training time. It appears then, that the LSTM-autoencoder presents a superior performance although it was slower than the standard autoencoder due to the complexity of the added LSTM layers