Deep residual network based fault detection and diagnosis of photovoltaic arrays using current-voltage curves and ambient conditions

Optimization of an off-grid PV/Biomass hybrid system with different battery technologies

Optimal photovoltaic array reconfiguration for alleviating the partial shading influence based on a modified harris hawks optimizer

Deep learning models for solar irradiance forecasting: A comprehensive review

Economic and environmental assessment of solar-wind-biomass hybrid renewable energy system supplying rural settlement load

Simple and efficient approach to detect and diagnose electrical faults and partial shading in photovoltaic systems

Assessing the potential of hybrid PV–Wind systems to cover public facilities loads under different Moroccan climate conditions

Clustering or grouping is among the most important image processing methods that aim to split an image into different groups. Examining the literature, many clustering algorithms have been carried out, where the K-means algorithm is considered among the simplest and most used to classify an image into many regions. In this context, the main objective of this work is to detect and locate precisely the damaged area in photovoltaic (PV) fields based on the clustering of a thermal image through the K-means algorithm. The clustering quality depends on the number of clusters chosen; hence, the elbow, the average silhouette, and NbClust R package methods are used to find the optimal number K. The simulations carried out show that the use of the K-means algorithm allows detecting precisely the faults in PV panels. The excellent result is given with three clusters that is suggested by the elbow method.

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Faults Detection for Photovoltaic Field Based on K-Means, Elbow, and Average Silhouette Techniques through the Segmentation of a Thermal Image

This paper presents an implementation of real-time energy management systems (EMS) to maximize the efficiency of the electricity distribution in an isolated hybrid microgrid system (HMGS) containing photovoltaic modules, wind turbine, battery energy storage system, and diesel generator (DG) which is used as a backup source. These systems are making progress worldwide thanks to their respect for the environment. However, hybridization of several sources requires power flow control (PFC). For this reason, in this work, a proper energy management system is developed using LabVIEW software and embedded in a suitable platform for the real-time management of the hybrid energy system. The developed EMS is tested and validated through a small-scale application which accurately represents the case study of an isolated mosque located in a remote area of Morocco. The aim of this paper is to (i) propose a novel modelling method and real-time monitoring interface under the LabVIEW software based on the real data obtained by an optimal sizing previously made using Homer-pro software and (ii) implement the power control system on a low-consumption embedded platform that is the Raspberry-pi3.

/pdf/design-and-embedded-implementation-of-a-power-management-1k7rgfesyg.pdf

Design and Embedded Implementation of a Power Management Controller for Wind-PV-Diesel Microgrid System

The increase in the use of renewable energy sources (RES) has been remarkable in recent years, especially photovoltaic energy, which is one of the most widely used renewable energy sources for electricity generation. In fact, the world has known the installation of a large number of autonomous or grid-connected photovoltaic systems. However the problem with the introduction of PV is the improvised nature of solar energy that can influence the stability of electricity grids and reliability of the grid, Accurate solar forecasting makes it easier to integrate solar generation into the grid by reducing the integration and operating costs associated with intermittent solar power.In this context, the objective of this work is to improve the development of appropriate forecasting models for the prediction of photovoltaic energy production. For that reason, this article presents new hybrid methods to optimize deep learning forecasting by using genetic algorithm based Deep Neural Network. The model is employed to forecast time series of solar irradiation output. Comparisons are made between the performances of three types of neural networks: long short-term memory (LSTM), Gate recurrent unit (GRU), and Recurrent Neural Network (RNN). GA is exploited to find the most appropriate number of window size, and number of units (neurons) in each and all three hidden layers.

Mohammed Boussetta

Papers

Simple and efficient approach to detect and diagnose electrical faults and partial shading in photovoltaic systems

Assessing the potential of hybrid PV–Wind systems to cover public facilities loads under different Moroccan climate conditions

Faults Detection for Photovoltaic Field Based on K-Means, Elbow, and Average Silhouette Techniques through the Segmentation of a Thermal Image

Design and Embedded Implementation of a Power Management Controller for Wind-PV-Diesel Microgrid System

Deep Learning Using Genetic Algorithm Optimization for Short Term Solar Irradiance Forecasting