• Energy Research

The role of clustering in unsupervised fault diagnosis is significant, but different clustering techniques can yield varied results and cause inevitable uncertainty. Ensemble clustering methods have been introduced to tackle this challenge. This study presents a novel integrated technique in the field of fault diagnosis using spectral ensemble clustering. A new dimensionality reduction technique is proposed to intelligently identify faults, even in ambiguous scenarios, by exploiting the informative segment of the underlying bipartite graph. This is achieved by identifying and extracting the most informative sections of the bipartite graph based on the eigenvector centrality measure of nodes within the graph. The proposed method is applied to experimental current-voltage (I-V) curve data collected from a real photovoltaic (PV) platform. The obtained results remarkably improved the accuracy of aging fault detection to more than 83.50%, outperforming the existing state-of-the-art approaches. We also decided to separately analyze the ensemble clustering part of our FDD method, which indicated surpassing performance compared to similar methods by evaluating commonly used datasets like handwritten datasets. This proves that the proposed approach inherently holds promise for application in various real-world scenarios that are indicated by ambiguity and complexity.

The role of clustering in unsupervised fault diagnosis is significant, but different clustering techniques can yield varied results and cause inevitable uncertainty. Ensemble clustering methods have been introduced to tackle this challenge. This study presents a novel integrated technique in the field of fault diagnosis using spectral ensemble clustering. A new dimensionality reduction technique is proposed to intelligently identify faults, even in ambiguous scenarios, by exploiting the informative segment of the underlying bipartite graph. This is achieved by identifying and extracting the most informative sections of the bipartite graph based on the eigenvector centrality measure of nodes within the graph. The proposed method is applied to experimental current-voltage (I-V) curve data collected from a real photovoltaic (PV) platform. The obtained results remarkably improved the accuracy of aging fault detection to more than 83.50%, outperforming the existing state-of-the-art approaches. We also decided to separately analyze the ensemble clustering part of our FDD method, which indicated surpassing performance compared to similar methods by evaluating commonly used datasets like handwritten datasets. This proves that the proposed approach inherently holds promise for application in various real-world scenarios that are indicated by ambiguity and complexity.

Project : CAptive Balloons for aLtitudE SOLAR farms Si solar cells encapsulated at IPVF with indoor I/V characterization Indoor tests at Tankers building Main goal Development of a long-duration flying platform using photovoltaic energy and thin film solar cells architecture to produce electricity

Project : CAptive Balloons for aLtitudE SOLAR farms Si solar cells encapsulated at IPVF with indoor I/V characterization Indoor tests at Tankers building Main goal Development of a long-duration flying platform using photovoltaic energy and thin film solar cells architecture to produce electricity

Health monitoring and diagnosis of photovoltaic (PV) systems is becoming crucial to maximise the power production, increase the reliability and life service of PV power plants. Operating under faulty conditions, in particular under shading, PV plants have remarkable shape of current-voltage (I-V) characteristics in comparison to reference condition (healthy operation). Based on real electrical measurements (I-V), the present work aims to provide a very simple, robust and low cost Fault Detection and Classification (FDC) method for PV shading faults. At first, we extract the features for different experimental tests under healthy and shading conditions to build the database. The features are then analysed using Principal Component Analysis (PCA). The accuracy of the data classification into the PCA space is evaluated using the confusion matrix as a metric of class separability. The results using experimental data of a 250 Wp PV module are very promising with a successful classification rate higher than 97% with four different configurations. The method is also cost effective as it uses only electrical measurements that are already available. No additional sensors are required.

Health monitoring and diagnosis of photovoltaic (PV) systems is becoming crucial to maximise the power production, increase the reliability and life service of PV power plants. Operating under faulty conditions, in particular under shading, PV plants have remarkable shape of current-voltage (I-V) characteristics in comparison to reference condition (healthy operation). Based on real electrical measurements (I-V), the present work aims to provide a very simple, robust and low cost Fault Detection and Classification (FDC) method for PV shading faults. At first, we extract the features for different experimental tests under healthy and shading conditions to build the database. The features are then analysed using Principal Component Analysis (PCA). The accuracy of the data classification into the PCA space is evaluated using the confusion matrix as a metric of class separability. The results using experimental data of a 250 Wp PV module are very promising with a successful classification rate higher than 97% with four different configurations. The method is also cost effective as it uses only electrical measurements that are already available. No additional sensors are required.

Abstract Due to its high absorption coefficient and variable bandgap, InGaN is being intensively studied for photovoltaic applications. Growth of thick homogenous InGaN absorbers is challenging due to relaxation, clustering, and transition from 2D to 3D growth. These issues can be avoided by a semibulk multilayer structure. In this work, we analyze InGaN-based semibulk-structured solar cells in detail. We show that for indium content lower than 15%, GaN interlayers’ thickness has no influence on carrier transport due to the low barrier height. A conversion efficiency of 2.4% can be expected for this indium content. However, for higher indium content (15–30%), we show that the thinner the GaN interlayers, the better the conversion efficiency. Beyond 30% of indium, the conversion efficiency is hindered by the barriers’ important height even for very thin thicknesses of GaN interlayers. We show also that, for semibulk structure, both growth direction (N-face and metal-face) have similar impact on efficiency. This theoretical study gives the guidelines for the fabrication of InGaN-based solar cells that can be used as a wide-bandgap top cell in multijunction solar cells.