DSpace Collection:http://hdl.handle.net/10174/295342024-03-28T19:41:27Z2024-03-28T19:41:27ZThe Impact of Dust Deposition on PV Panels’ Efficiency and Mitigation Solutions: Review ArticleNezamisavojbolaghi, MinaDavodian, ErfanBouich, AmalTlemçani, MouhaydineMesbahi, OumaimaJaneiro, Fernando M.http://hdl.handle.net/10174/362682024-02-06T09:42:19Z2023-12-01T00:00:00ZTitle: The Impact of Dust Deposition on PV Panels’ Efficiency and Mitigation Solutions: Review Article
Authors: Nezamisavojbolaghi, Mina; Davodian, Erfan; Bouich, Amal; Tlemçani, Mouhaydine; Mesbahi, Oumaima; Janeiro, Fernando M.
Abstract: Conversion efficiency, power production, and cost of PV panels’ energy are remarkably impacted by external factors including temperature, wind, humidity, dust aggregation, and induction characteristics of the PV system such as tilt angle, altitude, and orientation. One of the prominent elements affecting PV panel performance and capability is dust. Nonetheless, dust features including size, shape, type, etc. are geologically known. Several mitigation methods have been studied for the reduction of dust concentration on the exterior face of the PV modules. The outcomes have demonstrated that dust concentration and pollutants remarkably affect the PV panel energy production. This paper reviews the recently developed research on the outcomes of the dust effect on PV panels in different locations and meets the needs of future research on this subject. Moreover, different cleaning methods that could be advantageous for future researchers in opting for the most applicable technique for dust removal are reviewed.2023-12-01T00:00:00ZBattery Impedance Spectroscopy Embedded Measurement SystemCicioni, GabrielDe Angelis, AlessioJaneiro, Fernando M.Ramos, Pedro M.Carbone, Paolohttp://hdl.handle.net/10174/362662024-02-06T09:42:08Z2023-11-01T00:00:00ZTitle: Battery Impedance Spectroscopy Embedded Measurement System
Authors: Cicioni, Gabriel; De Angelis, Alessio; Janeiro, Fernando M.; Ramos, Pedro M.; Carbone, Paolo
Abstract: The evolution of rechargeable battery characteristics have led to their use in almost every device in our everyday life. This importance has also increased the relevance of estimating the remaining battery charge (state of charge, SOC) and their health (state of health, SOH). One of the methods for the estimation of these parameters is based on the impedance spectroscopy obtained from the battery output impedance measured at multiple frequencies. This paper proposes an embedded measurement system capable of measuring the battery output impedance while in operation (either charging or supplying power to the intended device). The developed system generates a small amplitude stimulus that is added to the battery current. The system then measures the battery voltage and current to estimate the impedance at the stimulus frequencies. Three batteries were measured at different SOC levels, demonstrating the system principle of operation. Complementarily, a battery impedance equivalent circuit was used, together with genetic algorithms, to estimate the circuit parameters and assess their dependence on the battery SOC.2023-11-01T00:00:00ZMeasurement Interval Effect on Photovoltaic Parameters EstimationMesbahi, OumaimaAfonso, DaruezTlemçani, MouhaydineBouich, AmalJaneiro, Fernando M.http://hdl.handle.net/10174/362652024-02-07T12:10:08Z2023-08-31T23:00:00ZTitle: Measurement Interval Effect on Photovoltaic Parameters Estimation
Authors: Mesbahi, Oumaima; Afonso, Daruez; Tlemçani, Mouhaydine; Bouich, Amal; Janeiro, Fernando M.
Abstract: Recently, the estimation of photovoltaic parameters has drawn the attention of researchers, and most of them propose new optimization methods to solve this problem. However, the process of photovoltaic parameters estimation can be affected by other aspects. In a real experimental setup, the I–V characteristic is obtained with IV tracers. Depending on their technical specifications, these instruments can influence the quality of the I–V characteristic, which in turn is inevitably linked to the estimation of photovoltaic parameters. Besides the uncertainties that accompany the measurement process, a major effect on parameters estimation is the size of the measurement interval of current and voltage, where some instruments are limited to measure a small portion of the characteristic or cannot reach their extremum regions. In this paper, three case studies are presented to analyse this phenomenon: different characteristic measurement starting points and different measurement intervals. In the simulation study the parameters are extracted from 1000 trial runs of the simulated I-V curve. The results are then validated using an experimental study where an IV tracer was built to measure the I–V characteristic. Both simulation and experimental studies concluded that starting the measurements at the open circuit voltage and having an interval spanning a minimum of half of the I–V curve results in an optimal estimation of photovoltaic parameters.2023-08-31T23:00:00ZDeep Learning for Power Quality Event Detection and Classification Based on Measured Grid DataRodrigues, Nuno M.Janeiro, Fernando M.Ramos, Pedro M.http://hdl.handle.net/10174/362542024-02-06T09:40:49Z2023-06-30T23:00:00ZTitle: Deep Learning for Power Quality Event Detection and Classification Based on Measured Grid Data
Authors: Rodrigues, Nuno M.; Janeiro, Fernando M.; Ramos, Pedro M.
Abstract: Energy consumption has increased over the years, and, due to the dependency on fossil energy, alternative and renewable energy sources have been integrated to address environmental concerns. However, it is important to maintain the efficiency, reliability, and safety of the power grid amid the integration of different energy sources. IEEE and IEC standards regulate power quality (PQ) and define thresholds for PQ events that traditionally have been detected through specialized algorithms. With machine learning, it is possible to detect and classify those events using deep-learning (DL) techniques that teach systems to learn by example, providing a more scalable approach to classification. Published studies in PQ with DL algorithms to detect disturbances rely only on simulated signals or imposed disturbances. In this article, a DL neural network is trained and used to detect and classify PQ events from a database built with real electrical power grid signals measured with monitoring devices.2023-06-30T23:00:00Z