Solar, Volume 2, Issue 1 (March 2022) – 6 articles

The timely detection of photovoltaic (PV) system failures is important for maintaining optimal performance and lifetime reliability. A main challenge remains the lack of a unified health-state architecture for the uninterrupted monitoring and predictive performance of PV systems. To this end, existing failure detection models are strongly dependent on the availability and quality of site-specific historic data. The scope of this work is to address these fundamental challenges by presenting a health-state architecture for advanced PV system monitoring. The proposed architecture comprises of a machine learning model for PV performance modeling and accurate failure diagnosis. The predictive model is optimally trained on low amounts of on-site data using minimal features and coupled to functional routines for data quality verification, whereas the classifier is trained under an enhanced supervised learning regime. The results demonstrated high accuracies for the implemented predictive model, exhibiting normalized root mean square errors lower than 3.40% even when trained with low data shares. The classification results provided evidence that fault conditions can be detected with a sensitivity of 83.91% for synthetic power-loss events (power reduction of 5%) and of 97.99% for field-emulated failures in the test-bench PV system. Finally, this work provides insights on how to construct an accurate PV system with predictive and classification models for the timely detection of faults and uninterrupted monitoring of PV systems, regardless of historic data availability and quality. Such guidelines and insights on the development of accurate health-state architectures for PV plants can have positive implications in operation and maintenance and monitoring strategies, thus improving the system’s performance. Full article

This paper presents a measurement-based stability analysis of commercially available single-phase inverters in public low-voltage networks. In practice, manufacturers typically do not disclose the parameters of the inverter design, although interactions with the low-voltage network need to be assessed and predicted. State-of-the-art modeling methods require knowledge about the internal parameters. The method proposed in the paper is based on measurements in the laboratory and does not require detailed knowledge about the specific inverter design for the identification of the black-box linear time-periodic representation. The gained information is used for the black-box stability analysis in the frequency range up to 2 kHz, which covers the bandwidth of the control of the inverters. The method is validated for a commercially available photovoltaic inverter in the laboratory. An instability that leads to a shutdown of the inverter is demonstrated, while the critical frequency range is predicted accurately. Full article

The significance of Distributed Generators (DGs) in the technical and economic operations of electric power distribution systems cannot be overemphasized in recent times. This is essential as a result of the incessant increase in electrical energy demand, which is becoming considerably difficult to meet with the conventional means of energy supply. Thus, DGs offer better alternatives for providing a quality supply of energy near the site of consumption. This type of energy supply is cleaner and cheaper most of the time due to the lessened transmission losses, which consequently reduced the cost of operation at the transmission and distribution levels of the power system. In this work, an approach for placement and sizing of solar PV DGs into radial distribution networks (RDN) based on the solar PV capacity factor of the site was analyzed using particle swarm optimization. The aim of this study is to analyze the effect of the approach on the real and reactive power losses within the network as well as the bus voltage profile. Constraints on credible system operation parameters, which includes bus voltage limits, power balance, and power flow limits, are considered in the formulation of the optimization problem. In order to verify the viability of the deployed approach, steady-state performance analyses were executed on IEEE 33-bus RDN; and the results obtained were compared with the results from other approaches reported in the literature. Full article

In the last few years, renewable energies became more socially and economically relevant, and among them, photovoltaic systems stand out. Residential self-consumption of electricity is a field with great potential, and implementation of grid-connected photovoltaic systems (GCPS) is in full rise. The installation of distributed generation systems in residential environments could alter the performance of low-voltage distribution networks, since these are designed for unidirectional power flow and adding these generators means fluctuations in power-flows. For these reasons, a study of the fundamental magnitudes of three low-voltage distribution networks located in Madrid was made for various photovoltaic penetration rates, making use of simulations via the software OpenDSS and subsequent analysis of results. The research concludes that, among other aspects, GCPS produce load flow variations that are dependent on: the penetration rates; the distance from the point of interest and the distribution transformer, increasing the voltage variation between the most productive hours and the night hours with that distance; and on the rate between consumption and generation, so that when it diminishes, the self-sufficiency of the system increases, and with it the voltage of all the buses that tend to the rated voltage. Moreover, there are wide seasonal fluctuations: specifically, in summer months, generation profiles override consumption fluctuations, while in winter months consumption guides voltage and power profiles. Both the code implemented and the results of the analysis were published in an open source website using a free software license. Full article

Recently, reactive dyes have attracted a lot of attention for dye-sensitized solar cell applications. This study endeavors to design dye sensitizers with enhanced efficiency for photovoltaic cells by modifying the reactive blue 5 (RB 5) and reactive brown 10 (RB 10) dyes. Three different strategies were used to design the sensitizers, and their efficiency was compared using the density functional theory (DFT). The optimized geometry, bang gap values, the density of states, electrostatic potential surface analysis, and theoretical FT-IR absorption spectra of the sensitizers were obtained. In the first strategy, functional groups (electron-donating (C₂H₅), electron-withdrawing (–NO₂) groups) were anchored onto dye molecules, and their effect on the charge transport properties was evaluated using the DFT analysis. The latter two designs were based on a donor-π-acceptor strategy. The second design consisted of intramolecular donor-acceptor regions separated by a benzodithiophene-based π-spacer. In the third strategy, an external acceptor unit was attached to the dye molecules through the benzodithiophene-based π-spacer. The electron-donating strengths of donor moieties in the donor-π-acceptor systems were studied using B3LYP/6-31G level DFT calculations. The quantum chemical analysis of the three designs revealed that the anchoring of functional groups (–NO₂ and C₂H₅) on the dye molecules showed no impact on the charge transport properties. The introduction of a benzodithiophene-based π-spacer improved the conjugation of the dye sensitizers, which enhanced the electron transport properties. The electron transport properties further improved when an external acceptor unit was attached to the dye molecule containing a π-spacer. It was thus concluded that attaching an external acceptor unit to the donor dye molecule containing a π-spacer produced desired results for both of the dyes. Full article

Renewable energy sources such as photovoltaic (PV) technologies are considered to be key drivers towards climate neutrality. Thin-film PVs, and particularly copper indium gallium selenide (CIGS) technologies, will play a crucial role in the turnaround in energy policy due to their high efficiencies, high product flexibility, light weight, easy installation, lower labour-intensiveness, and lower carbon footprint when compared to silicon solar cells. Nonetheless, challenges regarding the CIGS fabrication process such as moderate reproducibility and process tolerance are still hindering a broad market penetration. Therefore, cost-efficient and easily implementable in-line process control methods are demanded that allow for identification and elimination of non-conformal cells at an early production step. As part of this work, a practical approach towards industrial in-line photoluminescence (PL) imaging as a contact-free quality inspection tool is presented. Performance parameters of 10 CIGS samples with 32 individually contacted cells each were correlated with results from PL imaging using green and red excitation light sources. The data analysis was fully automated using Python-based image processing, object detection, and non-linear regression modelling. Using the red excitation light source, the presented PL imaging and data processing approach allows for a quantitative assessment of the cell performance. Full article