GIS-Based Optimal Photovoltaic Panel Floorplanning for Residential Installations

Shading is a crucial issue for the placement of PV installations, as it heavily impacts power production and the corresponding return of investment. Nonetheless, residential rooftop installations still rely on rule-of-thumb criteria and on gross estimates of the shading patterns, while more optimized approaches focus solely on the identification of suitable surfaces (e.g., roofs) in a larger geographic area (e.g., city or district). This work addresses the challenge of identifying an optimal (with respect to the overall energy production) placement of PV panels on a roof. The novel aspect of the proposed solution lies in the possibility of having a sparse, irregular placement of individual modules so as to better exploit the variance of solar data. The latter are represented in terms of the distribution of irradiance and temperature values over the roof, as elaborated from historical traces and Geographical Information System (GIS) data. Experimental results will prove the effectiveness of the algorithm through three real world case studies, and that the generated optimal solutions allow to increase power production by up to 28% with respect to rule-of-thumb solutions

Simulation support for performance assessment of building components

The determination of performance metrics for novel building components requires that the tests are conducted in the outdoor environment. It is usually difficult to do this when the components are located in a full-scale building because of the difficulty in controlling the experiments. Test cells allow the components to be tested in realistic, but controlled, conditions. High-quality outdoor experiments and identification analysis methods can be used to determine key parameters that quantify performance. This is important for achieving standardised metrics that characterise the building component of interest, whether it is a passive solar component such as a ventilated window, or an active component such as a hybrid photovoltaic module. However, such testing and analysis does not determine how the building component will perform when placed in a real building in a particular location and climate. For this, it is necessary to model the whole building with and without the building component of interest. A procedure has been developed, and applied within several major European projects, that consists of calibrating a simulation model with high-quality data from the outdoor tests and then applying scaling and replication to one or more buildings and locations to determine performance in practice of building components. This paper sets out the methodology that has been developed and applied in these European projects. A case study is included demonstrating its application to the performance evaluation of hybrid photovoltaic modules

Performance Analysis of Thermal Image Processing-Based Photovoltaic Fault Detection and PV Array Reconfiguration—A Detailed Experimentation

Due to the flexibility, sustainability, affordability, and ease of installation of solar photovoltaic systems, their use has significantly increased over the past two decades. The performance of a solar PV system can be constrained by a variety of external conditions, including hotspots, partial shade, and other minor faults. This causes the PV system to permanently fail and power losses. The power output in a partially shaded solar system is improved in this work by the introduction of a fault classifier based on thermal image analysis with a reconfiguration algorithm. For that purpose, the entire PV array is divided into two parts, with one of these being the male part and the other being the female part. MOSFET switches are used to build the switching matrix circuit that connects these parts. The Flir T420bx thermal camera captures thermal pictures, and MATLAB/Simulink® is used to extract the image properties. The pairing reconfiguration pattern is found using an algorithm based on image processing and the image attributes. The switching signals to the switching circuit are triggered by an Arduino controller. The image attributes of the thermal images may also be used to categorize PV system defects. This reconfiguration technique is easy, simple to use, and it can also be used to check the health of each PV module. The performance of the proposed work was validated using a 5 kW PV system with a 4 × 5 TCT array configuration at Sethu Institute of Technology’s renewable energy lab in India. The proposed method was simulated using the MATLAB-Simulink software program, and the outcomes were verified on different hardware setups.publishedVersio

Fault diagnosis for PV arrays considering dust impact based on transformed graphical feature of characteristic curves and convolutional neural network with CBAM modules

Various faults can occur during the operation of PV arrays, and both the dust-affected operating conditions and various diode configurations make the faults more complicated. However, current methods for fault diagnosis based on I-V characteristic curves only utilize partial feature information and often rely on calibrating the field characteristic curves to standard test conditions (STC). It is difficult to apply it in practice and to accurately identify multiple complex faults with similarities in different blocking diodes configurations of PV arrays under the influence of dust. Therefore, a novel fault diagnosis method for PV arrays considering dust impact is proposed. In the preprocessing stage, the Isc-Voc normalized Gramian angular difference field (GADF) method is presented, which normalizes and transforms the resampled PV array characteristic curves from the field including I-V and P-V to obtain the transformed graphical feature matrices. Then, in the fault diagnosis stage, the model of convolutional neural network (CNN) with convolutional block attention modules (CBAM) is designed to extract fault differentiation information from the transformed graphical matrices containing full feature information and to classify faults. And different graphical feature transformation methods are compared through simulation cases, and different CNN-based classification methods are also analyzed. The results indicate that the developed method for PV arrays with different blocking diodes configurations under various operating conditions has high fault diagnosis accuracy and reliability

A review for solar panel fire accident prevention in large-scale PV applications

Due to the wide applications of solar photovoltaic (PV) technology, safe operation and maintenance of the installed solar panels become more critical as there are potential menaces such as hot spot effects and DC arcs, which may cause fire accidents to the solar panels. In order to minimize the risks of fire accidents in large scale applications of solar panels, this review focuses on the latest techniques for reducing hot spot effects and DC arcs. The risk mitigation solutions mainly focus on two aspects: structure reconfiguration and faulty diagnosis algorithm. The first is to reduce the hot spot effect by adjusting the space between two PV modules in a PV array or relocate some PV modules. The second is to detect the DC arc fault before it causes fire. There are three types of arc detection techniques, including physical analysis, neural network analysis, and wavelet detection analysis. Through these detection methods, the faulty PV cells can be found in a timely manner thereby reducing the risk of PV fire. Based on the review, some precautions to prevent solar panel related fire accidents in large-scale solar PV plants that are located adjacent to residential and commercial areas

Investigation into the effects of flow distribution on the photovoltaic performance of a building integrated photovoltaic/thermal solar collector

The conversion of solar energy into usable forms of energy such as electricity and heat is attractive given the abundance of solar energy and the numerous issues recently raised in the consumption of fossil fuels. Solar conversion technologies may generally be categorised as either photovoltaic or solar thermal types capable of converting incidental sunlight into electricity and heat respectively. The photovoltaic cell is able to transform incidental sunlight into electricity via the Becquerel effect, however, the single junction crystalline silicon solar cell, the predominant cell type in today’s photovoltaic market is only able to utilise a small portion (less than 20%) of incidental sunlight for this purpose. A majority of the remaining portion is absorbed much like a traditional solar thermal collector and sunk as heat by the cell, elevating its operating temperature. Given the negative effect of temperature on photovoltaic cell operation, where a linearly proportional drop in conversion efficiency with elevated temperature can be expected, photovoltaic conversion can be reduced significantly particularly in areas of high irradiance and ambient temperatures. Based on the intrinsic absorption characteristics of the photovoltaic cell, a third type of solar panel referred to as the hybrid photovoltaic thermal collector (PVT) collector has been developed where fluid channels running along the underside of the photovoltaic panel transfer heat away from the cells to minimise this detrimental effect. Furthermore, heat captured from the cells may then be used for space heating or domestic hot water improving the overall collector efficiency. In this study a unique building integrated PVT (BIPVT) collector is investigated consisting of an aluminium extrusion with structural ribs, fluid channels, and solar conversion materials. In order to evaluate this design, a mathematical model of the collector was developed in order to determine both thermal and electrical yield of the proposed design. The thermal analyses of the building integrated PVT collector in previous studies have generally adopted the approach applied to traditional solar thermal collectors where the distribution of coolant fluid flowing through the piping array is assumed uniform. For a conventional solar thermal collector this simplification may be reasonable under certain circumstances, however, given the temperature sensitivity of photovoltaic cells and their electrical connection scheme, this assumption may lead to significant modelling error. In order to further investigate this issue, a mathematical model has been developed to determine the photovoltaic yield of a BIPVT collector operating under non-homogeneous operating temperature as a result of flow maldistribution. The model is composed of three steps individually addressing the issues of 1) fluid flow, 2) heat transfer, and 3) the photovoltaic output of a BIPVT array. Fluid analysis was conducted using the finite element method in order to obtain the individual fluid channel flow rates. Using these values, a heat transfer analysis was then conducted for each module forming the BIPVT array to calculate the photovoltaic operating temperature for the constituent cells forming the array. During this step the finite difference method was utilised to approximate the fin efficiency of the building integrated collector, taking into account its irregular geometry. Finally the photovoltaic yield was calculated using a numerical approach which considered the individual operating temperature of the PV cells. During this step a new method was identified to determine the values of series and shunt resistances and also the diode constant required for the modelling of photovoltaic devices based on the multi-dimensional Newton-Raphson method and current-voltage equations expressed using the Lambert W-function. Experimentation was carried out to validate the new modelling methods. These models were combined to quantify the detrimental impact of flow mal-distribution on photovoltaic yield for a number of scenarios. In the case where flow uniformity was poorest, only a 2% improvement in photovoltaic yield was obtained in comparison to a traditional photovoltaic panel operating under the same environmental conditions. For the case where flow uniformity was optimal however, photovoltaic output was improved by almost 10%. This work has shown that the effects of poor flow distribution has the potential to have a substantial negative impact on the photovoltaic output of a building integrated solar collector especially given the variability in its physical geometry. The appropriate design of this technology should therefore consider the effects of this phenomenon. The methodology presented in this study can be used to approximate PV output for a BIPVT array with different array geometries and operating characteristics. Furthermore, the method to calculate solar cell modelling parameters developed in this study is not only useful for the analysis of hybrid PVT systems, but for the general analysis of photovoltaic systems based on crystalline silicon solar cells

ANALYSIS AND OPTIMIZATION OF CRYSTALLINE SILICON WAFER BASED PHOTOVOLTAIC MODULES

Ph.DDOCTOR OF PHILOSOPH