DeepSolarEye: Power Loss Prediction and Weakly Supervised Soiling Localization via Fully Convolutional Networks for Solar Panels

The impact of soiling on solar panels is an important and well-studied problem in renewable energy sector. In this paper, we present the first convolutional neural network (CNN) based approach for solar panel soiling and defect analysis. Our approach takes an RGB image of solar panel and environmental factors as inputs to predict power loss, soiling localization, and soiling type. In computer vision, localization is a complex task which typically requires manually labeled training data such as bounding boxes or segmentation masks. Our proposed approach consists of specialized four stages which completely avoids localization ground truth and only needs panel images with power loss labels for training. The region of impact area obtained from the predicted localization masks are classified into soiling types using the webly supervised learning. For improving localization capabilities of CNNs, we introduce a novel bi-directional input-aware fusion (BiDIAF) block that reinforces the input at different levels of CNN to learn input-specific feature maps. Our empirical study shows that BiDIAF improves the power loss prediction accuracy by about 3% and localization accuracy by about 4%. Our end-to-end model yields further improvement of about 24% on localization when learned in a weakly supervised manner. Our approach is generalizable and showed promising results on web crawled solar panel images. Our system has a frame rate of 22 fps (including all steps) on a NVIDIA TitanX GPU. Additionally, we collected first of it's kind dataset for solar panel image analysis consisting 45,000+ images.Comment: Accepted for publication at WACV 201

Application of LANDSAT to the surveillance of lake eutrophication in the Great Lakes basin

The author has identified the following significant results. A step-by-step procedure for establishing and monitoring the trophic status of inland lakes with the use of LANDSAT data, surface sampling, laboratory analysis, and aerial observations were demonstrated. The biomass was related to chlorophyll-a concentrations, water clarity, and trophic state. A procedure was developed for using surface sampling, LANDSAT data, and linear regression equations to produce a color-coded image of large lakes showing the distribution and concentrations of water quality parameters, causing eutrophication as well as parameters which indicate its effects. Cover categories readily derived from LANDSAT were those for which loading rates were available and were known to have major effects on the quality and quantity of runoff and lake eutrophication. Urban, barren land, cropland, grassland, forest, wetlands, and water were included

Infrared Thermal Images of Solar PV Panels for Fault Identification Using Image Processing Technique

Among the renewable forms of energy, solar energy is a convincing, clean energy and acceptable worldwide. Solar PV plants, both ground mounting and the rooftop, are mushrooming thought the world. One of the significant challenges is the fault identification of the solar PV module, since a vast power plant condition monitoring of individual panels is cumbersome. This paper attempts to identify the panel using a thermal imaging system and processes the thermal images using the image processing technique. An ordinary and thermal image has been processed in the image processing tool and proved that thermal images record the hot spots. Similarly, the new and aged solar photovoltaic panels were compared in the image processing technique since any fault in the panel has been recorded as hot spots. The image recorded in the aged panels records hot spots, and performance has been analyzed using conventional metrics. The experimental results have also been verified

Novel utility-scale photovoltaic plant electroluminescence maintenance technique by means of bidirectional power inverter controller

Nowadays, photovoltaic (PV) silicon plants dominate the growth in renewable energies generation. Utility-scale photovoltaic plants (USPVPs) have increased exponentially in size and power in the last decade and, therefore, it is crucial to develop optimum maintenance techniques. One of the most promising maintenance techniques is the study of electroluminescence (EL) images as a complement of infrared thermography (IRT) analysis. However, its high cost has prevented its use regularly up to date. This paper proposes a maintenance methodology to perform on-site EL inspections as efficiently as possible. First, current USPVP characteristics and the requirements to apply EL on them are studied. Next, an increase over the automation level by means of adding automatic elements in the current PV plant design is studied. The new elements and their configuration are explained, and a control strategy for applying this technique on large photovoltaic plants is developed. With the aim of getting on-site EL images on a real plant, a PV inverter has been developed to validate the proposed methodology on a small-scale solar plant. Both the electrical parameters measured during the tests and the images taken have been analysed. Finally, the implementation cost of the solution has been calculated and optimised. The results conclude the technical viability to perform on-site EL inspections on PV plants without the need to measure and analyse the panel defects out of the PV installation

Use of laser beam diffraction for non-invasive characterisation of CdTe thin film growth structure

Characterisation of Cadmium Telluride (CdTe) thin films commonly requires the use of invasive techniques for the identification of their structural growth and the detection of defects which occur during the deposition process. Structural growth and the presence of defects can affect the performance of the final device. A non-invasive inspection system for CdTe films has been developed to identify the structural properties of this material, comparing two different deposition techniques, Close Space Sublimation (CSS) and Magnetron Sputtering (MS). The proposed system utilises a 1 μm diode laser which passes through the CdTe layer, originating detectable diffraction patterns, which are characterised using image processing techniques and assessed using a neural network-based cognitive decision-making support system. Results are found to be consistent with the conventional microscopic techniques (SEM and TEM) used to analyse morphological and structural properties of thin-film CdTe solar cells

Solar cell degradation : the role of moisture ingress

Moisture ingress is one of the key fault mechanisms responsible for photovoltaic (PV) devices degradation. Moisture and moisture induced degradation (MID) products can attack the solar cell and the PV module components which can lead to solar cell degradation (e.g., microcracks), corrosion, optical degradation, potential induced degradation (PID), etc. These MID mechanisms have dire implications for the performance reliability of PV modules. Understanding the influence of moisture ingress on solar PV device’s degradation will boost the interest in investing in solar PV power installations globally, especially in the Nordics. In this thesis, the effect of moisture ingress on 20-years old field-aged multicrystalline silicon (mc-Si) PV modules is investigated. The defective areas in the PV modules were identified using visual inspection, electroluminescence (EL), ultraviolet fluorescence (UV-F), and infrared thermal (IR-T) techniques. Scanning electron microscopy and energy dispersive Xray spectroscopy (SEM-EDS) analyses were used to elucidate the role of moisture on the observed degradation mechanisms. In addition, temperature coefficient profiling is used as a diagnostic tool to characterize different moisture induced defects. The ethylene vinyl acetate (EVA) front encapsulation was found to undergo optical degradation and the extracted cells show dark discolored Tedlar®/Polyester/Tedlar® (TPT) backsheets. Corrosion at the solder joint was dominant and is attributed to the dissolution of lead and tin (main components of solder) and the Ag grids in moisture and acetic acid due to galvanic corrosion. Degradation of the EVA encapsulation produces acetic acid, carbon dioxide, phosphorus, sulfur, fluorine, and chlorine. It was observed that under the influence of moisture ingress, leached metal ions e.g., Na, Ag, Pb, Sn, Cu, Zn, and Al migrate to the surface of the solar cells. This led to the formation of oxides, hydroxides, sulfides, phosphates, acetates, and carbonates of silver, lead, tin, copper, zinc, and aluminum. Also, other competing reactions led to the formation of stannates of copper, silver, sodium, and zinc. Similarly, migration of silver and aluminum to the surfaces of the TiO2 antireflection coating (ARC) nanoparticles (NPs) lead to the formation of titania-alumina and silver-titania complexes. Formation of these titania-metal complexes affects the opto-electrical efficiency of the TiO2 ARC in the PV module. Additionally, in the presence of moisture and acetic acid, Pb is preferentially corroded (to form lead acetate complexes) instead of the expected sacrificial Sn in the solder. In the EL and UV-F images, these degradation species appear as dark spots, and as hot spots in IR-T images. More importantly, these MID defects and fault modes lead to parasitic resistance and mismatch losses, and hence, degradation in the current-voltage (I-V) characteristics, temperature coefficients, and maximum power (Pmax) of the field-aged PV modules. The observed temperature sensitivities are characteristic of different moisture-induced defects. Taken together, this work has expounded on the understanding and detection of MID phenomenon in field-deployed solar PV modules.publishedVersio

Novel utility-scale photovoltaic plant electroluminescence maintenance technique by means of bidirectional power inverter controller

Producción CientíficaNowadays, photovoltaic (PV) silicon plants dominate the growth in renewable energies generation. Utility-scale photovoltaic plants (USPVPs) have increased exponentially in size and power in the last decade and, therefore, it is crucial to develop optimum maintenance techniques. One of the most promising maintenance techniques is the study of electroluminescence (EL) images as a complement of infrared thermography (IRT) analysis. However, its high cost has prevented its use regularly up to date. This paper proposes a maintenance methodology to perform on-site EL inspections as efficiently as possible. First, current USPVP characteristics and the requirements to apply EL on them are studied. Next, an increase over the automation level by means of adding automatic elements in the current PV plant design is studied. The new elements and their configuration are explained, and a control strategy for applying this technique on large photovoltaic plants is developed. With the aim of getting on-site EL images on a real plant, a PV inverter has been developed to validate the proposed methodology on a small-scale solar plant. Both the electrical parameters measured during the tests and the images taken have been analysed. Finally, the implementation cost of the solution has been calculated and optimised. The results conclude the technical viability to perform on-site EL inspections on PV plants without the need to measure and analyse the panel defects out of the PV installation.Ministerio de Industria, Economía y Competitividad (grant number RTC-2017-6712-3)Junta de Castilla y León (grant VA283P18

ReSCon '10, Research Student Conference: Book of Abstracts

The third SED Research Student Conference (ReSCon2010) was hosted over three days, 21-23 June 2010, in the Hamilton Centre at Brunel University. The conference consisted of oral and poster presentations, which showcased the high quality and diversity of the research being conducted within the School of Engineering and Design. The abstracts and presentations were the result of ongoing research by postgraduate research students from the School. The conference is held annually, and ReSCon plays a key role in contributing to research and innovations within the School