Anomaly detection and automatic labeling for solar cell quality inspection based on Generative Adversarial Network

Quality inspection applications in industry are required to move towards a zero-defect manufacturing scenario, withnon-destructive inspection and traceability of 100 % of produced parts. Developing robust fault detection and classification modelsfrom the start-up of the lines is challenging due to the difficulty in getting enough representative samples of the faulty patternsand the need to manually label them. This work presents a methodology to develop a robust inspection system, targeting thesepeculiarities, in the context of solar cell manufacturing. The methodology is divided into two phases: In the first phase, an anomalydetection model based on a Generative Adversarial Network (GAN) is employed. This model enables the detection and localizationof anomalous patterns within the solar cells from the beginning, using only non-defective samples for training and without anymanual labeling involved. In a second stage, as defective samples arise, the detected anomalies will be used as automaticallygenerated annotations for the supervised training of a Fully Convolutional Network that is capable of detecting multiple types offaults. The experimental results using 1873 EL images of monocrystalline cells show that (a) the anomaly detection scheme can beused to start detecting features with very little available data, (b) the anomaly detection may serve as automatic labeling in order totrain a supervised model, and (c) segmentation and classification results of supervised models trained with automatic labels arecomparable to the ones obtained from the models trained with manual labels.Comment: 20 pages, 10 figures, 6 tables. This article is part of the special issue "Condition Monitoring, Field Inspection and Fault Diagnostic Methods for Photovoltaic Systems" Published in MDPI - Sensors: see https://www.mdpi.com/journal/sensors/special_issues/Condition_Monitoring_Field_Inspection_and_Fault_Diagnostic_Methods_for_Photovoltaic_System

A COMPREHENSIVE ASSESSMENT METHODOLOGY BASED ON LIFE CYCLE ANALYSIS FOR ON-BOARD PHOTOVOLTAIC SOLAR MODULES IN VEHICLES

This dissertation presents a novel comprehensive assessment methodology for using on-board photovoltaic (PV) solar technologies in vehicle applications. A well-to-wheels life cycle analysis based on a unique energy, greenhouse gas (GHG) emission, and economic perspective is carried out in the context of meeting corporate average fuel economy (CAFE) standards through 2025 along with providing an alternative energy path for the purpose of sustainable transportation. The study includes 14 different vehicles, 3 different travel patterns, in 12 U.S. states and 16 nations using 19 different cost analysis scenarios for determining the challenges and benefits of using on-board photovoltaic (PV) solar technologies in vehicle applications. It develops a tool for decision-makers and presents a series of design requirements for the implementation of on-board PV in automobiles to use during the conceptual design stage, since its results are capable of reflecting the changes in fuel consumption, greenhouse gas emission, and cost for different locations, technological, and vehicle sizes. The decision-supports systems developed include (i) a unique decision support systems for selecting the optimal PV type for vehicle applications using quality function deployment, analytic hierarchy process, and fuzzy axiomatic design, (ii) a unique system for evaluating all non-destructive inspection systems for defects in the PV device to select the optimum system suitable for an automated PV production line. (iii) The development of a comprehensive PV system model that for predicting the impact of using on-board PV based on life cycle assessment perspective. This comprehensive assessment methodology is a novel in three respects. First, the proposed work develops a comprehensive PV system model and optimizes the solar energy to DC electrical power output ratio. Next, it predicts the actual contribution of the on-board PV to reduce fuel consumption, particularly for meeting corporate average fuel economy (CAFE) 2020 and 2025 standards in different scenarios. The model also estimates vehicle range extension via on-board PV and enhances the current understanding regarding the applicability and effective use of on-board PV modules in individual automobiles. Finally, it develops a life cycle assessment (LCA) model (well-to-wheels analysis) for this application. This enables a comprehensive assessment of the effectiveness of an on-board PV vehicle application from an energy consumption, Greenhouse Gas (GHG) emission, and cost life-cycle perspective. The results show that by adding on-board PVs to cover less than 50% of the projected horizontal surface area of a typical passenger vehicle, up to 50% of the total daily miles traveled by a person in the U.S. could be driven by solar energy if using a typical mid-size vehicle, and up to 174% if using a very lightweight and aerodynamically efficient vehicle. In addition, the increase in fuel economy in terms of combined mile per gallon (MPG) at noon for heavy vehicles is between 2.9% to 9.5%. There is a very significant increase for lightweight and aerodynamic efficient vehicles, with MPG increase in the range of 10.7% to 42.2%, depending on location and time of year. Although the results show that the plug-in electric vehicles (EVs) do not always have a positive environmental impact over similar gasoline vehicles considering the well-to-wheel span, the addition of an on-board PV system for both vehicle configurations, significantly reduces cycle emissions (e.g., the equivalent savings of what an average U.S. home produces in a 20 month period). The lifetime driving cost ($per mile) of a gasoline vehicle with adding on-board PV, compared to a pure gasoline vehicle, is lower in regions with more sunlight (e.g., Arizona) even of the current gasoline price in the U.S. ($4.0 per gallon) assuming battery costs will decline over time. Lifetime driving cost ($per mile) of a plug-in EV with added PV versus pure plug-in EV (assuming electricity price 0.18$/kWh) is at least similar, but mostly lower, even in regions with less sunlight (e.g., Massachusetts). In places with low electricity prices (0.13 $/kWh), and with more sunlight, the costs of operating an EV with PV are naturally lower. The study reports a unique observation that placing PV systems on-board for existing vehicles is in some cases superior to the lightweighting approach regarding full fuel-cycle emissions. An added benefit of on-board PV applications is the ability to incorporate additional functionality into vehicles. Results show that an on-board PV system operating in Phoenix, AZ can generate in its lifetime, energy that is the equivalent of what an American average household residential utility customer consumes over a three-year period. However, if the proposed system operates in New Delhi, India, the PV could generate energy in its lifetime that is the equivalent of what an Indian average household residential utility customer consumes over a 33-year period. Consequently, this proposed application transforms, in times of no-use, into a flexible energy generation system that can be fed into the grid and used to power electrical devices in homes and offices. The fact that the output of this system is direct current (DC) electricity rather than alternative current (AC) electricity reduces the wasted energy cost in the generation, transmission, and conversion losses between AC-DC electricity to reach the grid. Thus, this system can potentially reduce the dependency on the grid in third world countries where the energy consumption per home is limited and the grid is unstable or unreliable, or even unavailable