Multistress Testing of OPV Modules for Accurate Predictive Aging and Reliability Predictions

Stoichkov, Vasil; Kumar, Dinesh; Tyagi, Priyanka; Kettle, Jeff

doi:10.1109/jphotov.2018.2838438

Cited by 13 publications

(9 citation statements)

References 14 publications

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“…which environmental affects factors 'significantly' affect the selected response (i.e. mobility, drain Ion, Ioff) by using hypothesis tests to find a significance level [7]. Shown in Figure 3 is the Pareto chart which ranks the main effects and interactions that impact the sensor response by considering changes in the Ion of the ZnO TFT.…”

Section: Experimental Results and Data Analysismentioning

confidence: 99%

Understanding UV Sensor Performance in ZnO TFTs Through the Application of Multivariate Analysis

et al. 2018

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Zinc oxide (ZnO) thin film transistors are well suited to UV sensing application because they absorb predominantly in the UV region due to the wide bandgap (Eg = 3.37 eV) and possess a large exciton binding energy (60 meV) with high radiation hardness. When operated as a transistor, many device performance parameters alter such as threshold Voltage, on-off current and channel mobility. As a result, it is to distinguish between changes in electrical performance induced by UV light and environmental effects that add noise to the sensor performance. In this work, the UV response of zinc oxide thin film transistors (ZnO TFTs) is examined using Taguchi Design of Experiment (DOE) method. By using this multivariate analysis approach, it is possible to reduce the number of calibration tests required for the sensor to accurately assess UV irradiation It is observed that different input conditions (UV power, exposure time, temperature, bias conditions) affect different TFT performance parameters more or less significantly. From the perspective of UV sensing, ON current in the saturation region appears to be the best performance parameter in a ZnO TFT for examining differences in UV exposure.

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Section: Experimental Results and Data Analysismentioning

confidence: 99%

Understanding UV Sensor Performance in ZnO TFTs Through the Application of Multivariate Analysis

et al. 2018

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“…Due to the hotspots, PV degradation is enhanced and a high probability for the occurrence of permanent damage to PV panels prevails (Simon and Meyer, 2010). Another solidity problem that affects the PV panels is discontinuation [6] (Stoichkov et al, 2018). The above-mentioned problems affect the performance of output power in a PV panel but, the parameters such as temperature coefficient will decrease its annual energy production.…”

Section: Introductionmentioning

confidence: 99%

Solar PV’s Micro Crack and Hotspots Detection Technique Using NN and SVM

Winston¹,

Murugan²,

Elavarasan

et al. 2021

IEEE Access

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For lifelong and reliable operation, advanced solar photovoltaic (PV) equipment is designed to minimize the faults. Irrespectively, the panel degradation makes the fault inevitable. Thus, the quick detection and classification of panel degradation is pivotal. Among various problems that promote panel degradation, hot spots and micro-cracks are the prominent reliability problems which affect the PV performance. When these types of faults occur in a solar cell, the panel gets heated up and it reduces the power generation hence its efficiency considerably. In this study, the effect of the hotspot is studied and a comparative fault detection method is proposed to detect different PV modules affected by micro-cracks and hotspots. The classification process is accomplished by utilizing Feed Forward Back Propagation Neural Network technique and Support Vector Machine (SVM) techniques. Six input parameters like percentage of power loss (PPL), Open-circuit voltage (VOC), Short circuit current (ISC), Irradiance (IRR), Panel temperature and Internal impedance (Z) are accounted to detect the faults. Experimental investigation and simulations using MATLAB are carried out to detect five categories of faulty and healthy panels. Both methods exhibited a promising result with an average accuracy of 87% for feed-forward back propagation neural network and 99% SVM technique which exposes the potential of this proposed technique.

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“…2 ALT has been used for identifying optimal material sets, 3 benchmarking module stability 4 and also information on failure mechanisms. 5 However, it can only be used to estimate the operational lifetime when well-designed multistress and qualitative life tests are applied 6,7 and should be complemented with outdoor testing.…”

Section: Introductionmentioning

confidence: 99%

Predicting diurnal outdoor performance and degradation of organic photovoltaics via machine learning; relating degradation to outdoor stress conditions

David

Soares²,

Bristow

et al. 2021

Progress in Photovoltaics

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Accurate prediction of the future performance and remaining useful lifetime of next-generation solar cells such as organic photovoltaics (OPVs) is necessary to drive better designs of materials and ensure reliable system operation. Degradation is multifactorial and difficult to model deterministically; however, with the advent of machine learning, data from outdoor performance monitoring can be used for understanding the relative impact of stress factors and could provide a powerful method to interpret large quantities of outdoor data automatically. Here, we propose the use of artificial neural networks and regression models for forecasting OPV module performance and their degradation as a function of climatic conditions. We demonstrate their predictive capability for short-term energy forecasting of OPV modules, showing that energy yield can be predicted if climatic conditions are known. In addition, the model has been extended so that the impact of climatic conditions on degradation can be predicted. The combined model has been validated on unseen OPV module data and is able to predict energy yield to within 4% accuracy.

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Multistress Testing of OPV Modules for Accurate Predictive Aging and Reliability Predictions

Cited by 13 publications

References 14 publications

Understanding UV Sensor Performance in ZnO TFTs Through the Application of Multivariate Analysis

Understanding UV Sensor Performance in ZnO TFTs Through the Application of Multivariate Analysis

Solar PV’s Micro Crack and Hotspots Detection Technique Using NN and SVM

Predicting diurnal outdoor performance and degradation of organic photovoltaics via machine learning; relating degradation to outdoor stress conditions

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