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Forecasting degradation rates of different photovoltaic systems using Robust Principal Component Analysis and ARIMA
IET Renewable Power Generation
Abstract and Figures
Degradation rates based on forecasting of performance ratio, Rp, time series are computed
and compared with actual degradation rates. A three year forecasting of monthly Rp, measured
from PV connected systems of various technologies is performed using the seasonal ARIMA
(SARIMA) time series model. The seasonal ARIMA model is estimated using monthly Rp measured
over a 5 year period and based on this model forecasting is implemented for the subsequent
three years. The degradation rate at the end of the forecasting period, eighth year, is computed
using a robust principal component analysis (RCPA) based methodology. The degradation rates
obtained for various (PV) systems are then compared to the ones obtained using the actual eight
year data.
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Grid-connected photovoltaic (PV) systems have become a significant constituent of the power supply mix. A challenge faced by both users and suppliers of PV systems is that of defining and computing a reliable metric of annual degradation rate while in service. This paper defines a new measure to calculate the degradation rate of PV systems from the PV field measured performance ratio (PR). At first, the PR time series is processed by conventional principal component analysis, which yields seasonality as the dominant data feature. The environment, operating conditions, uncertainty, and hardware used for monitoring influence the outdoor measurements unpredictably. These influences are viewed as perturbations that render the dominant feature obtained by PCA unsuitable to be used in a degradation rate definition. Robust principal component analysis (RPCA) is proposed to alleviate these effects. The new measure is defined as the area enclosed by the time series of the corrected by the RPCA annual monthly PR values. The degradation rates obtained for different technologies are compared with those obtained in previous studies. The results have shown that the degradation rates estimated by RPCA were in good agreement with previous investigations and provided increased confidence due to mitigation of uncertainty.
Long-term reliability is critical to the cost effectiveness and commercial success of photovoltaic (PV) products. Today most PV modules are warranted for 25 years, but there is no accepted test protocol to validate a 25-year lifetime. The qualification tests do an excellent job of identifying design, materials, and process flaws that are likely to lead to premature failure (infant mortality), but they are not designed to test for wear-out mechanisms that limit lifetime. This paper presents a method for evaluating the ability of a new PV module technology to survive long-term exposure to specific stresses. The authors propose the use of baseline technologies with proven long-term field performance as controls in the accelerated stress tests. The performance of new-technology modules can then be evaluated versus that of proven-technology modules. If the new-technology demonstrates equivalent or superior performance to the proven one, there is a high likelihood that they will survive versus the tested stress in the real world.
We evaluate and compare several forecasting techniques using no exogenous inputs for predicting the solar power output of a 1 MWp, single-axis tracking, photovoltaic power plant operating in Merced, California. The production data used in this work corresponds to hourly averaged power collected from November 2009 to August 2011. Data prior to January 2011 is used to train the several forecasting models for the 1 and 2 h-ahead hourly averaged power output. The methods studied in this work are: Persistent model, Auto-Regressive Integrated Moving Average (ARIMA), k-Nearest-Neighbors (kNNs), Artificial Neural Networks (ANNs), and ANNs optimized by Genetic Algorithms (GAs/ANN). The accuracy of the models is determined by computing error statistics such as mean absolute error (MAE), mean bias error (MBE), and the coefficient of correlation (R2) for the differences between the forecasted values and the measured values for the period from January to August of 2011. This work also addresses the accuracy of the different methods as a function of the variability of the power output, which depends strongly on seasonal conditions. The findings show that the ANN-based forecasting models perform better than the other forecasting techniques, that substantial improvements can be achieved with a GA optimization of the ANN parameters, and that the accuracy of all models depends strongly on seasonal characteristics of solar variability.
A brief discussion of Statistical Quality Control Charting procedures is first presented with special reference to the relevance of the objectives and assumptions. An approach to the design of discrete feedforward and feedback control schemes, which are of great importance for example, in the chemical industry, is then given. This approach to control employs discrete stochastic and dynamic models discussed in Part I of this paper (Box and Jenkins, 1968) and has a close link with the forecasting problems discussed there. The control algorithms obtained are ideally suited to discrete digital computer control. However, for common simple situations the algorithms may be represented by suitable charts or nomograms which may be employed to obtain improved manual control. The paper ends with a discussion of a problem typical of that arising in the parts manufacturing industry.
For a large scale implementation of photovoltaics (PV) in the urban environment, building integration is a major issue. This includes installations on roof or facade surfaces with orientations that are not ideal for maximum energy production. To evaluate the performance of PV systems in urban settings and compare it with the building user’s electricity consumption, three-dimensional geometry modelling was combined with photovoltaic system simulations. As an example, the modern residential district of Scharnhauser Park (SHP) near Stuttgart/Germany was used to calculate the potential of photovoltaic energy and to evaluate the local own consumption of the energy produced.
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Long-term reliability is critical to the cost effectiveness and commercial success of photovoltaic (PV) products. Today most PV modules are warranted for 25 years, but there is no accepted test protocol to validate a 25-year lifetime. The qualification tests do an excellent job of identifying design, materials, and process flaws that are likely to lead to premature failure (infant mortality), but they are not designed to test for wear-out mechanisms that limit lifetime. This paper presents a method for evaluating the ability of a new PV module technology to survive long-term exposure to specific stresses. The authors propose the use of baseline technologies with proven long-term field performance as controls in the accelerated stress tests. The performance of new-technology modules can then be evaluated versus that of proven-technology modules. If the new-technology demonstrates equivalent or superior performance to the proven one, there is a high likelihood that they will survive versus the tested stress in the real world.