Alexander Phinikarides

ARIMA modeling of the performance of different photovoltaic technologies

In this paper, the performance of different technology photovoltaic (PV) systems was modeled using autoregressive integrated moving average (ARIMA) processes. Measurements from mono-crystalline (mono-c-Si), multi-crystalline (multi-c-Si) and amorphous (a-Si) silicon, cadmium telluride (CdTe) and copper indium gallium diselenide (CIGS) systems were used to construct monthly dc performance ratio (PR) time-series, from outdoor measurements. Each PR time-series was modeled a) with multiplicative ARIMA, b) with linear regression and c) with Seasonal-Trend Decomposition by Loess (STL) using the first 4 years of each time-series in order to compare the accuracy of the different methods. The models were used to forecast the PR of the 5th year of the different PV technologies and the results from the aforementioned statistical methods were compared based on the root-mean-square error (RMSE). The results showed that ARIMA produced the lowest RMSE for crystalline silicon (c-Si) technologies, whereas for thin-film technologies, STL was more accurate. The results from ARIMA also showed that thin-film technologies were optimally modeled with identical model orders, whereas for c-Si, each technology required a different optimal model order.

Comparison of trend extraction methods for calculating performance loss rates of different photovoltaic technologies

In this work, the performance loss rates of eleven grid-connected photovoltaic (PV) systems of different technologies were evaluated by applying linear regression (LR) and trend extraction methods to Performance Ratio, RP, time series. In particular, model-based methods such as Classical Seasonal Decomposition (CSD), Holt-Winters (HW) exponential smoothing and Autoregressive Integrated Moving Average (ARIMA), as well as non-parametric filtering methods such as LOcally wEighted Scatterplot Smoothing (LOESS) were used to extract the trend from monthly RP time series of the first five years of operation of each PV system. The results showed that applying LR on the time series produced the lowest performance loss rates for most systems, but with significant autocorrelations in the residuals, signifying statistical inaccuracy. The application of CSD and HW significantly reduced the residual autocorrelations as the seasonal component was extracted from the time series, resulting in comparable results for eight out of eleven PV systems, with a mean absolute percentage error (MAPE) of 6.22 % between the performance loss rates calculated from each method. Finally, the optimal use of multiplicative ARIMA resulted in Gaussian white noise (GWN) residuals and the most accurate statistical model of the RP time series. ARIMA produced higher performance loss rates than LR for all technologies, except the amorphous Silicon (a-Si) system. The LOESS non-parametric method produced directly comparable results to multiplicative ARIMA, with a MAPE of -2.04 % between the performance loss rates calculated from each method, whereas LR, CSD and HW showed higher deviation from ARIMA, with MAPE of 25.14 %, -13.71 % and -6.39 %, respectively.

Estimation of annual performance loss rates of grid-connected photovoltaic systems using time series analysis and validation through indoor testing at standard test conditions

This paper presents the results of an extensive testing campaign for validating the time series analysis approach to the estimation of the linear field performance loss rates (PLR) of grid-connected photovoltaic (PV) systems of different technologies operating side-by-side at the PV Technology test site of the University of Cyprus since June 2006. Fifteen-minute average measurements of the array power at the maximum power point, PA, were used to construct time series of the performance ratio, RP, of each array. The time series were analysed with regARIMA and classical seasonal decomposition (CSD) in order to extract the trend. Then, linear regression (LR) was used to calculate the slope. To validate the results, all arrays were disassembled and every module was tested at Standard Test Conditions (STC) in a class A+A+A+ solar simulator, in order to calculate the nominal array degradation rate. Comparison of both methods has shown good agreement between the time series analysis approach and the indoor testing approach, for PV arrays with no identified failures through electroluminescence (EL). On the contrary, for modules with identified failures through EL, the nominal array degradation rate was higher in comparison to the field PLR. Differences between the two methods have been shown to be due to cracked cells, hotspots and spectral response mismatch. Lastly, the comparison has shown that amongst the time series analysis methods, regARIMA produced statistically significant PLR with low uncertainty and the best agreement with the nominal array degradation rates.

Field performance evaluation and modelling of spectrally tuned quantum-well solar cells

Monolithic multi-junction solar cells are becoming prevalent in concentrator photovoltaic (CPV) systems due to their high demonstrated conversion efficiencies. However, these devices often operate at sub-optimal levels due to current mismatch losses arising between each p-n junction as a result of natural variation in the solar spectrum at the earths surface. The use of quantum wells in solar cell design affords improved control over the spectral response of the cells via band-gap engineering. This makes it possible to tailor the spectral response of a cell for optimum performance under a given annual spectral resource. Establishing the optimum spectral response for a given location is a major challenge of this approach to cell design, and relies heavily on averaged and modelled data based on combinations of satellite and sparse ground measurements. In addition, there is only limited field experience of such technology to date. The purpose of this work is to investigate through experiment the effect of incorporating multi-quantum-well (MQW) structures into photovoltaic cells to respond to a specific range of annual irradiation spectra and to compare the results obtained with those predicted through modelling. A number of triple-junction cells of different design have been placed side-by-side on an accurate solar tracker, and current-voltage characteristics of each taken at regular intervals over a few months. The outputs are then compared to those predicted by a model of cell performance that includes simulated spectra, generated with the SMARTS program, specific to that location and period of time. Results obtained from outdoor testing indicate that the cells designed to provide improved current matching under the spectral conditions in which they have been tested have performed more consistently than conventional cell designs. This is evident when the short-circuit current is normalised to a fixed direct normal irradiation and temperature, and plotted against atmospheric depth. Detailed analysis of the spectral resource is expected to reveal additional information on the performance of the cells.

Definition and Computation of the Degradation Rates of Photovoltaic Systems of Different Technologies With Robust Principal Component Analysis

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.

Performance Loss Rates of Different Photovoltaic Technologies After Eight Years of Operation under Warm Climate Conditions

In this paper, the performance loss rates of different technology crystalline silicon (c-Si) and thin-film photovoltaic (PV) systems were estimated and compared over their first eight years of operation at the test site of the Photovoltaic Technology Laboratory, University of Cyprus (UCY) in Nicosia, Cyprus, by applying different statistical trend analysis methods on monthly Performance Ratio, RP, time series. The statistical trend analysis methods include Linear Regression (LR), Classical Seasonal Decomposition (CSD), Holt-Winters exponential smoothing (HW) and LOcally wEighted Scatterplot Smoothing (LOESS) and were applied on monthly constructed time series of RP, calculated from the fifteen-minute average array DC power at the maximum power point, PA, of each grid-connected PV system. The comparison of the estimated performance loss rates for each technology showed that the average performance loss rate of the c-Si systems was 0.75 ± 0.17 %/year. On the other hand, the average performance loss rate for the thin-film systems was 1.95 ± 0.11 %/year for all methods, with a 95 % confidence interval. The good agreement in the results between the different methods for each system also provided evidence that the performance loss rates have started to converge to a steady value. Finally, it was demonstrated that trend extraction techniques produced similar estimates between them and with very low uncertainty, even with less than five years of outdoor exposure, whereas LR was the least robust method for all technologies, since it was greatly affected by the seasonality and outliers of the time series and needed more years of data to produce reliable estimates.

Photovoltaic model uncertainties based on field measurements

The purpose of this paper is to compare the combined uncertainties inherent in three PV models the single-point efficiency, the single-point efficiency with temperature correction and the PVUSA. This evaluation was performed using outdoor measurement data from 12 different, 1 kWp, grid-connected PV systems, operating in Cyprus since June 2006, along with measurement data from the meteorological sensors on-site. The different models were associated with different uncertainties which affected the accuracy of the annual energy yield prediction of the different PV technologies. For the single-point efficiency model, the combined uncertainties on the annual dc energy yield of all PV systems were in the range ±7%, resulting from the name-plate efficiency at standard test conditions (ηSTC) and the global irradiation measured on the plane of array (GPOA). By applying temperature correction on the model, the combined uncertainties dropped to an average of ±5.68% over the evaluation period. Lastly, the combined uncertainties for the PVUSA model were even lower, on average ±1.59%.

Development of a novel web application for automatic photovoltaic system performance analysis and fault identification

This paper details the development of the pvpaR (PV Performance Analysis in R) web application built on open-source technologies for the automated and user-friendly evaluation of photovoltaic (PV) system performance and identification and classification of faults. pvpaRs ecosystem is based on the R statistical computing project, both for the back-end as well as for the front-end which uses the Shiny web application framework. Currently, the core of the application incorporates models for synthesizing time-series of irradiance, module temperature, PV system voltage, current, DC and AC power. These are used to validate the imported field measurements and create the comparison which the fault identification function is dependent upon. The web application can currently import measurements from flat files and databases. It has been released under the GNU Affero GPL license to encourage contributions, with the goal to become a useful tool for the PV community and PV system owners.

Characterisation and mapping of daily sky conditions based on ground measurements of solar irradiance in mainland USA

The power produced from solar systems depends strongly on the prevailing weather conditions and more precisely on solar irradiance. Due to the dependence of such systems on the weather conditions the electricity injected into the grid is intermittent in nature with potentially negative impact on the grid operation. The quality and quantity of the electrical power produced from solar systems is directly related to the available solar irradiance and therefore any disturbances to the latter affect the produced power. In this paper, the characterisation and classification of the daily solar irradiance from 12 locations in mainland USA is presented based on 4 years of ground measurements. The K-POP method used evaluates the quantity and quality of solar irradiance for a day and as a consequence classifies the day based on these two indices. The statistical analysis of the results shows high correlation through the years for each station. This is a strong indication that the sky conditions are predictable and that with the utilisation of mechanisms, such as storage, spinning reserve, etc. solar systems can become a major contributor to the energy mix. Finally, this analysis provides better insight into the solar resource of a given location.