Application of the smoothing methods to the PV power output data for the evaluation of the performance photovoltaic system
Fragment książki (Rozdział w monografii)
MNiSW
50
Poziom II
| Status: | |
| Autorzy: | Gułkowski Sławomir |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Strony: | 37 - 43 |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | NIE |
| Abstrakty: | angielski |
| The analysis of the output data generated by large-scale photovoltaic power plant is of great importance not only from the scientific but also from the investment perspective, due to the possibility of system efficiency evaluation depending on many parameters related to the weather conditions, devices (PV modules, inverters) degradation or module soiling. Moreover, long-term system performance monitoring helps to detect system failures, and thus to evaluate the quality of the installation by system and array losses computations. The PV system performance evaluation requires the analysis of large sets of output data which is a complex and challenging task due to their dynamic behavior caused by a number of factors (irradiance, operating temperature, wind, partial shading by moving clouds). The purpose of this paper is the effectiveness analysis of the smoothing algorithms applied to the daily PV power output data collected during partly cloudy days. Such data is characterized by the highest power fluctuations. Three methods have been studied: locally weighted non-parametric regression (LOESS), moving average and Savitzky-Golay filtering. On the basis of the modified data of power production, the daily energy yields were computed and compared with the experimental results to find the smoothing technique of the highest accuracy. Relative mean error (%RE) was calculated. The results show an excellent agreement of the smoothed data with experimental measurements in terms of LOESS and Savitzky-Golay filtering. |
