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Spatiotemporal Performance Analysis to Identify Degradation Trends in a Utility-Scale PV Plant
Thesis (MEng)--Stellenbosch University, 2026.
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| Format: | Thesis |
| Language: | English |
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Stellenbosch : Stellenbosch University
2026
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| _version_ | 1867614136853069824 |
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| access_status_str | Open Access |
| author | Bekker, Neil Francois |
| author2 | Rix, A. J. |
| author_browse | Bekker, Neil Francois Rix, A. J. |
| author_facet | Rix, A. J. Bekker, Neil Francois |
| author_sort | Bekker, Neil Francois |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MEng)--Stellenbosch University, 2026. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/135612 |
| institution | Stellenbosch University (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:47:15.075Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/135612 Spatiotemporal Performance Analysis to Identify Degradation Trends in a Utility-Scale PV Plant Bekker, Neil Francois Rix, A. J. Stellenbosch University. Faculty of Engineering. Dept. of Electrical & Electronic Engineering. Thesis (MEng)--Stellenbosch University, 2026. Bekker, N. F. 2026. Spatiotemporal Performance Analysis to Identify Degradation Trends in a Utility-Scale PV Plant. Unpublished masters thesis. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/fbb32528-2ada-4102-9a3e-85bc385f0635 The rise in energy and electricity demand, along with the rapid expansion of renewable energy sources, has increased the use of photovoltaic (PV) power plants for power generation. This growth of PV systems necessitates an understanding of their long-term performance, especially under local conditions. The work presented in this project aims to investigate the performance and degradation trends of a 75 MWp PV plant as well as the environmental factors that affect this plant, using measured meteorological and operational data, over the course of nine years. The data is first processed, reducing its size by 79.8% to an accurate, workable dataset containing over 600 million individual data entries. The cleaning process involves manual cleaning, utilising three anomaly detection algorithms, and the implementation of the sliding standard deviation mutation (SSDM) method. Environmental factors within the meteorological dataset, such as ambient temperature, irradiance, humidity and wind, were analysed to determine yearly trends, as well as their effect on energy yield and the weather-corrected performance ratio (WCPR) of the PV plant. This analysis indicated that no single factor dominated performance loss, but rather it was caused by the combination of wind-induced soiling and thermal stress. Following this, a visualisation framework was implemented to analyse the interannual spatial and temporal performance trends of the PV plant at string-pair and inverter levels. This analysis highlighted the regional and zonal patterns in varying performance. The southeastern region is showing the highest rate of performance loss and the southwestern region the lowest. These variations were linked to environmental exposure, module batching differences, and local inverter irregularities. The PV plant experiences a loss of between 1%and 6% in string-pair performance over the nine years. Lastly, the degradation rate of each string-pair was calculated and compared to the manufacturer’s warranty specifications. The comparison showed that less than 0.4% of all string-pairs exceeded the expected degradation rate in any given year, indicating that the plant is performing well within acceptable limits. However, clusters of underperforming string-pairs were identified in the southeastern region, suggesting localised and progressive degradation that warrants maintenance attention. Overall, the findings demonstrate that the PV plant remains in a healthy operating state, while highlighting the value of spatial performance analysis for detecting emerging problem areas. Masters 2026-04-02T09:14:29Z 2026-04-02T09:14:29Z 2026-03 Thesis https://scholar.sun.ac.za/handle/10019.1/135612 en Stellenbosch University 179 pages : ill. application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Bekker, Neil Francois Spatiotemporal Performance Analysis to Identify Degradation Trends in a Utility-Scale PV Plant |
| title | Spatiotemporal Performance Analysis to Identify Degradation Trends in a Utility-Scale PV Plant |
| title_full | Spatiotemporal Performance Analysis to Identify Degradation Trends in a Utility-Scale PV Plant |
| title_fullStr | Spatiotemporal Performance Analysis to Identify Degradation Trends in a Utility-Scale PV Plant |
| title_full_unstemmed | Spatiotemporal Performance Analysis to Identify Degradation Trends in a Utility-Scale PV Plant |
| title_short | Spatiotemporal Performance Analysis to Identify Degradation Trends in a Utility-Scale PV Plant |
| title_sort | spatiotemporal performance analysis to identify degradation trends in a utility scale pv plant |
| url | https://scholar.sun.ac.za/handle/10019.1/135612 |
| work_keys_str_mv | AT bekkerneilfrancois spatiotemporalperformanceanalysistoidentifydegradationtrendsinautilityscalepvplant |