In this work, data quality control and mitigation tools have been developed for improving the accuracy of photovoltaic (PV) system performance assessment. These tools allow to demonstrate the impact of ignoring erroneous or lost data on performance evaluation and fault detection. The work mainly focuses on residential PV systems where monitoring is limited to recording total generation and the lack of meteorological data makes quality control in that area truly challenging. Main quality issues addressed in this work are with regards to wrong system description and missing electrical and/or meteorological data in monitoring. An automatic detection of wrong input information such as system nominal capacity and azimuth is developed, based on statistical distributions of annual figures of PV system performance ratio (PR) and final yield. This approach is specifically useful in carrying out PV fleet analyses where only monthly or annual energy outputs are available. The evaluation is carried out based on synthetic weather data which is obtained by interpolating from a network of about 80 meteorological monitoring stations operated by the UK Meteorological Office. The procedures are used on a large PV domestic dataset, obtained by a social housing organisation, where a significant number of cases with wrong input information are found. Data interruption is identified as another challenge in PV monitoring data, although the effect of this is particularly under-researched in the area of PV. Disregarding missing energy generation data leads to falsely estimated performance figures, which consequently may lead to false alarms on performance and/or the lack of necessary requirements for the financial revenue of a domestic system through the feed-in-tariff scheme. In this work, the effect of missing data is mitigated by applying novel data inference methods based on empirical and artificial neural network approaches, training algorithms and remotely inferred weather data. Various cases of data loss are considered and case studies from the CREST monitoring system and the domestic dataset are used as test cases. When using back-filled energy output, monthly PR estimation yields more accurate results than when including prolonged data gaps in the analysis. Finally, to further discriminate more obscure data from system faults when higher temporal resolution data is available, a remote modelling and failure detection framework is ii developed based on a physical electrical model, remote input weather data and system description extracted from PV module and inverter manufacturer datasheets. The failure detection is based on the analysis of daily profiles and long-term PR comparison of neighbouring PV systems. By employing this tool on various case studies it is seen that undetected wrong data may severely obscure fault detection, affecting PV system s lifetime. Based on the results and conclusions of this work on the employed residential dataset, essential data requirements for domestic PV monitoring are introduced as a potential contribution to existing lessons learnt in PV monitoring.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:727699 |
| Date | January 2017 |
| Creators | Koubli, Eleni |
| Publisher | Loughborough University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://dspace.lboro.ac.uk/2134/27508 |
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