Browsing by Author "Musango, Jones"
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- ItemMeasuring and modelling the influence of weather factors on CSP reflector soiling(Stellenbosch : Stellenbosch University, 2016-12) Musango, Jones; Dinter, Frank; Stellenbosch University. Faculty of Engineering. Dept. of Mechanical and Mechatronic Engineering.ENGLISH ABSTRACT: South Africa is among the countries with a plan to reduce GHG emissions of up to 34% by 2020 through investment in renewable energy in order to reduce its base load coal generation. High solar resource and the potential of concentrated solar power (CSP) to address the challenges facing other solar technologies have attracted CSP investment in the country. CSP uses reflector surfaces or mirrors to focus low solar energy radiation from a large field into a small area of high energy concentration. In their working conditions, CSP reflector surfaces are subjected to harsh outdoor environments which drastically degrade their performance. The main objective of the study was to develop and test reflector optical degradation measurement device and use these optical degradation measurements from the device to relate optical losses caused by soiling to weather. A reflector optical degradation assessment device termed as real-time cleanliness monitoring sensor (RCMS) was designed, developed and tested for its ability to measure soiling optical degradation. The device was then utilised to relate weather conditions with optical losses caused by soiling on CSP reflectors. Furthermore, a neural network model was developed to simultaneously relate various weather factors to the optical loss caused by soiling on CSP reflectors. Error analysis and calibration were undertaken for RCMS measurement in order to improve confidence in the data, which was further used in experimental analysis. Two experimental analyses were carried out. The results from the first experimental analysis showed only the variation of cleanliness with weather factors that directly influence the rate of soiling. Wind speed and humidity were observed to degrade cleanliness, while rain lead to reflector cleaning. In the second experimental analysis, factors that directly or indirectly influence cleanliness were statistically analysed using clustering method. The results showed that temperature and direct normal irradiation (DNI) correlate relatively well with cleanliness although they do not directly influence it. The neural network model demonstrated that a combination of weather factors could be used to estimate the optical degradation caused by soiling on CSP reflectors. High coefficient of determination was observed from the neural network model results, as compared to the correlations that considered the relationship between cleanliness and a single weather factor, done in the experimental analysis.