Construction and analysis of the receiver for a solar thermal cooker system.

M. Sc. University of KwaZulu-Natal, Durban 2013.One of the key components in a solar thermal cooker system is the receiver since the performance of the receiver greatly affects the entire system. Absorption of the maximum amount of reflected radiation is crucial for ensuring the system is operating at high efficiency. A small-scale solar thermal cooker was constructed and tested. The main focus was the design, construction and analysis of a receiver for the solar cooker. The receiver was constructed from mild steel and contains water as the heat transfer fluid. The receiver consists of two half shells fixed to either end of a short cylinder to form an elongated boiling chamber for the water as it is heated by concentrated solar radiation. One of the half shells is exposed to the concentrated solar radiation and is coated with a high-temperature resistant black paint. The size of the receiver was determined by the method of ray-tracing. The maximum temperature the water attained within the receiver during solar heating was 136 C. The highest receiver efficiency was 66%. It was shown that there has been effective heat transfer within the system

Clustering analysis for classification and forecasting of solar irradiance in Durban, South Africa.

Doctor of Philosophy. University of KwaZulu-Natal, Durban. 2017.Classification and forecasting of solar irradiance patterns has become increasingly important for operating and managing grid-connected solar power plants. A powerful approach for classification of irradiance patterns is by clustering of daily profiles, where a profile is defined as irradiance as a function of time. Classification is useful for forecasting because if the class of a day can be successfully forecast, then the irradiance profile of that day will share the general pattern of the class. In Durban, South Africa (29.871 °S; 30.977 °E), beam and diffuse irradiance profiles were recorded over a one-year period and normalized to a clear sky model to reduce the effect of seasonality, from which several variables were derived, namely minute-resolution beam, hourly-resolution beam and diffuse, and hourly-resolution beam variability. To these variables, individually and in combination, k-means clustering was applied, and beam irradiance was found to be the one that best distinguishes between sky conditions. In particular, clustering of hourly-resolution beam irradiance produced four classes with diurnal patterns characterized as sunny all day, cloudy all day, sunny morning-cloudy afternoon, and cloudy morning-sunny afternoon. These classes were then used to forecast beam and diffuse irradiance for the day ahead, in association with cloud cover forecasts from Numerical Weather Prediction (NWP) output. Two forecasting methods were investigated. The first used k-means clustering on predicted daily cloud cover percentage profiles from the NWP, which was a novel aspect of this research. The second used a rule whereby predicted cloud cover profiles were classified according to whether their averages in the morning and afternoon were above or below 50%. From both methods, four classes were obtained that had diurnal patterns associated with the irradiance classes, and these were used to forecast the irradiance class for the day ahead. The two methods had a comparable success rate of about 65%. In addition, hour-ahead forecasts of beam and diffuse irradiance were performed by using the mean profile of the forecast irradiance class to extrapolate from the current measured value to the next hour. The method showed an average improvement of about 22% for beam and diffuse irradiance over persistence forecasts. These results suggest that classification of predicted cloud cover and irradiance profiles are potentially useful for development of class-specific, multi-hour irradiance forecast models

Investigating diffuse irradiance variation under different cloud conditions in Durban, using k-means clustering

Diffuse irradiance is important for the operation of solar-powered devices such as photovoltaics, so it is important to analyse its behaviour under different sky conditions. The primary cause of short-term irradiance variability is clouds. One approach to analyse the diffuse irradiance variation is to use cluster analysis to group together days experiencing similar cloud patterns. A study was carried out to examine the application of k-means clustering to daily cloud data in Durban, South Africa (29.87 °S; 30.98 °E), which revealed four distinct day-time cloud cover (CC) patterns classified as Class I, II, III and IV, corresponding to cloudy, sunny, or a combination of the two. Diffuse irradiance was then correlated with each of the classes to establish corresponding diurnal irradiance patterns and the associated temporal variation. Class I had highest diffuse irradiance variation, followed by Classes III, IV and II. To further investigate the local cloud dynamics, cloud types were also analysed for Classes I−IV. It was found that stratocumulus (low cloud category); altocumulus translucidus, castellanus and altocumulus (middle cloud category); and cirrus fibrates and spissatus (high cloud category), were the most frequently occurring cloud types within the different classes. This study contributes to the understanding of the diurnal diffuse irradiance patterns under the four most frequently occurring CC conditions in Durban. Overall, knowledge of these CC and associated diffuse irradiance patterns is useful for solar plant operators to manage plant output where, depending on the CC condition, the use of back-up devices may be increased or reduced accordingly

Cluster analysis for classification and forecasting of solar irradiance in Durban, South Africa

Clustering of solar irradiance patterns was used in conjunction with cloud cover forecasts from Numerical Weather Predictions for day-ahead forecasting of irradiance. Beam irradiance as a function of time during daylight was recorded over a one-year period in Durban, to which k-means clustering was applied to produce four classes of day with diurnal patterns characterised as sunny all day, cloudy all day, sunny morning-cloudy afternoon, and cloudy morning-sunny afternoon. Two forecasting methods were investigated. The first used k-means clustering on predicted daily cloud cover profiles. The second used a rule whereby predicted cloud cover profiles were classified according to whether their average in the morning and afternoon were above or below 50%. In both methods, four classes were found, which had diurnal patterns associated with the irradiance classes that were used to forecast the irradiance class for the day ahead. The two methods had a comparable success rate of about 65%; the cloud cover clustering method was better for sunny and cloudy days; and the 50% rule was better for mixed cloud conditions