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Results obtained from monitoring an outdoor experimental 8 kW distribution gridtied solar photovoltaic (SPV) system installed at Energy Technology Station known as KwaZulu-Natal Industrial Energy Efficient Training and Resource Centre (IEETRC) of Durban University of Technology, South Africa is presented in this study. The study was carried out to investigate and compare the system performance with similar installations in a few selected countries. Data were collected between January 2018 and December 2018 and computational analysis was completed with the aid of Simulink and MS-Excel. The evaluated monthly average daily and annual performance parameters of the SPV systemarray yield, reference yield, final yield, system efficiency, inverter efficiency, capacity factor, and performance ratiowere discussed in term with the International Electrotechnical Commission (IEC) standard. A comparison of this study to other studies conducted in Dublin, Morocco, India, and Spain shows that this study final yield and performance ratio of 4.93 kWh/kWp/day and 87.1% is greater than what is reported in the extant literature.

The reliability of the power supply depends on the reliability of the structure of the grid. Grid networks are exposed to varying weather events, which makes them prone to faults. There is a growing concern that climate change will lead to increasing numbers and severity of weather events, which will adversely affect grid reliability and electricity supply. Predictive models of electricity reliability have been used which utilize computational intelligence techniques. These techniques have not been adequately explored in forecasting problems related to electricity outages due to weather factors. A model for predicting electricity outages caused by weather events is presented in this study. This uses the back-propagation algorithm as related to the concept of artificial neural networks (ANNs). The performance of the ANN model is evaluated using real-life data sets from Pietermaritzburg, South Africa, and compared with some conventional models. These are the exponential smoothing (ES) and multiple linear regression (MLR) models. The results obtained from the ANN model are found to be satisfactory when compared to those obtained from MLR and ES. The results demonstrate that artificial neural networks are robust and can be used to predict electricity outages with regards to faults caused by severe weather conditions.

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