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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.

A collisional-radiative (CR) model that extracts the electron temperature, T e , of hydrogen plasmas from Balmer-line-ratio measurements is examined for the plasma electron density, n e , and T e ranges of 10 10 -10 15 cm -3 and 5-500 eV, respectively. The CR code, developed and implemented in Python, has a forward component that computes the densities of excited states up to n = 15 as functions of T e , n e , and the molecular-to-atomic neutral ratio r(H 2 /H). The backward component provides n e and r(H 2 /H) as functions of the Balmer ratios to predict the T e . The model assumes Maxwellian electrons. The density profiles of the electrons and of the molecular and atomic hydrogen neutrals are shown to be of great importance, as is the accuracy of the line-ratio measurement method.

A femtosecond two-photon-absorption laser-induced-fluorescence (TALIF) diagnostic was designed, installed, and operated on the Princeton-Field-Reversed Configuration-2 device to provide non-invasive measurements of the time and spatially resolved neutral-atom densities in its plasmas. Calibration of the H o density was accomplished by comparison with Kr TALIF. Measurements on plasmas formed of either H 2 or Kr fill gases allowed examination of nominally long and short ionization mean-free-path regimes. With multi-kW plasma heating and H 2 fill gas, a spatially uniform H o density of order 10 17  m -3 was measured with better than ±2 mm and 10 µs resolution. Under similar plasma conditions but with Kr fill gas, a 3-fold decrease in the in-plasma Kr density was observed.