Pure and fresh water is being scarce day by day. Although Bangladesh is a riverine country, pure drinking potable water is not a cheap commodity. Most of the people in the cities boil water to drink, and people of the villages take tube-well's water directly, which might have high amount of heavy metal like arsenic. Solar still is a cheap and very useful renewable technology, which can be used in Bangladesh extensively everywhere at the rooftop as a pure water source. Objective of this paper was to find a low cost portable and easily maintainable passive solar still for southern part of Bangladesh. All the parameters of passive solar still are studied, and it is found that an inclined stepped solar still with passive condenser, internal and external reflectors, black cotton wick, and with optimum design values can be the desired still, which would give maximum yield. Finally, the optimum values of the parameters are used to propose a cheap design.

In this study, several regression models were employed to estimate global solar radiation from commonly available meteorological data such as sunshine duration, temperature, precipitation, and cloud cover for 34 meteorological stations of Bangladesh. The models studied were calibrated using five meteorological stations that are providing global solar radiation as well as other meteorological data. Estimated values were also compared with measured values in terms of statistical evaluation indicators like the coefficient of determination (R 2 ), mean percentage error, mean bias error, root mean square error (RMSE), mean absolute relative error, and t statistic. The statistical analysis showed that the models assessed were well suited to accurately estimate the solar potential. Sunshine duration-based models performed best, and cloud cover-based models performed worst. Among 45 developed models to predict solar radiation, the models with RMSE value lower than 0.2 are recommended for use.

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Electrical faults, which can occur at all voltage levels in an electricity supply system, are a health and safety risk. Multi-branch distribution networks represent a significant ongoing challenge for fault detection, with the greatest challenge being high impedance fault (HIF) detection. To date, research has focused on higher voltage levels, and fault monitoring sensors have traditionally only been installed in limited locations within the higher voltage networks. The main contributions of this paper are to characterize a high impedance fault (HIF) involving a tree branch and to experimentally verify the feasibility of giant magneto-resistive (GMR) sensors, located distant from the overhead lines, for fault detection. In a purpose-built 400 V physical simulation test facility, we have collected current and magnetic field data during HIF involving a tree branch. We have identified new characteristics in the early stages of this fault type, which persist for a reasonable length of time but are only observable when suitable signal processing techniques are applied. New detection schemes will, therefore, need to be developed to detect such faults. GMR sensors were found to be suitable for observing the characteristics of HIF, validating their potential use for fault detection.

Generation of realistic scenarios is an important prerequisite for analyzing the reliability of renewable-rich power systems. This paper satisfies this need by presenting an end-to-end model-free approach for creating representative power system scenarios on a seasonal basis. A conditional recurrent generative adversarial network serves as the main engine for scenario generation. Compared to prior scenario generation models that treated the variables independently or focused on short-term forecasting, the proposed implicit generative model effectively captures the cross-correlations that exist between the variables considering long-term planning. The validity of the scenarios generated using the proposed approach is demonstrated through extensive statistical evaluation and investigation of end-application results. It is shown that analysis of abnormal scenarios, which is more critical for power system resource planning, benefits the most from cross-correlated scenario generation.

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