Abstract-The power system using the clean decentralized renewable horizontal axis river current turbine can be an alternative option in delivering locally and sustainably the energies for the communities inaccessible to the electricity connection. One of the important aspects in the technology design is the selection of the blade number. This aspects can raise several consequences including the performance, the manufacturing cost and the construction constrain. The performance must be on the top of the priority in the technology design as the river velocity is typically low which effortlessly brings the technology uneconomically attractive. This study deals with the investigation of effect of the blade number on the performance of the horizontal axis river turbine for low speed condition. The investigation is conducted by a parametric study with the mathematical model of the Blade Element Momentum. The results indicate that turbines with high blade number deliver a better performance than those with the lower blade number. However, the consequences will be the low rotation for the high bladed turbines thus requiring a high gearing ratio for the mechanical transmission. This study also discusses the reason for the results of the investigation and presents a recommendation for designing of the river turbine for such low speed condition in relation to the decision of the blade number.

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Accurate and precisess estimation of spatio-temporal variability of solar radiation is critical. Some commonly used models evaluate this variability using methods in which the data required for estimating atmospheric attenuation may not be easily accessible for some study areas. Here, a daily solar radiation estimation method which uses ambient air temperature, a Digital Elevation Model, time of year, and monthly radiation estimates from Solar Analyst model has been proposed. The objective was to use air temperature-based empirical models for atmospheric transmissivity and diffuse fractions to vary total monthly radiation estimation from Solar Analyst, and then calculate total daily radiation as a fraction of total monthly radiation by applying a daily transmissivity-based ratio, as air temperature data are readily available at most locations on the planet. Results revealed that daily solar radiation can be estimated very well, with Mean Absolute Bias Error of around 40-53 W m −2 or Mean Bias Error of ± 10%, under all sky conditions at seven sites in diverse climate regions, using significantly less input data. The presented method is an improvement over previously used methods with Mean Bias Error of under 10% but more input parameters. Furthermore, the hourly solar radiation values can be calculated using the presented method using the ratio between daily and hourly radiation, for example from literature values and estimated daily insolation. The result also showed that the method is more useful for those stations with substantially higher numbers of sunny days than cloudy or partly cloudy days because the uncertainty of the model decreased from cloudy to sunny sky conditions. The implemented Digital Elevation Models environment of this method makes it applicable in many studies that need spatial estimation of solar radiation, especially for solar energy generation projects.