The energy conversion chain in a photovoltaic water pumping system was modelled.  Water collection by the inhabitants was included as a model input.  The model was validated by using data acquired on a system in rural Africa.  The accuracy of the model is higher than 95 % when using local climatic data.  Accuracy drops of 1 to 3 % when replacing local climatic data by satellite ones.

Recently we have developed a model of photovoltaic water pumping systems (PVWPS) for domestic water access in poor rural areas. In this article, we perform a sensitivity analysis over the 14 parameters of this model. We study how the variation of the parameters value influences the model output and the optimal sizing obtained from the model, for both the dry and the wet season. Results indicate that the peak power of the photovoltaic modules, the efficiency of the motor-pump and the tank volume have the highest impact on the model output. Besides, the parameters which significantly influence the optimal sizing are the position of the water entry in the tank, the position of the stop level of the float switch, the distance between the stop and restart levels of the float switch, the height between the floor and the bottom of the tank, and the static water level in the borehole. Finally, the thermal parameters of the PV modules and the hydraulic losses have a small impact on the model output and on the optimal sizing. This study can be useful to companies, governments and non-governmental organizations which install PVWPS for domestic water access. It can help them to determine the accuracy at which a given parameter has to be known to correctly model or size these systems. It can also allow them to evaluate the robustness of PVWPS sizing to parameters variation with time. Finally, it may guide the choice of components made by PVWPS installers.

Photovoltaic water pumping could significantly improve water access, particularly in off-grid rural villages of Sub-Saharan Africa. Earlier, we have developed and validated a numerical model of a photovoltaic water pumping system (PVWPS). Such model allows to include the water consumption profile as an input. The current study assesses the influence of the temporal resolution of the water consumption profile on the model accuracy and on PVWPS optimal sizing. This helps to select the adapted temporal resolution for data acquisition, modelling and optimization in order to keep data storage and computational time low without significantly changing the accuracy of the model and the sizing obtained from the optimization. Our study shows that the temporal resolution has a strong impact on modelling and optimal system sizing.

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Analyzing eigenfrequencies by acoustic resonance testing enables a fast screening of components regarding structural defects. The eigenfrequencies of each specific part depend on the general geometric and material properties, including tolerable part-to-part variations, as well as on possible structural flaws. Separating good parts from defective ones is not straightforward and each application-specific sorting algorithm is usually found from experimental training data. However, there are limitations and training data collection may be intricate. We worked on this challenge focusing on machine-made model parts varying slightly in geometry. The application objective was the eigenfrequency-based detection of parts featuring a through-hole test defect drilled into some of the parts and enlarged stepwise. The eigenfrequencies were measured concomitantly. Unlike the industry standard, our approach is based on synthetic training data created mainly by simulation techniques, which resulted in a principally satisfactory classification of the good and defective parts. However, the parts with small defects were not identified from the eigenfrequencies alone, due to overlaying geometric variations. In order to counteract such noise and to improve defect detection based on synthetic training data, the specific actual part geometry was used, in the sense of additional a priori information. A multimodal data evaluation model showed a clearly enhanced sorting power.