Sensitivity analysis of PV produced power in presence of measurement uncertainty

Photovoltaics represents one of the key sources of clean energy to help reduce the carbon footprint and fight climate change, enabling the so-called green energy transition. To maximize photovoltaic production in any irradiation and temperature conditions, Maximum Power Point Tracking techniques must be implemented to determine and set the working point at which the photovoltaic panel delivers the maximum power. Such techniques usually exploit real-time measurements of voltage and current on the photovoltaic cell, and possibly of the operating temperature. This paper proposes an assessment of the effects of measurement uncertainty on the maximum power point calculation. We compare the sensitivity to measurement noise of different tracking algorithms, including perturb and observe, incremental conductance and feedforward neural networks. The results show that neural networks become the most attractive solution when measurement uncertainty is introduced in the system.