Sensitivity analysis of PV produced power in presence of measurement uncertainty | IEEE Conference Publication | IEEE Xplore

Sensitivity analysis of PV produced power in presence of measurement uncertainty


Abstract:

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 transiti...Show More

Abstract:

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.
Date of Conference: 29-31 May 2024
Date Added to IEEE Xplore: 09 July 2024
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ISSN Information:

Conference Location: Firenze, Italy

I. Introduction

Climate change calls for a transition to a more sustainable energy system to support human activities. This process, known as green energy transition [1], poses several challenges, which can be ultimately grouped into two major objectives: first, the increase of the percentage of energy from renewable sources to meet certain goals [2], along with the necessary adaptation of the electricity infrastructure to sustain the passage from centralized to distributed generation; second, the development of new technologies to achieve optimization [3] - [5] and robustness [6] - [9] of production, transmission, distribution, storage and usage of electric energy.

References

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