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Electronic system for the control of the power production of photovoltaic arrays under partial shading

Lioudakis Emmanouil

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Summary

Photovoltaic (PV) systems constitute a significant percentage of the worldwide installed electrical power plants. They exploit a renewable energy source and they do not require frequent maintenance, since they do not contain mechanical parts. These, combined with the fact that their components get cheaper over time, explain the increasing penetration of PV power plants in modern electricity grids. To avoid frequency disturbance and to improve the grid’s power quality, a way of controlling the output power of PV systems is the Flexible Power Point Tracking (FPPT), where a reference value is configured for the PV array’s output power. Since PV modules are usually shaded, conventional FPPT algorithms cannot operate efficiently, thus the concept of the Global FPPT (GFPPT) algorithm was introduced. In this thesis, two novel GFPPT algorithms based on machine learning are proposed. To evaluate their effectiveness, an experimental system was developed. One GFPPT algorithm from the literature and the two new ones were tested experimentally on varying operating conditions, as it could happen in real world applications. The experimental results demonstrated that the two new algorithms developed within the framework of this thesis converged in significantly less time, while they achieved an almost same steady-state tracking error.

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