Artificial neural network-polar coordinated fuzzy controller based maximum power point tracking control under partially shaded conditions

TLDR

Partially shaded conditions reduce photovoltaic energy yield because conventional MPPT algorithms fail under non‑uniform insolation, producing multiple local maximum‑power points that cannot be distinguished from the global maximum, which shifts with shading patterns. This study proposes an ANN‑based fuzzy polar controller to track the global maximum‑power point and estimate maximum power and energy generation for partially shaded PV arrays. The three‑layer feed‑forward ANN is trained once on several shading scenarios to predict the global MPP voltage, which the fuzzy polar controller then uses as a reference to generate the power‑converter control signal, while the same ANN also estimates the system’s maximum power and energy. Experimental real‑time simulation on a dSPACE interface demonstrates the method’s effectiveness across series‑parallel, bridge‑link, and total‑cross‑tied PV array configurations.

Abstract

The one of main causes of reducing energy yield of photovoltaic systems is partially shaded conditions. Although the conventional maximum power point tracking (MPPT) control algorithms operate well under uniform insolation, they do not operate well in non-uniform insolation. The non-uniform conditions cause multiple local maximum power points on the power–voltage curve. The conventional MPPT methods cannot distinguish between the global and local peaks. Since the global maximum power point (MPP) may change within a large voltage window and also its position depends on shading patterns, it is very difficult to recognise the global operating point under partially shaded conditions. In this paper, a novel MPPT system is proposed for partially shaded PV array using artificial neural network (ANN) and fuzzy logic with polar information controller. The ANN with three layer feed-forward is trained once for several partially shaded conditions to determine the global MPP voltage. The fuzzy logic with polar information controller uses the global MPP voltage as a reference voltage to generate the required control signal for the power converter. Another objective of this study is to determine the estimated maximum power and energy generation of PV system through the same ANN structure. The effectiveness of the proposed method is demonstrated under the experimental real-time simulation technique based dSPACE real-time interface system for different interconnected PV arrays such as series-parallel, bridge link and total cross tied configurations.