Optimization of a Photovoltaic Solar Water Pumping System Under Partial Shading Using Intelligent Algorithms: Applied to the Ecuadorian Coast Context

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Abstract
Photovoltaic solar water pumping systems (PVSWPS) offer a sustainable solution for water access in remote rural areas, but their performance depends on tracking the maximum power point (MPP) of the photovoltaic (PV) array, which partial shading complicates by creating multiple local maxima in the power–voltage curve. This study presents the design and simulation in MATLAB/Simulink of a complete PVSWPS (PV array, Ćuk DC–DC converter, permanent magnet DC motor–centrifugal pump and hydraulic network) under partial shading scenarios representative of the Ecuadorian coast. Three MPPT strategies were compared: incremental conductance (IC-MPPT), particle swarm optimization exploiting empirically identified voltage clusters (PSO-MPPT), and an artificial neural network trained on the irradiance-to-MPP voltage relationship (ANN-MPPT). IC-MPPT converged to a local maximum in three of five scenarios, with a mean tracking error of 20.45%, whereas PSO-MPPT and ANN-MPPT reached 0.65% and 1.14%, increasing the pumped volume by approximately 6.5% and 7.0% in five stepped scenarios and by 3.9% and 4.6% in a 60 s continuous irradiance profile. This came with a lower converter efficiency (84.25% for IC-MPPT vs. 80.11% and 79.52%), a trade-off between tracking accuracy and efficiency. The net gain in delivered water supports intelligent MPPT for PV water pumping under frequent partial shading.
Funding Information
  • Escuela Superior Politécnica Agropecuaria de Manabí Manuel Félix López
  • INIAC research group of ESPAM MFL and the ISII research group of the Universidad Técnica de Manabí

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