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Perovskite solar cells (PSCs) are in the forefront of third-generation of photovoltaics and gained a lot of attention as a very promising green technology toward direct solar energy conversion to electricity. PSCs are fabricated following solution-processed techniques at low temperature and they present high power conversion efficiency exceeding 25%, enabling them to be attractive alternative to the silicon-based devices. This research work proposes an efficient and cost-effective photovoltaic (PV) pumping system based on PSCs. For this purpose, lab-scale PSCs were fabricated and their characteristics were determined. In parallel, the geometry of a synchronous reluctance motor (SynRM) driving a 350 m 3 /day water pump was optimized for maximizing the output power, while minimizing the torque ripple simultaneously. In addition, a perovskite solar array feeding the SynRM via an inverter was designed and implemented. The inverter was properly regulated by a control system which optimized the maximum available power of the PSCs solar array and the SynRM characteristics. Finally, laboratory measurements were performed, including a power generator simulating the behavior of the PSCs array feeding the SynRM. The obtained results confirmed the experimental validation of the proposed approach.

To prevent the degradation of perovskite solar cells (PSCs) and optimize the solar energy conversion process, a donor–π–acceptor (D–π–A) organic blue dye as a passivation layer and as a hole‐transporting layer is introduced. The terminal chains of D–π–A dye confer the ultrahydrophobic character (contact angle > 100°) of the interface layer, protecting the perovskite from ambient moisture while mitigating ionic diffusion in the device. The dye interlayer primarily improves the perovskite by reducing grain boundary defects. The perovskite/D–π–A architecture enhances the interfacial hole extraction, suppressing nonradiative carrier recombination and enabling power conversion efficiency (PCE) reaching 20.90%, outperforming by 2.05% the PCE of control cells. Unsealed PSCs retain 84% and 62% of their efficiency after photovoltaic operation for 1000 and 3000 h, respectively. Statistical correlation of bivariant and multivariant analyses of photovoltaic parameters is performed and Pearson's correlation identifies underlying patterns in experimental data collections. Machine learning (ML) of regression algorithms is used to predict the minimum errors and the coefficient of determination, which confirm the analysis quality. The linear regression ML model suggests the importance of photovoltaic parameters (Rs > Vmpp > Jsc > Voc > fill factor > Jmpp > Rsh) toward higher PCE. An efficient online prediction model is also developed to support the estimation of PCEs with high accuracy.

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