Recently, embedded systems and wireless sensor nodes have been gaining importance. For operating these devices several vibration-based energy harvesters have been successfully developed and reported, such as piezoelectric, electromagnetic, and electrostatic energy harvesters (EEHs). This paper presents the state-of-the-art in the field of vibration-based EEHs. Mainly, two types of EEHs, electret-free and electret-based, are reported in the literature. The developed EEHs are mostly of the centimeter scale. These energy harvesters, with resonant frequencies ranging from 2 Hz to 1.7 kHz, when subjected to excitation on the order of 0.25 g to 14.2 g, generate power that ranges from 0.46 nW to 2.1 mW.

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The most cost-effective electrical energy is produced by photovoltaic (PV) systems, and with the smallest carbon footprint, making it a sustainable renewable energy. They provide an excellent alternative to the existing fossil fuel-based energy systems, while providing 4% of global electricity demand. PV system efficiency is significantly reduced by the intrinsic non-linear model, maximum power point (MPP), and partial shading (PS) effects. These two problems cause major power loss. To devise the maximum power point tracking (MPPT) control of the PV system, a novel group teaching optimization algorithm (GTOA) based controller is presented, which effectively deals with the PS and complex partial shading (CPS) conditions. Four case studies were employed that included fast-changing irradiance, PS, and CPS to test the robustness of the proposed MPPT technique. The performance of the GTOA was compared with the latest bio-inspired techniques, i.e., dragon fly optimization (DFO), cuckoo search (CS), particle swarm optimization (PSO), particle swarm optimization gravitational search (PSOGS), and conventional perturb and observe (P&O). The GTOA tracked global MPP with the highest 99.9% efficiency, while maintaining the magnitude of the oscillation <0.5 W at global maxima (GM). Moreover, 13–35% faster tracking times, and 54% settling times were achieved, compared to existing techniques. Statistical analysis was carried out to validate the robustness and effectiveness of the GTOA. Comprehensive analytical and statistical analysis solidified the superior performance of the proposed GTOA based MPPT technique.

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