Automated Solar PV Simulation System Supported by DC–DC Power Converters

Cordeiro, Armando; Chaves, Miguel; Gâmboa, Paulo; Barata, Filipe; Fonte, P. M.; Lopes, Helio; Pires, V. Fernão; Foito, Daniel; Amaral, Tito G.; Martins, João

doi:10.3390/designs7020036

Cited by 5 publications

(1 citation statement)

References 38 publications

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“…Given the relatively low efficiency of a photovoltaic system, MPPT is an essential step as it helps the system achieve the best overall performance [15]. This can be achieved through a designed control technique that can be incorporated into a DC-DC converter [16,17]. However, PV cells, as well as PV modules, strings, and arrays, are characterized by a nonlinear power-voltage (P-V) curve that depends on incident irradiance [18], cell temperature [19] and, when present, partial shading [20].…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization-Based Control for Maximum Power Point Tracking Implemented in a Real Time Photovoltaic System

del Rio,

Barambones,

Uralde

et al. 2023

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Photovoltaic panels present an economical and environmentally friendly renewable energy solution, with advantages such as emission-free operation, low maintenance, and noiseless performance. However, their nonlinear power-voltage curves necessitate efficient operation at the Maximum Power Point (MPP). Various techniques, including Hill Climb algorithms, are commonly employed in the industry due to their simplicity and ease of implementation. Nonetheless, intelligent approaches like Particle Swarm Optimization (PSO) offer enhanced accuracy in tracking efficiency with reduced oscillations. The PSO algorithm, inspired by collective intelligence and animal swarm behavior, stands out as a promising solution due to its efficiency and ease of integration, relying only on standard current and voltage sensors commonly found in these systems, not like most intelligent techniques, which require additional modeling or sensoring, significantly increasing the cost of the installation. The primary contribution of this study lies in the implementation and validation of an advanced control system based on the PSO algorithm for real-time Maximum Power Point Tracking (MPPT) in a commercial photovoltaic system to assess its viability by testing it against the industry-standard controller, Perturbation and Observation (P&O), to highlight its advantages and limitations. Through rigorous experiments and comparisons with other methods, the proposed PSO-based control system’s performance and feasibility have been thoroughly evaluated. A sensitivity analysis of the algorithm’s search dynamics parameters has been conducted to identify the most effective combination for optimal real-time tracking. Notably, experimental comparisons with the P&O algorithm have revealed the PSO algorithm’s remarkable ability to significantly reduce settling time up to threefold under similar conditions, resulting in a substantial decrease in energy losses during transient states from 31.96% with P&O to 9.72% with PSO.

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Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization-Based Control for Maximum Power Point Tracking Implemented in a Real Time Photovoltaic System

del Rio,

Barambones,

Uralde

et al. 2023

Information

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Harnessing ambient sound: Different approaches to acoustic energy harvesting using triboelectric nanogenerators

Jean,

Khan,

Alazzam

et al. 2024

Journal of Science: Advanced Materials and Devices

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Advancement in piezoelectric nanogenerators for acoustic energy harvesting

Jean,

Khan,

Alazzam

et al. 2024

Microsyst Nanoeng

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The demand for sustainable energy sources to power small electronics like IoT devices has led to exploring innovative solutions like acoustic energy harvesting using piezoelectric nanogenerators (PENGs). Acoustic energy harvesting leverages ambient noise, converting it into electrical energy through the piezoelectric effect, where certain materials generate an electric charge in response to mechanical stress or vibrations. This review paper provides a comprehensive analysis of the advancements in PENG technology, emphasizing their role in acoustic energy harvesting. We begin by discussing the essential principles of piezoelectricity and the design considerations for nanogenerators to optimize energy capture from sound waves. The discussion includes a detailed examination of various piezoelectric materials, such as polyvinylidene fluoride (PVDF), lead zirconate titanate (PZT), and zinc oxide (ZnO) nanowires, which are known for their superior piezoelectric properties. A critical aspect of this review is the exploration of innovative structural designs and resonance devices that enhance the efficiency of PENGs. We delve into the mechanisms and benefits of using Helmholtz resonators, quarter-wavelength tubes, and cantilever beams, which are instrumental in amplifying acoustic signals and improving energy conversion rates. Each device’s design parameters and operational principles are scrutinized to highlight their contributions to the field. The review addresses practical applications of PENGs in various domains. Environmental monitoring systems, wearable electronics, and medical devices stand to benefit significantly from the continuous and sustainable power supplied by PENGs. These applications can reduce reliance on batteries and minimize maintenance by harnessing ambient acoustic energy, leading to more efficient and longer-lasting operations. Despite the promising potential of PENGs, several challenges remain, including material degradation, efficiency limitations, and integrating these devices into existing technological frameworks. This paper discusses these obstacles in detail and proposes potential solutions to enhance the longevity and performance of PENG systems. Innovations in material science and engineering are crucial to overcoming these hurdles and realizing the full potential of acoustic energy harvesting.

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Automated Solar PV Simulation System Supported by DC–DC Power Converters

Cited by 5 publications

References 38 publications

Particle Swarm Optimization-Based Control for Maximum Power Point Tracking Implemented in a Real Time Photovoltaic System

Particle Swarm Optimization-Based Control for Maximum Power Point Tracking Implemented in a Real Time Photovoltaic System

Harnessing ambient sound: Different approaches to acoustic energy harvesting using triboelectric nanogenerators

Advancement in piezoelectric nanogenerators for acoustic energy harvesting

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