Enhanced Proportional Resonant – Second-Order General Integrators (EPR-SOGI) With Fuzzy Logic Control in Hybrid Renewable Energy Source-Based STATCOM

Kalaiselvi, P.; Chenthur Pandian, S.; Anand, M.

doi:10.1080/15325008.2024.2332396

Cited by 1 publication

(1 citation statement)

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“…The design of adaptive switch module architecture took into account the battery packs of different sizes and how to protect the battery packs from over-discharge by controlling the relationship between load and available renewable power generation. Compared to conventional methods, Kalaiselvi et al [32] enhance power quality control in hybrid renewable energy systems with their development of an enhanced proportional resonant second-order general integrators (EPR-SOGI) method combined with fuzzy logic control. In addition, Mbey et al [33] address the challenges posed by the intermittent nature of solar photovoltaic (PV) generation and fluctuating electrical demands in smart grids by developing a novel hybrid deep learning model.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Fuzzy Control and Neural Network Control in the Commercial Development of Sustainable Energy System

Xie,

Guan,

Peng

et al. 2024

Sustainability

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Sustainable energy systems (SESs) occupy a prominent position in the modern global energy landscape. The purpose of this study is to explore the application of fuzzy control and neural network control in photovoltaic systems to improve the power generation efficiency and stability of the system. By establishing the mathematical model of a photovoltaic system, the nonlinear and uncertain characteristics of photovoltaic system are considered. Fuzzy control and neural network control are used to control the system, and their performance is verified by experiments. The experimental results show that under the conditions of low light and moderate temperature, the fuzzy neural network control achieves a 3.33% improvement in power generation efficiency compared with the single control strategy. Meanwhile, the system can still maintain relatively stable operation under different environmental conditions under this comprehensive control. This shows that fuzzy neural network control has significant advantages in improving power generation efficiency and provides beneficial technical support and guidance for the commercial development of SESs.

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Section: Literature Reviewmentioning

confidence: 99%