2022
DOI: 10.1109/tia.2021.3131923
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A Multifunctional Three-Phase Grid Coupled Solar PV Energy Conversion System Using Delayed µ-Law Proportionate Control for PQ Improvement

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Cited by 18 publications
(6 citation statements)
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“…Applications include industrial power systems, renewable energy integration, and smart grids. The synergy of fuzzy logic and PID control in D-STATCOM systems ensures robust and reliable power quality improvement, making it a promising solution for modern electrical infrastructure challenges [18]. FLC is a very basic controller.…”
Section: Proposed Control Approachmentioning
confidence: 99%
“…Applications include industrial power systems, renewable energy integration, and smart grids. The synergy of fuzzy logic and PID control in D-STATCOM systems ensures robust and reliable power quality improvement, making it a promising solution for modern electrical infrastructure challenges [18]. FLC is a very basic controller.…”
Section: Proposed Control Approachmentioning
confidence: 99%
“…Leading the charge for green energy investment, solar photovoltaic (SPV)-integrated DSTATCOM topologies have gained prominence in recent times, contrasting with selfsupporting DSTATCOM topologies [4] [6]- [15]. The latter, documented in the literature, involves withdrawing active power from the grid to maintain the dc-link capacitor voltage.…”
Section: ░ 1 Introductionmentioning
confidence: 99%
“…In response to these challenges, researchers have displayed significant interest in the single-stage Solar Photovoltaic (SPV)integrated Distribution Static Compensator (DSTATCOM) [10]- [15]. In [10], a flexible control approach is introduced, extracting the reference grid current through a reweighted zeroattracting control technique.…”
Section: ░ 1 Introductionmentioning
confidence: 99%
“…For the purpose of producing grid reference currents, the fundamental weight components of load current are calculated using adaptive filtering algorithms, with the aim of minimizing error between the actual and estimated load currents. Some of the control schemes employed with adaptive filtering algorithms are least mean square (LMS), 15 improved versions of LMS, 16 least mean fourth (LMF), 17 normalized LMS (NLMS), 18 decorrelation NLMS, 19 leaky LMS, 20 delayed μ‐law proportionate NLMS, 21 and leaky least logarithmic absolute difference (LLLAD) 22 . In general, an appropriate cost function adapted to specific noise environments is required in developing an adaptive filtering algorithm.…”
Section: Introductionmentioning
confidence: 99%