2019
DOI: 10.17775/cseejpes.2017.01000
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A combined reinforcement learning and sliding mode control scheme for grid integration of a PV System

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Cited by 20 publications
(3 citation statements)
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“…The multiple peaks may mislead; therefore, some algorithms are proposed to identify the global maximum power point (GMPP) [2]. The literature [3,4] has reported in a variety of ways, such as the number of PV system designs connected to the grid, PV array layout or reconfiguration, etc. The local maximum points may be chosen using conventional MPPT methods.…”
Section: Introductionmentioning
confidence: 99%
“…The multiple peaks may mislead; therefore, some algorithms are proposed to identify the global maximum power point (GMPP) [2]. The literature [3,4] has reported in a variety of ways, such as the number of PV system designs connected to the grid, PV array layout or reconfiguration, etc. The local maximum points may be chosen using conventional MPPT methods.…”
Section: Introductionmentioning
confidence: 99%
“…Many methods have been developed to determine the Maximum Power Point (MPP) under varying conditions [5][6][7]. Some of them are based on the well-known principle of perturb and observe (P&O) [8], others are based on sliding mode control [9][10][11][12], on artificial neural networks, or on fuzzy logic algorithms [1,2,7]. In [12][13][14], Maximum Power Voltage (MPV) based approaches are developed using a two-loop MPPT control scheme.…”
Section: Introductionmentioning
confidence: 99%
“…
The digital industrialisation and advancements in technology offers bulk amount electrical energy demand. The present energy fuel vagaries show profound influence on required bulk amount electrical energy demand [1]. In view of this, renewable energy utilities such as hydal, solar, wind etc.
…”
mentioning
confidence: 99%