Indirect adaptive neurofuzzy Hermite wavelet based control of PV in a grid-connected hybrid power system

Owing to the evolution of the smart grid, the emergence of hybrid power systems (HPSs), and the proliferation of plug-in-hybrid electric vehicles, the development of efficient and robust maximum power point tracking (MPPT) algorithms for renewable energy sources due to their inherent intermittent nature has overwhelmed the power industry. In this paper, an incremental conductance (IC) based Hermite wavelet incorporated neurofuzzy indirect adaptive MPPT control paradigm for a photovoltaic (PV) system in a grid-connected HPS is proposed. The performance of the proposed adaptive Hermite wavelet incorporated neurofuzzy MPPT control paradigm is validated through a comprehensive grid- connected HPS test-bed developed in MATLAB n Simulink by comparison with an IC based adaptive indirect neurofuzzy Takagi{Sugeno{Kang (TSK) control scheme, IC based adaptive direct neurofuzzy TSK control system, IC based adaptive proportional-integral-derivative (adapPID) control scheme, and IC algorithm for PV systems

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[1] Eltawil MA, Zhao Z. MPPT techniques for photovoltaic applications. Renew Sust Energ Rev 2013; 25: 793-813.
[2] Reisi AR, Moradi MH, Jamasb S. Classi cation and comparison of maximum power point tracking techniques for photovoltaic system: a review. Renew Sust Energ Rev 2013; 19: 433-443.
[3] Esram T, Chapman PL. Comparison of photovoltaic array maximum power point tracking techniques. IEEE T Energy Conver 2007; 22: 439-449.
[4] Fermia N, Granozio D, Petrone G, Vitelli M. Predictive and adaptive MPPT perturb and observe method. IEEE T Aero Elec Sys 2007; 43: 934-950.
[5] Kjr SB. Evaluation of the hill climbing" and the incremental conductance" maximum power point trackers for photovoltaic power systems. IEEE T Energy Conver 2012; 27: 922-929.
[6] Lin CH, Huang CH, Du YC, Chen JL. Maximum photovoltaic power tracking for the PV array using the fractional- order incremental conductance method. Appl Energy 2011; 88: 4840-4847.
[7] Gokmen N, Karatepe E, Ugranli F, Silvestre S. Voltage band based global MPPT controller for photovoltaic systems. Sol Energy 2013; 90: 322-334.
[8] Noguchi T, Togashi S, Nakamoto R. Short-current pulse-based maximum power point tracking method for multiple photovoltaic and converter module system. IEEE T Ind Electron 2002; 49: 217-223.
[9] Kulaksiz AA, Akkaya R. A genetic algorithm optimized ANN-based MPPT algorithm for a stand-alone PV system with induction motor drive. Sol Energy 2012; 86: 2366-2375.
[10] Adly M, Besheer AH. A meta-heuristics search algorithm as a solution for energy transfer maximization in stand- alone photovoltaic systems. Int J Elec Power 2013; 51: 243-54.
[11] Liu YH, Liu CL, Huang JW, Chen JH. Neural-network-based maximum power point tracking methods for photo- voltaic systems operating under fast changing environments. Sol Energy 2013; 89: 42-53.
[12] Alajmi BN, Ahmed KH, Finney SJ, Williams BW. Fuzzy logic control approach of a modi ed hill-climbing method for maximum power point in micro-grid standalone photovoltaic system. IEEE T Power Electr 2011; 26: 1022-1030.
[13] Badar R, Khan L. Coordinated adaptive control of multiple exible AC transmission systems using multiple-input- multiple-output neuro-fuzzy damping control paradigms. Electr Pow Compo Sys 2014; 42: 818-830.
[14] Khan L, Lo KL, Jovanovic S. Hybrid GA neuro-fuzzy damping control system for UPFC. COMPEL 2006; 25: 841-861.
[15] Khan L, Badar R. Hybrid adaptive neuro-fuzzy B-spline based SSSC damping control paradigm using online system identi cation. Turk J Elec Eng & Comp Sci 2015; 23: 395-420.
[16] Abiyev RH, Kaynak O. Identi cation and control of dynamic plants using fuzzy wavelet neural networks. IEEE Int Symp Intell 2008; 1295-1301.
[17] Basso TS, DeBlasio R. IEEE 1547 series of standards: interconnection issues. IEEE T Power Electr 2004; 19: 1159-62.