Faculty Publications

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Author: search.filters.author.Gudimindla, H.Subject: search.filters.subject.fuel cell

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    Accurate parametrization and methodology for selection of pertinent single diode photovoltaic model with improved simulation efficiency
    (Elsevier Ltd, 2018) Gudimindla, H.; Sharma K, M.
    An accurate model of photovoltaic (PV) panel is indispensable for simulations studies. In general, the PV circuit parameters for simulation studies are extracted from the manufacturer's data sheet under different environmental conditions. The PV characterizing equations are nonlinear and requires a more involved computation. This paper presents a fast convergent third order Newton-type method to solve such nonlinear equations and thereby, to accurately parameterize any of the possible PV circuit models. The applicability and suitability of the proposed method are demonstrated through modeling of multi and mono-crystalline PV cells. Further an algorithm to evaluate the efficacy of the available methods and the proposed method is presented. PV characteristics of the suitable circuit model at various levels of temperature and irradiation are also examined. Finally, the effectiveness of the developed method is comprehensively assessed through comparison with the most recent and available effective techniques by considering various performance indices based on current-voltage, power-voltage curves and experimental data is carried out. © 2018 Elsevier Ltd
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    Dynamic performance evaluation of automated QFT robust controller for grid-tied fuel cell under uncertainty conditions
    (Elsevier Ltd, 2020) Gudimindla, H.; K, M.S.
    Power flow control and peak point tracking are significant in grid-tied renewable energy systems to improve power factor and efficient energy extraction. In this paper, the design of robust controllers for the power electronic converters of the grid-connected PEM fuel cell with thermal modeling is deliberated. Further, the transfer function model of the power electronic converters is derived by considering uncertainty in system parameters. A low complexity algorithm is used to design the converter parameters from the uncertainty range. The proposed robust automated power flow controller is designed to minimize the objective function using a genetic algorithm in the quantitative feedback theory framework. The robustness and disturbance rejection with enhanced transient response of the proposed controller is evaluated under heavy and light loading conditions, DC-link voltage and grid voltage distortion uncertainty conditions are investigated. Finally, comprehensive simulations are performed to validate the proposed controller performance with the existing controller under the above-mentioned uncertainty conditions. © 2020 Elsevier Ltd