Faculty Publications
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Item Performance analysis of automated quantitative feedback theory based robust controller for photovoltaic converter(Institute of Electrical and Electronics Engineers Inc., 2018) Gudimindla, H.; Manjunatha Sharma, K.M.Robust controller design for photovoltaicconverters (PVC) has received considerable critical attention due to solar panels operated under uncertain environmental circumstances. This paper presents automated loop-shaping based robust controller design for PVC voltage regulation with the aid of quantitative feedback theory (QFT) using Genetic algorithm. The step by step design guidelines for the automated QFT (AQFT) robust controller is deliberated in detail. The proposed AQFT controller exhibits decreasing modular plot, descending phase response and nearer to the universal bound to replicate the controller ideal characteristics. Finally, benchmarking of the proposed controller with affine parameterization design method available in the literature is performed through simulations. © 2018 IEEE.Item Performance analysis of automated QFT robust controller for long-term grid tied PV simulations(Institute of Electrical and Electronics Engineers Inc., 2020) Gudimindla, H.; Krishnamurthy, M.S.; Sandhya, S.Long-term simulations are significant to understand the real-time operation of grid-tied renewable energy system configurations. Grid-tied photovoltaic system (GPV) is highly non-linear due to the dependency of real-time meteorological conditions. The non-linear behavior of the photovoltaic (PV) system with the power electronic converter makes the long-term simulation inefficient and slow. This paper presents an efficient and simple modelling approach for GPV modelling suitable for long-term simulations. The recent advancements in control strategies and system configurations, sub-module level controller operation gained much interest but the simulation of such systems can be very challenging due to a large number of power electronic components and their control, non-linear behavior of PV system. This paper proposed a genetic algorithm based robust controller design in the quantitative feedback theory (QFT) framework to extract the maximum power from GPV at the sub-module level to extradict the power losses due to partial shading conditions. The performance of the proposed controller at the PV sub-module level is evaluated through comparison with the Q-parameterization based controller. The proposed QFT methodology based robust controller is shown to have advantages over Q-parameterization approach to simulate long-term GPV operation. © 2020 IEEE.Item 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