@article{roselyn_ravi_devaraj_venkatesan_2021, title={Optimal SoC Estimation Considering Hysteresis Effect for Effective Battery Management in Shipboard Batteries}, volume={9}, ISSN={["2168-6785"]}, DOI={10.1109/JESTPE.2020.3034362}, abstractNote={The energy storage is critical in shipboard systems since there is no alternate energy in case of primary energy source failure. Hence, a battery management system (BMS) is necessary to monitor the state of the battery. Since a BMS operates the battery based on its state, an accurate estimation of the state of charge (SoC) is essential. Hysteresis is predominantly present in lead–acid batteries, whose effect is generally not accounted for in existing SoC estimation methods. In this work, the effect of hysteresis on SoC estimation is considered and the differential evolution-based SoC estimation technique is applied to accurately estimate the SoC while minimizing the hysteresis effect. A dynamic charge and discharge dynamic model of a 100-Ah lead–acid battery with ship thruster load is used to study the battery hysteresis. The battery model with the proposed SoC estimation and the BMS algorithm is simulated in MATLAB/SIMULINK 2018A and is tested under different charging and discharging conditions. The proposed model is validated in a real-time testbed comprising of a 62-Ah lead–acid battery connected to a 25 HP thruster of a boat. From the test results, it has been demonstrated that the proposed estimation algorithm reduces the estimation error thereby achieving an accurate SoC.}, number={5}, journal={IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS}, author={Roselyn, J. Preetha and Ravi, Anirudhh and Devaraj, D. and Venkatesan, R.}, year={2021}, month={Oct}, pages={5533–5541} }
 @article{sadees_roselyn_vijayakumar_ravi_2021, title={Techno economic analysis of microgrid with an efficient energy management system and inverter control strategies}, volume={48}, ISSN={["2213-1396"]}, DOI={10.1016/j.seta.2021.101602}, abstractNote={A microgrid comprises of distributed energy resources with the capability of operating independently as an islanded mode or in a grid connected mode. The efficacy of a microgrid is based on the performance of the control strategy and the energy management strategy. Therefore, in this paper the feasibility of an efficient inverter control strategy and energy management strategy for microgrid are studied. The proposed microgridis implemented with master-slave energy management control and battery management system for effective power flow control in an islanded and grid connected mode. A three-layer hierarchical energy management strategy comprising of master-slave system to provide continuous supply at all conditions and effective switchover operations between grid connected and islanded modes of operation is proposed. The voltage-frequency control under standalone mode of operation and P-Q control using hysteresis current control under grid-connected mode of operation are developed and the system parameters like real power, reactive power and voltage at the PCC are analyzed after the implementation of proposed controllers under islanded and grid connected modes of operation. The proposed model achieves voltage and frequency regulation under varying system operating conditions. Also, a techno-economic analysis is performed in HOMER software where the cost of energy and return on investment are studied for the proposed microgrid system by which levelized cost and payback period is reduced. The proposed control algorithms are implemented through MATLAB simulation and tested in a real-time for 1 kW grid-connected solar PV system.}, journal={SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS}, author={Sadees, M. and Roselyn, J. Preetha and Vijayakumar, K. and Ravi, Anirudhh}, year={2021}, month={Dec} }
 @article{roselyn_ravi_devaraj_venkatesan_sadees_vijayakumar_2020, title={Intelligent coordinated control for improved voltage and frequency regulation with smooth switchover operation in LV microgrid}, volume={22}, ISSN={["2352-4677"]}, DOI={10.1016/j.segan.2020.100356}, abstractNote={During demand changes and incidence of grid faults, the microgrid system is subjected to an imbalance between the generation and demand. The maintenance of power balance under such dynamic conditions is a major concern for the proper functioning of the microgrid network. During those dynamic periods, achieving voltage and frequency regulation, in particular, during islanded/standalone mode of operation is a challenging task. For reliable operation, in addition to power balance, voltage and frequency regulation, reduced switchover transients and fast switchover operations are necessary. Conventional coordinated controllers comprising of droop, voltage and current control loops, aim at achieving stability of the microgrid network but fails to ensure a quick and smooth response. This work proposes an Adaptive Neuro-Fuzzy Inference System based intelligent coordinated control strategy by combining control techniques namely, inverse droop control, virtual impedance control and current control. The proposed coordinated control scheme provides improved voltage and frequency regulation, reduced switching transients and quick adaptation of the system during demand changes and fault events. The proposed system uses an inverse droop control technique to achieve power decoupling. Additionally, a virtual impedance-based voltage control loop is implemented which ensures voltage regulation of the microgrid and a feed-forward current control loop is developed to minimize the transients during load switching and switchover operations. The reverse droop based PLL strategy is implemented for grid synchronization and smooth switchover operations. The proposed system is simulated in a 20-kVA grid-tied microgrid system in MATLAB/SIMULINK R2018b and tested in real-time of 5-kVA grid-tied solar PV system which demonstrated that the proposed approach is effective under varying irradiance, changing demand conditions and switchover operations. The experimental results prove that the proposed scheme is performing better under different system conditions with fast switching response when compared with other control algorithms.}, journal={SUSTAINABLE ENERGY GRIDS & NETWORKS}, author={Roselyn, J. Preetha and Ravi, Anirudhh and Devaraj, D. and Venkatesan, R. and Sadees, M. and Vijayakumar, K.}, year={2020}, month={Jun} }