Faculty Publications
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Item Model predictive control of three level buck/boost converter for bipolar DC microgrid applications(Institute of Electrical and Electronics Engineers Inc., 2019) Nisha, K.S.; Gaonkar, D.N.Emergence of bipolar dc microgrids calls for the need of bipolar converter configurations for the integration of battery energy storage system (BESS), electric vehicle dc fast charging stations (EVCS) etc. This paper proposes model predictive control of a bipolar bidirectional buck/boost converter derived from three level converter (TLC) configuration in a bipolar dc microgrid. Bipolar dc microgrid is fed by power from solar PV systems and BESS. State space analysis is done and discrete model is developed. Simulation of the proposed system with model predictive control (MPC) is done in Simulink MATLAB and analysed for the voltage unbalance issues of bipolar dc microgrid under varying conditions of photovoltaic generations and load disturbance. From the simulation results, proposed converter with model predictive control technique gives faster response in mitigating the voltage unbalance and grid voltage regulation issues arising in bipolar dc microgrid. © 2019 IEEE.Item Predictive Control of Three Level Bidirectional Converter in Bipolar DC Microgrid for EV Charging Stations(Institute of Electrical and Electronics Engineers Inc., 2020) Nisha, K.S.; Gaonkar, D.N.This paper proposes model predictive control (MPC) of a bipolar bidirectional buck/boost converter derived from three level converter (TLC) configuration for integrating with electric vehicle charging station or battery energy storage system (BES) in bipolar dc microgrid structure. Bipolar dc microgrid considered here consists of two solar PV systems, dc loads and battery. The bidirectional power flow between grid and battery or EV charging stations is controlled considering the battery state of charge (SOC), total power generated and load demanded. Advantage of this converter is that it can address the dc grid voltage regulation and capacitance voltage balancing issues during variation of load and solar irradiation in bipolar dc microgrid. State space analysis is done and discrete model is developed. Simulation is done in Simulink MATLAB and analysed for voltage unbalance issues of bipolar dc microgrid under varying conditions of photovoltaic generations and load disturbance. Real time performance is tested and verified in hardware in loop environment using Typhoon HIL 402. © 2020 IEEE.Item Model predictive controlled three-level bidirectional converter with voltage balancing capability for setting up EV fast charging stations in bipolar DC microgrid(Springer Science and Business Media Deutschland GmbH, 2022) Nisha, K.S.; Gaonkar, D.N.Transportation electrification and charging infrastructure development has to gain momentum in order to go hand-in-hand with the fast advances in the electric vehicle technology. Setting up dc fast charging stations connected to bipolar DC microgrid is a great viable option to utilize the distributed energy resources for transportation electrification. It also helps to eliminate power quality issues in ac grid that may arise due to the unpredictable charging/discharging behaviour of EVs. This paper focuses on model predictive control of a three-level bidirectional dc–dc converter suitable for interconnecting bipolar DC microgrid with dc fast charging stations or battery energy storage. State space analysis is done, and discrete model is developed. Simulation of the proposed system with model predictive control is done in Simulink MATLAB. Real-time hardware in loop performance is tested and verified using Typhoon HIL 402. The proposed converter is able to mitigate the voltage unbalance issues arising in the bipolar DC microgrid and is capable of controlling bidirectional power flow, hence suitable for V2G/G2Voperation. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.Item Operation and control of multiple electric vehicle load profiles in bipolar microgrid with photovoltaic and battery energy systems(Elsevier Ltd, 2023) Nisha, K.S.; Gaonkar, D.N.; Sabhahit, N.S.Charging of electric vehicles is going to be a major electrical load in the near future, as more and more population shift to electric auto-motives from conventional internal combusted engine-powered vehicles. Integration of electric vehicle charging stations (EVCS) might even burden the existing grid to a point of collapse or grid failure. Establishing charging stations interfaced with bipolar DC microgrids along the roads and highways is the most realistic and feasible solution to avoid the overburdening of the existing power system. The bipolar DC microgrid is a far better microgrid structure than the unipolar microgrid structure in many aspects like reliability, flexibility, and controllability. It can provide multiple voltage level interfaces according to the load demands, which is very apt for different charging levels of electric vehicles (EVs). Operation of multiple sources and multiple loads connected to bipolar DC microgrid will affect DC voltage regulation, capacitance-voltage balancing, and overall stable operation of the grid. In order to mitigate these power quality problems arising in multi-node bipolar DC microgrids, a decentralized model predictive control is proposed in this paper. EV charging load profiles are modeled and developed by considering standard driving cycles, state of charge, and power demand of multiple vehicles to study the effect of unpredictable varying EV loads in the bipolar DC microgrid. EVCS thus modeled are connected to solar photovoltaic-battery energy storage fed bipolar DC microgrid with three-level/bipolar converters and analyzed under dynamic conditions for capacitance–voltage unbalance mitigation, voltage regulation, and the stability of operation with model predictive control. Simulation studies are carried out in MATLAB/Simulink to verify the effectiveness of the system. © 2022 Elsevier LtdItem A finite control set model predictive controller for single-phase transformerless T-type dynamic voltage restorer(Springer Science and Business Media Deutschland GmbH, 2023) Rajkumar, K.; Grimm, F.; Parthiban, P.; Baghdadi, M.; Lokesh, N.This paper presents a five-level T-type multilevel inverter (MLI) with a finite control set model predictive control (FCS-MPC) scheme for a single-phase transformerless dynamic voltage restorer (DVR). Typical two-level voltage source inverters are not suitable for high-power and medium-voltage applications due to high dv/dt, large size, and high cost of the filter, as well as high voltage stress on all switches. To overcome these issues, a reduced switch count T-type MLI-based transformerless DVR is proposed. The literature does not yet describe an FCS-MPC control scheme for transformerless T-type DVR. The FCS-MPC controller predicts the future of the trajectory of the controlled variables based on a prediction model. The optimal state is then selected using a cost function that is formed by combining predicted and reference variables. The proposed control technique minimizes the total harmonic distortion to a very low value compared to a linear PI controller. In addition, this control scheme is not dependent on a modulation scheme and linear control technique. The proposed system is validated by both simulation and experimental results. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.Item ARIMA-PID: container auto scaling based on predictive analysis and control theory(Springer, 2024) Joshi, N.S.; Raghuwanshi, R.; Agarwal, Y.M.; Annappa, B.; Sachin, D.N.Containerization has become a widely popular virtualization mechanism alongside Virtual Machines (VMs) to deploy applications and services in the cloud. Containers form the backbone of the modern architectures around microservices and provide a lightweight virtualization mechanism for IoT and Edge systems. Elasticity is one of the key requirements of modern applications with various constraints ranging from Service Level Agreements (SLA) to optimization of resource utilization, cost management, etc. Auto Scaling is a technique used to attain elasticity by scaling the number of containers or resources. This work introduces a novel mechanism for auto-scaling containers in cloud environments, addressing the key elasticity requirement in modern applications. The proposed mechanism combines predictive analysis using the Auto-Regressive Integrated Moving Average (ARIMA) model and control theory utilizing the Proportional-Integral-Derivative (PID) controller. The major contributions of this work include the development of the ARIMA-PID algorithm for forecasting resource utilization and maintaining desired levels, comparing ARIMA-PID with existing threshold mechanisms, and demonstrating its superior performance in terms of CPU utilization and average response times. Experimental results showcase improvements of approximately 10% in CPU utilization and 30%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.Item Finite control set model predictive control of three-port converter for interfacing a PV-battery energy storage system to a three-phase stand-alone AC system(Oxford University Press, 2024) Preeti, G.A.; Karthikeyan, A.This paper proposes a multiport bidirectional non-isolated converter topology that provides advantages in terms of simultaneous multiple operations, single-stage conversion, high power density and reduced power losses due to the lower number of switches. The proposed multiport converter uses a centralized non-linear controller known as a finite control set model predictive controller to manage the flow of power between different ports. It deals with the parallel operation of photovoltaic and battery energy storage systems for stand-alone alternating current (AC) systems. The converter connects the lower voltage battery to the photovoltaic port using a bidirectional buck/boost converter and the photovoltaic port is linked to the stand-alone AC load through a three-phase full-bridge inverter. Each leg of the three-phase converter will act as a bidirectional direct current (DC)/DC converter as well as an inverter simultaneously. Only six switches manage the power transfer between all the connected ports of photovoltaic-battery energy storage system linked to the stand-alone AC load. The proposed multiport converter is mathematically modelled and controlled by a finite control set model predictive controller. The system is validated in simulation (1-kW rating) and experimental environment (200-W rating). The hardware prototype is developed in the laboratory and the controller is implemented on the field-programmable gate array board. Two independent case studies are carried out to validate the efficacy of the system. The first scenario is for a change in solar irradiance, while the second scenario is for a change in the output load. © The Author(s) 2024. Published by Oxford University Press on behalf of National Institute of Clean-and-Low-Carbon Energy.Item Enhanced Power Management in Multiport Converter with SRF-PI Control and SVPWM for PV-Battery Standalone Systems(Springer, 2025) Gangashetty, P.A.; Karthikeyan, K.This paper presents a novel single-stage three-port power converter topology for standalone renewable energy systems that integrate photovoltaic (PV) generation and battery energy storage to supply a three-phase AC load. The proposed converter architecture combines a multi-phase bidirectional interleaved direct current-to-direct current (DC/DC) converter with a full-bridge inverter, forming a compact and modular power interface that reduces the number of conversion stages and minimizes component count and volume. A synchronous reference frame-based proportional–integral (SRF-PI) controller is employed for decoupled regulation of the DC-link and AC output voltages, while Space Vector Pulse Width Modulation (SVPWM) ensures fixed-frequency switching and optimal DC bus utilization. The control strategy enables effective power flow management between the PV, battery, and load under dynamic irradiance and load variations. Real-time implementation on an FPGA-based platform validates the feasibility and performance of the proposed control method, with a 300 W experimental prototype demonstrating practical applicability. The system is also modeled and simulated in MATLAB/Simulink to evaluate transient and steady-state behavior under different operating conditions. A comparative analysis with the Finite Control Set Model Predictive Control (FCS-MPC) technique highlights that the SRF-PI controller offers improved lower transient overshoot, reduced steady-state error, and superior power quality while significantly reducing the computational burden and implementation complexity. The proposed system offers a scalable, efficient, and hardware-friendly solution suitable for standalone PV-battery-based microgrids and rural electrification applications. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.Item Effects of specimen thickness and compositions on the fracture toughness investigations of Al7075-SiC/Al2O3 hybrid composites utilizing Taguchi optimization and FEA analysis(Springer-Verlag Italia s.r.l., 2025) Bharath, P.B.; Shivakumar, S.P.; Rajesh, A.M.; Prabhuswamy, G.S.; Doddamani, S.The primary objective of this study is to investigate the influence of process parameters on the fracture toughness of aluminium–silicon carbide/alumina particulate composites. The composite is fabricated using the stir-casting method, and the study aims to explore the relationship between process parameters and the resulting mechanical properties of the material. The research seeks to answer how varying process parameters such as reinforcement composition, specimen thickness, and crack length-to-width ratio affect the fracture toughness of aluminium-based hybrid composites. A comprehensive experimental approach is employed, utilizing compact tension specimens of varying thicknesses, compositions, and crack length-to-width ratios to assess fracture toughness. Taguchi's optimization techniques, including the design of experiments with an L9 orthogonal array, analysis of variance (ANOVA), and regression analysis, are used to analyze the specified parameters. The three key factors and their respective levels considered in the study are reinforcement composition (3, 6, and 9 wt%), specimen thickness (10, 12, and 15 mm), and crack length-to-width ratio (0.45, 0.47, and 0.50). The experimental results indicate that increasing the composition of reinforcements beyond 6 wt% and certain crack length-to-width ratios decreases the fracture toughness of the hybrid composites. Through Taguchi's analysis, it is revealed that for a crack length-to-width ratio of 0.45, specimens with a thickness of 12 mm and 6 wt% reinforcements exhibit the highest fracture toughness. Further analysis underscores that the crack length-to-width ratio (a/W ratio) significantly affects fracture toughness (94%), followed by reinforcement composition and specimen thickness. The study provides valuable insights into optimizing the fracture toughness of aluminium–silicon carbide/alumina particulate composites. The identified optimized parameters 12 mm specimen thickness, 6 wt% reinforcement, and a 0.45 crack length-to-width ratio lead to enhanced fracture toughness. Additionally, finite element simulations support the experimental findings, with less than a 12% error, confirming the robustness of the optimized conditions. This research contributes to a deeper understanding of the interplay between process parameters and mechanical properties in particulate composite materials. © The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature 2025.