Browsing by Author "Gautam, Aditya R."

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Decoupled Control of Single-Input Double-Output Boost-Flyback Converter
(IEEE, 2023) Gautam, Aditya R.
The paper proposes a modified single-input double-output boost-flyback converter and its decoupling control technique. The converter avails two different outputs i.e. one to supply a light load at low-voltage and another to supply heavier load at higher voltage in single-stage itself. The availability of different voltage/power outputs is need of toady's automation in power applications such as in personal computers, electric vehicles, smart grids, shipboards. The major challenges of the coupled-inductor or flyback transformer-based multi-output converter are cross-coupling and cross-regulation issues. The proposed converter not only supplies two different outputs but also eliminate coupling and regulation issues. Moreover, the low voltage/power output of converter remains isolated from higher voltage/power output. The working principle of proposed converter and its PWM scheme, dynamic model and a procedure to find decoupling condition to design a decoupled control technique are discussed. Also, to verify the working of converter and its decoupled-control the simulation results are presented using MATLAB Simulink.
Digital twin simulation and comparative study to understand the microgrid operation and control
(IEEE, 2025-01) Mathur, Hitesh Datt; Gautam, Aditya R.
Solar photovoltaic (PV) microgrid (MG) systems are the future of the growing electrical industry. However, these systems have a dependency on the environment. To mitigate this dependency, better control and technologies are required. The digital twin (DT) technology can be utilized to make these MGs more sustainable and promising. In this paper, the DT of the installed MG has been developed on the Simulink platform. The irradiance data, and load power consumption data have been used as input for the DT of the MG. Moreover, it is found that the DT of the MG is working stably with the given data. On the output side, the grid power data of the physical MG has been compared with the output grid power of DT. Further, it has been found that the solar power generation, load power consumption data, and grid power data have matched the results of the physical MG.
Disturbance observer-based sliding mode control of boost-flyback converter
(IEEE, 2025-04) Bansal, Hari Om; Gautam, Aditya R.
This paper presents a disturbance observer based sliding mode control of single-input double-output Boost-flyback dc-dc converter. The converter supplies two outputs i.e. flyback side output and boost side output through common magnetic path of flyback transformer. The power flow through common magnetic path poses the challenges of cross-coupling and crossregulation at line and load transient operations of converter. In this paper, these challenges are alleviated using sliding mode control approach. Moreover, a disturbance observer is also designed to alleviate the effect of cross-regulation at the load transients. The performance of proposed control technique is validated using simulation results.
Experimental Investigations on Particle Swarm Optimization Based Control Algorithm for Shunt Active Power Filter to Enhance Electric Power Quality
(IEEE, 2022-05) Bansal, Hari Om; Gautam, Aditya R.
Quality power is a very important factor for the proper functioning of appliances and can be enhanced using shunt active power filters (SAPF). This work demonstrates the hardware implementation of synchronous reference frame (SRF) theory based SAPF. This paper presents the technique of particle swarm optimization (PSO) to tune the gain values of the SAPF PI controller to control the voltage of the DC-link and enhance its dynamic efficiency. The estimation of gain values of PI controllers using conventional methods does not yield the expected outcomes under varying load conditions. The PSO tuned controller provides better results as compared to traditional tuning methods. The switching scheme is implemented using hysteresis controller to control the SAPF. The main objectives of this approach are to extract the compensating currents and cancel out the harmonics produced by balanced, unbalanced and nonlinear loads. The planned scheme is designed and implemented in MATLAB/Simulink and then its performance on a developed laboratory prototype is validated experimentally.
Techno-economic and reliability assessment of an off-grid solar-powered energy system
(Elsevier, 2024-10) Mathur, Hitesh Datt; Gautam, Aditya R.
In the current era of the world, electricity has become a basic need of every individual. Therefore, the global energy demand has doubled from 77 trillion kWh to nearly 155 trillion kWh since 1980. However, despite the rising energy demand in developing countries, the per capita energy consumption only grew by about 14% globally. It is mainly because of the rural and remote areas where grid supply infrastructure is unavailable or inaccessible. Therefore, a solar-based microgrid (MG) can be considered an alternative energy solution. These MGs with energy storage support can be considered a viable solution because of the intermittent nature of solar photovoltaic (PV). In this regard, this paper describes the design, development, and deployment of a solar rooftop microgrid (SRMG) at the commercial building of BITS Pilani, India. Further, a comprehensive economic analysis is performed using a novel method of levelized cost of energy (LCOE) and payback period, and the results are compared with the traditional method's results. In order to improve the reliability of the SRMG, a demand side management (DSM) strategy is developed and implemented. It can effectively cater the unbalance between load and generation by shifting the non-critical loads to unused solar PV generation hours. It was found from the results that a significant energy of BESS is conserved which is reflected by the high state of charge (SOC) value. The developed design, economic analysis, and proposed DSM make the SRMG an economical and reliable energy solution.
Transformer-based time series prediction of the maximum power point for solar photovoltaic cells
(Wiley, 2022-06) Bansal, Hari Om; Gautam, Aditya R.
This paper proposes an improved deep learning-based maximum power point tracking (MPPT) in solar photovoltaic cells considering various time series-based environmental inputs. Generally, artificial neural network-based MPPT algorithms use basic neural network architectures and inputs which do not represent the ambient conditions in a comprehensive manner. In this article, the ambient conditions of a location are represented through a comprehensive set of environmental features. Furthermore, the inclusion of time-based features in the input data is considered to model cyclic patterns temporally within the atmospheric conditions leading to robust modeling of the MPPT algorithm. A transformer-based deep learning architecture is trained as a time series prediction model using multidimensional time series input features. The model is trained on a dataset containing typical meteorological-year data points of ambient weather conditions from 50 locations. The attention mechanism in the transformer modules allows the model to learn temporal patterns in the data efficiently. The proposed model achieves a 0.47% mean average percentage error of prediction on non-zero operating voltage points in a test dataset consisting of data collected over a period of 200 consecutive hours; resulting in the average power efficiency of 99.54% and peak power efficiency of 99.98%. The proposed model is validated through real-time simulations. The proposed model performs power point tracking in a robust, dynamic, and nonlatent manner, over a wide range of atmospheric conditions.