PRERNA GAUR
@nsut.ac.in
Director, Others
Professor, Electrical Engineering (Main Campus)
Netaji Subhas University of Technology
RESEARCH, TEACHING, or OTHER INTERESTS
Renewable Energy, Sustainability and the Environment, Control and Systems Engineering, Energy Engineering and Power Technology
Scopus Publications
Scopus Publications
Akash Deep Verma, Prerna Gaur, and Anuradha Tomar
Elsevier BV
Diwaker Pathak, Aanchal Katyal, and Prerna Gaur
Springer Science and Business Media LLC
Huma Khan, Shahida Khatoon, and Prerna Gaur
Springer Science and Business Media LLC
Akash Deep Verma, Prerna Gaur, and Anuradha Tomar
IEEE
The squirrel cage induction machine (SCIM) based water pumping system (WPS) remains a cost-effective and robust option compared to other alternatives. However, the voltage-to-frequency-based controllers and direct-on-line (DOL) start of SCIM within solar PV-based microgrids (MGs) result in voltage dips and harmonic disturbances. Hence, other electrical applications can't be used in parallel. The SCIM needs a dedicated system that increases the overall cost. Hence, maintaining load voltage and frequency constant in a power system with IEEE 519 standards is very critical with a SCIM-based WPS. To address this issue, a secondary droop-control supported cascaded proportional resonant (CPR) controller has been proposed and tested for a three-phase voltage source inverter (VSI) in a grid-interactive solar PV system for SCIM-based WPS. The objective is to keep constant load voltage and frequency with enhanced power quality as per IEEE 519 standards and optimize power management so that other applications can also be used in parallel with the same controller. Additionally, a “Grid-Start Grid-Run” mode has been discussed to protect solar modules from over-current during the DOL start of the WPS. The DC link voltage is regulated to ensure the motor torque and speed remain within specified operational parameters. The proposed control is stable, robust, and cost-effective, offering the flexibility to operate in islanded mode when necessary.
Prabhat Srivastava, Alok Agrawal, and Prerna Gaur
IEEE
Mobile charging stations (MCS) consist of vehicle equipped with battery banks and rapid chargers. The idea of using static charging station is to refuel electric cars. These charging stations are scheduled and routed to meet the needs of electric vehicle users who are heterogeneous in terms of geography and time. In order to meet the requirements of mobile charging stations, charging requests deadlines, cost estimation, acceptance of both the user etc. conditions should be required. Also, to optimize the problem to turn down the cost of charging, it should require to get the solar enabled charging station that provide surplus of energy at the peak hours and increase the reliability of system. Numerical findings demonstrate that the approach can significantly lower the cost of charging at peak hours and off peak too and also improving customer satisfaction by improvising the charging strategy. Solar based MCS have the potential to significantly contribute to the acceleration of EV adoption by offering charging services based on the location and time-agnostic.
Huma Khan, Shahida Khatoon, Prerna Gaur, Mohamed Abbas, Chanduveetil Ahamed Saleel, and Sher Afghan Khan
MDPI AG
Mobile robot is an automatic vehicle with wheels that can be moved automatically from one place to another. A motor is built in its wheels for mobility purposes, which is controlled using a controller. DC motor speed is controlled by the proportional integral derivative (PID) controller. Kinematic modeling is used in our work to understand the mechanical behavior of robots for designing the appropriate mobile robots. Right and left wheel velocity and direction are calculated by using the kinematic modeling, and the kinematic modeling is given to the PID controller to gain the output. Motor speed is controlled by the PID low-level controller for the robot mobility; the speed controlling is done using the constant values Kd, Kp, and Ki which depend on the past, future, and present errors. For better control performance, the integral gain, differential gain, and proportional gain are adjusted by the PID controller. Robot speed may vary by changing the direction of the vehicle, so to avoid this the Social Spider Optimization (SSO) algorithm is used in PID controllers. PID controller parameter tuning is hard by using separate algorithms, so the parameters are tuned by the SSO algorithm which is a novel nature-inspired algorithm. The main goal of this paper is to demonstrate the effectiveness of the proposed approach in achieving precise speed control of the robot, particularly in the presence of disturbances and uncertainties.
Surabhi Chandra and Prerna Gaur
Wiley
The solar photovoltaic (PV) sources have low voltage output and hence cannot be used directly in micro‐grid applications. Therefore, to boost the low voltage output, a novel high gain single‐ended primary inductor converter (SEPIC) is proposed in this manuscript. The proposed high gain SEPIC (HGS) has an advantage of obtaining high voltage gain at a low duty ratio and continuous input current, which makes it viable for PV applications. The proposed converter offers simple control, and the high gain is obtained without the use of any transformer or coupled inductor so the semiconductor switch will not experience a voltage overshoot during the turn‐off operation. Hence, the conduction losses of the converter switch are reduced, and the performance of the converter is enhanced. Additionally, this manuscript also investigates the effectiveness of the proposed HGS in integration with the PV array. An extensive performance evaluation of the proposed HGS is investigated and compared with different SEPIC topologies in irradiance change conditions. Experimental implementation of the proposed HGS confirms the theoretical analysis and verifies the performance. Also, the real time (RT) analysis of the proposed HGS with PV array is conducted to show the validation of high gain operation for the PV application.
Agrima Chandra, Prema Gaur, and Anuradha Tomar
IEEE
The designing and modeling of renewable based Electric Vehicle Charging Station (EVCS), using Solar Photovoltaics (SPV), SEPIC converter along with Maximum Power Point Tracking (MPPT) control, a BUCK converter as Voltage Regulation (VR) mechanism and Constant Current (CC) and Constant Voltage (CV) battery control for Li-ion battery charging which here is assumed as EV battery is proposed. A detailed modeling of the system is done in MATLAB/SIMULINK. The increase in Electric Vehicle (EV) has led to increasing demand of EVCS. The design proposed here is for off grid standalone charging station with renewable source integration enhancing green and clean energy source for sustainable transportation. Standalone systems are required to power the charging stations where grid feasibility is not available like in rural areas. SEPIC along with MPPT algorithm is used to dynamically optimize operating points of SPV to achieve maximum energy under different environmental conditions. In this paper a sustainable power generation system for Electric Vehicle Charging Station (EVCS) is proposed. The adoption of FLC for CC-CV control is advantageous over PID control in dealing with nonlinearities and adapting real time conditions parameters in changing environment. The performance and feasibility of system is demonstrated via results of simulations in SIMULINK. The dynamic responses of FLC based CC-CV charging system and overall system is observed.
Agrima Chandra, Prerna Gaur, and Anuradha Tomar
IEEE
In this paper a sustainable power generation system for Electric Vehicle Charging Station (EVCS) is proposed. This study explores the feasibility of an EVCS powered by a combination of Fuel Cell (FC), Biomass Generator (BMG) and Solar Photovoltaic (PV) generation. The Hybrid Optimization of Multiple Energy Resources (HOMER) software, a simulation software tool, is employed to perform the techno-economic analysis of the proposed EVCS system. Software’s optimization capabilities are utilized to determine the optimized size and configuration of the renewable energy systems, accounting for the EV charging demand using available resources, and local conditions. The results demonstrate the potential of the integrated system to provide reliable and sustainable power for EV charging. The site taken into consideration for the real time load data is of the area CRPW+8G8, Himmat Ganj, Prayagraj, Uttar Pradesh, India (25°26.1’N, 81°50.8E). Overall, this research contributes to the understanding of the feasibility and benefits of integrating fuel cells, biomass, and solar PV in an EVCS and the potential of this sustainable system to reduce greenhouse gas emissions over conventional fossil fuel plants, promote renewable energy utilization, and enhance the sustainability of the transportation sector without stressing on the grid system.
Prabhat Srivastava, Alok Agrawal, and Prerna Gaur
IEEE
The traditional grid networks could be upgraded to smart grids via Internet of Things (IoT) technology. This paper discusses the various grid system architecture's, technologies, and latest communication protocols for the IoT enabled smart grids. Literature survey shows that the technological advancements in power systems are making the grids better in terms of stability and sustainability; but one of the major issue related to such advancements, especially in the IoT enabled smart grids is related to data theft. This paper summarizes the IoT enabled mitigation techniques for such smart grid system vulnerabilities. Use of technologies such as bigdata, machine learning, augmented reality and blockchain, can give support to the IoT smart energy systems so that it become more flexible, safe, and secure. This paper summarizes the knowledge of such advanced technologies which are indeed helpful to make the IoT enabled smart grid system more effective technically, economically, and socially.
Akash Deep Verma, Anuradha Tomar, and Prerna Gaur
Informa UK Limited
Akash Deep Verma, Virat Shishodia, Anuradha Tomar, and Prerna Gaur
IEEE
In developing countries, E-rickshaw is becoming popular and plays a vital role in initial and last-mile connectivity. The main reason for the wide acceptance of this mode of public transport is that it is economical as compared to a CNG auto, reduces air pollution, and provides better income opportunities. Over time, three major issues emerge related to the rise in fares, increased grid stress due to an increase in load demand for charging, and unsystematic disposal of lead-acid batteries. Presently, architectures for the charging station are available in the market but they are based on-grid supply which results in additional stress on the existing grid. In this proposed work a dedicated real-time socio-economic case study has been done to explore issues at the ground level to develop a photovoltaic (PV) based off-grid battery charging & swapping station (BCSS) and define a new category of E-rickshaw. The BCSS is proposed keeping objectives in mind to increase the return on investment (ROI) of both E-rickshaw owners by defining new a category & charging stations with fast recovery on the initial investments.
Mridul Kapur, Agamdeep Singh Mahal, Priyanshu Kumar, Diwaker Pathak, Manisha, and Prerna Gaur
IEEE
Partial shading imposes a major issue while generating power using solar photovoltaics (SPV) and consequences in the formation of hot spots and manifold peaks in the power-voltage (P-V) curve of the SPV arrays. As a result, a main deforming concern arises i.e., The power loss due to mismatch in the SPV system is dependent on and directly caused by the shading pattern. Mitigation of this MPL can be assessed by selecting a proper configuration of the SPV array and optimal shading dispersion over the complete SPV array. This paper focuses on the implementation of renowned metaheuristic algorithms (MAs) to optimally disperse the shading effect for a partially shaded total-cross-tied (TCT) SPV array configuration. A partial shading effect is generated using a realistic moving cloud model by incorporating various affecting environmental parameters. Furthermore, MAs are tested for the designed moving cloud model and a comprehensive analysis is presented among the various SPV array configurations.
Abhishek Verma, Diwaker Pathak, and Prerna Gaur
Elsevier BV
Huma Khan, Shahida Khatoon, Prerna Gaur, and Salman Ahmad Khan
Springer Science and Business Media LLC
Rohit Rana, Prerna Gaur, Vijyant Agarwal, and Harish Parthasarathy
Cambridge University Press (CUP)
AbstractIn this paper, a novel statistical application of large deviation principle (LDP) to the robot trajectory tracking problem is presented. The exit probability of the trajectory from stability zone is evaluated, in the presence of small-amplitude Gaussian and Poisson noise. Afterward, the limit of the partition function for the average tracking error energy is derived by solving a fourth-order system of Euler–Lagrange equations. Stability and computational complexity of the proposed approach is investigated to show the superiority over the Lyapunov method. Finally, the proposed algorithm is validated by Monte Carlo simulations and on the commercially available Omni bundleTM robot.
Richa Sharma, Prerna Gaur, Shaurya Bhatt, and Deepak Joshi
Institute of Electrical and Electronics Engineers (IEEE)
Transfemoral amputation commonly occurs due to some stroke, diabetes, physical or mental trauma, which reduces the person's movement capability. Therefore, an efficient prosthetic leg is essential to improve the life of an amputee by replacing the lost limb. This letter addresses the fuzzy logic-based control strategy for magneto-rheological damper based prosthetic leg for transfemoral amputees. The primary focus of this letter is to present the performance analysis of the control to achieve the entire gait cycle (both swing and stance phases) for a transfemoral prosthesis. The performance and robustness analysis of the developed prosthetic leg is tested for real-time gait data to obtain the precise and more natural gait by the transfemoral amputee.
Rohit Rana, Prerna Gaur, Vijyant Agarwal, and Harish Parthasarathy
Springer Science and Business Media LLC
Varun Bhardwaj and Prerna Gaur
Springer Nature Singapore
Abhishek Verma, Diwaker Pathak, and Prerna Gaur
IEEE
Budding Micro, Small and Medium Enterprises (MSMEs) dealing with solar PV manufacturing, installation, and maintenance need a way to test their outsourced and inhouse PV modules. To keep up with the required power generation and efficiency, PV modules are needed to be tested periodically. The only commercially available equipment to test solar modules is a solar simulator, which provides accurate standard testing conditions (STC) to check the reliability and performance of photovoltaic (PV) panels. However, these simulators are expensive and very delicate because of which their acquisition is quite expensive and maintenance is difficult for MSMEs. Therefore, in this paper, the performance of a commonly available PV module is assessed and discussed comprehensively in a generalized approach. An economical and easily available microcontroller is utilized to obtain the power-voltage (P-V) and current-voltage (I-V) characteristics of the solar PV module. Henceforth, the implementation of the complete setup is interpreted thoroughly at a real-time platform.
Aanchal Katyal, Diwaker Pathak, and Prerna Gaur
IEEE
Under standard climatic circumstances, solar PV systems track maximum power point (MPP) using classic hill-climbing approaches such as perturb and observe (P&O) and incremental conductance (INC). Traditional algorithms, suffer difficulties in dynamic and unpredictable environments, such as excessive voltage and current ripple and becoming trapped on local minima. Metaheuristic optimization strategies are used to solve the aforementioned challenge. Therefore, the purpose of this study is to compare several metaheuristic optimization methods for MPP tracking, such as grey wolf optimization (GWO), grasshopper optimization algorithm (GOA), whale optimization algorithm (WAO), and hybrid particle swarm optimization with GWO (PSO-GWO) (MPPT). The performance of algorithms has been observed using the experimental evaluation on standard benchmark functions. Based on the performance of these MPPT algorithms, tracking efficiency is investigated thoroughly for a mathematically modelled solar PV module. Furthermore, the best-performing algorithm's data is sent to an internet of things (IoT) cloud for monitoring and creation of a dataset for smart controller which may learn from every best performing algorithm.