PRERNA TUNDWAL

@anandice.ac.in

ASSISTANT PROFESSOR IN ELECTRICAL ENGINEERING DEPARTMENT
ANAND INTERNATIONAL COLLEGE OF ENGINEERING, JAIPUR

PRERNA TUNDWAL


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https://researchid.co/prernatundwal

RESEARCH, TEACHING, or OTHER INTERESTS

Electrical and Electronic Engineering, Engineering
7

Scopus Publications

Scopus Publications

  • Solar energy classification, mitigation, detection, and applications in engineering practice: A comprehensive review
    Sustainability Principles and Applications in Engineering Practice, 2024
  • Optimization techniques for solar energy system design and operation
    Prerna Tundwal, Vikramaditya Dave
    Fostering Cross Industry Sustainability with Intelligent Technologies, 2024
    This chapter focuses on the application of optimization techniques in the design and operation of solar energy systems. Solar energy has emerged as a viable and sustainable alternative to traditional energy sources, and optimizing the performance of solar energy systems is crucial for maximizing energy production, improving system efficiency, and reducing costs. Various optimization methods and algorithms are explored in this chapter, including mathematical programming, evolutionary algorithms, and machine learning. The chapter highlights their advantages, challenges, and potential applications in solar energy system design and operation. Moreover, the application is presented to illustrate the effectiveness of these optimization techniques in improving the performance and economic viability of solar energy systems.
  • Design and Simulation of 546kWp Grid Connected Solar PV System for an Academic Institute
    Prerna Tundwal, Hitesh Kumawat, Vikramaditya Dave
    2024 3rd International Conference on Power Electronics and Iot Applications in Renewable Energy and Its Control Parc 2024, 2024
    The integration of solar photovoltaic (PV) systems of an academic institute is a appropriate solution to reduce carbon emissions and promote sustainable energy practices. This paper presents a comprehensive study on the design and simulation of 546kWp grid-connected solar PV system for Maharana Pratap University of Agriculture & Technology, CTAE, Udaipur. This study includes a detail analysis of load demands, site specific solar irradiance data, and energy consumption patterns. The PV system is modelled, its performance is simulated, and the system design is optimised for maximum energy production using PVsyst software. The paper outlines for choosing suitable PV modules, inverters, and balance-of-system components to ensure optimal energy output. The system’s effectiveness is shown by the simulation results, which account for weather fluctuations and the effects of shade. An economic analysis that assesses the system’s financial viability and payback duration finishes the study. The proposed grid connected solar PV system contribute in sustainable energy solution for an academic institute, aligning with environmental goals and promoting renewable energy. The solar system is very beneficial in educational institute to avoid bills and also helpful in research area.
  • Enhancing Power Quality in Grid-Tied Solar Photovoltaic Systems
    Prerna Tundwal, Hitesh Kumawat, Vikramaditya Dave
    Lecture Notes in Electrical Engineering, 2024
  • Empowering sustainability: The role of artificial intelligence in renewable energy
    Prerna Tundwal
    Crafting A Sustainable Future Through Education and Sustainable Development, 2023
    This chapter explores the significant role that artificial intelligence (AI) plays in advancing renewable energy technologies and promoting sustainability. It discusses how AI can address the challenges and complexities associated with renewable energy systems, improve their efficiency, and enable their seamless integration into existing power grids. The chapter also explores various AI applications in renewable energy generation, forecasting, grid optimization, energy management, and demand response. Additionally, it highlights the potential benefits of AI-driven solutions in accelerating the global transition to a sustainable energy future.
  • Artificial Intelligence-Enabled Fault Detection and Diagnosis for Improved Power Quality in Hybrid Renewable Energy Systems
    Hitesh Kumawat, Prerna Tundwal, Vikramaditya Dave
    2023 3rd International Conference on Advancement in Electronics and Communication Engineering Aece 2023, 2023
    Hybrid renewable energy systems (HRES) have drawn a lot of interest as a viable answer to the world's rising energy needs. On the other hand, combining many renewable energy sources into HRES makes it challenging to maintain excellent power quality. The optimization methodology for improving power quality in HRES using clever control tactics is proposed in this research. The suggested framework successfully manages and controls the power flow, voltage stability, and frequency control in HRES by utilizing cutting-edge control approaches including artificial intelligence and machine learning. To reduce power fluctuations and maintain a dependable and stable power supply, artificial intelligence dynamically alters the operation of RES, energy storage systems, and power converters. According to simulation results, the suggested optimization framework is effective at enhancing power quality in HRES, making it a promising strategy for the deployment of HRES in the future.
  • Intelligent Control Strategies for Enhancing Power Quality in Hybrid Renewable Energy System for Agricultural Applications
    Hitesh Kumawat, Prerna Tundwal, Vikramaditya Dave
    Proceedings 2023 IEEE World Conference on Applied Intelligence and Computing Aic 2023, 2023
    This paper presents a study on intelligent control strategies aimed at improving power quality in hybrid renewable energy systems (HRES) for agricultural applications. The increasing adoption of renewable energy sources, such as solar and wind, in agricultural settings necessitates effective control techniques to ensure reliable and stable power supply. The proposed intelligent control strategies leverage advanced algorithms to optimize the operation of various system components, including renewable energy sources, energy storage systems, and loads. The strategies encompass energy management systems (EMS) that monitor and coordinate the operation of different energy sources and storage systems, predictive control algorithms that utilize real-time data and predictive models to anticipate power generation and demand, smart grid integration techniques for seamless interaction between the hybrid system and the grid, and fault detection and diagnostic algorithms for identifying and mitigating issues that may affect power quality. The effectiveness of these intelligent control strategies is assessed through simulation studies and experimental validations in representative agricultural environments. The results demonstrate improved power quality, enhanced energy utilization, and increased system reliability, highlighting the potential of intelligent control strategies for optimizing hybrid renewable energy systems in agricultural applications.