PNIT LAB - OVERVIEW

RESEARCH OVERVIEW

Machine Learning and Reinforcement Learning-Based Integrated Operation Technique for Enhanced Renewable Energy Integration in Distribution Systems

This research focuses on developing a cutting-edge approach that utilizes reinforcement learning to enhance the integration of renewable energy sources into distribution systems, such as local power grids. The key challenge is to optimize the operation of the grid by intelligently managing both active power and reactive power. By leveraging reinforcement learning, the research aims to create a dynamic and adaptable system that maximizes the utilization of renewable energy, leading to higher efficiency and lower environmental impact.

Real-Time Power Demand Forecasting Algorithm Development

This research project aims to create innovative algorithms for predicting real-time power demand. Accurate and timely power demand forecasting is essential for efficient energy management and distribution. By leveraging data-driven methods, statistical models, and possibly machine learning techniques, this research seeks to improve the accuracy of power demand predictions. The ultimate goal is to enable utilities and grid operators to better match power supply with demand, leading to optimized energy consumption.

AI-Based Fault Record Waveform Analysis and Classification Technique Development

This research focuses on the development of advanced techniques that leverage artificial intelligence to analyze and classify fault record waveforms. The primary objective is to enhance the accuracy and efficiency of identifying faults in complex systems, such as electrical grids and industrial machinery. By applying AI algorithms, the research aims to automate the process of detecting and categorizing faults, which is critical for maintaining the reliability and safety of various systems.

Optimization of Data-Driven Building Energy Management System

A data-driven building energy management system (BEMS) is a sophisticated approach that utilizes real-time data and advanced analytics to optimize the energy consumption of buildings. This system integrates information from various sensors, weather forecasts, occupancy patterns, and energy consumption data to create a comprehensive view of a building's energy needs. By applying advanced algorithms, a BEMS can make intelligent decisions to regulate heating, cooling, lighting, and other energy-consuming systems to achieve maximum efficiency without sacrificing comfort or functionality.

Electric Vehicle Charging Scheduling

Electric Vehicle (EV) Charging Scheduling is a critical component of modern transportation systems, especially as the adoption of electric vehicles continues to grow. This concept revolves around developing intelligent strategies to manage when and how electric vehicles are charged, with the goals of optimizing energy usage, reducing costs, ensuring grid stability, and providing convenience to EV owners.

Power System Inertia Estimation

This research revolves around the accurate estimation of power system inertia, a critical parameter for the stability and control of power systems. Inertia estimation is essential, especially as power systems undergo transformations due to the increased integration of renewable energy sources. Accurate estimation of system inertia allows grid operators to design effective control strategies, ensuring the reliability and resilience of the power grid. The research aims to develop advanced techniques for estimating inertia in real-time, considering the dynamic nature of modern power systems.

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