Abstract
Energy management and monitoring are significant obstacles to microgrid implementation in smart homes. As a result, further research is required to address the modeling and operational aspects of the system's future findings for various applications. This work describes a novel technique for managing microgrid energy based on a robust control approach and the construction of a real-time monitoring platform. The proposed controller is based on an Artificial Neural Network (ANN), which is already in use in the energy internet paradigm, where Distributed Generators are connected to microgrids (DGs). When paired with the Thingspeak platform for real-time data analysis, the suggested technique manages the microgrid's power flow, and frequency/voltage regulation improves the load management system's performance and monitoring capabilities. The MATLAB simulation results validate the feasibility and effectiveness of the suggested microgrid control method and technique under various operational scenarios. Additionally, the findings demonstrate that the proposed monitoring platform can supervise data in real time.
Publication Date
4-30-2022
Document Type
Master's Project
Student Type
Graduate
Degree Name
Electrical Engineering (MS)
Advisor
None provided
Recommended Citation
Jalal, Fatima Hassan, "Microgrid Energy management using ANN and Monitoring platform for micro-grid systems using the Internet of Things" (2022). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/12198
Campus
RIT Dubai
