ANN-augmented adaptive droop/PI control residential is a M.Tech project topic for Electrical Engineering. It gives students a clear starting point for research, implementation planning, and documentation.
ANN-augmented adaptive droop/PI control residential Project Details
| Abstract |
This research looks at voltage and frequency instability in residential hybrid microgrids that combine many renewable sources and energy‑storage units. Standard droop and proportional‑integral (PI) controllers often can’t keep performance good when loads change quickly or generation is intermittent. To overcome this, we propose an adaptive droop‑and‑PI control that uses an artificial neural network (ANN). The ANN continuously adjusts the droop coefficients and PI gains in real time, so the system can handle fast fluctuations and non‑linear behavior. We also add an Internet of Things (IoT) monitoring setup. It gathers data live, displays the system state, and lets users track performance remotely. The method starts by modeling the hybrid AC/DC
microgrid in a simulation environment. We train the ANN with representative operating data and then test the transient response under several disturbance cases. The goal is to improve power quality, enable smooth mode changes, and provide strong voltage and frequency regulation. The IoT monitoring makes the solution scalable for decentralized energy management in modern residential power systems.
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| Reference Paper |
ANN-augmented adaptive droop/PI control for residential hybrid microgrids with IoT monitoring. |
| Domain |
Electrical Engineering |
| Sub-Domain |
Power Systems / Renewable Energy / Hybrid Microgrids |
| PDF Download |
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