Project

# Title Team Members TA Documents Sponsor
48 Intelligent Net-Energy Optimization System for Distributed Photovoltaic Nodes in Microgrids
 Minghao Fang
Yifei Liu
Yikai Zhang
Ziru Niu
 design_document1.pdf
final_paper1.pdf
other1.pdf
 Ruisheng Diao
Problem
In modern microgrids, Distributed Energy Resources (DERs), particularly small-scale photovoltaic systems, suffer from significant efficiency losses due to the misalignment between solar panels and the sun. While dual-axis tracking systems exist, traditional active tracking methods often consume more power in actuation (motors) than they gain in generation, especially during intermittent cloud cover or low-light conditions. There is a lack of low-cost, adaptive control strategies that can autonomously evaluate the "net energy gain"—balancing the energy cost of moving against the potential generation revenue—in real-time.

Solution Overview
This project seeks to develop an intelligent, edge-computing-based control solution that maximizes the net energy yield of a PV node using accessible, cost-effective hardware, ensuring economic viability for small-scale microgrid applications. Our system will utilize a Master-Slave architecture, integrating a Raspberry Pi for high-level computing (e.g., net-energy optimization algorithms) and a microcontroller (e.g., STM32) for hard-real-time motor execution. It will feature one-button autonomous calibration and real-time visualization of energy data.

Solution Components
Software Component:

Edge-computing logic (e.g., Q-Learning or threshold-based algorithms) on the Raspberry Pi to decide optimal tracking strategies based on real-time irradiance and motor power consumption.

Real-time embedded control code on the microcontroller for accurate sensor polling, PWM generation for motors, and serial communication with the Raspberry Pi.

Data visualization software to drive an OLED screen, displaying current voltage, net power gain, and AI status.

Hardware Component:

A custom-designed PCB integrating robust power management (essential for simultaneously powering the Raspberry Pi and motors via battery/PV), stepper/servo motor driver circuits, and sensor interfaces.

Microcontroller (e.g., STM32/ESP32) and Raspberry Pi boards.

Sensor array: Current/Voltage sensors (e.g., INA219) for power calculation, and photoresistors/LDRs for light tracking.

Dual-axis pan-tilt mechanical structure, solar panel, and a stable chassis.

Criteria of Success

The system initiates self-calibration and begins autonomous tracking immediately upon a single button press, requiring no external computer connection.

The system successfully tracks the brightest light source under normal conditions.

The adaptive control algorithm successfully pauses motor actuation during simulated low-light or rapidly fluctuating light conditions, demonstrating an avoidance of negative net energy gain compared to a continuous tracking baseline.

The OLED display accurately shows real-time system metrics (voltage, current, power status).

Distribution of Work

Ziru, Niu (EE) & Yifei, Liu (ECE): Responsible for the custom PCB design, power management circuitry, hardware sensor integration, and underlying microcontroller programming for motor control and data acquisition.

Minghao, Fang (ECE): Responsible for developing the edge-computing optimization algorithms on the Raspberry Pi, serial communication protocols, and the OLED data visualization software.

Yikai, Zhang (ME): Responsible for the physical design and fabrication of the dual-axis pan-tilt mechanism, ensuring the structural stability of the chassis, and managing the heat dissipation and mounting of the electronic components.

Seat U: Sensing System for Real-time Library Seat Occupation Detection

Jiayuan Huang, Hangzheng Lin, Jiaqi Lou, Hanyin Shao

Featured Project

# Problem

During the exam week, it is very difficult to find a seat in the library. Sometimes students cannot find a satisfying seat even if they walk through the library all around. Some students complain about unknown traffic in the library. For more convenient library seats seeking, students would like to know which other seats are empty ahead of time in order to decide whether they will go to the library and where to find available seats.

# Solution Overview

We will design a sensor-based device for each table to detect occupancy. The occupancy data will be uploaded through wifi to the cloud. There will be three states for each seat: occupied by people, occupied by items, or unoccupied. Then we will design an APP to visualize these data.

# Components

## The sensing subsystem:

• Data preprocessing and WiFi module to transfer data (ESP32)

• Multi-kinds of sensors to detect objects and collect data

• Wired power supply to support long-term real-time detection

## Human-computer interaction subsystem:

• Database server to store the collected data

• APP on the phone that allows clients to check the status of library seats

• It can indicate whether the seat is occupied with people (reserved by personal items), occupied without people, or available

# Criteria of Success

• Classify three different states of seats (occupied by people, occupied by items, or unoccupied)

• The accuracy of detecting whether a seat is reserved by items is above 90%

• The accuracy of detecting whether a seat is occupied by people is above 95%

• The sensor-based device APP is user-friendly and accurately visualizes the seat occupation

• The states of the seats get updated every 1 minute in the APP

• Adaptive to different kinds of table in the library (flexibility)

• Implement the database server bidirectionally: upload data from the device and download data to the APP