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
 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.

Cypress Robot Kit

Featured Project

Cypress is looking to develop a robotic kit with the purpose of interesting the maker community in the PSOC and its potential. We will be developing a shield that will attach to a PSoC board that will interface to our motors and sensors. To make the shield, we will design our own PCB that will mount on the PSoC directly. The end product will be a remote controlled rover-like robot (through bluetooth) with sensors to achieve line following and obstacle avoidance.

The modules that we will implement:

- Motor Control: H-bridge and PWM control

- Bluetooth Control: Serial communication with PSoC BLE Module, and phone application

- Line Following System: IR sensors

- Obstacle Avoidance System: Ultrasonic sensor

Cypress wishes to use as many off-the-shelf products as possible in order to achieve a “kit-able” design for hobbyists. Building the robot will be a plug-and-play experience so that users can focus on exploring the capabilities of the PSoC.

Our robot will offer three modes which can be toggled through the app: a line following mode, an obstacle-avoiding mode, and a manual-control mode. In the manual-control mode, one will be able to control the motors with the app. In autonomous modes, the robot will be controlled based off of the input from the sensors.