Hybrid Solar Tracker Boosts Voltage by Up to 14% in All Weather

Global energy challenges stem from heavy reliance on fossil fuels, which drive greenhouse‑gas emissions that harm human health and the environment. Projections indicate an 80 % decline in fossil‑fuel consumption and a 50 % rise in non‑fossil energy use over the next 30 years. Solar power is poised to supply about 11 % of worldwide electricity by 2050, making efficient capture technologies critical for the energy transition.

Design and Implementation

The solar‑tracking system integrates electronic and mechanical components. Light Dependent Resistors (LDRs), an AMC 5050 A87L module, an ACS712 current sensor, a solar panel, an Arduino Mega microcontroller, and servo/stepper motors form the hardware core. Mechanically, the panel moves on two axes—north‑south and east‑west—to follow the sun.

Two control algorithms guide the movement. The active algorithm reads LDR signals, compares light intensity across sensors, and commands the motors to align the panel with the brightest direction. The chronological algorithm calculates azimuth and elevation angles from mathematical models based on the day of the year and local latitude, enabling sun‑position prediction when sensors are blocked or clouds obscure light.

A web‑based monitoring interface, built with HTML/CSS and backed by an SQL database, receives data from the Arduino and displays real‑time solar metrics.

Experiments

Tests were performed on both sunny and cloudy days. Measurements were taken from 8 a.m. to 7 p.m. at 15‑minute intervals, comparing the hybrid tracker’s output with that of a stationary panel under identical conditions.

Results and Discussion

The hybrid algorithm consistently produced higher voltage than the fixed panel. On sunny days, voltage rose up to 13 %; on cloudy days, the increase reached 14 %. These gains demonstrate that combining active sensing with chronological prediction optimizes energy capture across diverse weather conditions.

Mechanisms & Explanations

• Active Tracking operates as a closed‑loop system: LDRs detect light intensity, the Arduino processes the feedback, and motors adjust the panel to maintain optimal alignment.
• Chronological Tracking relies on sun‑position equations, moving the panel based on calculated azimuth and elevation without needing real‑time light data.
• Hybrid Algorithm merges both methods, ensuring reliable tracking when sensors are obstructed or illumination is low.
• Data Monitoring System streams sensor readings from the Arduino Mega to a server, where an SQL database stores each 15‑minute record. The web interface queries this database to present live performance graphs.