A Multiprocessing-Based GPS Solar Tracking System with Dual-Axis Control for Remote Energy-Aware Stations

Abstract

This work presents the design, control architecture, and initial performance characterization of a fully autonomous, GPS-based dual-axis photovoltaic tracking system intended for off-grid and remote energy applications. Conventional fixed-tilt PV installations are passive and low-cost but cannot continuously align to the sun, which limits incident irradiance and daily energy yield, especially at low solar elevation. Commercial sun trackers address this limitation but typically rely on optical sensors, require continuous actuation, or assume permanent infrastructure and backend connectivity. The gap we address is the need for a self-contained, low-power, self-orienting tracker that can operate without external supervision, compute sun position from first principles, manage its own energy budget, and report performance data over telemetry. The proposed system combines a Raspberry Pi (high-level computation, telemetry, and supervisory power management) with an Arduino Nano (deterministic sensor acquisition and motor actuation). Using GPS latitude/longitude, day-of-year, and UTC time, the controller computes sun azimuth and elevation through astronomical equations, converts those angles into azimuth/elevation motor commands, and steers two geared DC drives accordingly. The system also measures PV voltage, current, and power in real time and logs these quantities alongside orientation commands. A three-day rooftop deployment (15–17 June 2025, Cairo latitude ~30°N) was used to simulate energy capture for a 10 W panel under different tracking update scenarios. For realistic scenarios between 5 and 40 minutes, total daily harvested energy remained approximately 96.2–96.3 Wh, while an extremely slow, quasi-fixed behavior (60-minute cadence) produced about 95.75 Wh/day, indicating only ~0.5–0.7% loss under favorable summer conditions. These data confirm that high actuation frequency is not always required; instead, a hybrid strategy with dense updates near sunrise/sunset and slower updates midday can preserve energy yield while reducing mechanical wear and control overhead. The study establishes the functional baseline embedded control, tracking logic, autonomous power management, and telemetry framing and sets the stage for future work on long-duration field validation, adaptive energy management policies, and multi-panel scaling.