Project-Based Engineering Education Through an Open-Source Smart Energy Meter Platform: Integrating Electrical Measurement, Embedded Systems, and IoT
Authors/Creators
- 1. Department of Electrical and Electronics Engineering, Faculty of Çorlu Engineering, Tekirdağ Namık Kemal University, Tekirdağ, Türkiye.
Description
Engineering students often encounter electrical measurement, embedded systems, and IoT technologies in separate courses, with limited opportunities to integrate these domains within a complete engineering system. This study presents a project-based engineering education framework built around an open-source smart energy meter platform and structured to connect theoretical concepts with hardware implementation, firmware development, measurement validation, IoT integration, and systematic troubleshooting. The educational model organizes the platform into seven technical learning modules and a seven-stage project workflow covering system analysis, hardware examination and PCB implementation, firmware configuration, measurement verification and calibration, IoT integration and data monitoring, testing and troubleshooting, and technical reporting. The underlying platform provides a 17-channel measurement structure consisting of one main line and 16 auxiliary circuits and supports approximately 7 kHz waveform observation, enabling students to investigate measurement principles, signal conditioning, resource sharing, real-time firmware, and data communication within a single system. Learning is assessed through five weighted criteria: technical performance (25%), calibration and validation awareness (20%), IoT functionality (20%), troubleshooting (20%), and documentation quality (15%). Real implementation problems are incorporated as structured learning activities; for example, missing anti-aliasing filter components produced approximately 3–5% IRMS deviation under noisy operating conditions, while permanent integration of a 3.579545 MHz SMD oscillator improved clock frequency accuracy from approximately ±1.6% to ±0.005%. The proposed framework demonstrates how open-source hardware can support an end-to-end, evidence-based learning environment integrating measurement, embedded systems, IoT, verification, debugging, and engineering documentation.
Files
PJSE v12n4(22-42)Arslan_2.pdf
Files
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