Low-Cost GSM-Based Smart DOL Motor Automation System for Agricultural and Industrial Applications
Conventional Direct-On-Line (DOL) motor starters used in agricultural irrigation and small industrial applications require physical operator presence, offer no remote accessibility, and lack automated protection mechanisms. This paper presents a low-cost, GSM-based smart DOL motor automation system designed specifically to address these limitations in regions where internet connectivity is unavailable or unreliable. The proposed system integrates an Arduino Nano microcontroller, SIM800L GSM module, EL817 optocoupler isolation stage, 5V relay, RTC module, and an AC-to-DC power supply derived directly from the 230V mains supply. A key experimental finding of this work is that the EL817 optocoupler, which carries a datasheetrecommended input drive current of 5 mA, operates reliably and without signal degradation at input currents as low as 1 mA when interfacing 230V AC mains sensing circuits, effectively reducing power dissipation and improving component longevity. The system enables farmers to start, stop, and schedule motor operation via SMS, voice call, or a mobile application — entirely without internet connectivity — from any geographic location. Additionally, the system implements autonomous dry-run protection and faulty supply detection to prevent motor damage. Experimental evaluation across 100 switching trials demonstrated a 100% motor ON/OFF command success rate, a relay switching response time below 50 ms, and a GSM command latency in the range of 500–1000 ms. Component temperatures remained below 40°C under sustained operation. The proposed system was implemented at a hardware cost below INR 3,000, representing a reduction of more than 50% compared to commercially available mobile-controlled motor automation units. These results establish the proposed architecture as a practical, scalable, and cost-effective foundation for smart agricultural motor automation.
Authors
- Sanmay Kotkar
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-05-12
- DOI
- https://doi.org/10.5281/zenodo.20145400
- Primary Topic
- Smart Agriculture and AI
- Type
- preprint