Mini hydropower plants (MHPPs) in mountainous and remote regions often face low power generation and operational failures due to abnormal water flow conditions and canal blockages. These blockages occur due to heavy rainfall, landslides, and debris. This study aims to design and implement a real-time obstruction detection system using the Internet of Things (IoT) with LoRaWAN (Long-Range Wide Area Network). Solar-powered ultrasonic sensors were installed at 500 m intervals along the canal to measure water levels, and the acquired data were transmitted via LoRa nodes to a centralized gateway for further processing and analysis. Cloud-based analytics with real-time IoT system, including sensors, Arduino Uno, Raspberry Pi, and a LoRaWAN gateway, was used to detect abnormalities. The system was deployed and tested at the Mai Phase-I MHPP (4 × 500 kW), which is a shadow zone of cellular network. Seasonal Autoregressive Integrated Moving Average (SARIMA) predictive models were applied using Python to historical and synthetic power generation data to forecast performance trends. Experimental results showed that the system generated alerts for normal, overflow, and no-flow conditions, enabling timely obstruction detection and improving operational efficiency. The findings demonstrate that the proposed system is low-cost, low-power, enhances monitoring efficiency, and provides a scalable solution for improving MHPP operations in remote areas without cellular connectivity. The system enhances operational reliability and supports improved power generation efficiency in MHPPs.
Taday et al. (2026) studied this question.