Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/80270
Title: Non-destructive testing for sediment deposition monitoring in the Nam Sana hydropower plant reservoir using internet of things
Other Titles: การทดสอบแบบไม่ทำลายสำหรับการเฝ้าสังเกตการสะสมตัวของตะกอนในอ่างเก็บน้ำของโรงไฟฟ้าพลังน้ำของน้ำสนาโดยใช้อินเทอร์เน็ตของสรรพสิ่ง
Authors: Nitsakhone Keochomsi
Authors: Anucha Promwungkwa
Nitsakhone Keochomsi
Keywords: Nam Sana hydropower plant reservoir
Issue Date: 2-Oct-2024
Publisher: Chiang Mai : Graduate School, Chiang Mai University
Abstract: This study introduces a method to monitor sediment deposition using the non-destructive testing (NDT) techniques combined with Internet of Things (IoT) technology with a focus on the Nam Sana Hydropower Plant reservoir in Laos. Sediment accumulation in hydropower reservoirs poses a significant challenge to plant efficiency by reducing water retention capacity, impacting electricity production, and accelerating the corrosion of turbine components. Traditional monitoring methods which rely on manual measurements using dipping poles, are prone to errors due to equipment limitations and human factors. This research demonstrates the feasibility and effectiveness of using NDT in conjunction with IoT systems to overcome these challenges. The proposed method employs a 4–20 mA submersible water level sensor to gather sediment deposition data within the reservoir. An ESP32 microcontroller is used to transmit and process this data through an A/D converter, utilizing the MQTT internet protocol. This technology allows operators to estimate sedimentation trends accurately. Data collection is optimized by assessing multiple grid points within the reservoir area. The calibration of the sensor, which involved immersion in laboratory water, yielded an average accuracy of 98.5%. The study also examines the impact of sediment accumulation on the performance of the hydropower plant, including electricity production and turbine efficiency. Data collected from 2015 to 2023 show an average annual sedimentation volume of 6,459 cubic meters, affecting 28.3% of the total reservoir volume. Following sediment removal, a 12.1% increase in production was observed over a 30-day period, comparing post- and pre-sedimentation removal conditions. To further address sediment accumulation, the study recommends constructing a sediment trap with a capacity of 4,000 cubic meters, designed to capture 62% of the average annual sediment load. This solution aims to enhance the operational efficiency and sustainability of the hydropower plant by improving sediment management practices. In conclusion, this research highlights the viability and effectiveness of integrating NDT and IoT technologies for sediment monitoring, providing valuable insights for optimizing hydropower reservoir management.
URI: http://cmuir.cmu.ac.th/jspui/handle/6653943832/80270
Appears in Collections:ENG: Theses

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