Evolution of Reliable and Cost-Effective Power Systems for Buoys Used in Monitoring Indian Seas

Venkatesan, R.; Vedachalam, N.; Muthiah, M. Arul; Kesavakumar, Balakrishnan; Sundar, R.; Atmanand, M. A.

doi:10.4031/mtsj.49.1.8

Cited by 20 publications

(3 citation statements)

References 10 publications

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“…The buoy platform is fitted with 1000 mm x 670 mm solar panels which mounted horizontally on the buoy platform [16]. The installed system hardware powered by a solar power supply system with 30W small solar panel, 12V solar charger, 12V 7AH lead-acid rechargeable battery and a low voltage converter [17][18][19]. The voltage regulator is used to supply different voltage to different components, which is 5Vdc to the embedded controller and 9Vdc to the sensor network.…”

Section: Sustainable Solar Power Supply Systemmentioning

confidence: 99%

Sustainable Surface Water Dissolved Oxygen Monitoring at Lake 7/1F, Shah Alam, Selangor

Ali¹,

Thamrin²,

Misnan³

et al. 2021

JMechE

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Shah Alam is in Selangor, Malaysia and symbolized with several lakes that are popular among local communities and visitors for leisure and recreational activities. Among of these lakes, few lakes need management to look after its hygienic and well-being to serve its purpose as the recreational lake for the nearby community, such as Lake 7/1F, Shah Alam, Selangor. The secluded location of this water reservoir has made it unpopular, and improper management of it could invite an imbalance environment to its surrounding. Therefore, to initiate the effort, an Internet-of-Thing (IoT) water monitoring system is deployed at this lake to study the dissolved oxygen (DO) level in the water that could indicate the health status of this lake. The system consists of a DO sensor, embedded controller, self-charging power supply, wireless data transmission, and IoT dashboard for in-situ DO measurement. From the analysis, the lake has the normal cycle of DO production, but a dangerous condition is detected when the concentrated DO in the lake water is mostly below the saturation value.

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Section: Sustainable Solar Power Supply Systemmentioning

confidence: 99%

Sustainable Surface Water Dissolved Oxygen Monitoring at Lake 7/1F, Shah Alam, Selangor

Ali¹,

Thamrin²,

Misnan³

et al. 2021

JMechE

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“…The total capacity of the battery is calculated based on the power budget ( Figure 6). The number of sensors, sampling frequency and back up duration are other factors determining the battery capacity (Venkatesan et al, 2015a).…”

Section: Power Management Systemmentioning

confidence: 99%

Best Practices for the Ocean Moored Observatories

et al. 2018

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Real-time spatio-temporal meteorological and oceanographic data, from the Ocean moored observatories, are essential for the precise forecast of the ocean state, climate variability studies and reliable weather prediction. Precise spatio-temporal measurement of subsurface parameters such as temperature, salinity and current are essential to understand the intra-seasonal and inter-annual evolution of monsoons and tropical cyclones. To cater to this time-critical information, moored observatories have to continuously be operational in the harsh marine environment to measure these essential ocean variables. However, bio-fouling and corrosion limits the life time and accuracy of the highly precise measuring instruments. Thus, best practices in these moored observations are essential for long term accurate and cost-effective ocean observation. The Indian moored buoy network which has been operational since 1997, has been providing quality data over the past decade. This paper describes the best operational practices and quality control processes followed in the Indian moored buoy system design, sensor calibration, testing, integration, deployment, retrieval, and data quality control over the past two decades, which has helped to achieve an average meteorological data return of 90%.

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“…Sea surface salinity (SSS, expressed in PSU) is a key property controlling seawater density and plays a critical role in the global hydrological cycle, density-driven global ocean circulation, climate dynamics, vertical mixing, air-sea exchange, biogeochemistry, acidification, and ocean heat content [1]- [3]. Changes in the SSS are driven by fresh water flux from the continents, evaporation, precipitation, glacier melts, stratification and vertical mixing.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Sea Surface Salinity Estimates Using Machine-Learning Algorithm With SMAP and High-Resolution Buoy Data

2022

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Despite the recent advances in satellite-based L-band microwave radiometry and retrieval algorithms to provide a unique capability to measure sea surface salinity (SSS) from space and explore its utility for understanding mesoscale dynamics, global ocean circulation, vertical mixing, evaporation rates and climate oscillations, SSS retrieval from L-band microwave radiometric data from the NASA-SMAP (Soil Moisture Active Passive) mission is often biased with systematic errors on larger temporal, spatial scales and random errors on short-time and length scales. To improve the SSS retrieval from SMAP data, we developed a robust algorithm based on a machine-learning approach with high-resolution in-situ data from the Ocean Moored Buoy Network in the Northern Indian Ocean (OMNI), which includes the Arabian Sea (AS) and Bay of Bengal (BoB)). The new algorithm was rigorously trained, tested and validated using the in-situ SSS time series measurements from the AS and BoB. Several sensitive variables were examined and used to improve the SSS estimatessuch as radiometric and ancillary data from the satellite observations, sea-surface wind and precipitation from the ERA5 data, and SSS/ SST from the OMNI buoy measurements. The OMNI timeseries measurements provided the spatially averaged satellite products to characterize the variability and gross features of SSS in BoB and AS waters on weekly, monthly, seasonal and annual time scales. The systematic validation of SMAP SSS products on a range of spatio-temporal scales showed that the new algorithm improved the SSS estimates by more than 15% in open ocean waters and 25% in river-discharge and precipitation-dominated regions in BoB and AS, when compared to the standard (operational) algorithm. Further analysis demonstrated that the new algorithm reduced clear biases and significant anomalies in SMAP SSS retrievals in the regions of river runoff, surface-freshened ocean and intense tropical cyclones, and captured synoptic/mesoscale SSS features and their seasonal variations in the North Indian Ocean.

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Evolution of Reliable and Cost-Effective Power Systems for Buoys Used in Monitoring Indian Seas

Cited by 20 publications

References 10 publications

Sustainable Surface Water Dissolved Oxygen Monitoring at Lake 7/1F, Shah Alam, Selangor

Sustainable Surface Water Dissolved Oxygen Monitoring at Lake 7/1F, Shah Alam, Selangor

Best Practices for the Ocean Moored Observatories

Enhanced Sea Surface Salinity Estimates Using Machine-Learning Algorithm With SMAP and High-Resolution Buoy Data

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