Data-driven internet of things and cloud computing enabled hydropower plant monitoring system
Kumar, Krishna LU
and Saini, R. P.
(2022)
In Sustainable Computing: Informatics and Systems
36.
- Abstract
Hydropower is one of the renewable energy sources that can play a crucial role to fulfil the global energy demand. However, the performance of the hydro turbine is severely affected by silt erosion and cavitation problems which causes a reduction in the overall efficiency of the plant. Various studies have been carried out and are available in the literature to investigate silt erosion and cavitation issues in hydro turbines. It has been reported that cavitation and silt erosion varies with the variation in discharge under part load and overload operating conditions of the machine. However, very few studies are available to predict the performance of the machine under variable operating conditions. Hence, there is a scope of study for... (More)
Hydropower is one of the renewable energy sources that can play a crucial role to fulfil the global energy demand. However, the performance of the hydro turbine is severely affected by silt erosion and cavitation problems which causes a reduction in the overall efficiency of the plant. Various studies have been carried out and are available in the literature to investigate silt erosion and cavitation issues in hydro turbines. It has been reported that cavitation and silt erosion varies with the variation in discharge under part load and overload operating conditions of the machine. However, very few studies are available to predict the performance of the machine under variable operating conditions. Hence, there is a scope of study for monitoring the performance under these conditions in real-time, as it is difficult to predict the behavior of the machine using the existing models. In view of the above, an architecture of a data-driven IoT-based cloud computing-enabled hydropower plant monitoring system has been proposed under the present study. In order to develop this system, historical plant data has been collected and correlations are developed, which are validated with real-time data on the ThingSpeak cloud. It has been found that the developed model can accurately predict the condition of the hydro turbine with an R2-value of 0.9693 having a mean absolute percentage error (MAPE) of 0.67% at 0.89% of root mean square percentage error (RMSPE), and the power factor with an R2-value of 0.9503, having a MAPE of 0.798% at 0.91% of RMSPE.
(Less)
- author
- Kumar, Krishna
LU
and Saini, R. P.
- publishing date
- 2022-12
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Cloud computing, Hydropower, IoT, Monitoring system, Operation and maintenance
- in
- Sustainable Computing: Informatics and Systems
- volume
- 36
- article number
- 100823
- publisher
- Elsevier
- external identifiers
-
- scopus:85141435715
- ISSN
- 2210-5379
- DOI
- 10.1016/j.suscom.2022.100823
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2022 Elsevier Inc.
- id
- 7f9c94ee-9a2c-4444-934b-e40fdf84e683
- date added to LUP
- 2024-04-15 13:22:55
- date last changed
- 2024-05-16 14:46:49
@article{7f9c94ee-9a2c-4444-934b-e40fdf84e683,
abstract = {{<p>Hydropower is one of the renewable energy sources that can play a crucial role to fulfil the global energy demand. However, the performance of the hydro turbine is severely affected by silt erosion and cavitation problems which causes a reduction in the overall efficiency of the plant. Various studies have been carried out and are available in the literature to investigate silt erosion and cavitation issues in hydro turbines. It has been reported that cavitation and silt erosion varies with the variation in discharge under part load and overload operating conditions of the machine. However, very few studies are available to predict the performance of the machine under variable operating conditions. Hence, there is a scope of study for monitoring the performance under these conditions in real-time, as it is difficult to predict the behavior of the machine using the existing models. In view of the above, an architecture of a data-driven IoT-based cloud computing-enabled hydropower plant monitoring system has been proposed under the present study. In order to develop this system, historical plant data has been collected and correlations are developed, which are validated with real-time data on the ThingSpeak cloud. It has been found that the developed model can accurately predict the condition of the hydro turbine with an R<sup>2</sup>-value of 0.9693 having a mean absolute percentage error (MAPE) of 0.67% at 0.89% of root mean square percentage error (RMSPE), and the power factor with an R<sup>2</sup>-value of 0.9503, having a MAPE of 0.798% at 0.91% of RMSPE.</p>}},
author = {{Kumar, Krishna and Saini, R. P.}},
issn = {{2210-5379}},
keywords = {{Cloud computing; Hydropower; IoT; Monitoring system; Operation and maintenance}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{Sustainable Computing: Informatics and Systems}},
title = {{Data-driven internet of things and cloud computing enabled hydropower plant monitoring system}},
url = {{http://dx.doi.org/10.1016/j.suscom.2022.100823}},
doi = {{10.1016/j.suscom.2022.100823}},
volume = {{36}},
year = {{2022}},
}