WATERWATER AND ENERGY INTERNATIONAL · 2019-07-17 · 4 WATER And EnERGY InTERnATIOnAL May 2019 C O...

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CENTRAL BOARD OF IRRIGATION & POWERMalcha Marg, Chanakyapuri, New Delhi

ISO 9001:2015

ENERGYENERGYENERGYANDANDWATERWATERWATERINTERNATIONALINTERNATIONAL

ISSN: 0974-4711Rs. 200/-May 2019

VOLUME 62/RNI, NO. 2

Special Highlights

International Conference on Integration of Renewables with

Synchronous Grid by CIGRE-India during GRIDTECH 2019

Inauguration of the National Workshop on “Developing Comprehensive and Scientific Mechanism/Model for Forecasting of Hydrology for Hydro Electric Projects” held on 25 April 2019 at New Delhi, by Shri Sanjiv Nandan Sahai, Additional Secretary, Ministry of Power, Government of India,

joined by (L to R) Dr. S.K. Jain, Director, National Institute of Hydrology; Dr G.P. Patel, Director (Water Resources), CBIP; Shri Nand Lal Sharma, Chairman-cum-Managing Director, SJVN Ltd.; Shri A.S. Bindra, Director (Finance), SJVN Ltd. and Shri V.K. Kanjlia, Secretary, CBIP

WATER And EnERGY InTERnATIOnAL May 2019 3

CEnTRAL BOARd Of IRRIGATIOn & POWEROffICE BEARERs

InTERnATIOnAL EdITORIAL AdVIsORY BOARdV.C. Patel - Distinguished Professor and Director,

University of IOWA, U.S.A.donggi Zuo - Prof. & President’s Advisor of Hohai University,

Ministry of Water Resources, People’s Rep. of China, K.G. Ranga Raju - Former Professor of Civil Engineering

University of Roorkee (Now IIT Roorkee) secretary General, CIGRE, France,

Jose M. Martin Mendiluce - Doctor Ingeniero de Carninos ASINC, S.L.. Spain,

Alison Bartle - Editor, Aqua-Media International Ltd., United Kingdom.

lThe statements and opinions expressed in this Journal are not necessarily of Central Board of Irrigation & Power.

lReproduction of articles from Water and Energy International is subject to approval of the Editor.

l Interested authors from India and abroad are requested to contribute their articles to this Journal and send the same to Secretary, Central Board of Irrigation & Power, well in advance.

All communications should be addressed to :

Secretary

Central Board of Irrigation & PowerMalcha Marg, Chanakyapuri

New Delhi 110 021

Ph. : 91-11-2611 5984

Fax : 91-11-2611 6347

E-mail : [email protected]

Website : www.cbip.org

President : Mr. s. Masood Husain, Chairman, CWC

(i) Vice President : Mr. P.s. Mhaske, Chairperson, CEA

(ii) Vice President : Mr. K.s. Popli, Former CMD, IREDA

(iii) Vice President : Mr. Ashok sethi, Former COO & ED, Tata Power

secretary : Mr. V.K. Kanjlia

director : Mr. P.P. Wahi

director (WR) : dr. G.P. Patel

director (Energy) : Mr. A.K. Bhatnagar

fROM EdITOR’s dEsK V.K. Kanjlia, Secretary, CBIP 5nEWs LETTEREnergy Section– 2 Weeks Training Program for Fresh Recruited Trainee Engineers/Jr.EngineersTrainees/TraineeOfficersof Chenab Valley Power Project Pvt. Ltd. (CVPPPL) on Hydro Power Development 6Energy Section– Utilizing Retiring Thermal Units as Synchronous Condenser for Reactive Power Management in Renewable Energy Rich Scenario – Karan Sareen, B.S Bairwa, (Ms) Rita Nagdeve and Pardeep Jindal 10– EfficiencyEnhancementofSolarGenerationusing Solar Optimizer – Nilesh Kane, Raj Kumar Sharma, B. Karunakaran and Pankaj Patel 14– Designing and Integration of Solar Energy in the Energy Intensive Transport Sector – Case Study on Formation of a Micro Grid – Manuj Singhal, Ashish Arora and Reddy Sai Suraj 18– Improvement Needed for Accurate Wind Generation Forecasting - Wind Generation Forecasting in Present Era – B.B. Mehta, A.P. Shah and S.P. Nayak 25

Publisher, Printer and Editor Mr. V.K. Kanjlia on behalf of Central Board of Irrigation and Power, Printed at M/s Chandu Press, D-97, Shakarpur, Delhi, published from Central Board of Irrigation and Power, Malcha Marg, Chanakyapuri, New Delhi 110 021

Total Pages 84

4 WATER And EnERGY InTERnATIOnAL May 2019

C O N T E N T S (contd.)

– Large Scale Battery Energy Storage System Supplementary Application for Improving Grid Performance – Case Study – Edla Srinivas and Dr. Neelima Tambey 31– Application of Point-on-Wave Switching for Integration of Renewable Sources on Main Grid in Context to Handing Switching Transients – Dr. Urmil Parikh 38

– Frequently Asked Questions & Answers – (SPARK Ignited to share – A Power Engineers WhatsApp Group) 43

W&E InTERnATIOnALWater Resources Section– IoT Based Real-Time Liquid Flow Rate Monitor System and Leakage Detection – I Made Agus Hary Setiawan, I Ketut Gede Darma Putra and Kadek Suar Wibawa 51– Estimation of Scour of Ski-Jump Bucket Type Spillways Using on Field Data – Arun Goel 55– Analysis of Rain Event Properties for Mumbai, India – Amit Parchure, Shirish Kumar Gedam and Anil S. Parlikar 60– Assessment of Shape and Size of End Sill on Hydraulic Energy Indulgence Behaviour in SAF Stilling Basin – Shatruhan Jaiswal and Akhilesh Kumar 66

nEWs LETTERWater Resources Section

– NationalWorkshoponDevelopingComprehensiveandScientificMechanism/Modelfor Forecasting of Hydrology for Hydro Electric Projects, 25 April 2019, New Delhi 71

sTATIsTICAL dATAWater Resources Section • 76Energy Section • 77

WATER & EnERGY ABsTRACTs` Energy Section • 79Water Resources Section • 80

fAR & nEAR In WATER And EnERGY

News - Energy Section • 81News - Water Resources Section • 82

For Advertisement Please Contact :SecretaryCentral Board of Irrigation and PowerMalcha Marg, Chanakyapuri, New Delhi 110 021Phone: 91-11-26115984/1294/26116567 Fax: 91-11-2611 6347 E-mail : [email protected]

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V.K. Kanjlia Secretary, CBIP

Climate change is one of the biggest and most complex problem the development community has ever faced. The impacts of higher temperatures, variable precipitation, and extreme weather events have already begun to impact the economic performance of countries and the lives and livelihoods of millions of poor people. India is among the countries most vulnerable to climatechange.Oneofthemostsignificantway,bywhichclimatechangewillimpact the lives of people in India, will be through its water resources.

Climate change is expected to increase the frequency and intensity of current extreme weather events, greater monsoon variability and also the emergence of

newdisasteri.e.sealevelriseandnewvulnerabilitieswithdifferentialspatialandsocio-economicimpactson communities. This unprecedented increase is expected to have severe impact on the hydrological cycle, water resources, forest and ecosystems, losses of coastal wetlands and mangroves, food security, health and other related areas. The impact would be disastrous particularly for developing countries, including India. It may further reduce the resilience of poor, vulnerable communities, which make up between one quarter and one half of the population of most Indian cities.

The world economy is now exposed to considerable risk and uncertainty caused by climate changes. Global average temperature would increase by 1.4 to 5.8ºC during the period 1990–2100. Annual precipitation may also increase or decrease, depending on regions, by 5-20%, compared with the past 30-yearmean.Although these figuresmay be debatable, but still can be considered for preliminaryestimate. It is worth considering potential impacts of global warming on any aspect of the economy and possible mitigation measures. In particular, anticipated climate changes may bring about a dramatically differentsituationofenergydemand.

In recent years special attention has been given to hydropower generation, because it is source of renewable energy. At the same time, hydropower is clearly among the most vulnerable areas to global warming because water resources are closely linked to climate changes.

Hydropower is the second largest contributor of energy consumed in the Indian power sector. The most significanthydropowerpotentialinIndiaexistsinthethreemajortransboundaryriverbasins(Ganges,Indus, and the Brahmaputra). However, all these basins have experienced substantial changes in precipitationandairtemperaturethataffectedwateravailabilityforhydropowergeneration.

Sincehydropowerproductionanditspotentialdependsonstreamflow,itissensitivetoclimatechange.The impacts of climate change on hydropower potential have been studied globally. India has experienced significantwarmingover thepast fewdecades,which is likely tocontinuealongwith thechanges inprecipitation in the 21stcentury.Despitetheprofoundimplicationsofclimatechangeonstreamflow,thelinkage between climate change and hydropower production in India remains unexplored.

There is a strong need for hydrology prediction in river basins through short term and medium term weatherforecastingalongwithquantumofflood,rainfall,snowfall,glacierssizeinthecatchmentareacombined with temperatures etc. These issues are of great importance for correct use of forecasts to make it possible to optimise and predict the Hydroelectric Power output over short term and medium term and todecideannualgenerationtargetforourHydroElectricPowerPlantsonamorescientificbasis.

Ihopethereaderswillbehighlybenefittedfromtheinformationgiveninthisissue.

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iot Based real-time liquid Flow rate Monitor system and

leakage DetectionI Made agus Hary setIawan, I Ketut gede darMa Putra

and KadeK suar wIbawaDepartment of Information Technology, Udayana University, Indonesia

ABSTRACTWater is a primary human need that cannot be replaced. Communities in Bali get water from several water springs that are spread over several points on the Bali Island. Water from natural water spring is distributed to areas that need it. Water distribution in Bali is still done manually, its often causing some problems. The problem that often occurs in water distribution is water leakage. Water flow leakage is very difficult to detect especially when the leakage point is found in the distribution of underground water. This leakage problem can be anticipated by monitoring water flow and pressure directly. Flow monitoring can be a solution to overcome this leakage problem, with real-time monitoring stakeholders can take necessary actions to deal with leaks quickly. A monitoring system that is integrated and IoT technology is a solution to provide real-time flow and water pressure data to overcome leaks that occur in the water distribution flow.Keywords : water, water leak, IoT, real-time monitoring system.

InTROdUCTIOnMonitoring explained as a method used to monitor, control, manage and optimize the network by using different computational methods that will provide users with relevant tools and information (Radhakrishnan & Wu, 2018). The Monitoring process is needed to record every movement of flow and pressure data in the water distribution process. The Monitoring process purposed to obtain recent water flow and pressure data to detect any leakages. Leakages can occur in every pipeline, even well-maintained, water pipeline networks. It is estimated, that average water loss in public water network ranges from about 5% in well-maintained, to 30% in older pipeline networks (Klepka & Michalik, 2015). The leakages often occur when the pressure inside the distribution pipeline is exceeding the pipeline manufacture’s maximum pressure (Daadoo & Daraghmi, 2017). That can happen in every single water distribution pipeline anytime and anywhere. By the high amount of percentage of leakage mentioned above, it is important to keep the water distribution network stays under control and stays monitored. The monitoring system can be built using any kind of method.The water monitoring system explained in this article used a wireless sensor network and IoT concept to obtain the current flow and pressure data from the pipeline. Its important to make this system work on every landmark condition, so then the device used for monitoring must be built in very flexible way. The sensors must able to reach the pipeline in any situation on the land. There will be a lot of obstacle on the field. To

solve this problem, the sensors and other component must be built in flexibly. One of the solution is create one nodes which can connect the sensors and the internet. There the Arduino and a GSM/GPRS module used. The sensors must be easy to be replaced anytime it needs to be changed for maintenance or broken, there come the solution to make the sensors detachable with the node (Ijaradar, Chatterjee, & Pi, 2018). It make it easier to maintenance and attachment to the pipeline anywhere it placed.The water flow sensors needed to detect current water flow in the pipe line. It is used the Hall Effect to calculate every bit and transform it into flow rates (Anandhavalli, Sangeetha, Dharshini & Fathima, 2018). The flow rate than collected and compared its record with another node record to detect leakage. Its important to continue monitoring the flow rate data. It will help the stake holder to control the amount of water delivered to a certain place. The pressure sensors work using the piezoresistive effect. The main reason behind the leak is damage to the pipe due to pressure that exceeds the capacity that can be borne by the pipe. Its really important to keep it under control. Leakage also can be detected when the pressure is drastically decreased. This paper develop an IoT system aimed to detect leaks automatically and also inform the users about the latest flow and pressure data from the pipeline. To make it easier in development, design and maintenance, the water monitoring system developed using 7 layers architecture issued by Cisco to organized every components and make it approachable for other companies to develop the same kind of project (Cisco, 2014). This kind of IoT system is an improvement from another project. This improve the

water resources section

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processing and simplify the algorithm that used to detect leakage to improve processing time (Deuerlein, Piller, & Montalvo, 2014). Using GSM/GPRS Technology also improving efficiency and ease in data transfer (Daadoo & Daraghmi, 2017). IoT approach will help the stakeholder to takes strategic action to handle water leakage (Swetha Reddy, V. Chanakya, Eswari & Bhupati, 2018).

SYSTEM dEvELOpMEnTThis monitoring system constructed using the IoT Referenced Model that issued by Cisco. This Water Monitoring system construct by these layers: Physical Devices and Controllers, Connectivity, Edge (Fog) Computing, Data Accumulation, Data Abstraction, Application and Collaboration and Process (Cisco, 2014). These layers described below.

1. Layer 1 : physical devices and ControllersPhysical Devices and Controllers layer contain every single element that consists of any physical devices either sensors and controllers (Cisco, 2014). This Water Monitoring system consists of Arduino, Water Flow Sensor, Water Pressure Sensors and GSM SIM800L v2. Each item’s function and workflow is shown as described below.(a) Arduino Arduino is a microcontroller that is open-source

platform. Arduino built on easy-to-use hardware and software. Arduino boards are able to read inputs from various sensors and process it into many response outputs. Arduino board programmed using Arduino IDE. Arduino is open source microcontrollers, it’s cheap and easy to use.

On this project Arduino used to process sensors data and send it through the server online. Arduino reads data from water flow sensors. The data then processed in Arduino and send it wirelessly through 2G network using SIM 800L v2.

(b) YF-S201 (Liquid Flow Rate Sensor) YF-S201 is a water flow rate sensor which measure how

much liquid has moved through the pipeline. There are many type of water flow sensors, the one used in this system is mechanical water flow sensors using Hall Effect. When water flows through the rotor inside the water flow sensor, the rotor start rolling after feeling the pressure. Its speed changes with different rate of flow. The hall-effect sensor outputs the corresponding pulse signal[6].

The Hall Effect theory is utilized in the mechanical water flow meter using a fan/propeller shaped rotor and placed inside water flow sensor and will move by the water flowing. The water pushed the fan and make rotate. The shaft of the rotor is connected to a Hall Effect sensor. It is an arrangement of a current flowing coil and a magnet connected to the shaft of the rotor, thus a voltage/pulse is induced as this rotor rotates. In this flow meter, for every liter of water passing through

it per minute, it outputs about 7.5 pulses Calculate the flow rate in liters per minutes (L/minutes) using a simple conversion formula (Anandhavalli et al., 2018).

Q = L / s ...(2,1) On the equations above shows how the calculation

process to get the water flow. Q is a symbolic of water flow rate, L is a symbolic of much liquid that past through of the sensors and s is a duration. The Pulse frequency is in Hz, it’s value comes from 7.5. Q where Q is flow rate in Liters/minute.

Pulse frequency (Hz) = 7.5 x Q ...(2,2) Using the equation above, we can get the flow of the

water in Liters/minute. It’s then delivered through Arduino and send it to the server.

(c) GSM/GPRS SIM 800lv2 GSM/GPRS SIM 800l v2 modules provides an internet

connection for the Arduino. Using this module will make the Arduino send data from the sensor to the server through the internet. This connection uses a GPRS technology. GPRS technology is available on 2nd and 3rd generation cellular communication network. GPRS connection allow data speed up to 115 kbps (Amin & Khan, 2014), its more than enough to send sensors data to the server.

2. Layer 2 : ConnectivityConnectivity includes every communication that occurs on the IoT system (Cisco, 2014). The communication that covered at this level are:• Communication between IoT hardware and its

network.• Communication between network systems.• Inter-network communication with low-level

information processing (Level 3)Communication that occurs on the IoT system can run on an existing Communication network (ISP), but giving IP addresses on IoT devices remains an agreed matter. The communication that occurred at Layer 2 was published by the OSI 7 Network Layer (Cisco, 2014).In this water monitoring system, connection were made using GPRS network from ISP providers. The GSM modules will connect to the internet in GPRS network and used it to send data to the server.

3. Layer 3 : Edge (Fog) ComputingEdge Computing layers consist of any method of evaluating, formatting, expanding and any other process that needed to store data in the databases. This Water Monitoring system already get the clean data from the device sensors. But the data can’t be used for monitoring immediately. The data that’s comes from the devices will be accepted by the API then sent to the databases using stored procedures. In the stored procedure the received data then will be reducted. The reduction process is needed to improve referential

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integrity constrains (Carpenter, 2008). The data then stored in database and ready for any other process needed by the stakeholder especially for leak detection and identification.

4. Layer 4 : data AccumulationData accumulation process in this Water Monitoring done by the MySQL queries. The data from the devices received then stored in server databases using query. The database used in this system is a SQL (Structured Query Language). The uses of SQL will provide more spatial function rather than NoSQL database (Agarwal, 2017).

5. Layer 5 : data AbstractionData abstraction process in this Water Monitoring done by the API. The data that was stored in database then can be called as needed for the function. The API built using laravel framework. API can provide full control of authorization, access control and monitoring of the API uses (Fremantle & Boakes, 2014). The API make it possible to modify the database’s data and present it to the user as needed.

6. Layer 6 : ApplicationThe Application used in this project based on android. Android is an mobile operating system were runs on Java. Android use custom VM created by Google called Dalvik (Holla & Katti, 2012). In android operating system runs for mobile. It includes an operating system, middleware and key applications. Android OS were chosen because its high mobility and flexibility in development.

7. Layer 7 : Collaboration and processThis level of system defined how the data used and what information will be produced. This process includes the business logic that runs on the system (Cisco, 2014). This system runs monitoring and leak detecting process. The user will able to get a notification and monitor the nodes live from their mobile phone.

TESTInG, RESULT And AnALYTICTesting were done by monitoring the water flow in system’s pipe for one hour. It done 3 times in different time leakage scenario, weather and water condition. Here is the condition of the testing.The first testing (Testing 1) held in duration of 1 hour, first 30 minutes is leakage the rest is no leakage. The testing were done on a fine condition weather, no raining. The water comes from the river. The water is clean and no pollutant.The second testing (Testing 2) held in duration of 1 hour, 15 minutes leakage, 15 minutes no leakage, 15 leakage, 15 no leakage. The testing were done on a cloudy condition weather, no raining. The water comes from the river. The water is clean and no pollutant.The third testing (Testing 3) held in duration of 1 hour, first 30 minutes no leakage, the rest is leakage. The testing were done on a rainy condition weather. The water comes from the river. The water is clean and no pollutant.

The testing used two nodes. The first node is used to capture the water’s flow rate on the start edge of the pipe. The second node is used to capture the water’s flow rate on the end edge of the pipe. The first node Serial Number is SN001 and the second node is SN002. The testing’s result shows different result for each testing. The result shown as the graph to compare each node’s sensor record. The result shows as figure below.The first result (Fig 1) result shows the Test 1’s result. The water flow in the first 30 minutes (first half of the graph) shows a leakage occurred. There was a difference between the SN001 and SN002. The figure shows that SN002 record less water flow rate than SN001. That can be concluded leakage.

The third testing ( Testing 3 ) held in duration of 1 hour, first 30 minutes no leakage, the rest is leakage, The testing were done on a rainy condition weather . The water comes from the river. The water is clean and no polutan.

The testing used two nodes. The first node is used to capture the water’s flow rate on the start edge of the pipe. The second node is used to capture the water’s flow rate on the end edge of the pipe. The first node Serial Number is SN001, and the second node is SN002. The testing’s result shows different result for each testing. The result shown as the graph to compare each node’s sensor record. The result shows as figure below.

( a )

( b )

( c )

Figure 4. 1 Testing result : (a) Testing result for Test 1, (b) Testing result for Test 2, (c) Testing result for

Test 3

The first result ( Figure 4.1 (a) ) result shows the Test 1’s result. The water flow in the first 30 minutes ( first half of the graph ) shows a leakage occurred. There was a different between the SN001 and SN002. The figure shows that SN002 record less water flow rate than SN001. That can be concluded leakage.

The second test ( Figure 4.2 (b) ) result shows the Test 1’s result. The water flow in the first 15 minutes shows a leakage occurred and so the third 15 minutes. There was a different between the SN001 and SN002. The figure shows that SN002 record less water flow rate than SN001. That can be concluded leakage already detected based on the scenario that were made.

The third test ( Figure 4.1 (b) ) result shows the Test 1’s result. The water flow in the last 30 minutes ( last half of the graph ) shows a leakage occurred. There was a different between the SN001 and SN002. The figure shows that SN002 record less water flow rate than SN001. That can be concluded leakage already detected based on the

scenario that were made. The third result test shows that the SN001

record were not consistent. It occurred because the sensors were broken by the mud on the polutan water. It shows that the sensors is not effective enough to face a dirt polutaned water. That can be concluded leakage already detected based on the scenario that were made.

Overall the system has could detect a leakage from the pipeline. It’s shown on the figure above that it already shows the differences between the start flow rate and the end flow rate as the scenario we were made.

References 1. Agarwal, S. (2017). Analyzing the

performance of NoSQL vs . SQL databases for Spatial and Aggregate queries. In Free and Open Source Software for Geospatial (FOSS4G) Conference Proceedings Volume (Vol. 17).

2. Amin, A., & Khan, M. N. A. (2014). A Survey of GSM Technology to Control Remote Devices Adeel. International Journal of U- and e- Service, Science and Technology, 7(6), 153–162.

3. Anandhavalli, D., Sangeetha, K. S., Dharshini, V. P., & Fathima, B. L. (2018). Smart Meter for Water Utilization using IoT, 4–7.

4. Carpenter, D. a. (2008). Clarifying Normalization. Journal of Information Systems Education, 19(4), 379–383.

5. Cisco. (2014). The Internet of Things Reference Model. Internet of Things World Forum, 1–12.

6. Daadoo, M., & Daraghmi, Y. (2017). Smart Water Leakage Detection Using Wireless Sensor Networks ( SWLD ). International Journal of Networks and Communications, 7(January), 1–16. http://doi.org/10.5923/j.ijnc.20170701.01

7. Deuerlein, J., Piller, O., & Montalvo, I. (2014). Improved real-time monitoring and control of water supply networks by use of graph decomposition. Procedia Engineering, 89, 1276–1281. http://doi.org/10.1016/j.proeng.2014.11.436

8. Fremantle, P., & Boakes, R. (2014). A history and future of Web APIs Information Technology A History and Future of Web APIs. ITIT-13-1035R2, (4). http://doi.org/10.1515/itit-2013-1035

9. Holla, S., & Katti, M. M. (2012). Android Based Mobile Application. International Journal of Computer Ttrends and Technology, 3(3), 486–490. http://doi.org/10.1080/10304312.2012.706462

10. Ijaradar, J., Chatterjee, S., & Pi, A. R. (2018). Real-Time Water Quality Monitoring System, 1166–1171.

11. Klepka, A., & Michalik, J. (2015). LEAKAGE DETECTION IN PIPELINES -THE CONCEPT OF SMART WATER LEAKAGE DETECTION IN PIPELINES - THE CONCEPT OF SMART WATER SUPPLY SYSTEM. In 7th ECCOMAS Thematic Conference on Smart Structures and



( a )

( b )

( c )


Test 3





















( a )

( b )

( c )


Test 3



















Fig. 3 : Flow rate vs time graph of the third test

Fig. 1 : Flow rate vs time graph of the first test

Fig. 2 : Flow rate vs time graph of the second test

The second test (Fig 2) result shows the Test 1’s result. The water flow in the first 15 minutes shows a leakage occurred and so the third 15 minutes. There was a difference between the SN001 and SN002. The figure shows that SN002 record less water flow rate than SN001. That can be concluded leakage already detected based on the scenario that were made.

The third test (Fig 3) result shows the Test 1’s result. The water flow in the last 30 minutes (last half of the graph) shows a leakage occurred. There was a difference between the SN001 and SN002. The figure shows that SN002 record less water flow rate than SN001. That can be concluded leakage already detected based on the scenario that were made.

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The third result test shows that the SN001 record were not consistent. It occurred because the sensors were broken by the mud on the pollutant water. It shows that the sensors is not effective enough to face a dirt polluted water. That can be concluded leakage already detected based on the scenario that were made.Overall the system can detect a leakage from the pipeline. It’s shown on the figure above that it already shows the differences between the start flow rate and the end flow rate as the scenario we were made.

REFEREnCES1. Agarwal, S. (2017). Analyzing the performance of

NoSQL vs . SQL databases for Spatial and Aggregate queries. In Free and Open Source Software for Geospatial (FOSS4G) Conference Proceedings Volume (Vol. 17).

2. Amin, A., & Khan, M.N.A. (2014). A Survey of GSM Technology to Control Remote Devices Adeel. International Journal of U- and e- Service, Science and Technology, 7(6), 153–162.

3. Anandhavalli, D., Sangeetha, K.S., Dharshini, V. P., & Fathima, B. L. (2018). Smart Meter for Water Utilization using IoT, 4–7.

4. Carpenter, D.A. (2008). Clarifying Normalization. Journal of Information Systems Education, 19(4), 379–383.


6. Daadoo, M., & Daraghmi, Y. (2017). Smart Water Leakage Detection Using Wireless Sensor Networks

(SWLD). International Journal of Networks and Communications, 7 (January), 1–16. http://doi.org/10.5923/j.ijnc.20170701.01


8. Fremantle, P., & Boakes, R. (2014). A history and future of Web APIs Information Technology. A History and Future of Web APIs. ITIT-13-1035R2, (4). http://doi.org/10.1515/itit-2013-1035



11. Klepka, A., & Michalik, J. (2015). Leakage Detection in Pipelines - The Concept of Smart Water Leakage Detection in Pipelines - The Concept of Smart Water Supply System. In 7th ECCOMAS Thematic Conference on Smart Structures and Materials SMART 2015.

12. Radhakrishnan, V., & Wu, W. (2018). IoT technology for Smart water system, (November). http://doi.org/10.1109/HPCC/SmartCity/DSS.2018.00246

13. Swetha Reddy, P., V.Chanakya, K., Eswari, B., & Bhupati, C. (2018). Water leakage detection monitoring and controlling system using IOT. International Journal of Engineering & Technology, 7(2–7), 12. http://doi.org/10.14419/ijet.v7i2.7.10276

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