Control Engineering in Water Resources Abubakr Muhammad Director, - - PowerPoint PPT Presentation

Control Engineering in Water Resources

• EE361. Lectures on Control Engineering in Environment & Sustainability

March 2015

Abubakr Muhammad

Director, Laboratory for Cyber Physical Networks and Systems Dept of Electrical Engineering SBA School of Science & Engineering Lahore University of Management Sciences (LUMS), Pakistan

ISLAMABAD: Anticipating a water crisis in the wake of extreme weather conditions, the Indus River System Authority (Irsa) has asked the government to freeze the country‟s entire development programme for five years and divert funds for construction of major water reservoirs on war footing as a national priority. The water regulator, comprising irrigation and engineering experts from all the four provinces and the centre set up following the 1991 water apportionment accord, did not specifically name the major water reservoirs but pointed out that at the very minimum 22 million acre feet (MAF) storage capacity should be developed at the earliest. “To put an end to the misery faced by the country, the PSDP for all sectors be frozen for at least five years and funds may be diverted for the construction of mega storages on priority basis in the best interest of public,” Irsa chairman Raqib Khan wrote to the secretary of water and power. The letter was issued after a meeting of the Authority, attended by the five members.

http://www.dawn.com/news/1118725 ISLAMABAD: The water regulation and irrigation authorities have found unprecedented variations in river flow records, resulting in disharmony among provinces and massive water losses. ―According to an official of the Ministry of Water and Power, “disappearance of Indus water” upstream Tarbela Dam and “erroneous flow measurement by Wapda at Chashma Barrage” had become so serious ….” “He said Sindh, Balochistan and Punjab had protested over huge water losses between Besham and Tarbela and faulty measurements at Chashma.” “Sindh has also complained against wrong measurements between the Chashma and Taunsa and Taunsa and Guddu barrages, showing more than 50,000 cusec variation, disappearance or unauthorised diversion.” “The incoming information on various gauging stations along the river was keenly observed and during the course an astonishing variation in discharge measurement in the range of 33 per cent was observed,” Mr Bazai said. “He said the measuring site at Besham, set up by Wapda, was 141km upstream Tarbela and it took just seven hours for the discharge to reach the dam, yet the recording variation was so huge. On the contrary, the figure between Tarbela and Kotri, involving 1,451 kilometres and 13 days, was less than 20pc.”

http://www.dawn.com/news/1125409 ISLAMABAD: A fact-finding mission of the Indus River System Authority (Irsa) has said three major stakeholders in the water sector — Punjab, Sindh and Wapda — are misreporting river flow data, putting consumers, particularly the tail-end users, at a disadvantage. “ …. during inspection they recorded river flow at a canal at 12,112 cusecs, but the irrigation department had been reporting a flow of 11,450 cusecs.” “ …. at the BS-Feeder canal downstream powerhouse the flow was measured at 16,627 cusecs during the visit but the officials at the powerhouse had been reporting 14,052 cusecs.” “ ….. the Irsa team reported 10-15 per cent difference between the flow measured by the independent team and that reported by the irrigation authorities at Taunsa barrage.” “ ….. the total difference in the flow recorded by Sindh‟s irrigation officials and that measured by the Irsa team was as high as 60 per cent. Although the province was getting its full share as allocated by Irsa its internal situation was very bad.” “At Ghotki Canal ….. the difference between actual flow and discharges reported by the irrigation authorities (14,000 cusecs and 8,000 cusecs, respectively) stood at more than 60 per cent.”

http://www.nation.com.pk/lahore/10-May-2014/wapda-irsa-s-negligence-costs-rs3-8b-to-national-kitty “The need……. was to help improve the water distribution system within the provinces …..for installation of telemetry system for equitable distribution of river waters among the provinces.” “The Irsa, with the financial cooperation of the World Bank, had agreed to revive the telemetry system for automatic measurement of water flows. The bank will give soft loan of $38 million for the project.” “As per the agreement, 23 sites would be monitored for water regulation and measurement without human involvement through a satellite system. The Irsa, Wapda and the provincial governments would jointly control the monitoring system for measurement of water flows.” “The government should rather introduce modern water measuring systems at the internal canals to remove grievances of the tail farmers.”

Objectives

• PLO-7: Environment and Sustainability (PEC )
• Deeper motivation: Connecting technology to real-

world and societal grand challenges 

• Accessible introduction to cutting-edge research 
• Pay attention to the Right Problems! 
• Demonstrate how student involvement helps develop

high impact research 

Outline

• Motivation
• Water networks : A CPS / IoT perspective
• Open channel hydraulics : obtaining simple models
• System identification : theory to experiments
• Sensing: building telemetry networks
• Control: putting it all together
• Conclusions and outlook

Motivation / Concerns

8

Annual canal diversions and sea escapage Flow reduction due to climate change

Vulnerability sources

• Source. UNEP South Asia report, 2008

Agricultural Productivity Loss

Main reason is water!

Basin Management Problem: A complex system of systems

10

Water Silt Salt

• Courtesy. Asad Abidi, 2009

Managing the World’s Largest Irrigation Network

90,000 Km of watercourses 3 reservoirs, 23 barrages 45 canal commands 36 million acre irrigated area

Regulation Structure

Outline

• Motivation
• Water networks : A CPS / IoT perspective
• Open channel hydraulics : obtaining simple models
• System identification : theory to experiments
• Sensing: building telemetry networks
• Control: putting it all together
• Conclusions and outlook

A Networked Smart Water Grid

Embedded controller Gate control Flow Measurements Wireless connectivity

A Networked Smart Water Grid

Cyber Physical Systems / Internet of Things perspectives

• Physical elements: rivers, watercourses, barrages, weirs, gates, pumps
• Cyber elements : sensors, controllers, comm., services

What type of problems can be solved?

• Increase of distribution efficiency
• Demand based delivery
• Control of nontechnical losses

– Detection of Leak or unauthorized takeoff – Detection of unauthorized dumps

• System health monitoring
• Flood/breach security
• Real-time scheduling and planning
• Improvement and enforcement of water rights

Outline

• Motivation
• Water networks : A CPS / IoT perspective
• Open channel hydraulics : physical models
• System identification : theory to experiments
• Sensing: building telemetry networks
• Control: putting it all together
• Conclusions and outlook

Open Channel Flows

Typical Canal Pool Structure

• Pools or Reaches.
• Two gates in each pool/reach.

Models of Open Channel Flows

• Two ways to simulate:

– Simple volume balance equations. – Navier Stokes in 1D (Saint Venant equations).

(Lumped) Volume Balance Model

( ) ( ),

in

• ut

dV Q t Q t dt  

3 2

0.6 . Q gbh 

Where,

(Lumped) Volume Balance Model

). ( ) ( t Q t Q dt dV

• ut

in

 

. ,

2 3 2 3

h c Q h c Q

• ut
• ut

in in

 

3 3 1 2 2 , 1, 1

( ) ( ) ( ).

i i in i i

• ut

i

dy t c h t c h t dt 

  

  

Distributed Model Geometry

Q: Water flow A: Cross-sectional area h: Height P: Wetted Perimeter B: Base width R: Hydraulic Radius

Modeling of flow of water

Saint Venant equations Continuity Equation Momentum Equation Frictional Slope Hydraulic Radius

. , , . ) ( 2 ) ( ,

3 4 2 2 2 2 2

P A R R A n Q S where S S gA x Q A Q x A A Q B gA t Q x Q t A

f f

                   

Preissmann’s Scheme

Preissmann’s Scheme contd. ). ( ) )( 1 ( ), ( 2 1 ), ) 1 ( ( 2 1 ) ) 1 ( ( 2 1

1 1 1 1 1 1 1 1 1 1 1 1

t f f x f f x f t f f t f f t f f f f f f

k i k i k i k i k i k i k i k i k i k i k i k i p

                      

           

     

Discretizing PDE

System of equations

• Solved by Newton-Raphson method
• Applying Preissmann‟s equation to St. Venant equation.
• Boundary equations are given as:

. ,

2 3 2 3

h c Q h c Q

• ut
• ut

in in

 

Example: Breaches and Dumps

. ) ( 2 ) ( ,

2 2

A Qd S S gA x Q A Q x A A Q B gA t Q d x Q t A

f

                 

For Rectangular Channel:

Leak Simulations

Dumping Simulation

End of Lecture 1

Outline

• Motivation
• Water networks : A CPS / IoT perspective
• Open channel hydraulics: physical models
• System identification : theory to experiments
• Sensing: building telemetry networks
• Control: putting it all together
• Conclusions and outlook

Model Learning for Control

3 3 1 2 2 , 1, 1

( ) ( ) ( ).

i i in i i

• ut

i

dy t c h t c h t dt 

  

  

Abstraction

Physical Models Data Driven Models

(Lumped) Volume Balance Model

). ( ) ( t Q t Q dt dV

• ut

in

 

. ,

2 3 2 3

h c Q h c Q

• ut
• ut

in in

 

3 3 1 2 2 , 1, 1

( ) ( ) ( ).

i i in i i

• ut

i

dy t c h t c h t dt 

  

  

Plant Model

• Declare
• W.r.t. inflow, a linear transfer function emerges:

ℎ3/2 𝑢 = 𝑣 𝑢 .

System Identification

Idea prototyped in a LUMS MS Thesis 2011.

System Identification contd.

). ( ) ( ) (

1 2 3 , 1 2 3 , 1

t h c t h c t y

i

• ut

i i in i i    

   

Date extracted

Pool 1 2 3

Time delay (min) 2 3 1 Wave Period (min) 8 13 7 Ci,in 0.1090 0.1010 0.2340 Ci+1,out 0.1460 0.0910 0.2010

System Identification

System ID: Experiments

Location:

– KHAIRA Distributory – Length 87000 feet – Width 10 feet – Max height 4 feet – 3 Minors – Discharge 87 cusecs

Tested in a LUMS FYP 2013 !

System Identification

System ID: Experiments

Procedure:

– Water level sensors are placed at appropriate sites along the canal and communicate through mobile or other networks. – At the Upstream, Gate is closed and then subsequently

• pened to generate a step input.

– The readings are recorded and then used as empirical

• utput, in conjunction with the input, to perform System

Identification.

System Identification

System ID: Gate Modeling

Wat ater Flo low: Ov Overshot Gat ate Wat ater Flo low: Und Undershot Gat ate

System Identification

System ID: Parametric Equation for the given channel

• The parameters

in 𝜄 matrix are estimated by the minimization of a least-squares criterion.

System ID: Experiment

System Identification

System ID: Least Squares Estimation

• Experimental Setup

– Upstream Gate was closed and then opened – The water level was measured at 50m, 350m and 550m, every 10s. – The corresponding data was processed and interpolated to

• btain a uniformly sampled and synchronized set.
• Estimation

– A model was fit to the observed response at 350m and 550m sensors by linear regression. – As mentioned earlier, yu was assumed to be constant and p2[k] was taken to be zero to model an „always opened – hypothetical – downstream gate’. – In addition, yd [k] was taken to be the values of 50m sensor. – The response delay were inspected from the raw data, which came out be approximately 200s and 350s for the 50m, 350m and 550m sensors respectively. – Using the above conditions, the response for the sensors at 350m and 550m was estimated

System ID: Least Squares Estimation

System ID: Least Squares Estimation

System Identification

System ID: Least Squares Estimation

• Results

– For 350m the estimated parameters were:

• θ = [0.0160 -0.3271]x10-3

– For 550m the estimated parameters were:

• θ = [0.0152 -0.2737]x10-3
• The estimated parameter values make sense

from a physical point of view.

– θ1 is positive. It is associated with the inflow of water – θ2 is negative. It is associated with the outflow of water – θ2 has a greater magnitude than θ1 because there exists no hydraulic structure at the downstream sensor position, and there is always an outflow at the hypothetical downstream end.

System Identification

System ID: Model Validation

• Simulation of Model
• Average Squared Prediction Error
• Comparison of predicted water level with the measured one

System ID: Model Validation

Outline

• Motivation
• Water networks : A CPS / IoT perspective
• Open channel hydraulics: physical models
• System identification : theory to experiments
• Sensing: building telemetry networks
• Control: putting it all together
• Conclusions and outlook

Hydrometry for Open Channel Flows

Objective: Measure flows in distributary canal networks

Hydrometry for Open Channel Flows

• Outdated infrastructure
• Gaps in monitoring expertise
• Objective: To develop a low-cost low-power robust flow gauge

Challenges

• Power / energy autarky
• Communication mode
• Physical security
• Cost / scalability
• Calibration / maintenance
• Data dissemination / services

Solution: A smart metering like approach

Smart Water Grid : LUMS-IWMI collaboration

Goal: To install a network of 20+ sensors at a real site (distributary network on Hakra Branch, Bahawalnagar)

Smart Water Grid in Bahawalnagar

• Ref. Ahmad, Muhammad. IECON 2013

Hakra Branch Distributaries

Packaging / Assembly

Circuitry Enclosure

• Material die cast

Aluminum

• IP 67 Enclosures
• Connectors for external

antenna and temperature sensors are also IP67 standard

Prototyped in a LUMS FYP 2012 !

Stilling well / Civil Infrastructure

• 60cm x 90cm
• To secure

electronics

• High strength PCC

concrete

• No steel

reinforcement for good GSM reception

Ultrasonic Sensor

• Maxbotix MB7380 Ultra Sonic Sensor
• 1mm resolution, 1% accuracy

Block diagram of Smart Water Meter

Unit Performance

• 10 months data of a field deployed unit (5R) with 10

minutes sampling interval.

• Average signal level -69dBm
• 42,187 samples

Flow Calibration

• Level to flow calibration
• Hydraulic rating equation (Manning equation)
• “Calibrating” flow from level measurements

Model based Filtering for Sensor Data

• Physical models for

– Pipe blockage – False ultrasound returns – Sensor failures

Installation at LUMS

End of Lecture 2

Outline

• Motivation
• Water networks : A CPS / IoT perspective
• Open channel hydraulics: physical models
• System identification : theory to experiments
• Sensing: building telemetry networks
• Control: putting it all together
• Conclusions and outlook

Low level downstream control

Controller Design

• Model

Controller Design

• Model
• Root locus (with 2nd order Pade approx. of delay)

Controller Design

• Model
• Root locus (with 4th order Pade approx. of delay)

Model Refinement

• Wave excitations in the channel: damped oscillations.
• Model is approximate. There are higher-order invisible

modes.

Model Refinement

• Model is approximate. There are higher-order

invisible modes.

Introduce damping / friction

Model Refinement

• Model is approximate. There are higher-order

invisible modes.

Oscillatory mode + damping

How to choose a Controller?

• Water off-takes from channel act as disturbances

– Therefore, Integral action needed for disturbance rejection (PI control)

• At some higher frequencies, waves in channels may

get excited. – Therefore, controller should have “low gain” at wave frequency. (LPF with roll-off)

• Both plant and controller (PI) introduce integrators.

– Therefore, need lead compensation.

Controller Design

• Model
• PI-control + low-pass + lead compensator

PI control LPF Phase Lead

) 1 ( ) 1 ( ). ( ) (

2 1

s T s T s K K s C

i p

   

Level regulation (physical simulation)

Closed Loop On-Off Control

Gate Controller: Prototyped in a LUMS FYP 2014 ! (Farwa Akhtar, Shibal Ibrahim, Muhammad Soban, Usama Munir)

Downstream Control in Other Parts of the World

• Australia, Europe, USA, China

Networked Control Issues

• So far, plant is single pool
• Control problem is downstream water level

regulation for one pool.

• But irrigation networks are extremely complex,

specially in the Indus basin

• Control effects propagate
• Enters Networked Control Systems !

Network effects

TOP VIEW SIDEVIEW Controller of last gate sends signal of water scarcity

Network effects

TOP VIEW SIDEVIEW Controller of a gate sends signal of water scarcity

Network effects

TOP VIEW SIDEVIEW Controller of a gate sends signal of water scarcity

Network effects

TOP VIEW SIDEVIEW Water starts entering the canal

Network effects

TOP VIEW SIDEVIEW After reaching the set value controller sends signal to close upstream gate

Network effects

TOP VIEW SIDEVIEW After reaching the set value controller sends signal to close upstream gate

Network effects

TOP VIEW SIDEVIEW Off take creates water scarcity in a pool

Network effects

TOP VIEW SIDEVIEW Controller sends signal of water scarcity

Network effects

TOP VIEW SIDEVIEW Controller sends signal of water scarcity

Network effects

TOP VIEW SIDEVIEW After reaching set value gates are closed

Network effects

TOP VIEW SIDEVIEW All gates closed

Irrigation Networked Control Models

• Multiple pools, multiple inputs, multiple outputs
• What about control?
• Ref. Cantoni et al. “Control of Large-Scale Irrigation Networks,” IEEE proceedings 2007.

Irrigation Networked Control Models

• Distributed control
• Local controller for each pool

Irrigation Networked Control Models

• Distributed control with feed-forward paths
• Local controller for each pool + comm. with neighbors

Irrigation Networked Control Models

• Centralized control
• All feedback loops closed via a “central processor”.

Irrigation Networked Control Models

• Centralized Vs Distributed control – which is better?

Solid (distributed), dashed (centralized), dotted (with feedfoward)

• Ref. Cantoni et al. IEEE proceedings 2007.

CPS Security

• Physical security + Network security = CPS security
• Can we detect

– Illegal dumps? – Breaches? – Seepages? – Non-technical losses? – covert misappropriations?

Nominal Networked Control System

• LTI plant
• Measurements
• Actuation
• Closed loop response

where,

Ref.. Roy. IEEE Control Systems Magazine, 2015.

Compromised System : Covert Agent

• LTI plant
• Measurements
• Offsets
• Closed loop response

Ref.. Roy. IEEE Control Systems Magazine, 2015.

Impossible to detect if cover agent‟s model ∏u = reality Pu

Irrigation Network Security – Explaining theft

Misappropriation by covert agent Nominal responses 2-input 2-output model ???

Cyber-physical Security (Aside)

• Stuxnet worm attack on Iran‟s nuclear centrifuges (June 2010) and

put its nuclear program many years back

• Relayed fake measurements and destructive actuations in SCADA

control systems to destroy centrifuge yields.

• As we automate further, other critical infrastructures are also prone

to such attacks. – Power networks – Water supply – Traffic control systems – Air traffic control – Aviation systems

• Beware the demons of automation !

Ref.. “Cybernetics” by Norbert Wiener. 1948

Conclusions / Final Thoughts

• Canal networks are good candidates for CPS / IoT driven solutions to improve

water efficiency, specially in developing world settings.

• Complex channel models can be simplified to ones that can experimentally
• btained, validated and used for controller synthesis.
• Scalable low-power hydrometry is the key to making complex decision support
• systems. There is both a need and a market for it.
• Power and communications are key challenges towards water metering.
• Fault diagnosis, state estimation and autonomic services will be key to deploying

large scale networks.

• Huge potential for system theorists, control engineers, instrumentation and

automation experts. (in addition to informatics, systems analysis, decision support systems)

Related Publications (2011-2014)

• Saad Aleem, Hasan Nasir, Abubakr Muhammad, "System Identification of Distributory Canals in the

Indus Basin." 19th World Congress of the International Federation of Automatic Control (IFAC 2014), Cape Town, South Africa, 24-29 August 2014. Preprint

• Talha Manzoor, Sergey Aseev, Elena Rovenskaya, Abubakr Muhammad, "Optimal Control for

Sustainable Consumption of Natural Resources." 19th World Congress of the International Federation

• f Automatic Control (IFAC 2014), Cape Town, South Africa, 24-29 August 2014. Preprint
• Zahoor Ahmad and Abubakr Muhammad, "Design, Calibration and Performance of a Low-Power

Wireless Sensor Node for Open Channel Flows", 39th Annual Conference of the IEEE Industrial Electronics Society (IECON), Vienna, Austria, 2013.

• Zahoor Ahmad, Ehsan U. Asad, Abubakr Muhammad, Waqas Ahmad and Arif Anwar, "Development of

a Low-Power Smart Water Meter for Discharges in Indus Basin Irrigation Network", Wireless Sensor Networks for Developing Countries, Springer Communications in Computer and Information Science (CCIS), Volume 366, 2013, pp 1-13. Preprint

• Zaeem Hussain and Abubakr Muhammad, "Sample size reduction in groundwater surveys via sparse

data assimilation," IEEE International Conference on Networking, Sensing and Control (ICNSC), Paris- Evry University, France 2013. Preprint

• Hasan Nasir and Abubakr Muhammad, "Locating Leaks & Dumps in Open Channels with Minimal

Sensing," IEEE Conference on Control Applications (CCA), Dubrovnik, Croatia 2012. Preprint

• Muhammad Umer Tariq, Hasan Arshad Nasir, Abubakr Muhammad and Marilyn Wolf, “Model-Driven

Performance Analysis of Large Scale Irrigation Networks,” IEEE/ACM International Conference on Cyber-Physical Systems (ICCPS), Beijing, China, 2012. Preprint

• Hasan Nasir and Abubakr Muhammad, "Feedback Control of Very-Large Scale Irrigation Networks: A

CPS Approach in a Developing-World Setting." 18th World Congress of International Federation of Automatic Control (IFAC), Milano, Italy, 2011. Preprint