Mar 29, 2018 - (Rengali Reservoir). Catchment. Delineation. Parameter Estimation. Rossman (2010),. Huber & Dickinson (1992),. Temprano et al. (2007).
SIMULATION AND CONTROL OF FLOODING IN RIVER AND URBAN SYSTEMS
PAWA N K U M A R R A I Under supervision of
D r. D h a n y a C . T. & P r o f . B . R . C h a h a r Department of Civil Engineering, I n d i a n I n s t i t u t e o f Te c h n o l o g y D e l h i N e w D e l h i − 11 0 0 1 6
26-March-2018
ORGANIZATION 2
Introduction 2. Research Objectives and Framework 1.
Study Area and Data Description Methodology Results and Discussions Conclusion
Publications
3.
Journal Conferences
29-03-2018
ORGANIZATION 3
Introduction 2. Research Objectives and Framework 1.
Study Area and Data Description Methodology Results and Discussions Conclusion
Publications
3.
Journal Conferences
29-03-2018
INTRODUCTION River System and Water Management. River Flooding- Adverse effect due to Urbanization (El Alfy, 2016) • Ill-effects can be minimized – 1. Flood protection works ex-dams, weirs, barrages etc. 2. Developing flood warning systems • Advantages of flood warning system • 1d-models-HEC-1, TR-55, MOUSE, SWMM5, SWAT (U.S. Army Corps of Engineers 1985; SCS 1986; DHI 1997; Rossman 2010) SWMM model : Advantages 1. Practical applications in urban as well as non-urban drainage systems (Kim et al. 2015). 2. Incorporates the capabilities of both hydrological and hydraulic models. 3. Minimum data requirement, open source software. Disadvantage: 1d modelling does not provide detailed information on flood analysis. • 2d models-MIKE 21, DELFT-FLS, DELFT-2D, RiverCAD, and the Nays2DFlood solver from the iRIC (International River Interface Cooperative) Major Disadvantage: High computational requirement, Data requirement. • Evolution of Hybrid models- Coupling of 1d and 2d models 4
29-03-2018
Urban Flooding • Monitoring/Controls-1. Structural- Source controls, Collection system controls, etc 2.Non-structural measures- Soft computing technique Linear controllers- Ex. Proportional Integral Derivative (PID) Non-Linear controllers-Ex- Dynamic Inversion (DI) controller
Water Distribution System- Optimum design of water distribution network The efficient operation and control of such water supply systems is vital for meeting inconsistent water demand.
5
29-03-2018
OBJECTIVES 6
The objectives of the present study are mention below: 1.
To simulate the response of a catchment to flood events using a storm water
management model. 2.
To map the flood extent, i.e., spread and depth, using a computationally efficient hybrid (coupled 1D and 2D) model.
3.
To simulate the flood events in an urban system and develop the control mechanism using a linear controller, for effective management of flood in an urban drainage system.
4.
To investigate the applicability of a nonlinear controller in an urban drainage system for controlling the flood events. 29-03-2018
OBJECTIVES 7
The objectives of the present study are mention below: 1.
To simulate the response of a catchment to flood events using a storm water management model.
2.
To map the flood extent, i.e., spread and depth, using a computationally efficient hybrid (coupled 1D and 2D) model.
3.
To simulate the flood events in an urban system and develop the control mechanism using a linear controller, for effective management of flood in an urban drainage system.
4.
To investigate the applicability of a nonlinear controller in an urban drainage
system for controlling the flood events. 29-03-2018
River Flood Modelling ? 8
River flooding has been causing extensive socio-economic loss globally.
In India, the eastern states of Orissa, Andhra Pradesh and West Bengal have been witnessing high magnitude monsoonal floods annually. In the recent times, there is ever-increasing emphasis on the development of flood warning systems as they offer a relatively cheaper option as compared to the costlier flood protection works. SWMM model : Advantages 1. Practical applications in urban as well as non-urban drainage systems (Kim et al. 2015). 2. Incorporates the capabilities of both hydrological and hydraulic models. 3. Minimum data requirement, open source software. 29-03-2018
River Flood Modelling ? 9
In view of the above, a GIS-based auto-calibrated SWMM model was used to model the flood prone Brahmani river watershed of India. The model was then integrated with Monte Carlo algorithm for calibration and then evaluated for its applicability in floods applications on natural catchments. This modeling study can facilitate water resource managers in efficient planning and management of natural disasters such as floods.
29-03-2018
Why SWMM ? 10
Overall Functional Feature of SWMM (1) Hydrologic (2) Hydraulic (3) Water Quality • • • • • •
Hydrologic Modeling Feature of SWMM Spatially and time varying rainfall Evaporation Snow accumulation and melting Infiltration into soil layers Percolation into shallow groundwater Interflow between groundwater & channels
Hydraulic Modeling Feature of SWMM • Handles drainage networks of any size • Accommodates various conduit shapes as well as irregular natural channels • Models pumps, regulators, storage units 29-03-2018
STUDY AREA
DATA 11 S. No.
Data
Resolution
Source
1
Digital Elevation Model (DEM)
90 m × 90 m
Shuttle Radar Topography Mission (SRTM)
2
Landuse/ Land Cover (LU/LC) of 2010
1:2,50,000
National Remote Sensing Centre (NRSC)
3
Soil Data
1:2,50,000
National Bureau of Soil Survey & Landuse Planning (NBSS-LUP)
4
Meteorological Data
5
Precipitation (1901-2013)
0.25 × 0.25
Temperature (1951-2013)
1.0 x 1.0
Daily Streamflow Data (1980-2012)
Station Data
Indian Meteorological Department (IMD)
India-WRIS Web Portal 29-03-2018
METHODOLOGY 12
Derive Depth -Area Relationship (Rengali Reservoir)
Depth-Area Curve
Auto-calibration of SWMM using Monte-Carlo technique
Parameter Estimation Catchment Delineation
Rossman (2010), Huber & Dickinson (1992), Temprano et al. (2007)
Parameter N-perv N-imperv Imperv, Width and Slope Des-imperv (mm) Des-perv (mm) Zero-Imperv (%) Max. Infilt (mm/h) Min. Infilt (mm/h) Decay constant (1/h) Drying time (days)
Initial Value 0.1 0.012 0.4 3 15
4 7
Calibration Interval 0.02–0.8 0.011–0.033 ±25% 0.3–2.5 2.0–5.1 5–20 25–110 0–10 2–7 2–14 29-03-2018
METHODOLOGY 13
The developed SWMM model was calibrated and validated on monthly as well as daily basis using Monte Carlo based optimization. • Latin-hypercube sampling method was employed to generate 2000 samples for each parameters. • For calibration, SWMM was run for each set of above-mentioned parameters and the obtained simulated results were compared with the observed data at Gomlai and Jenapur gauging stations. • During calibration-validation, SWMM outputs was evaluated based on NashSutcliffe Efficiency (NSE) (Nash and Sutcliffe 1970), Percent Bias (PBIAS) (Gupta et al. 1999) and Root mean square error-observations Standard deviation Ratio (RSR) (Singh et al. 2004; Moriasi et al. 2007).
RESULTS & DISCUSSION Model Calibration and Validation Monthly Basis
Location
NSE
Daily Basis
14
RSR
PBIAS
Calibration
Validation Calibration
Validation
Calibration
Gomlai
0.90
0.87
4.2705e-06
2.6467e-06 7.66
7.80
Jenapur
0.88
0.85
3.1945e-06
2.3026e-06 -9.04
-6.85
Location
NSE Calibration
Validation
RSR
Validation Calibration
Validation
PBIAS Calibration Validation
Gomlai
0.73
0.71
7.977e-07
3.074e-07
0.41
0.09
Jenapur
0.62
0.66
8.353e-07
3.30e-007
-10.63
-14.13
29-03-2018
RESULTS & DISCUSSION Maximum Yearly and Monthly Discharge 15
There is a good agreement between the observed and simulated flood peaks for maximum yearly and maximum monthly flood from year 2003 to 2012. Thus, the developed SWMM model can be said to be an effective tool for operational flood warning. 29-03-2018
RESULTS & DISCUSSION 16
Capacity, Velocity and Cross-Section at Jenapur and its Downstream
Flow in Deltaic Region of Brahmani Basin
29-03-2018
CONCLUSION 17
The performance of Monte Carlo optimization was quite good in auto-
calibrating SWMM on monthly and daily basis. The model was able to identify the exact flooding location and predicted that the maximum flow should be regulated below 7515.8 m3/s. In addition to SWMM’s application in urban catchments, it can also be effectively used for Simulating the Catchment Response to Flood Events in natural systems like river. Therefore, instead of setting up flood monitoring stations that involve great cost, SWMM can be employed as an early warning flood prediction tool at relatively small price. Rai, Pawan Kumar, B. R. Chahar, and C. T. Dhanya. "GIS-based SWMM model for simulating the catchment response to flood events." Hydrology Research 48.2 (2016): 384-394. (SCI Journal) 29-03-2018
OBJECTIVES 18
The objectives of the present study are mention below: 1.
To simulate the response of a catchment to flood events using a storm water management model.
2.
To map the flood extent, i.e., spread and depth, using a computationally efficient hybrid (coupled 1D and 2D) model.
3.
To simulate the flood events in an urban system and develop the control mechanism using a linear controller, for effective management of flood in an urban drainage system.
4.
To investigate the applicability of a nonlinear controller in an urban drainage
system for controlling the flood events. 29-03-2018
Hybrid Model For Flood Modelling (Coupled 1D-2D Model)? 19
Flash floods occur almost every year in the deltaic region of Brahmani and
Baitarani river basin in India, during the monsoon season. 1D Model:Confined flow and mostly unidirectional; No need of detailed velocities; With many complex structures Disadvantages :Flow paths must be known; No detailed flow descriptions in floodplains. 2D Model :When detailed velocity, depth, flood flow patterns are important Disadvantages:Costly in computational time; Requires fine grid in rivers/channels Hybrid Model A hybrid model has been developed for this deltaic region to identify the extent of inundation and its depth during the flooding, in the absence of uniformity of observed data. 29-03-2018
ArcSWAT Model (1D Model) 20
• The SWAT model was developed for the United States Department of Agriculture’s (USDA) Agricultural Research Service (ARS) by Dr. Jeff Arnold (Nietsch 2005). Stream Basin and Sub basin
DEM
Watershed Delineation
Watershed Outlets
Land Cover
HRU (Hydrologic Response Unit) Analysis
Soil Slope
Temperature
ArcSWAT Simulation
Weather Data
Precipitation
Flowchart of ArcSWAT Preprocessing Steps 29-03-2018
STUDY AREA
DATA 21 S. No.
Spatial data
Resolution
Source
1
Digital Elevation Model (DEM)
30 m × 30 m
Shuttle Radar Topography Mission (SRTM)
2
Landuse/ Land Cover (LU/LC)
1:2,50,000
National Remote Sensing Centre (NRSC)
3
Soil
1:2,50,000
National Bureau of Soil Survey & Landuse Planning (NBSS-LUP)
4
Meteorologic al Data
Precipitation: 0.25 × 0.25 gridded data Temperature: 1.0 x 1.0 gridded data
Indian Meteorological Department (IMD)
5
Flow and Gauge Data
Station Data
India-WRIS
29-03-2018
METHODOLOGY 22
Calibrate SWAT model using SWAT-CUP (SUFI2) for Brahmani Parameters
Description
r_CN2.mgt v_ALPHA_BF.gw v_GW_DELAY.g w v_GWQMN.gw
Curve number Base flow alfa factor Groundwater delay time
v_GW_REVAP.g w v_CH_N2.rte v_CH_K2.rte
Threshold depth of water in shallow aquifer Groundwater ‘revaporation’ coefficient Manning roughness for main channel Effective hydraulic conductivity
Minimum value -0.38 0 200
Maximum value 0.008 0.65 450
0.80
2.43
0.09
0.27
0.04
0.1
-22
79
29-03-2018
METHODOLOGY 23
Calibrate SWAT model using SWAT-CUP (SUFI2) for Brahmani Calibrate SWMM model using Monte-Carlo technique for Baitarni
Give output from 1D (SWAT and SWMM) model as input to iRIC (2D mdel)
Formulate Hybrid Model
29-03-2018
RESULTS & DISCUSSION SWAT model formulation Model Calibration
The values of p-factor, r-factor, NSE and PBIAS for the validation period are 0.30, 0.05, 0.74 and -1.9, respectively.
24
Model Validation
The values of p-factor, r-factor, NSE and PBIAS for the validation period are 0.29, 0.06, 0.78 and -5.4, respectively. 29-03-2018
RESULTS & DISCUSSION SWMM model formulation 25
Number of Subcatchments = 55 Number of Junction = 57 Number of Channel = 57 Number of Outfall = 1 29-03-2018
RESULTS & DISCUSSION SWMM model formulation 26
Station Champua Anandpur
Akhuapada
NSE Calibration 0.525 0.616
Validation 0.511 0.664 Calibration 0.87
RSR Calibration 4.1e-07 1.5e-07 RMSE
Validation 1.05e-07 2.4e-007
PBIAS Calibration 4.41 17.63
Validation 7.09 24.13
Validation 0.83 29-03-2018
RESULTS & DISCUSSION iRIC model formulation (Jenapur) 27
RMSE values during the calibration and validation were 0.77 and 0.79 respectively
29-03-2018
RESULTS & DISCUSSION 28
(a) Flood depth (Brahmani) (b) Velocity (Brahmani) (c) Flooding (Both) (d) Flood Depth (Bartarani)
Hybrid Model Simulation Results of Brahmani and Baitarni River Delta (August)
29-03-2018
CONCLUSION 29
The performance of the hybrid model was observed to be
satisfactory for mapping the extent of flood inundation. The performance of the hybrid model can be improved if the features like regulators, drainage network, waterbodies (ponds and lakes) were considered in the hybrid model. The calibrated and validated hybrid model can be used as a tool to identify the extent of inundation and its depth caused due to various extreme flood events Rai, Pawan Kumar, C. T. Dhanya and B. R. Chahar. "Coupling of 1D models (SWMM and SWAT) with 2D model (iRIC) for mapping inundation in Brahmani and Baitarni river delta." Natural Hazards (Accepted for Publication) SCI Journal 29-03-2018
OBJECTIVES 30
The objectives of the present study are mention below: 1.
To simulate the response of a catchment to flood events using a storm water management model.
2.
To map the flood extent, i.e., spread and depth, using a computationally efficient hybrid (coupled 1D and 2D) model.
3.
To simulate the flood events in an urban system and develop the control mechanism using a linear controller, for effective management of flood in an urban drainage system.
4.
To investigate the applicability of a nonlinear controller in an urban drainage
system for controlling the flood events. 29-03-2018
Urban Drainage Flood Control ? 31
The Najafgarh catchment is being flooded mainly due to the flow coming
from Shahibi river. Due to rapid urbanization, the local drainage system does not able to
accommodate the excess runoff generated during heavy storm. Thus a computational modelling is needed to identify the critical flooding
locations. The aim of this study is to develop a methodology that will minimize
flooding by linking SWMM program for the hydraulic results with the PID Controller for efficient operation of urban drainage system. 29-03-2018
STUDY AREA
DATA 32 S. No.
Data
Resolution
1
Digital Elevation Model (DEM)
5m×5m
2
Landuse/ Land Cover (LU/LC) of 2010
1:1000
Geo-Spatial Delhi Ltd (GSDL)
3
Shapefile of Conduit and Nodes
1:2,50,000
National Bureau of Soil Survey & Landuse Planning (NBSS-LUP)
4
Soil Data
Meteorological Data 5
Precipitation
Station data
Temperature
Station data
Source
Indian Meteorological Department (IMD)
29-03-2018
METHODOLOGY 33
Delineation of basin
Calculate different parameters by drawing Thiessen polygon for different nodes
Execute SWMM modelling to identify the critical flood locations
Apply Controls
29-03-2018
METHODOLOGY 34
PID Controller For Flood Control Input data to SWMM Error (e) = SWMM return hydraulic result
Error (e) = 0
Yes
No Control Module (PID Controller)
Modify the SWMM Input File
STOP
RESULTS & DISCUSSION 35
Development of SWMM Model for Virgin Flow Conditions
Simulation Results (a) Conduits Flow, (b) Conduits Capacity and (c) Node Flooding
29-03-2018
RESULTS & DISCUSSION SWMM Model virgin flow results 36
29-03-2018
RESULTS & DISCUSSION 37
Results Obtained from PID Controller (a) Location of Orifice Connecting Drain to Difference of the Water Level in the Waterbody Waterbody
and (b) Variation in the Orifice Setting
29-03-2018
CONCLUSION 38
Connecting drainage system to nearby water body can mitigate
flood problems to certain extent. Linear controller is discussed with the examples of urban drainage system with the real time field data. Proportional Integral Derivative has been designed for this test problem for level control to waterbody. It is clearly observed that using PID controller, the target water level has been slowly achieved by the controller. Rai, Pawan Kumar, Dhanya, C. T., & Chahar, B. R. Flood control in an urban drainage system using a linear controller. Water Practice and Technology, 12.4(2017),:942-952. Scopus Journal 29-03-2018
OBJECTIVES 39
The objectives of the present study are mention below: 1.
To simulate the response of a catchment to flood events using a storm water management model.
2.
To map the flood extent, i.e., spread and depth, using a computationally efficient hybrid (coupled 1D and 2D) model.
3.
To simulate the flood events in an urban system and develop the control mechanism using a linear controller, for effective management of flood in an urban drainage system.
4.
To investigate the applicability of a nonlinear controller in an urban drainage
system for controlling the flood events. 29-03-2018
Why controls in water systems? 40
Many water systems do not employ control systems, because human
operators provide logic and decisions required for control action. Ex: Pump Speeds Usually control decisions are based on operator skill and experience
rather than algorithms Manual control is purely Trial & Error procedure. (Brdys, 1994)
Particularly very complex under dynamic flow conditions May not be easy if a system is very complex
Ex: In a sequential operation of pumping stations, simultaneous supply to different target reservoirs etc 29-03-2018
Why controls in water systems? 41
To reach the targets/set points (reservoir flows / levels) To reach the targets as fast as possible To ensure the smoothest possible operation of valves/pumps
To control the slow transients Particularly useful for complex pipe networks
29-03-2018
Why controls in water systems? 42
Non linear controller approach for the urban drainage system is not common in practice due to various reasons. Complex flow dynamics of urban drainage system. Highly accurate real time field data is very tedious. Hence, applicability of this technique is limited under present scenario. Also, in urban areas, flow depends on conduits geometry, manholes, gate operations, etc., and the management and coordination of all these components are very difficult. In addition, existence of leakages and sedimentation in the drains, changes the flow dynamics in the drain due to which the model output never converse. 29-03-2018
STUDY AREA
PARAMETERS 43
Pipes
Schematic diagram of the water distribution system, Eker et al. (2003)
Reservoirs
Pumps
Lpipe1 = 669.27m
HSH1 = 113.4m
A0 = 0.0001433
Lpipe2 = 13805.04m
HSH2 = 210.4m
B0 = 0.005015
Lpipe3 = 20094.69m
HSH3 = 283.4m
C0 = 3.98
Lpipe4 = 4689.04m
HSH4 = 279.7m
np=1
D = 1.4m
AR = 475m2
f = 0.0183374
29-03-2018
METHODOLOGY Mathematical model of a water supply system 44
Dynamical behavior of fluid in the pipeline
𝑑𝑄(𝑡) 𝑑𝑡
=
𝑔𝐴𝑝𝑖𝑝𝑒 𝐿
[∆ℎ − ℎ𝑙𝑜𝑠𝑠 𝑡 ]
Friction Loss
where
ℎ𝑙𝑜𝑠𝑠−𝑓𝑟𝑖𝑡𝑖𝑜𝑛
ℎ𝑙𝑜𝑠𝑠 (𝑡)= ℎ𝑙𝑜𝑠𝑠−𝑓𝑟𝑖𝑐𝑡𝑖𝑜𝑛 𝑡 + ℎ𝑙𝑜𝑠𝑠−𝑙𝑜𝑐𝑎𝑙 (𝑡
10.7𝐿𝑝𝑖𝑝𝑒 𝑡 = 𝑄1.852 (𝑡 1.852 4.87 𝐻𝑊𝐶 𝐷
Head versus Flow characteristics of Pump
ℎ𝑝 𝑁, 𝑄𝑝 = 𝐴0
𝑁2
𝐵0 𝐶0 + 𝑁𝑄𝑝 − 𝑄𝑝2 𝑛𝑝 𝑛 2 𝑝
Continuity equation at Reservoir
𝑑(𝑉 𝑡 ) 𝑑𝑡
= 𝑄𝑖 𝑡 − 𝑄0 (𝑡) 29-03-2018
METHODOLOGY Introduction to control theory 45
If the model is of form
.
X f ( X ) g ( X )u
x denotes state: - minimum set of variables that characterize the state of the system - knowing the state, know everything about the system Reservoir levels, Pipe flows etc. u denotes the input to the system: - manipulated or control variable pump speed
29-03-2018
METHODOLOGY Dynamic Inversion Controller 46
Nonlinear control design Technique of feedback linearization May be implemented as PID
Nonlinear DI Controller Equation
. u g ( X ) X des f ( X ) 1
for
.
X f ( X ) g ( X )u
29-03-2018
METHODOLOGY Dynamic Inversion Controller 47
State Vector
𝑋 𝑡 = 𝑥1 𝑥2 𝑥3 𝑥4 𝑥5 𝑥6 𝑥7
Control Vector
𝑈 𝑡 = 𝑢1 𝑢2 𝑢3
State Equations 𝑄𝑜𝑢𝑡
𝑇
= 𝑁1 𝑁2 𝑁3
= 𝑄𝑜𝑢𝑡 𝐻𝐷3 𝑄3 𝐻𝐷2 𝑄2 𝐻𝐷1 𝑄1 𝑇
8 𝑓 𝐿𝑝𝑖𝑝𝑒4 𝑄𝑜𝑢𝑡 2 𝑑𝑄𝑜𝑢𝑡 𝜋 𝑔 𝐷2 = = 𝐻 + 𝐻𝑆𝐻3 − 𝐻𝑆𝐻4 − 𝑑𝑡 4 𝐿𝑝𝑖𝑝𝑒4 𝐷3 𝜋 2 𝑔 𝐷5
𝐻𝐷3 =
𝑑𝐻𝐷3 𝑄3 − 𝑄𝑜𝑢𝑡 = 𝑑𝑡 𝐴𝑅
8 𝑓 𝐿𝑝𝑖𝑝𝑒3 𝑄3 2 𝑑𝑄3 𝜋 𝑔 𝐷2 𝑄3 = = 𝐻𝐷2 − 𝐻𝐷3 + 𝐻𝑆𝐻2 − 𝐻𝑆𝐻3 − + 𝐴0 𝑁32 + 𝐵0 𝑄3 𝑁3 − 𝐶0 𝑄32 2 5 𝑑𝑡 4 𝐿𝑝𝑖𝑝𝑒3 𝜋 𝑔𝐷
𝐻𝐷2 =
𝑑𝐻𝐷2 𝑄2 − 𝑄3 = 𝑑𝑡 𝐴𝑅
8 𝑓 𝐿𝑝𝑖𝑝𝑒2 𝑄2 2 𝑑𝑄2 𝜋 𝑔 𝐷2 𝑄2 = = 𝐻𝐷1 − 𝐻𝐷2 + 𝐻𝑆𝐻1 − 𝐻𝑆𝐻2 − + 𝐴0 𝑁22 + 𝐵0 𝑄2 𝑁2 − 𝐶0 𝑄22 2 5 𝑑𝑡 4 𝐿𝑝𝑖𝑝𝑒2 𝜋 𝑔𝐷 29-03-2018
METHODOLOGY Dynamic Inversion Controller 48
𝐻𝐷1 =
𝑑𝐻𝐷1 𝑄1 − 𝑄2 = 𝑑𝑡 𝐴𝑅
8 𝑓 𝐿𝑝𝑖𝑝𝑒1 𝑄1 2 𝑑𝑄1 𝜋 𝑔 𝐷2 𝑄1 = = −𝐻𝐷1 − 𝐻𝑆𝐻1 − + 𝐴0 𝑁12 + 𝐵0 𝑄1 𝑁1 − 𝐶0 𝑄12 𝑑𝑡 4 𝐿𝑝𝑖𝑝𝑒1 𝜋 2 𝑔 𝐷5
Control Synthesis Procedure Error dynamics used to shape the system response (Piazzi & Visioli 2001) 𝑒 (𝑡) + 𝐾𝑝 𝑒(𝑡) + 𝐾𝑖 𝑒 𝑡 𝑑𝑡 + 𝐾𝑑 𝑒(𝑡) = 0 After substituting the state variables and their derivatives 1
𝑔𝜋 𝐷 2
1
4𝐴𝑅
𝐿𝑝𝑖𝑝𝑒 1
−𝐻𝐷1 − 𝐻𝑆𝐻1 − 8 𝑓𝐿𝑝𝑖𝑝𝑒 2 𝑄2 2 𝜋 2 𝑔 𝐷5
2
𝐻𝑆𝐻1 − 𝐻𝑆𝐻2 −
3
𝐾𝑖 (𝐻𝐷1 − 𝐻𝐷1 ∗ ) 𝑑𝑡 + 𝐾𝑑
8 𝑓𝐿𝑝𝑖𝑝𝑒 1 𝑄1 2 𝜋 2 𝑔 𝐷5
+ 𝐴0 𝑁12 + 𝐵0 𝑄1 𝑁1 − 𝐶0 𝑄12 − 𝐿
+ 𝐴0 𝑁22 + 𝐵0 𝑄2 𝑁2 − 𝐶0 𝑄22 𝑄1 −𝑄2 𝐴𝑅
1 𝑝𝑖𝑝𝑒 2
𝐻𝐷1 − 𝐻𝐷2 +
+ 𝐾𝑝 (𝐻𝐷1 − 𝐻𝐷1 ∗ ) +
=0
29-03-2018
METHODOLOGY Dynamic Inversion Controller 49
1 2 3
1 2
𝑔𝜋 𝐷 2
1
4𝐴𝑅
𝐿𝑝𝑖𝑝𝑒 2
1 𝐿𝑝𝑖𝑝𝑒 3
𝐻𝐷1 − 𝐻𝐷2 + 𝐻𝑆𝐻1 − 𝐻𝑆𝐻2 −
𝐻𝐷2 − 𝐻𝐷3 + 𝐻𝑆𝐻2 − 𝐻𝑆𝐻3 −
8 𝑓𝐿𝑝𝑖𝑝𝑒 2 𝑄2 2 𝜋 2 𝑔 𝐷5
8 𝑓𝐿𝑝𝑖𝑝𝑒 3 𝑄3 2 𝜋 2 𝑔 𝐷5
𝐾𝑝 (𝐻𝐷2 − 𝐻𝐷2 ∗ ) + 𝐾𝑖 (𝐻𝐷2 − 𝐻𝐷2 ∗ ) 𝑑𝑡 + 𝐾𝑑
𝑔𝜋 𝐷 2
1
4𝐴𝑅
𝐿𝑝𝑖𝑝𝑒 3
𝐻𝐷2 − 𝐻𝐷3 + 𝐻𝑆𝐻2 − 𝐻𝑆𝐻3 −
+ 𝐴0 𝑁32 + 𝐵0 𝑄3 𝑁3 − 𝐶0 𝑄32 𝑄2 −𝑄3 𝐴𝑅
8 𝑓𝐿𝑝𝑖𝑝𝑒 3 𝑄3 2 𝜋 2 𝑔 𝐷5
𝐾𝑝 (𝐻𝐷3 − 𝐻𝐷3 ∗ ) + 𝐾𝑖 (𝐻𝐷3 − 𝐻𝐷3 ∗ ) 𝑑𝑡 + 𝐾𝑑
+ 𝐴0 𝑁22 + 𝐵0 𝑄2 𝑁2 − 𝐶0 𝑄22 −
=0
+ 𝐴0 𝑁32 + 𝐵0 𝑄3 𝑁3 − 𝐶0 𝑄32
𝑄3 −𝑄𝑜𝑢𝑡 𝐴𝑅
+
+
=0
In the last equation, only one control variable, N3 is the unknown and is quadratic in nature, and hence the equation is solved for N3. Once N3 is calculated, other equations can be solved for N1 and N2 . 29-03-2018
METHODOLOGY Dynamic Inversion Controller Tuning Methods 50
Ziegler-Nichols (Z-N) Tuning • It is done by setting the integral and derivative gains to zero value. • The proportional gain, is then augmented till it reaches the ultimate gain, at which the
system output has steady and regular oscillations.
Particle Swarm Optimization Comparison with other evolutionary computation techniques • Unlike in genetic algorithms, in PSO, there is no selection operation.
• All particles in PSO are kept as members of the population through the course of run 29-03-2018
RESULTS & DISCUSSION 51
Kp1
Kp2
Kp3
0.0656
0.0033
0.0032
Ki1 0.0017
Ki2
Ki3
Kd1
3.32e-06 1.00e-07 0.6963
Kd2
Kd3
0.0929
0.09
PSO based controller parameters To check the developed control model, the system has been simulated by considering an initial condition based on Eker et al. (2003) study
Target outflow rate (Qo*): 2.4 m3s-1 Target reservoir levels (ht1*, ht2*, ht3*): 4.0 m, 2.5 m and 3.91 m 1st Initial condition: X (0) = (2.83, 3.20, 2.83, 2.15, 2.83, 4.20, 2.83)
2nd Initial condition: X (0) = (2.20, 3.50, 2.20, 2.70, 2.20, 3.80, 2.20) 29-03-2018
RESULTS & DISCUSSION 52
Reservoir levels and pump speeds for the 1st initial condition with constant target outflow (Qout)
Reservoir levels and pump speeds for the 2nd initial condition with constant target outflow (Qout) 29-03-2018
RESULTS & DISCUSSION 53
Targets: Outflow: 2.55 to 2.7 m3s-1 2.7 to 2.8 m3s-1
2.8 to 2.6 m3s-1 For every 30 min duration 1st reservoir level: 4.0m 2nd reservoir level: 2.5m Outflow, reservoir levels and pump speeds for the case of step changes in target outflow (Qout) 29-03-2018
RESULTS & DISCUSSION PSO DI compared with Eker et al. (2003). 54
•
Maximum water level in Reservoir 1 was to be kept below overflow limit of 4.3 m and hence a level controller was required for a stable operation.
• The speed of Pump 1 was changed for controlling the water level in Reservoir 1, whereas other two pumps were assumed to operate at a nominal speed of 985 rpm. • Output flow disturbance of ± 0.142m3/s was applied after Reservoir 1 at every 27.7 hours upto 138.5 hours.
29-03-2018
RESULTS & DISCUSSION PSO DI compared with Eker et al. (2003). 55
Water levels in the reservoirs for a flow disturbance of ± 0.142m3/s
Reservoir 1 - PSO DI is able to successfully keep level of 4 m. Reservoir 2 - Similar pattern can be seen in the present results (3.8m-0.25m (Eker
2003)). Reservoir 3 - Similar pattern can be seen in the present results (Peak difference 0.5m (Eker 2003)). 29-03-2018
CONCLUSION 56
An attempt has also been made to derive the non linear control algorithm for the
urban drainage system. Non linear control technique have also been applied to the water distribution system which is governed by the ordinary differential equations. The controller is working effectively for both the problems: (1) constant outflow rate and constant reservoir levels, and (2) non-constant demand input, i.e., outflow and constant reservoir levels. When PSO tuned Dynamic Inversion controller is used in place of the Z-N tuned controller, targets can be achieved quickly without creating undue transients. PSO has very good convergence characteristics too. Hence, it could be a very good controller for real time operations.
Rai, Pawan Kumar, C. T. Dhanya, and B. R. Chahar. "A PSO approach for optimum design of dynamic inversion controller in water distribution systems." Journal of Water Supply: Research and Technology-Aqua 65.7 (2016): 570-581. SCI Journal 29-03-2018
PUBLICATION 57
Journals 1. Rai, P. K., Chahar, B. R., & Dhanya, C. T. (2016). GIS-based SWMM model for simulating the catchment response to flood events. Hydrology Research, nh2016260.(SCI Journal) 2. Rai, P. K., Dhanya, C. T., & Chahar, B. R. (2016). A PSO approach for optimum design of dynamic inversion controller in water distribution systems. Journal of Water Supply: Research and Technology-Aqua, 65(7), 570-581.(SCI Journal) 3. Rai, P. K., Mohan Kumar, M. S., & Dhanya, C. T. (2013). A constrained tuning approach for optimal pump operation. ISH Journal of Hydraulic Engineering, 19(3), 219-226.(Scopus Journal) 29-03-2018
PUBLICATION 58
4. Rai, Pawan Kumar., Dhanya, C. T., & Chahar, B. R. (2017). Flood
control in an urban drainage system using a linear controller. Water Practice and Technology, 12(4), 942-952. 5. Rai, Pawan Kumar, C. T. Dhanya and B. R. Chahar. "Coupling of
1D models (SWMM and SWAT) with 2D model (iRIC) for mapping inundation in Brahmani and Baitarni river delta." Natural Hazards (Accepted for Publication) SCI Journal
29-03-2018
PUBLICATION 59
Conferences 1.
2.
3.
Rai, P. K., Chahar, B. R., & Dhanya, C. T. (2015). Flood forecasting for lower Tapi River with GIS based SWMM model. 20th International Conference on Hydraulics, Water Resources and River Engineering (HYDRO-2015), December 17-20, 2015. Rai, P.K., Mohan Kumar, M.S., and C.T. Dhanya (2012). A Constrained Tuning Approach for Optimal Pump Operation, Proceedings of National Conference on Hydraulic and Water Resources (HYDRO 2012), IIT Bombay, India, 7 & 8 December 2012. Rai, P. K., Dhanya, C. T., & Chahar, B. R. (2016). Optimal Pumping Location in an Urban Drainage System Using SWMM Linked Pattern Search Optimization, National Conference on Water Resources Management in Coastal Regions, Deltaic Regional Centre National Institute of Hydrology, Kakinada, December 8-9, 2016. 29-03-2018
THANK YOU
60
29-03-2018