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TOPFARM
TopFarm2
Commits
9e18c46a
Commit
9e18c46a
authored
6 years ago
by
Mikkel Friis-Møller
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Part 2 of update to include example 3
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examples/example_3_turbine_type_optimization.png
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examples/example_3_turbine_type_optimization.png
examples/example_3_turbine_type_optimization.py
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examples/example_3_turbine_type_optimization.py
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"""
Example: optimizing a layout with constraints
This example uses a dummy cost function to optimize a simple wind turbine
layout that is subject to constraints. The optimization pushes the wind turbine
locations to specified locations in the farm.
"""
import
os
from
matplotlib.patches
import
Polygon
import
matplotlib.pyplot
as
plt
import
numpy
as
np
from
topfarm
import
TopFarm
from
topfarm.cost_models.dummy
import
DummyCost
from
topfarm._topfarm
import
TurbineTypeOptimizationProblem
from
openmdao.drivers.doe_generators
import
FullFactorialGenerator
from
topfarm.plotting
import
TurbineTypePlotComponent
,
NoPlot
# ------------------------ INPUTS ------------------------
# define the conditions for the wind farm
positions
=
np
.
array
([[
0
,
0
],
[
6
,
6
]])
# initial turbine pos
optimal_types
=
np
.
array
([[
2
],
[
6
]])
# optimal layout
#===============================================================================
# Setup the problem and plotting
#===============================================================================
try
:
import
matplotlib.pyplot
as
plt
plt
.
gcf
()
plot_comp
=
TurbineTypePlotComponent
(
turbine_type_names
=
[
"
Turbine %d
"
%
i
for
i
in
range
(
10
)],
delay
=
0.1
)
plot
=
True
except
RuntimeError
:
plot_comp
=
NoPlot
()
plot
=
False
# create the wind farm
tf
=
TurbineTypeOptimizationProblem
(
cost_comp
=
DummyCost
(
optimal_types
,
[
'
turbineType
'
]),
turbineTypes
=
[
0
,
0
],
lower
=
0
,
upper
=
9
,
plot_comp
=
plot_comp
,
driver
=
FullFactorialGenerator
(
10
))
#===============================================================================
# # Run the optimization
#===============================================================================
state
=
{
'
turbineX
'
:
positions
[:,
0
],
'
turbineY
'
:
positions
[:,
1
]}
cost
,
state
,
recorder
=
tf
.
optimize
(
state
)
#===============================================================================
# plot and prin the the final, optimal types
#===============================================================================
print
(
state
[
'
turbineType
'
])
tf
.
evaluate
(
state
)
# save the figure
if
plot
:
plt
.
savefig
(
os
.
path
.
basename
(
__file__
).
replace
(
'
.py
'
,
'
.png
'
))
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