def test_smart_start_aep_map_PyWakeAEP():
site = IEA37Site(16)
n_wt = 4
x, y = site.initial_position[:n_wt].T
wd_lst = np.arange(0, 360, 45)
ws_lst = [10]
turbines = hornsrev1.HornsrevV80()
site = UniformSite([1], .75)
site.default_ws = ws_lst
site.default_wd = wd_lst
aep = PyWakeAEP(wake_model=NOJ(site, turbines))
aep_1wt = aep.calculate_AEP([0], [0]).sum()
tf = TopFarmProblem(design_vars={
'x': x,
'y': y
},
cost_comp=aep.get_TopFarm_cost_component(n_wt),
driver=EasyScipyOptimizeDriver(),
constraints=[
SpacingConstraint(160),
CircleBoundaryConstraint((0, 0), 500)
])
x = np.arange(-500, 500, 10)
y = np.arange(-500, 500, 10)
XX, YY = np.meshgrid(x, y)
tf.smart_start(XX,
YY,
aep.get_aep4smart_start(wd=wd_lst, ws=ws_lst),
radius=40,
seed=1)
tf.evaluate()
if 0:
wt_x, wt_y = tf['x'], tf['y']
for i, _ in enumerate(wt_x, 1):
print(aep.calculate_AEP(wt_x[:i], wt_y[:i]).sum((1, 2)))
X_j, Y_j, aep_map = aep.aep_map(x,
y,
0,
wt_x,
wt_y,
ws=ws_lst,
wd=wd_lst)
print(tf.evaluate())
import matplotlib.pyplot as plt
c = plt.contourf(X_j, Y_j, aep_map, 100)
plt.colorbar(c)
plt.plot(wt_x, wt_y, '2r')
for c in tf.model.constraint_components:
c.plot()
plt.axis('equal')
plt.show()
npt.assert_almost_equal(aep_1wt * n_wt, tf['AEP'], 5)
def test_setup_as_penalty_none():
driver = SimpleGADriver()
tf = TopFarmProblem(dict(zip('xy', initial.T)),
DummyCost(desired),
driver=driver)
# check that it does not fail if xy and z is not specified
assert tf.evaluate()[0] == 121
assert tf.evaluate({
'x': [2.5, 7, 4.5],
'y': [-3., -7., -3.],
'z': [0., 0., 0.]
})[0] == .5
def test_smart_start_aep_map(seed, radius, resolution, tol):
site = IEA37Site(16)
n_wt = 4
x, y = site.initial_position[:n_wt].T
wd_lst = np.arange(0, 360, 45)
ws_lst = [10]
turbines = hornsrev1.HornsrevV80()
site = UniformSite([1], .75)
site.default_ws = ws_lst
site.default_wd = wd_lst
wfm = NOJ(site, turbines)
aep_comp = PyWakeAEPCostModelComponent(wfm, n_wt=n_wt)
aep_1wt = wfm([0], [0]).aep().sum()
tf = TopFarmProblem(design_vars={
'x': x,
'y': y
},
cost_comp=aep_comp,
driver=EasyScipyOptimizeDriver(),
constraints=[
SpacingConstraint(160),
CircleBoundaryConstraint((0, 0), radius)
])
x = np.arange(-radius, radius, resolution)
y = np.arange(-radius, radius, resolution)
XX, YY = np.meshgrid(x, y)
tf.smart_start(XX,
YY,
aep_comp.get_aep4smart_start(wd=wd_lst, ws=ws_lst),
radius=40,
plot=0,
seed=seed)
tf.evaluate()
if 0:
wt_x, wt_y = tf['x'], tf['y']
for i, _ in enumerate(wt_x, 1):
print(wfm(wt_x[:i], wt_y[:i]).aep().sum(['wd', 'ws']))
aep_comp.windFarmModel(wt_x, wt_y, ws=ws_lst,
wd=wd_lst).flow_map().aep_xy().plot()
print(tf.evaluate())
import matplotlib.pyplot as plt
plt.plot(wt_x, wt_y, '2r')
for c in tf.model.constraint_components:
c.plot()
plt.axis('equal')
plt.show()
npt.assert_almost_equal(aep_1wt * n_wt, tf['AEP'], tol)
def test_setup_as_penalty_xy():
driver = SimpleGADriver()
tf = TopFarmProblem(dict(zip('xy', initial.T)),
DummyCost(desired),
constraints=[XYBoundaryConstraint(boundary)],
driver=driver)
# check normal result if boundary constraint is satisfied
assert tf.evaluate()[0] == 121
# check penalized result if boundary constraint is not satisfied
assert tf.evaluate({
'x': [2.5, 7, 4.5],
'y': [-3., -7., -3.],
'z': [0., 0., 0.]
})[0] == 1e10 + 1
def test_design_var_list(turbineTypeOptimizationProblem, design_vars):
tf = TopFarmProblem(design_vars=design_vars,
cost_comp=DummyCost(np.array([[2, 0, 1]]).T, ['type']),
driver=DOEDriver(FullFactorialGenerator(3)))
cost, _, = tf.evaluate()
npt.assert_equal(tf.cost, cost)
assert tf.cost == 5
def test_AEPMaxLoadCostModelComponent_constraint():
tf = TopFarmProblem(
design_vars={
'x': ([1]),
'y': (.1, 0, 2.5)
},
# design_vars={'x': ([2.9], [1], [3])},
cost_comp=AEPMaxLoadCostModelComponent(input_keys='xy',
n_wt=1,
aep_load_function=lambda x, y:
(np.hypot(x, y), x),
max_loads=3),
constraints=[CircleBoundaryConstraint((0, 0), 7)],
)
tf.evaluate()
cost, state, recorder = tf.optimize()
npt.assert_allclose(state['x'], 3) # constrained by max_loads
npt.assert_allclose(state['y'], 2.5) # constrained by design var lim
