current_dqp_iparams = PI_params(kP=0.005, kI=200, limits=(-1, 1))
# PI gain parameters for the PLL in the current forming inverter
pll_params = PLLParams(kP=10, kI=200, limits=None, f_nom=freq_nom)
# Droop characteristic for the active power Watts/Hz, W.s/Hz
droop_param = InverseDroopParams(DroopGain,
delta_t,
freq_nom,
tau_filt=0.04)
# Droop characteristic for the reactive power VAR/Volt Var.s/Volt
qdroop_param = InverseDroopParams(50, delta_t, v_nom, tau_filt=0.01)
# Add to dict
ctrl.append(
MultiPhaseDQCurrentController(current_dqp_iparams,
pll_params,
i_lim,
droop_param,
qdroop_param,
ts_sim=delta_t,
name='slave'))
# Define the agent as StaticControlAgent which performs the basic controller steps for every environment set
agent = StaticControlAgent(
ctrl, {
'master': [[f'lc1.inductor{k}.i' for k in '123'],
[f'lc1.capacitor{k}.v' for k in '123']],
'slave': [[f'lcl1.inductor{k}.i' for k in '123'],
[f'lcl1.capacitor{k}.v' for k in '123'],
[f'inverter2.i_ref.{k}' for k in '012']]
}) # np.zeros(3)]})
def update_legend(fig):
# Current control PI gain parameters for the voltage sourcing inverter
current_dqp_iparams = PI_params(kP=0.012, kI=90, limits=(-1, 1)) # kp=0.012
# Define a current sourcing inverter as master inverter using the pi and droop parameters from above
ctrl.append(MultiPhaseDQ0PIPIController(voltage_dqp_iparams, current_dqp_iparams, droop_param_master,
qdroop_param_master, ts_sim=ts, ts_ctrl=2 * ts, name='master'))
###############
# define slave
current_dqp_iparams = PI_params(kP=0.005, kI=200, limits=(-1, 1)) #
# PI gain parameters for the PLL in the current forming inverter
pll_params = PLLParams(kP=10, kI=200, limits=(-10000, 10000), f_nom=nomFreq)
# Droop characteristic for the active power Watts/Hz, W.s/Hz
# Add to dict
ctrl.append(MultiPhaseDQCurrentController(current_dqp_iparams, pll_params, iLimit, droop_param_slave,
qdroop_param_slave, ts_sim=ts, name='slave'))
#####################################
# Definition of the optimization agent
# The agent is using the SafeOpt algorithm by F. Berkenkamp (https://arxiv.org/abs/1509.01066) in this example
# Arguments described above
# History is used to store results
agent = SafeOptAgent(mutable_params,
abort_reward,
j_min,
kernel,
dict(bounds=bounds, noise_var=noise_var, prior_mean=prior_mean,
safe_threshold=safe_threshold, explore_threshold=explore_threshold),
ctrl,
{'master': [[f'lc1.inductor{k}.i' for k in '123'],
[f'lc1.capacitor{k}.v' for k in '123'],
current_dqp_iparams = PI_params(kP=0.005, kI=200, limits=(-1, 1))
# PI gain parameters for the PLL in the current forming inverter
pll_params = PLLParams(kP=10, kI=200, limits=None, f_nom=nomFreq)
# Droop characteristic for the active power Watts/Hz, W.s/Hz
droop_param = InverseDroopParams(DroopGain,
delta_t,
nomFreq,
tau_filt=0.04)
# Droop characteristic for the reactive power VAR/Volt Var.s/Volt
qdroop_param = InverseDroopParams(50, delta_t, nomVoltPeak, tau_filt=0.01)
# Add to dict
ctrl.append(
MultiPhaseDQCurrentController(current_dqp_iparams,
pll_params,
delta_t,
iLimit,
droop_param,
qdroop_param,
name='slave'))
# Define the agent as StaticControlAgent which performs the basic controller steps for every environment set
agent = StaticControlAgent(
ctrl, {
'master': [[f'lc1.inductor{k}.i' for k in '123'],
[f'lc1.capacitor{k}.v' for k in '123']],
'slave': [[f'lcl1.inductor{k}.i' for k in '123'],
[f'lcl1.capacitor{k}.v' for k in '123'],
np.zeros(3)]
})
env = gym.make('openmodelica_microgrid_gym:NormalizedEnv_test-v1',
###############
# define slave
current_dqp_iparams = PI_params(kP=0.005, kI=200, limits=(-1, 1))
# PI gain parameters for the PLL in the current forming inverter
pll_params = PLLParams(kP=10,
kI=200,
limits=(-10000, 10000),
f_nom=freq_nom)
# Droop characteristic for the active power Watts/Hz, W.s/Hz
# Add to dict
ctrl.append(
MultiPhaseDQCurrentController(current_dqp_iparams,
pll_params,
delta_t,
i_lim,
droop_param_slave,
qdroop_param_slave,
name='slave'))
#####################################
# Definition of the optimization agent
# The agent is using the SafeOpt algorithm by F. Berkenkamp (https://arxiv.org/abs/1509.01066) in this example
# Arguments described above
# History is used to store results
agent = SafeOptAgent(mutable_params,
abort_reward,
j_min,
kernel,
dict(bounds=bounds,
noise_var=noise_var,