# -*- coding: utf-8 -*-
"""
Created on Thu Aug 04 09:24:51 2016
@author: tlbl
"""
import numpy as np
class Control(object):
def torque_controller_tuning(self, r0, lambda_opt, ksi_pole_part,
omega_pole_part, j_dt):
"""THIS IS A WIP
"""
# TODO: THIS IS STILL A WIP
i12, i23, i34 = self.select_regions()
# Compute K factor for optimal CP tracking in region 1 as
# K=0.5*rho*A*Cp_opt*R^3/lambda_opt^3
# HAWCStab2/Computation_LIB/compute_controller_input_mod.f90:83-88
# Compute K factor for optimal CP tracking in region 1 as
# K=0.5*rho*A*Cp_opt*R^3/lambda_opt^3
# pi_torque_controller%kfactor=0.d0
# do i=1,iregion2-iregion1-1
# !kfactor=kfactor+0.85d0*kfactor_fit(i)*substructure(3)
# pi_torque_controller%kfactor
# = pi_torque_controller%kfactor
# + kfactor_fit(i)*substructure(3)%opstate(i+iregion1)%r0**3/lambda_opt**3
# enddo
# HAWCStab2/Computation_LIB/compute_controller_input_mod.f90:89
# pi_torque_controller%kfactor =
# pi_torque_controller%kfactor/max(dfloat(iregion2-iregion1-1), 1.d0)
Qg = (r0*r0*r0)/(lambda_opt*lambda_opt*lambda_opt)
# PI generator torque controller in region 2 (constant speed, variable power)
# HAWCStab2/Computation_LIB/compute_controller_input_mod.f90:104-105
# pi_torque_controller%kp = 2.d0*ksi_pole_part*omega_pole_part*j_dt
# pi_torque_controller%ki = omega_pole_part**2*j_dt
pgTorque = 2.0*ksi_pole_part*omega_pole_part*j_dt
igTorque = omega_pole_part**2*j_dt
return Qg, pgTorque, igTorque
def pitch_controller_tuning(self, pitch, I, dQdt, P, Omr, om, csi):
"""
Function to compute the gains of the pitch controller of the Basic DTU
Wind Energy Controller with the pole placement technique implemented
in HAWCStab2.
Parameters
----------
pitch: array
Pitch angle [deg]. The values should only be those of the full load
region.
I: array
Drivetrain inertia [kg*m**2]
dQdt: array
Partial derivative of the aerodynamic torque with respect to the
pitch angle [kNm/deg]. Can be computed with HAWCStab2.
P: float
Rated power [kW]. Set to zero in case of constant torque regulation
Omr: float
Rated rotational speed [rpm]
om: float
Freqeuncy of regulator mode [Hz]
csi: float
Damping ratio of regulator mode
Returns
-------
kp: float
Proportional gain [rad/(rad/s)]
ki: float
Intagral gain [rad/rad]
K1: float
Linear term of the gain scheduling [deg]
K2: float
Quadratic term of the gain shceduling [deg**2]
"""
pitch = pitch * np.pi/180.
I = I * 1e-3
dQdt = dQdt * 180./np.pi
Omr = Omr * np.pi/30.
om = om * 2.*np.pi
# Quadratic fitting of dQdt
A = np.ones([dQdt.shape[0], 3])
A[:, 0] = pitch**2
A[:, 1] = pitch
b = dQdt
ATA = np.dot(A.T, A)
iATA = np.linalg.inv(ATA)
iATAA = np.dot(iATA, A.T)
x = np.dot(iATAA, b)
kp = -(2*csi*om*I[0] - P/(Omr**2))/x[2]
ki = -(om**2*I[0])/x[2]
K1 = x[2]/x[1]*(180./np.pi)
K2 = x[2]/x[0]*(180./np.pi)**2
return kp, ki, K1, K2
def K_omega2(V, P, R, TSR):
Va = np.array(V)
Pa = np.array(P)
Ra = np.array(R)
TSRa = np.array(TSR)
K = Ra**3 * np.mean(Pa/(TSRa*Va)**3)
return K
def select_regions(self, pitch, omega, power):
"""Find indices at wich point the controller should switch between the
different operating mode regions.
Parameters
----------
Returns
------
i12 : int
123 : int
134 : int
"""
i12 = 0
n = len(pitch)
for i in range(n-1):
if (abs(power[i]/power[i+1] - 1.) > 0.01):
if (abs(omega[i] / omega[i+1] - 1.) > 0.01):
i12 = i
break
i23 = n-1
for i in range(i12, n-1):
if (abs(omega[i] / omega[i+1] - 1.) < 0.01):
i23 = i
break
i34 = i23
for i in range(i23, n-1):
if (abs(power[i]/power[i+1] - 1.) > 0.01):
if (abs(omega[i] / omega[i+1] - 1.) < 0.01):
i34 = i+1
return i12, i23, i34
if __name__ == '__main__':
pass