48 lines
1.2 KiB
Python
48 lines
1.2 KiB
Python
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# Paul Gasper, NREL
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# Functions for updating time-varying states
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import numpy as np
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def update_power_state(y0, dx, k, p):
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if y0 == 0:
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if dx == 0:
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dydx = 0
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else:
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y0 = k*(dx**p)
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dydx = y0/dx
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else:
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if dx == 0:
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dydx = 0
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else:
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dydx = k*p*((y0/k)**((p-1)/p))
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return dydx * dx
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def update_power_B_state(y0, dx, k, p):
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if y0 == 0:
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if dx == 0:
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dydx = 0
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else:
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y0 = (k*dx)**p
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dydx = y0/dx
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else:
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if dx == 0:
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dydx = 0
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else:
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z = (y0 ** (1/p)) / k
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dydx = (p * (k*z)**p)/z
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return dydx * dx
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def update_sigmoid_state(y0, dx, y_inf, k, p):
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if y0 == 0:
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if dx == 0:
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dydx = 0
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else:
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dy = 2 * y_inf * (1/2 - 1 / (1 + np.exp((k * dx) ** p)))
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dydx = dy / dx
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else:
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if dx == 0:
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dydx = 0
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else:
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x_inv = (1 / k) * ((np.log(-(2 * y_inf/(y0-y_inf)) - 1)) ** (1 / p) )
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z = (k * x_inv) ** p
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dydx = (2 * y_inf * p * np.exp(z) * z) / (x_inv * (np.exp(z) + 1) ** 2)
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return dydx * dx
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