trjhtr

The script below is used to determine relay operating times, as defined in the standard IEC 60255. The equation and relevant information can be found here.

curve_equation takes three input values, and outputs a list of values representing the trip times for currents in the fixed range np.logspace(np.log10(Is), 5, 1e2).

The part in the bottom plots the trip times for the different curves. The function curve_equation will be used to much more than that, so I'm mostly interested in improvements to that one.

# -*- coding: utf-8 -*-
"""
Created on Sun Apr 29 13:28:26 2018

@author: Stewie
"""
import numpy as np
import matplotlib.pyplot as plt

def curve_equation(curve_name, T, Is):
 """ 
 This function calculates and returns the inverse curves for 
 the selected curve type.
 Input:
 curve_name: one of the strings: "SIT", "VIT", "LTI", "EIT", "UIT"
 T: The time dial setting. Typically in the range 0.1 ... 1
 Is: The current setting. Typically in the range 100-10000

 The appropriate k, alpha and theta values are selected based on the
 curve type.
 """

 # Define constants to be used in the curve equation 
 k = (0.14, 13.5, 120, 80, 315.2)
 alpha = (0.02, 1, 2, 2, 2.5)
 beta = (2.97, 1.5, 13.33, 0.808, 1) 
 curve_types = ("SIT", "VIT", "LTI", "EIT", "UIT")

 # idx is the index of the values we want
 try:
 idx = curve_types.index(curve_name)
 except:
 print("Non-existing curve-type")
 return

 I = np.logspace(np.log10(Is), 5, 1e2)

 def td_equation():
 td = k[idx]/((I/Is)**alpha[idx]-1) * (T / beta[idx])
 return(td)

 return(td_equation())

# For testing / plotting purposes: 
curve_types = ("SIT", "VIT", "LTI", "EIT", "UIT")
I = np.logspace(1, 5, 1e2)
for curve_type in curve_types:
 plt.loglog(I, curve_equation(curve_type, 1, 1e2), label=curve_type)

plt.xlabel("Current [A]")
plt.ylabel("Time [s]")
plt.title("IEC Time/current curves")
plt.grid()
plt.legend()

You need not declare # -*- coding: utf-8 -*- in Python 3: UTF-8 is the default.

Writing I = np.logspace(…) inside your curve_equation function is bad practice, because it hard-codes certain x-values for your plot. In fact, your plot is wrong, because the calculations are for $I_s$ values ranging from 10²Ã‚ A to 10⁵ A (due to the inner I = np.logspace(np.log10(Is), 5, 1e2)), but your plot's x axis is labelled as if the values ranged from 10¹Ã‚ A to 10⁵ A (due to the outer I = np.logspace(1, 5, 1e2)). Specifically, four of the curves should intersect at 1 kA, 10 s.

I also find the way in which the curves are specified very cumbersome. You have five magic strings ("SIT", "VIT", etc.) which you search for in curve_types. Then, you retrieve the k, alpha, and beta parameters by using the corresponding index. Printing an error message and returning None is an inappropriate error-handling mechanism; you should raise or propagate an exception for such a fatal error.

I'd rather specify the parameters the other way: each curve should be defined as a $(k, alpha, beta)$ tuple. To do that, I would use namedtuples. I'd go a bit further and make each namedtuple smart enough to know what its own $td(I)$ function looks like, when $T$ and $I_s$ are specified.

from collections import namedtuple
import numpy as np
import matplotlib.pyplot as plt

class IEC_Curve(namedtuple('IEC_Curve', 'name k alpha beta')):
 def td(self, T, Is):
 """
 The td(I) function for a given IEC curve, time dial setting, and
 current setting.
 """
 return lambda I: self.k * T / self.beta / ((I / Is)**self.alpha - 1)

SIT = IEC_Curve('SIT', k=0.14, alpha=0.02, beta=2.97) # Standard inverse / A
VIT = IEC_Curve('VIT', k=13.5, alpha=1, beta=1.5) # Very inverse / B
LTI = IEC_Curve('LTI', k=120, alpha=2, beta=13.33) # Long time inverse / B
EIT = IEC_Curve('EIT', k=80, alpha=2, beta=0.808) # Extremely inverse / C
UIT = IEC_Curve('UIT', k=315.2, alpha=2.5, beta=1) # Ultra inverse

I = np.logspace(2, 5, 1e2)
for curve in (SIT, VIT, LTI, EIT, UIT):
 plt.loglog(I, curve.td(T=1, Is=1e2)(I), label=curve.name)
plt.xlabel("Current [A]")
plt.ylabel("Time [s]")
plt.title("IEC Time/current curves")
plt.grid()
plt.legend()
plt.show()

Note that td is a method that returns a function. To calculate values of $td$ with fixed $T$, fixed $I_s$, and swept $I$:

UIT.td(T=1, Is=1e2)(I=np.logspace(2, 5, 1e2))

To calculate a value of $td$ for a given $T$, $I_s$, and $I$:

UIT.td(T=1, Is=1e2)(I=1e2)

(This happens to cause a ZeroDivisionError, which manifests itself as a RuntimeWarning: divide by zero encountered in divide at the beginning of the sweep.)

In case you feel intimidated by my use of inheritance of namedtuple, you could use a manually written constructor instead:

class IEC_Curve:
 def __init__(self, name, k, alpha, beta):
 self.name = name
 self.k = k
 self.alpha = alpha
 self.beta = beta

 def td(self, T, Is):
 return lambda I: self.k * T / self.beta / ((I / Is)**self.alpha - 1)

One difference is that namedtuples are immutable, whereas the members here are mutable.