PyTHTA

- EC317 - UNIFEI - June, 2018

DISCLAIMER

More about programming than electromagnetic transients

Agenda

  • Why Python?
  • Developing good software
  • PyTHTA
  • Future works

Python

  • Very popular in science and engineering
  • Easy to use and prototype
  • Lots of libraries
  • Open Source

Good software

  • Easy to maintain
  • Easy to add features
  • Easy to fix bugs
  • Stable environment
  • Easy to set up

Open source software has a good chance to become good software

PyTHTA

Python + THTA + Open Source => High chances to become a good software!

Work in progress

https://github.com/hannelita/pyTHTA

PyTHTA Stages

  1. Replicate the basic THTA features
  2. Organise the code
  3. Plot charts
  4. Replicate advanced THTA features
  5. Create a full library

PyTHTA Stages

  1. Replicate the basic THTA features
  2. Organise the code (Under development)
  3. Plot charts
  4. Replicate advanced THTA features
  5. Create a full library

PyTHTA Stages

  1. Replicate the basic THTA features

The libraries

 

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

PyTHTA Stages

  1. Replicate the basic THTA features

Importing the file

self.df = pd.read_csv('test.csv', header=None)

For this initial step, we shall use the .csv format

PyTHTA Stages

  1. Replicate the basic THTA features

Calculate the condutance and map elements

for index, row in self.elements.iterrows():
      if row[0] == 'R':
        resistor = Resistor(row[3])
        self.gkm[index-1] = 1/resistor.value
        self.nh = self.nh + 1
      elif row[0] == 'C':
        capacitor = Capacitor((row[3]*1e-6))
        self.gkm[index-1] = 2*capacitor.value/self.dt
        self.nh = self.nh + 1
      elif row[0] == 'L':
        inductor = Inductor((row[3]*1e-3))
        self.gkm[index-1] = self.dt/(2*inductor.value)
      elif row[0] == 'EDC':
        self.ndcvs = self.ndcvs + 1

PyTHTA Stages

  1. Replicate the basic THTA features

Build the G matrix

self.g_matrix = np.zeros((self.elements.shape[0], self.elements.shape[0]))
    for index, row in self.elements.iterrows():
      k = row[1]
      m = row[2]
      if m == 0:
        self.g_matrix[k,k] = self.g_matrix[k,k] + self.gkm[index-1]
      elif k == 0:
        self.g_matrix[m,m] = self.g_matrix[m,m] + self.gkm[index-1]
      else:
        self.g_matrix[k,k] = self.g_matrix[k,k] + self.gkm[index-1]
        self.g_matrix[m,m] = self.g_matrix[m,m] + self.gkm[index-1]
        self.g_matrix[k,m] = self.g_matrix[k,m] + self.gkm[index-1]
        self.g_matrix[m,k] = self.g_matrix[m,k] + self.gkm[index-1]

PyTHTA Stages

  1. Replicate the basic THTA features

Build the GAA and GAB matrices

d = self.nodes_qty - self.sources_qty
self.gaa = self.g_matrix[0:d, 0:d]
self.gab = self.g_matrix[0:d, d:self.nodes_qty]
self.gba = self.g_matrix[d:self.nodes_qty, 0:d]
self.gbb = self.g_matrix[d:self.nodes_qty, d:self.nodes_qty]

PyTHTA Stages

  1. Replicate the basic THTA features

Simulation (see resolver.py)

Example

CSV

T,2,1,100E-6,50E-6,0,0,0,0,0
EDC,2,0,10,0,0,0,0,0,0
R,2,1,4,0,0,0,0,0,2
C,1,0,7,0,0,0,0,0,2
R,1,0,6,0,0,0,0,0,2
NV,1,2,0,0,0,0,0,0,0

G matrix

IA, IB, I

VA, VB, V

What is not (yet) included in this version

  1. Switches, non linear elements, current sources
  2. Charts
  3. Edge cases
  4. Automated Tests

PyTHTA Stages

  1. Replicate the basic THTA features
  2. Organise the code (Under development)

Classes and object oriented programming (work in progress)

Future work

Different languages and paradigms

Article

"Open Source Implementations of Electromagnetic Transient Algorithms "