Basics of Digital Signal Processing for Power Engineers
4.8 (32 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
293 students enrolled

Basics of Digital Signal Processing for Power Engineers

Filter design using Python with examples related to power electronics
Bestseller
4.8 (32 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
293 students enrolled
Created by Shivkumar Iyer
Last updated 5/2020
English
English [Auto]
Current price: $13.99 Original price: $19.99 Discount: 30% off
5 hours left at this price!
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This course includes
  • 15 hours on-demand video
  • 77 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Signal processing with analog filters
  • Anaysis of analog filters
  • The concept of discrete time systems in comparison to continuous time systems
  • Analog to digital conversion theory
  • Laplace transforms and it's applicaton in analog filters
  • Laplace transforms in the digital domain
  • Continuous to discrete time conversion in the frequency domain
  • Installing and setting up Python, Numpy and Matplotlib
  • Generating and plotting signals
  • Sampling signals and simulating discrete time systems
  • Simulating the capacitor as a digital filter
  • Simulating the inductor as a digital filter
  • Simulating non-ideal capacitors and inductors as digital filters
  • Simulating an LC filter digitally
  • Using the signal package in Scipy
  • Synthesizing transfer functions in Python with signal
  • Generating Bode plots
  • Using frequency response characteristics to design filters
  • Designing and implementing a low pass and a notch filter
Requirements
  • Basic electrical engineering, basic mathematics, basic programming
Description

This course introduces signal processing to a power engineer with the objective of fulfilling one of the most pressing needs faced in power engineering - filter design. The course begins with a basic introduction to the concept of signal processing, discrete time systems and basic hardware applications. The course dives into the mathematics behind signal processing in order to translate many of the obscure concepts into plain English with the final objective of implementation in hardware. The course will then have code-along sessions where students will learn how filters are designed, analyzed and implemented using Python, Numpy, Scipy and Matplotlib. The course has a section on how to install and setup software on different operating systems and used only free and open source software, making the course and the materials accessible to students irrespective of their background.

Who this course is for:
  • Electrical engineering students and professionals
Course content
Expand all 93 lectures 14:55:46
+ Introduction
3 lectures 14:22
Target audience and requirements
06:21
Expected goals
02:33
+ What are discrete systems?
10 lectures 01:17:19
Discrete versus continuous - using a common example
09:54
A continuous time filtering example
11:08
The need for Digital Signal Processing
10:11
The concept of Digital Signal Processing
06:29
Advantages of Digital Signal Processing
07:25
Conversion from continuous to digital
12:04
Analog to Digital Converters (ADCs)
07:50
Interfacing processors and ADCs
07:18
Conclusions
02:13
+ Introduction to signal processing
13 lectures 01:56:29
Introduction
02:16
Reviewing capacitors and inductors
07:51
The need for transformations
06:36
Laplace Transforms
12:48
Transformed inductors and capacitors
09:02
Original variables
07:32
Advantages of Laplace Transform
08:08
What is s?
20:49
Laplace Transform in the digital domain
13:25
Conversion from continuous to digital domain
12:33
Summarizing
06:22
Conclusion
02:08
+ Installation, setup and a basic tutorial
16 lectures 03:01:37
Introduction
03:25
Anaconda
07:04
WINDOWS - Installing Anaconda
07:08
LINUX - Installing Anaconda
11:13
Environments in Anaconda
05:14
WINDOWS - Setting up an Anaconda environment
13:59
LINUX - Setting up an Anaconda environment
23:05
Code editors
06:34
Python packages for signal processing
12:53
Launching Jupyter notebook
09:02
Introduction to Numpy arrays
14:09
Generating signals using Numpy arrays
09:32
Getting started with Matplotlib
15:44
Sampling Numpy arrays
17:45
Generating a power frequency sinusoid
20:58
Conclusion
03:52
+ Emulating analog filters digitally
20 lectures 02:57:55
Introduction
03:25
Digital model of the capacitor
08:42
Implementation issues in digital realizations
09:52
Difference equation for a capacitor filter
03:28
Coding the capacitor filter
16:54
Analyzing the results of the digital capacitor filter
15:38
Dc offsets in the capacitor filter implementation
10:08
A lossy capacitor
09:23
Digital model for a lossy capacitor filter
04:27
Results of a lossy capacitor filter
09:50
Digital model of an inductor filter
03:48
Results of the digital inductor filter
10:11
Modeling the loss in the inductor
05:36
Coding the lossy inductor
04:01
Results of a lossy digital inductor filter
11:22
Digital model of a LC filter
10:36
Coding the LC filter
15:37
Analyzing the operation of a digital LC filter
14:37
Behaviour of a digital LC filter
06:59
Conclusion
03:21
+ Frequency Response Characteristics and Filter Design
30 lectures 05:21:30
Introduction
02:35
Bode plots
13:47
Using the semi-logarithmic scale for Bode plots
06:52
Linear Time Invariant (LTI) system representation
12:54
Sample Bode plots using Scipy
12:30
Bode plots for an LC filter
13:32
Generalized second order pole
13:02
Continuous to discrete conversion
13:05
Coding the generalized second order pole
15:24
Simulating the working of the generalized second order pole
12:05
Performance of the generalized second order pole
21:51
Generalized first order pole
02:29
Generalized fist order zero
01:33
Generalized second order zero
01:53
Synthesizing higher order transfer functions
09:35
Re-examnining the working of the second order pole filter
09:29
Requirements of an improved filter
08:26
Using the polymul function to synthesize higher order polynomials
09:33
Designing a double pole filter
14:44
Operation of a double pole filter
22:55
Improving the double pole filter
12:48
Sample filter design with second order pole and first order pole
12:19
The concept of a notch filter
18:43
Getting started with notch filter design
09:39
Issues in implementing a zero
13:45
Overcoming the limitation in discretization of a zero
06:35
Completing notch filter implementation
06:16
Operation of a notch filter
11:48
Design rules
08:45
Conclusion
02:38