Course Overview
This course introduces signal processing to a power engineer to fulfil 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 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 set up 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.
What you'll learn
- Signal processing with analog filters
- Analysis of analog filters
- The concept of discrete-time systems in comparison to continuous-time systems
- Analog to digital conversion theory
- Laplace transforms and its application 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
Course notes
Course name |
Basics of digital signal processing for power engineers |
Start & end date |
Open for enrolment anytime
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Mode of delivery |
Online, recorded video lessons & self-paced
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Software used |
Python, Numpy, Scipy & Matplotlib
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Course pre-requisites |
Basic electrical engineering, basic mathematics, basic Python programming
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Applicable for |
Students, Faculties, or Industry professionals from the background of Electrical & Electronics engineering.
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Certification by & Host details |
Decibels Lab Pvt Ltd (Recognised as Start-up by Department for Promotion of Industry and Internal Trade Ministry of Commerce & Industry Government of India) (Certificate Number: DIPP45372)
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Course duration |
15 hours |
Course access duration |
90 days |
Doubt clarification |
It's 100% practical & self-paced, provided with a step-by-step guide to achieve the learning. To address any of the queries in person, we have a Discussion feature, where you can directly interact with the course author.
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Course Curriculum
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Welcome
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How to use Discussions option
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Target audience and requirements
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Expected goals
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Course access duration
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Piracy & infringement warning
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Introduction
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Discrete versus continuous - using a common example
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A continuous time filtering example
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The need for Digital Signal Processing
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The concept of Digital Signal Processing
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Advantages of Digital Signal Processing
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Conversion from continuous to digital
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Analog to Digital Converters (ADCs)
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Interfacing processors and ADCs
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Conclusions
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Introduction
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Reviewing capacitors and inductors
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Analog filters
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The need for transformations
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Laplace Transforms
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Transformed inductors and capacitors
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Original variables
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Advantages of Laplace Transform
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What is s?
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Laplace Transform in the digital domain
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Conversion from continuous to digital domain
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Summarizing
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Conclusions
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Overview
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Introduction to Anaconda Python
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Windows - installing Anaconda
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Linux/Mac - installing Anaconda
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Environments in Anaconda
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Windows - setting up the Anaconda environment
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Linux/Mac - setting up the Anaconda environment
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Editors for Python programming
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Python packages for signal processing
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Launching Jupyter notebook
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Introduction to Numpy arrays
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Generating signals using Numpy arrays
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Getting started with Matplotlib
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Sampling Numpy arrays
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Generating a power frequency sinusoid
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Conclusions
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Introduction
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Digital model of the capacitor
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Implementation issues in digital realizations
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Difference equation for a capacitor filter
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Coding the capacitor filter
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Analyzing the results of the digital capacitor filter
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Dc offsets in the capacitor filter implementation
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A lossy capacitor
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Digital model for a lossy capacitor filter
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Results of a lossy capacitor filter
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Digital model of an inductor filter
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Results of the digital inductor filter
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Modeling the loss in the inductor
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Coding the lossy inductor
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Results of a lossy digital inductor filter
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Digital model of a LC filter
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Coding the LC filter
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Analyzing the operation of a digital LC filter
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Behaviour of a digital LC filter
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Conclusions
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Introduction
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Bode plots
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Using the semi-logarithmic scale for Bode plots
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Linear Time Invariant (LTI) system representation
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Sample Bode plots using Scipy
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Bode plots for an LC filter
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Generalized second order pole
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Continuous to discrete conversion
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Coding the generalized second order pole
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Simulating the working of the generalized second order pole
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Performance of the generalized second order pole
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Generalized first order pole
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Generalized fist order zero
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Generalized second order zero
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Synthesizing higher order transfer functions
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Re-examining the working of the second order pole filter
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Requirements of an improved filter
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Using the polymul function to synthesize higher order polynomials
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Designing a double pole filter
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Operation of a double pole filter
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Improving the double pole filter
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Sample filter design with second order pole and first order pole
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The concept of a notch filter
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Getting started with notch filter design
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Issues in implementing a zero
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Overcoming the limitation in discretization of a zero
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Completing notch filter implementation
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Operation of a notch filter
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Design rules
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Conclusion
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About this course
- ₹2,999.00
- 98 lessons
- 15 hours of video content
Shivkumar Iyer
Instructors profile
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