Basics of Digital Signal Processing for Power Engineers

[mitsumi]

Basics of Digital Signal Processing for Power Engineers
Video: .mp4 (1280x720, 30 fps(r)) | Audio: aac, 44100 Hz, 2ch | Size: 4.39 GB
Genre: eLearning Video | Duration: 93 lectures (14 hour, 55 mins) | Language: English

 Filter design using Python with examples related to power electronics

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 MatDescriptionlib
    Generating and Descriptionting 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 Descriptions
    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 MatDescriptionlib. 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

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