All classes Power Quality Disturbance Classification Wavelet Syllabus · Episode 1 of 6

Class Classification of Power Quality Disturbances Wavelet #1: Types of Power Quality Disturbances: Sag, Swell, Harmonics, Transient, Flicker

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Power quality tutorial with Python. Episode 1: types of power quality disturbances including sag, swell, interruption, harmonics, transients, flicker, notch, and RMS per cycle.

Software: Python (PyWavelets, scikit-learn, XGBoost)
Level: Intermediate
Duration: ± 9 min
Series total: 40 min · 6 videos
Published: October 25, 2026

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Class Classification of Power Quality Disturbances Wavelet #1: Types of Power Quality Disturbances: Sag, Swell, Harmonics, Transient, Flicker: Cover: Class Classification of Power Quality Disturbances Wavelet #1: Types of Power Quality Disturbances: Sag, Swell, Harmonics, Transient, Flicker

Class Classification of Power Quality Disturbances Wavelet #1: Types of Power Quality Disturbances: Sag, Swell, Harmonics, Transient, Flicker: Before you start, steps list, and Step 1

Class Classification of Power Quality Disturbances Wavelet #1: Types of Power Quality Disturbances: Sag, Swell, Harmonics, Transient, Flicker: Step 2: Disturbance magnitude: sag, swell, interruption

Class Classification of Power Quality Disturbances Wavelet #1: Types of Power Quality Disturbances: Sag, Swell, Harmonics, Transient, Flicker: Done: Final Result and Further Practice

Cover: Class Classification of Power Quality Disturbances Wavelet #1: Types of Power Quality Disturbances: Sag, Swell, Harmonics, Transient, Flicker

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Introduction

Here is the final result, 9 voltage waveforms, from normal to disturbances such as Sag, Swell, Harmonic, Transient, Flicker, and Knock, complete with RMS values per cycle. At the end of this series, the machine will recognize everything with an accuracy above 98%. Let's get started. Hello, welcome to the tutorial series on research, detection, and classification of power quality disturbances using Wavelet Transform and Machine Learning. Poor power quality causes industrial machines to shut down suddenly, data centers to fail, and equipment to break quickly. To address this, we must automatically identify the type of disturbance from voltage recordings. In this 6-episode series, we will build everything. Disturbance models, IEEE 1159 compliant datasets, Wavelet Transform, feature extraction, classification with SVM, Random Forest, and XGBoost, up to testing against NICE. In the first episode, we will learn about the types of disturbances.

Step 1: Installing the library

We open the terminal through the terminal menu, new terminal, then install numpy, matplotlib, pywavelet for Wavelet Transform, scikit-learn, and XGBoost. This series does not require a downloaded dataset, as all signals are generated itself in a controlled manner, a common method used in power quality papers.

Step 2: Magnitude disturbances: sag, swell, interruption

Create a file named signal_underscore_peki.py Voltage signal at 50 Hz, 10 cycles, sampled at 3.2 kHz, so 64 samples per cycle and 640 samples per signal. The event function creates a disturbance time window that starts and lasts randomly. Sag is a voltage drop of 10-90%, swell is a voltage rise of 10-80%, and interruption is voltage loss over 90%, all lasting 1-6 cycles. The next disturbance changes the waveform shape.

Step 3: Waveform shape disturbances

Harmonics add components at multiples of 3, 5, and 7 with amplitudes of 5-15%. Transient oscillation is a 300-900 Hz wave that decays, like when a capacitor bank is connected. Flicker is an amplitude modulation of 8-25 Hz that causes lights to flicker. Knock is a periodic flicker caused by power electronic converters. There is also a combination of sag and harmonics. The add noise function will be used in the final episode, create a file named type_underscore_disturbance.py.

Step 4: RMS per cycle

The RMS per cycle function calculates the effective value per cycle in per unit, where 1 means nominal voltage. Run. Sag in this example drops to 0.26 per unit, interruption drops to 0.17, and swell rises to 1.3. Interestingly, harmonics, transients, and knock almost do not change RMS, even though the waveform is distorted. So RMS alone is not enough.

Step 5: Nine waveform shapes

Let's look at the nine waveforms. The red line is RMS per cycle. Sec, swell, and interruptions are clearly visible from the red line going down or up. Harmonics make the waveform no longer smooth sine wave, transients introduce fast decaying oscillations, flicker makes the amplitude slowly wave, and knock is just a small spike that is hard to see. For waveform disturbances, we need wave-flat.

Done!

Done. We have now learned the nine types of power quality disturbances, modeled them with Python, and seen that RMS per cycle is only sufficient for magnitude disturbances. In the next episode, we will generate a dataset of 2700 signals with random parameters according to IEEE-1159. Currently working on a thesis, dissertation, or research in the field of power quality and signal processing? Cerdas Inovasi Digital helps with the implementation of machine learning, from generating disturbance signals according to IEEE-1159, wave-flat transformation, feature extraction, up to classification with SVM, Random Forest, and XGBoost, as well as opening Python programming courses for electrical engineering. Want to master the entire series, from synthetic signals to AIS testing? All 6 episodes, complete with PDF step-by-step guides and source code, are available for premium tutorial subscribers. Subscribe starting at Rp69.000 per month, save 28% with the 12-month package, or pay once Rp999.000 for lifetime access. Want just one class? Buy a single class with lifetime access. Subscribe now at the premium page cerdasinovasidigital.com Contact us via WhatsApp, or visit cerdasinovasidigital.com Thank you for watching, see you in the next episode.

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Transcript

0:03Introduction

1:09Step 1: Installing the library

1:30Step 2: Magnitude disturbances: sag, swell, interruption

3:28Step 3: Waveform shape disturbances

5:30Step 4: RMS per cycle

6:46Step 5: Nine waveform shapes

7:58Done!

Ask the class material

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Recommended classes for you

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Transcript

0:03Introduction

1:09Step 1: Installing the library

1:30Step 2: Magnitude disturbances: sag, swell, interruption

3:28Step 3: Waveform shape disturbances

5:30Step 4: RMS per cycle

6:46Step 5: Nine waveform shapes

7:58Done!

Ask the class material

My notes

Questions & answers

Your instructor

Introduction

Step 1: Installing the library

Step 2: Magnitude disturbances: sag, swell, interruption

Step 3: Waveform shape disturbances

Step 4: RMS per cycle

Step 5: Nine waveform shapes

Done!

Introduction

Step 1: Installing the library

Step 2: Magnitude disturbances: sag, swell, interruption

Step 3: Waveform shape disturbances

Step 4: RMS per cycle

Step 5: Nine waveform shapes

Done!