All classes Power Quality Disturbance Classification Wavelet Syllabus · Episode 2 of 6

Class Wavelet-Based Power Quality Disturbance Classification #2: Generating Synthetic Disturbance Signals According to IEEE 1159 Standard

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Tutorial generating power quality disturbance dataset according to IEEE 1159 with Python: 2,700 signals, 9 classes. Episode 2 of the wavelet power quality disturbance classification series.

Software: Python (PyWavelets, scikit-learn, XGBoost)
Level: Intermediate
Duration: ± 5 min
Series total: 40 min · 6 videos
Published: October 25, 2026

Episode 2 of 6 · view syllabus

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Class Wavelet-Based Power Quality Disturbance Classification #2: Generating Synthetic Disturbance Signals According to IEEE 1159 Standard: Cover: Class Wavelet-Based Power Quality Disturbance Classification #2: Generating Synthetic Disturbance Signals According to IEEE 1159 Standard

Class Wavelet-Based Power Quality Disturbance Classification #2: Generating Synthetic Disturbance Signals According to IEEE 1159 Standard: Before you start, steps list, and Step 1

Class Wavelet-Based Power Quality Disturbance Classification #2: Generating Synthetic Disturbance Signals According to IEEE 1159 Standard: Step 2: Checking IEEE 1159 Range

Class Wavelet-Based Power Quality Disturbance Classification #2: Generating Synthetic Disturbance Signals According to IEEE 1159 Standard: Done: Final Result and Further Practice

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Introduction

Here is the final result, 2700 power quality disturbance signals, 300 for each class, each with different depth, duration, and occurrence time. We check the value range to ensure it meets the IEEE 1159 standard. Let's start. Hello, welcome to episode two of the power quality disturbance classification series with Wayflat. Recording thousands of real disturbances in the power grid takes years. Therefore, many studies use synthetic signals with parameters randomized within the IEEE 1159 standard, the standard for power quality monitoring practices. Today, we are generating our own dataset and checking it.

Step 1: 300 random signals per class

Create file underscore dataset.py The random generator is given SID 0 so the dataset can be recreated exactly the same. For each class, we call its generator function 300 times, and each call selects a different random parameter, depth in seconds, swell rise, harmonic amplitude, transient frequency, and so on. The result is saved to dataset underscore peki.npz.

Step 2: Checking IEEE 1159 range

Now we check with RMS for each cycle. Run The dataset is 2700 x 640, 9 classes, 300 signals per class. Sek has the lowest RMS between 0.1 and 0.9 per unit, exactly as defined by IEEE 1159. Swell has the highest RMS between 1.1 and 1.8 per unit. Interuption can go up to 0, but sometimes it reads higher because the disturbance does not cover a full cycle. Flicker fluctuates around 1, whereas harmonics, transients, and knock remain close to 1 per unit.

Step 3: Variation within each class

Let's see the variations. Six random examples from each class show different depths and occurrence times. Transients appear at different times with different frequencies and decay rates. Knock varies in position and depth of the notch. This variation is what allows the model to learn general patterns, not memorize a single shape.

Done!

Done. We have generated a dataset of 2700 power quality disturbance signals with random parameters, Checking RMS range according to IEEE 1159, and observing its variations. In the next episode, we will distinguish signals using the Straight Away Flat Transform. Currently working on a thesis, dissertation, or research in the field of power quality and signal processing? Cerdas Inovasi Digital helps with machine learning implementation. From generating disturbance signals according to IEEE 1159, wavelet 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 NICE durability 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. Thank you for watching, see you in the next episode. Thank you for watching!

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Transcript

0:03Introduction

0:50Step 1: 300 random signals per class

2:02Step 2: Checking IEEE 1159 range

2:44Step 3: Variation within each class

3:41Done!

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Transcript

0:03Introduction

0:50Step 1: 300 random signals per class

2:02Step 2: Checking IEEE 1159 range

2:44Step 3: Variation within each class

3:41Done!

Ask the class material

My notes

Questions & answers

Your instructor

Introduction

Step 1: 300 random signals per class

Step 2: Checking IEEE 1159 range

Step 3: Variation within each class

Done!

Introduction

Step 1: 300 random signals per class

Step 2: Checking IEEE 1159 range

Step 3: Variation within each class

Done!