All classes Power Quality Disturbance Classification Wavelet Syllabus · Episode 4 of 6

Class Wavelet Power Quality Disturbance Classification #4: Energy and Statistical Feature Extraction from Wavelet Coefficients

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Wavelet feature extraction tutorial: energy, statistics, and entropy of DWT coefficients for power quality disturbance classification. Episode 4 of the Wavelet Power Quality Disturbance Classification series.

Software: Python (PyWavelets, scikit-learn, XGBoost)
Level: Advanced
Duration: ± 7 min
Series total: 40 min · 6 videos
Published: October 25, 2026

Episode 4 of 6 · view syllabus

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Transcript

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Introduction

Here is the final result, each 640-sample signal is summarized into 32 features from the wavelet coefficients and RMS. From the feature distribution alone, we can already see the separated classes of disturbances. Let's start. Hello, welcome to episode four of the series on power quality disturbance classification using wavelet analysis. Wavelet coefficients are still too numerous and have varying lengths to be directly used in machine learning. We need to summarize them into meaningful features. Today we create 32 features and see how well they separate the classes.

Step 1: Energy features, statistics & entropy

Create file feature.py For each wavelet coefficient, we calculate 5 features. Relative energy compared to total energy, standard deviation, average absolute value, maximum absolute value, and Shannon entropy, which measures how spread out the energy is. Short transients have low D1 entropy because their energy is concentrated in a few coefficients. 6 coefficients x 5 features result in 30 features, plus the lowest and highest RMS per cycle for magnitude disturbances.

Step 2: Feature names & feature matrix

Feature names are automatically generated, for example, energy underscore D1 or entropy underscore A5, and the feature matrix function calculates features for multiple signals at once. Create file extraction.py

Step 3: Features for the entire dataset

We create the dataset and calculate features for all signals, then save them to feature underscore peki.npz. Run. The feature matrix is 2700 x 32, completed in less than 1 second. Average per class shows clear patterns. D1 transient and NOP energy is the highest, D3 energy is highest on harmonics and sec plus harmonics because harmonics three to seven are present in that band. Lowest RMS separates sec, interruption, and sec plus harmonics, while highest RMS separates swell.

Step 4: Feature distribution per class

Let's look at the box plot of 4 features. Lowest RMS separates interruption near 0 and sec in the middle. Highest RMS highlights swell. D3 energy highlights two harmonic classes. There are still classes overlapping in one feature, for example, NOC and NORMAL But the combination of 32 features gives machine learning enough information to distinguish them.

Done!

Done. We have extracted 32 energy, statistical, and entropy features from the wavelet coefficients plus RMS, then looked at how the features separate the classes. In the next episode, we will train SVM, Random Forest, and XGBoost. 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-159, 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 up to NICE testing? All 6 episodes, complete with PDF step-by-step guides and source code, are available to 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

0:43Step 1: Energy features, statistics & entropy

2:08Step 2: Feature names & feature matrix

2:39Step 3: Features for the entire dataset

3:59Step 4: Feature distribution per class

5:04Done!

Ask the class material

My notes

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Need more than a video?

Transcript

0:03Introduction

0:43Step 1: Energy features, statistics & entropy

2:08Step 2: Feature names & feature matrix

2:39Step 3: Features for the entire dataset

3:59Step 4: Feature distribution per class

5:04Done!

Ask the class material

My notes

Questions & answers

Your instructor

Introduction

Step 1: Energy features, statistics & entropy

Step 2: Feature names & feature matrix

Step 3: Features for the entire dataset

Step 4: Feature distribution per class

Done!

Introduction

Step 1: Energy features, statistics & entropy

Step 2: Feature names & feature matrix

Step 3: Features for the entire dataset

Step 4: Feature distribution per class

Done!