Classification of Partial Discharges Originating from Multi-level PWM Using Machine Learning
Artikel i vetenskaplig tidskrift, 2022

A Partial discharge (PD) is a small, localized breakdown which often appears in high voltage insulation systems. PDs can be initiated within material defects such as voids or cracks when exposed to sufficiently high electric field levels. In this research work, we study and propose a machine learning approach to detect and classify PDs originating from multilevel pulse width modulation (PWM) waveforms as utilized in power electronic devices such as inverters and variable frequency drives. Due to the increased use of power conversion units, new types of higher magnitude PDs have been observed to increase insulation degradation. By creating a cavity placed at different locations within the test object and exposing it to PWM voltage waveforms with two different rise times, a total of 345660 PD events are recorded and organized into 10 different classes. The maximum PD amplitude, duration, the time distance between consecutive PDs, and the area under the PD are used as features for PD classification. A unique way of concatenating a sequence of the extracted features to capture the temporal dependence of consecutive PDs is also presented. It is observed that when creating a sequence of information from consecutive observed PDs, a significant increase in classification accuracy can be obtained. Trained classifiers based on ensemble bagged decision trees (DT) and long short-term memory (LSTM) architecture resulted in 95.3% and 98.5% average classification accuracy on test data respectively.

Pulse width modulation

Partial discharges

High-voltage techniques

Partial discharges

Voltage measurement

Feature extraction

Deep learning

Machine learning

pulse width modulation

Feature engineering




Ebrahim Balouji

Chalmers, Mekanik och maritima vetenskaper, Förbränning och framdrivningssystem

Thomas Hammarström

Chalmers, Elektroteknik, Elkraftteknik

Tomas McKelvey

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik

IEEE Transactions on Dielectrics and Electrical Insulation

1070-9878 (ISSN) 15584135 (eISSN)

Vol. In Press


Datorseende och robotik (autonoma system)

Annan elektroteknik och elektronik



Mer information

Senast uppdaterat