An intelligent system based on optimized ANFIS and association rules for power transformer fault diagnosis | JoVE Visualize

An intelligent system based on optimized ANFIS and association rules for power transformer fault diagnosis

Lilia Tightiz¹, Morteza Azimi Nasab², Hyosik Yang¹

¹Department of Computer Science and Engineering, Sejong University, 05006, Seoul, South Korea.

ISA Transactions|March 22, 2020

View abstract on PubMed

Summary

This study introduces an intelligent method for diagnosing power transformer faults using Dissolved Gas Analysis Method (DGAM) and machine learning. The approach enhances fault detection accuracy by selecting key DGAM attributes and optimizing the Adaptive Neuro-Fuzzy Inference System (ANFIS) with the Black Widow Optimization Algorithm (BWOA).

Area of Science:

Electrical Engineering
Artificial Intelligence
Materials Science

Background:

Power transformer failures can lead to significant economic losses and grid instability.
Accurate fault diagnosis is crucial for timely maintenance and preventing catastrophic failures.
Dissolved Gas Analysis Method (DGAM) provides essential data for transformer health assessment.

Purpose of the Study:

To develop an intelligent method for accurate diagnosis and classification of power transformer faults.
To enhance fault detection and recognition precision using optimized machine learning algorithms.
To improve the robustness and efficiency of power transformer fault diagnosis systems.

Main Methods:

Utilized 14 attributes from Dissolved Gas Analysis Method (DGAM) as input for Adaptive Neuro-Fuzzy Inference System (ANFIS).
Applied Association Rules Learning Technique (ARLT) for selecting the most informative DGAM attributes.
Employed Black Widow Optimization Algorithm (BWOA) for optimal training of the ANFIS model.

Main Results:

The proposed method demonstrated high accuracy in fault detection and classification.
The system exhibited robust performance and short run times when evaluated on industrial datasets.
Attribute selection and optimized ANFIS training significantly improved diagnostic precision.

Conclusions:

The developed intelligent diagnosis system effectively identifies and classifies power transformer faults.
Optimizing ANFIS with BWOA and selecting informative DGAM attributes are key contributions.
This research offers a reliable and efficient solution for power transformer fault diagnosis.