Detection of Incipient Faults in Power Transformers using Fuzzy Logic and Decision Tree Models Based on Dissolved Gas Analysis
This paper proposes an integrated approach utilizing Fuzzy Logic and Decision Tree algorithms to diagnose early-stage faults in power transformers based on Dissolved Gas Analysis (DGA) test results of transformer insulation oil. Overcoming limitations in conventional methods such as Duval Triangle,...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
College of Engineering of Afe Babalola University, Ado-Ekiti (ABUAD), Ekiti State, Nigeria
2024-03-01
|
| Series: | ABUAD Journal of Engineering Research and Development |
| Subjects: | |
| Online Access: | https://journals.abuad.edu.ng/index.php/ajerd/article/view/329 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846099600950165504 |
|---|---|
| author | Felix Olowolafe Kehinde Olukunmi Alawode |
| author_facet | Felix Olowolafe Kehinde Olukunmi Alawode |
| author_sort | Felix Olowolafe |
| collection | DOAJ |
| description |
This paper proposes an integrated approach utilizing Fuzzy Logic and Decision Tree algorithms to diagnose early-stage faults in power transformers based on Dissolved Gas Analysis (DGA) test results of transformer insulation oil. Overcoming limitations in conventional methods such as Duval Triangle, Key Gas Analysis, Rogers Ratio, IEC Ratio, and Doernenburg Ratio, our Fuzzy Logic and Decision Tree models address issues like inaccurate diagnosis, inconsistent diagnosis, lack of decisions or out-of-code results, and time-intensive manual calculations for large DGA datasets. The Decision Tree algorithm, a machine learning technique is applied to categorize faults into thermal and electrical types. Trained with over 300 DGA samples from transformers with known faults, the models exhibit robust performance during testing with different datasets. Notably, the Duval Triangle decision tree model attains the highest accuracy among the ten developed models, achieving a 98% accuracy rate when tested with 50 samples with known faults. Moreover, Decision Tree models for KGA, Doernenburg, Rogers, and IEC also demonstrate substantial prediction accuracy at 92%, 86%, 92%, and 90% respectively underscoring the efficacy of artificial intelligence methods over traditional approaches.
|
| format | Article |
| id | doaj-art-a7c232d14157401b82239d25daffb9c7 |
| institution | Kabale University |
| issn | 2756-6811 2645-2685 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | College of Engineering of Afe Babalola University, Ado-Ekiti (ABUAD), Ekiti State, Nigeria |
| record_format | Article |
| series | ABUAD Journal of Engineering Research and Development |
| spelling | doaj-art-a7c232d14157401b82239d25daffb9c72024-12-31T10:19:20ZengCollege of Engineering of Afe Babalola University, Ado-Ekiti (ABUAD), Ekiti State, NigeriaABUAD Journal of Engineering Research and Development2756-68112645-26852024-03-017110.53982/ajerd.2024.0701.06-j280Detection of Incipient Faults in Power Transformers using Fuzzy Logic and Decision Tree Models Based on Dissolved Gas AnalysisFelix Olowolafe0Kehinde Olukunmi Alawode1System Operations Department, Osogbo Area Control Centre, Osogbo, Transmission Company of Nigeria, NigeriaDepartment of Electrical and Electronic Engineering, Osun State University, Osogbo, Nigeria This paper proposes an integrated approach utilizing Fuzzy Logic and Decision Tree algorithms to diagnose early-stage faults in power transformers based on Dissolved Gas Analysis (DGA) test results of transformer insulation oil. Overcoming limitations in conventional methods such as Duval Triangle, Key Gas Analysis, Rogers Ratio, IEC Ratio, and Doernenburg Ratio, our Fuzzy Logic and Decision Tree models address issues like inaccurate diagnosis, inconsistent diagnosis, lack of decisions or out-of-code results, and time-intensive manual calculations for large DGA datasets. The Decision Tree algorithm, a machine learning technique is applied to categorize faults into thermal and electrical types. Trained with over 300 DGA samples from transformers with known faults, the models exhibit robust performance during testing with different datasets. Notably, the Duval Triangle decision tree model attains the highest accuracy among the ten developed models, achieving a 98% accuracy rate when tested with 50 samples with known faults. Moreover, Decision Tree models for KGA, Doernenburg, Rogers, and IEC also demonstrate substantial prediction accuracy at 92%, 86%, 92%, and 90% respectively underscoring the efficacy of artificial intelligence methods over traditional approaches. https://journals.abuad.edu.ng/index.php/ajerd/article/view/329Dissolved Gas AnalysisDecision TreeFuzzy LogicMembership FunctionIncipient FaultsConventional Methods |
| spellingShingle | Felix Olowolafe Kehinde Olukunmi Alawode Detection of Incipient Faults in Power Transformers using Fuzzy Logic and Decision Tree Models Based on Dissolved Gas Analysis ABUAD Journal of Engineering Research and Development Dissolved Gas Analysis Decision Tree Fuzzy Logic Membership Function Incipient Faults Conventional Methods |
| title | Detection of Incipient Faults in Power Transformers using Fuzzy Logic and Decision Tree Models Based on Dissolved Gas Analysis |
| title_full | Detection of Incipient Faults in Power Transformers using Fuzzy Logic and Decision Tree Models Based on Dissolved Gas Analysis |
| title_fullStr | Detection of Incipient Faults in Power Transformers using Fuzzy Logic and Decision Tree Models Based on Dissolved Gas Analysis |
| title_full_unstemmed | Detection of Incipient Faults in Power Transformers using Fuzzy Logic and Decision Tree Models Based on Dissolved Gas Analysis |
| title_short | Detection of Incipient Faults in Power Transformers using Fuzzy Logic and Decision Tree Models Based on Dissolved Gas Analysis |
| title_sort | detection of incipient faults in power transformers using fuzzy logic and decision tree models based on dissolved gas analysis |
| topic | Dissolved Gas Analysis Decision Tree Fuzzy Logic Membership Function Incipient Faults Conventional Methods |
| url | https://journals.abuad.edu.ng/index.php/ajerd/article/view/329 |
| work_keys_str_mv | AT felixolowolafe detectionofincipientfaultsinpowertransformersusingfuzzylogicanddecisiontreemodelsbasedondissolvedgasanalysis AT kehindeolukunmialawode detectionofincipientfaultsinpowertransformersusingfuzzylogicanddecisiontreemodelsbasedondissolvedgasanalysis |