Differential protection of indirect symmetrical phase shift transformer using wavelet transform

Bhasker, Shailendra Kumar; Bera, Pallav Kumar; Kumar, Vishal; Tripathy, Manoj

doi:10.1109/indicon.2015.7443404

Cited by 3 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Considering the primary and secondary sides of these two transformers, identification of internal faults becomes more complex and extensive than in the case of Power Transformers. Wavelet transform was used for protection of ISPST by Bhasker et al [10]. Further, they used two Multi-Layer Feed Forward Neural Networks (MLFFNN) to classify different internal faults [11].…”

Section: Introductionmentioning

confidence: 99%

Identification of internal faults in indirect symmetrical phase shift transformers using ensemble learning

Bera

Kumar

Işık

2018

2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

Self Cite

View full text Add to dashboard Cite

This paper proposes methods to identify 40 different types of internal faults in an Indirect Symmetrical Phase Shift Transformer (ISPST). The ISPST was modeled using Power System Computer Aided Design (PSCAD)/ Electromagnetic Transients including DC (EMTDC). The internal faults were simulated by varying the transformer tapping, backward and forward phase shifts, loading, and percentage of winding faulted. Data for 960 cases of each type of fault was recorded. A series of features were extracted for a, b, and c phases from time, frequency, time-frequency, and information theory domains. The importance of the extracted features was evaluated through univariate tests which helped to reduce the number of features. The selected features were then used for training five state-of-the-art machine learning classifiers. Extremely Random Trees and Random Forest, the ensemble-based learners, achieved the accuracy of 98.76% and 97.54% respectively outperforming Multilayer Perceptron (96.13%), Logistic Regression (93.54%), and Support Vector Machines (92.60%).

show abstract

Section: Introductionmentioning

confidence: 99%

Identification of internal faults in indirect symmetrical phase shift transformers using ensemble learning

Bera

Kumar

Işık

2018

2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

Self Cite

View full text Add to dashboard Cite

show abstract

Utilization of Resilient Back Propagation Algorithm and Discrete Wavelet Transform for the Differential Protection of Three Phase Power Transformer

Reddy¹,

Patel²,

Shah

2020

2020 21st National Power Systems Conference (NPSC)

View full text Add to dashboard Cite

Detection and Classification of Internal Faults in Power Transformers using Tree based Classifiers

Pani

Bera

Kumar

2020

2020 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES)

Self Cite

View full text Add to dashboard Cite

This paper proposes a Decision Tree (DT) based detection and classification of internal faults in a power transformer. The faults are simulated in Power System Computer Aided Design (PSCAD)/ Electromagnetic Transients including DC (EMTDC) by varying the fault resistance, fault inception angle, and percentage of winding under fault. A series of features are extracted from the differential currents in phases a, b, and c belonging to the time, and frequency domains. Out of these, three features are selected to distinguish the internal faults from the magnetizing inrush and another three to classify faults in the primary and secondary of the transformer. DT, Random Forest (RF), and Gradient Boost (GB) classifiers are used to determine the fault types. The results show that DT detects faults with 100% accuracy and the GB classifier performed the best among the three classifiers while classifying the internal faults.

show abstract

Differential protection of indirect symmetrical phase shift transformer using wavelet transform

Cited by 3 publications

References 13 publications

Identification of internal faults in indirect symmetrical phase shift transformers using ensemble learning

Identification of internal faults in indirect symmetrical phase shift transformers using ensemble learning

Utilization of Resilient Back Propagation Algorithm and Discrete Wavelet Transform for the Differential Protection of Three Phase Power Transformer

Detection and Classification of Internal Faults in Power Transformers using Tree based Classifiers

Contact Info

Product

Resources

About