Fault Detection Method based on Artificial Neural Network for 330kV Nigerian Transmission Line

Alhassan Musa Oruma; Ismaila Mahmud; Umar Alhaji Adamu; Simon Yakubu Wakawa; Gambo Idris; Mahmud Mustapha

doi:10.38124/ijisrt/ijisrt24apr651

Abstract

This research focused on identifying various types of faults occurring on 330kV transmission lines through the use of artificial neural networks (ANN). A MATLAB model for the Gwagwalada-Katampe 330kV transmission line in Nigeria was implemented to generate fault datasets. Voltage and current fault parameters were utilized to train and simulate the ANN network architecture selected for each stage of fault detection. Four types of faults were considered, along with a fifth condition representing no fault. The results illustrated the success of the developed model in identifying various fault conditions and system parameters on the Gwagwalada-Katampe 330kV transmission line, modelled using MATLAB Simulink.

Keywords

Artificial neural networkComputer scienceTransmission lineFault (geology)Fault detection and isolationArtificial intelligenceTransmission (telecommunications)Line (geometry)Electric power transmissionPattern recognition (psychology)EngineeringTelecommunicationsElectrical engineeringMathematicsBiology

Related Publications

Global optimization of a neural network-hidden Markov model hybrid

Yoshua Bengio , Renato De Mori , Giovanni Flammia +1 more

The integration of multilayered and recurrent artificial neural networks (ANNs) with hidden Markov models (HMMs) is addressed. ANNs are suitable for approximating functions that...

1992 IEEE Transactions on Neural Networks 200 citations

State-of-the-art in artificial neural network applications: A survey

Oludare Isaac Abiodun , Aman Jantan , Abiodun Esther Omolara +3 more

This is a survey of neural network applications in the real-world scenario. It provides a taxonomy of artificial neural networks (ANNs) and furnish the reader with knowledge of ...

2018 Heliyon 2822 citations

Power curves for pattern classification networks

Janet Twomey , Alice E. Smith

The authors discuss the development of a methodology for evaluating and predicting the goodness of a pattern classification neural network based on the statistical concept of po...

2002 IEEE International Conference on Neur... 4 citations

Global optimization of a neural network-hidden Markov model hybrid

Yoshua Bengio , Renato De Mori , Giovanni Flammia +1 more

An original method for integrating artificial neural networks (ANN) with hidden Markov models (HMM) is proposed. ANNs are suitable for performing phonetic classification, wherea...

2002 18 citations

Cooperative spectrum sensing in cognitive radio networks

Ghurumuruhan Ganesan , Ye Li

The demand for radio spectrum has dramatically increased over the past decade due to the growing proliferation of wireless services and applications. However, under the current ...

2005 First IEEE International Symposium on... 559 citations

Publication Info

Year: 2024
Type: article
Pages: 896-902
Citations: 1767
Access: Closed

External Links

Download PDF (Free) View on DOI.org Semantic Scholar

Social Impact

Altmetric

Fault Detection Method based on Artificial Neural Network for 330kV Nigerian Transmission Line

PlumX Metrics

Social media, news, blog, policy document mentions

Citation Metrics

1767

OpenAlex

Influential

CrossRef

Cite This

APA Style

                            
                                    Alhassan Musa Oruma, 
                                
                                    Ismaila Mahmud, 
                                
                                    Umar Alhaji Adamu
                                
                                et al.
                            
                            (2024). 
                            Fault Detection Method based on Artificial Neural Network for 330kV Nigerian Transmission Line. 
                            International Journal of Innovative Science and Research Technology (IJISRT)
                            
                            , 896-902.
                            https://doi.org/10.38124/ijisrt/ijisrt24apr651

Identifiers

DOI: 10.38124/ijisrt/ijisrt24apr651

Data Quality

Data completeness: 77%