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Fault Detection and Classification of Power System Busbar Using Artificial Neural Network

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Fault Detection and Classification of Power System Busbar Using Artificial Neural Network

Adrita Anika, Md. Junaed-Al-Hossain, Sk. Hasibul Alam, Nahid-Al-Masood

Abstract:

Fault analysis is an important consideration in power system planning, protection and overall system reliability assessment. When a fault occurs at some point in the network, normal operating conditions are upset; if the fault is persistent severe loss of load, property damage and steep economic losses can arise as undesirable consequences. Relay, circuit breakers and other protective elements are used to prevent such damages. Different types of faults in busbar are classified using the bus voltages and line fault current. In this paper, we have proposed an effective way of fault detection and classification in busbars using Artificial Neural Network (ANN). This can make the power system protection more effective. We have considered IEEE 9- bus system and a dataset has been generated using PSAF CYME software. This dataset is used to train and test our network in MATLAB software. The algorithm can be used for any bus system given the voltage magnitude and angles, which will be helpful for the authorities to get notified and solve the problem as soon as possible, since repair mechanism of each type of fault is different from others.

Conference / Journal Link:

https://ieeexplore.ieee.org/document/9071944

DOI:

10.1109/PEEIACON48840.2019.9071944

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