Open Access Open Access  Restricted Access Subscription Access

Transmission Line Fault Location and Classification Using Machine Learning Technique

Unika Benson, Bharsat Jagruti, Shubham Jadhav, Bhagyashri Pawar

Abstract


ABSTRACT

The fault types and location in a power transmission line are detected based on the voltage and current wave forms. Fault types and location varies slightly depending on the location of the accident, which is not easy to grasp with the human eye. Therefore, many sensors are needed to diagnose faults, and it is very difficult for an administrator to determine the type and location of fault using voltage and current waveforms. The traditional systems adopted for fault classification result in complexity, lack of economy, nonhomogeneity and sometimes even cause unacceptable classification errors. The detection of fault in transmission line is done by using magnetic field sensors. The k-Nearest-Neighbour (kNN) algorithm and linear regression Machine Learning Technique can be used to locate and classify fault. This will reduce the computation techniques and will help in improving the power transmission system protection. It will also reduce the time necessary to clear the faults, especially for a long transmission line.

 

Keywords: Overhead transmission line fault detection, fault classification, magnetic field sensing coils, application of ML, kNN

 


Full Text:

PDF

References


Ferreira, K. J. (2007). Fault location for power transmission systems using magnetic field sensing coils (Doctoral dissertation, Worcester Polytechnic Institute).

Huang, Q., Zhen, W., & Pong, P. W. (2012). A novel approach for fault location of overhead transmission line with noncontact magnetic-field measurement. IEEE Transactions on power delivery, 27(3), 1186-1195.

Jamil, M., Sharma, S. K., & Singh, R. (2015). Fault detection and classification in electrical power transmission system using artificial neural network. SpringerPlus, 4(1), 1-13.

Chen, K., Huang, C., & He, J. (2016). Fault detection, classification and location for transmission lines and distribution systems: a review on the methods. High voltage, 1(1), 25-33.

Asadi Majd, A., Samet, H., & Ghanbari, T. (2017). k-NN based fault detection and classification methods for power transmission systems. Protection and Control of Modern Power Systems, 2(1), 1-11.

Hasan, A. N., Eboule, P. P., & Twala, B. (2017, May). The use of machine learning techniques to classify power transmission line fault types and locations. In 2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) & 2017 Intl Aegean Conference on Electrical Machines and Power Electronics (ACEMP) (pp. 221-226). IEEE.

Silva, K. M., Souza, B. A., & Brito, N. S. (2006). Fault detection and classification in transmission lines based on wavelet transform and ANN. IEEE Transactions on Power Delivery, 21(4), 2058-2063.

Che, J., Park, J., Park, G., & Park, T. (2019, June). A new fault location identification method for transmission line using machine learning algorithm. In 2019 3rd International Conference on Smart Grid and Smart Cities (ICSGSC) (pp. 81-84). IEEE.

Baskar, D., & Selvam, P. (2019). Machine Learning Framework for Power System Fault Detection and Classification. Int. J. Sci. Technol. Res, 9.

Liu, X. W. (2021). Research on Transmission Line Fault Location Based on the Fusion of Machine Learning and Artificial Intelligence. Security and Communication Networks, 2021.


Refbacks

  • There are currently no refbacks.