Abstract
The smart grid is an intelligent power system network that should be reliable and resilient for sustainable operation. Wide area measurements systems are deployed in the power grid to provide real-time situational awareness to the power grid operators. Deriving meaningful insights from the growing voluminous data will be an excellent approach toward effectively using data being captured. This paper proposes an ensemble machine learning model for fault detection and fault type classification. The model is trained with features derived from data using an Ensemble feature extraction method. The ensemble feature extraction method’s efficacy is compared with state-of-the-art feature extraction methods. The proposed model gives an accuracy of 99.9% for fault detection and above 90% for fault classification. A considerable decrease in model training time is also a beneficial characteristic of this model. The model is trained and validated by data from IEEE 39 bus system.
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Ani Harish, Prince, A., Jayan, M.V. (2023). A Data-Driven Machine Learning Model for Transmission Line Faults Detection and Classification for the Smart Grid. In: Namrata, K., Priyadarshi, N., Bansal, R.C., Kumar, J. (eds) Smart Energy and Advancement in Power Technologies. Lecture Notes in Electrical Engineering, vol 926. Springer, Singapore. https://doi.org/10.1007/978-981-19-4971-5_56
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