Representational and sequential feature learning model for fault classification in power system

ABSTRACT

The classification of three-phase faults in electrical power systems is essential. The vast majority of fault classification techniques are focused on machine learning framework that requires pre-processing algorithms and rely highly on feature engineering. In this paper, a hybrid Convolutional Neural Network and Long Short Term Memory (CNN-LSTM) deep learning model is designed and employed for fault classification. With this combination, both representational and sequential features are automatically extracted and effectively learned using lesser trainable parameters from the raw temporal data. The raw three-phase current and voltage data are used as input for the proposed model. The performance of the proposed model is tested using a 10-fold cross-validation technique on large simulated data produced from a standard IEEE 30-bus system. The efficacy of the proposed model is confirmed by comparing it with different standards and existing models from the literature. The impact of noisy data and load variation conditions are also studied and analysed on the model’s performance.

KEYWORDS:

• Fault classification
• IEEE 30 bus system
• deep learning
• convolution neural network
• long short term memory

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author, Dr. Narendra D. Londhe, upon reasonable request.