Three-Tier-Deep-Learning Model for Fault Classification in Power System using PMU Data: A Hybrid Meta-Heuristic Optimization - Yenagimath, Mahesh; Ankaliki, Shekhappa; V., Girish (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The process of categorising and recognising various sorts of defects that occur inside a power system is referred as fault classification. It involves classifying and identifying the faults that occur in the system like Short Circuit errors, Frequency Disturbance events, High Impedance faults and Phase-balance errors. To overcome these faults, we proposed a three-tier deep learning model using PMU Data. The proposed a methodology consist of Pre-Processing stage followed by Feature extraction and Feature Selection. In Pre-Processing phase, the data are collected from the PMU Dataset and then pre-processed via Missing Data Imputation, Outlier Detection and Handling and Data normalization via Z-score normalization. The pre-processed PMU data are used to extract the features using Higher-Order Statistical Moments and Multivariate Statistical Measures. The extracted optimal features are used to select the relevant features using the Hybrid meta-heuristic optimization model which is combination of Darts Game Optimization (DGO) and Monarch Butterfly Optimization (MBO). In this paper work a Three-Tier-Deep Learning-Based model is developed for fault diagnosis which combines Artificial Neural Network (ANN), Optimized Convolutional Neural Network (CNN) and Bi-LSTM. And finally, the model will be fine-tuned by adjusting hyper parameters, architecture, or the optimization process to enhance fault diagnosis accuracy. The proposed model is executed using PYTHON Platform.