Fig 1.
The single line workflow analysis in electrical transmission.
Table 1.
The state of art research studies analysis for fault detection in electrical power transmission systems.
Fig 2.
The proposed electrical fault detection abstract workflow analysis.
Fig 3.
The proposed electrical fault detection stepwise workflow analysis.
Table 2.
Different types of faults analysis in the three-phase transmission line in the dataset.
Fig 4.
The voltage and current analysis overtime during a fault or not fault in electrical power transmission systems.
Fig 5.
The fault class distribution analysis before and after balancing.
Table 3.
The layers architecture stack of applied neural network based GRU method.
Fig 6.
The proposed glassbox-based optimized explainable boosting workflow analysis.
Table 4.
Analysis of parameter optimization in machine learning applications.
Table 5.
The testing results of applied neural network methods for fault detection with balanced dataset.
Table 6.
The testing results of applied neural network approaches for fault detection.
Fig 7.
The confusion matrix-based error rates analysis for fault detection.
Table 7.
The testing results of proposed EB method for fault detection.
Fig 8.
The histogram-based results comparisons of applied machine learning models.
Fig 9.
The ROC curve analysis of applied neural network methods.
Fig 10.
The radar chart-based performance area coverage analysis.
Fig 11.
The confusion matrix-based error rates analysis of proposed EB method for fault detection.
Table 8.
The k-fold-based performance validations of applied machine learning methods for fault detection.
Table 9.
The fault detection training time analysis of applied methods.
Fig 12.
The SHAPE chart-based XAI analysis of proposed EB method for fault detection.
Table 10.
The state of art research studies analysis for fault detection in electrical power transmission systems.