Fig 1.
Classification of SML algorithms.
These methods are in a supervised paradigm.
Fig 2.
This case study is considered a distribution network.
Fig 3.
Voltage profile related to switching Sub2A-CB1.
This shows the voltage curve when circuit breaker CB1 is switched.
Fig 4.
Voltage profile related to switching Sub2B-CB3.
This shows the voltage curve when circuit breaker CB3 is switched.
Fig 5.
Voltage profile related to switching Sub3-S2.
This shows the voltage curve when switch S2 is switched.
Fig 6.
Voltage profile related to switching Sub3-S3.
This shows the voltage curve when switch S3 is switched.
Fig 7.
Voltage profile related to switching Sub3-S1.
This shows the voltage curve when switch S1 is switched.
Fig 8.
Machine Learning (ML) process.
The methodology of topology identification.
Table 1.
Percentage error of alternative KNN algorithms.
Table 2.
Percentage error of alternative SVM algorithms.
Table 3.
Percentage error of alternative Ensemble algorithms.
Fig 9.
Error classification for KNN random subspace method.
This curve indicates the performance of the algorithm.
Fig 10.
Voltage profile for switching the static load.
This curve indicates the voltage curve when the static load is disconnected from the system.
Fig 11.
Voltage profile for switching the induction motor.
This curve indicates the voltage curve when the induction motor is disconnected from the system.
Fig 12.
Voltage profile for switching the electric vehicle.
This curve indicates the voltage curve when the electric vehicle is disconnected from the system.
Fig 13.
Error classification for KNN random subspace method.
This curve indicates the performance of the algorithm.
Table 4.
Percentage error of alternative SVM algorithms.