TY - GEN
T1 - Design of power distribution network fault data collector for fault detection, location and classification using machine learning
AU - Sowah, Robert A.
AU - Dzabeng, Nicholas A.
AU - Ofoli, Abdul R.
AU - Acakpovi, Amevi
AU - Koumadi, Koudjo M.
AU - Ocrah, Joshua
AU - Martin, Deborah
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/24
Y1 - 2018/10/24
N2 - The protection and maintenance of a power transmission system during fault condition is indispensable to ensure efficient and reliable power supply to consumers. Most methods of fault detection and location rely on measurements of electrical quantities provided by current and voltage transformers. In this paper, a prototype data collecting device was built for collecting data during different faulted conditions in a single-phase distribution network. Machine learning algorithms were developed for fault detection, location and classification on single-phase distribution lines. The transmission line was modelled using resistor network in the device; the current and voltage sensors were used in the prototype model with the data collection device for current and voltage readings under open-circuit and short-circuit faulted conditions. Training data was collected by varying the load on the line during the simulation of the fault type, sensor location on the node and analyzed. The test data was assessed using three (3) machine learning algorithms namely: K-Nearest Neighbor (KNN), Decision Trees and Support Vector Machines (SVM) for prediction of fault, location and classification within the single-phase distribution network. Test results showed that a higher accuracy rate of 99.42 % was obtained by using the Decision Trees algorithm compared to the others investigated.
AB - The protection and maintenance of a power transmission system during fault condition is indispensable to ensure efficient and reliable power supply to consumers. Most methods of fault detection and location rely on measurements of electrical quantities provided by current and voltage transformers. In this paper, a prototype data collecting device was built for collecting data during different faulted conditions in a single-phase distribution network. Machine learning algorithms were developed for fault detection, location and classification on single-phase distribution lines. The transmission line was modelled using resistor network in the device; the current and voltage sensors were used in the prototype model with the data collection device for current and voltage readings under open-circuit and short-circuit faulted conditions. Training data was collected by varying the load on the line during the simulation of the fault type, sensor location on the node and analyzed. The test data was assessed using three (3) machine learning algorithms namely: K-Nearest Neighbor (KNN), Decision Trees and Support Vector Machines (SVM) for prediction of fault, location and classification within the single-phase distribution network. Test results showed that a higher accuracy rate of 99.42 % was obtained by using the Decision Trees algorithm compared to the others investigated.
KW - Fault classification
KW - Fault detection
KW - Fault location
KW - Power distribution network
KW - Power distribution protection
UR - http://www.scopus.com/inward/record.url?scp=85056839403&partnerID=8YFLogxK
U2 - 10.1109/ICASTECH.2018.8506774
DO - 10.1109/ICASTECH.2018.8506774
M3 - Conference contribution
AN - SCOPUS:85056839403
T3 - IEEE International Conference on Adaptive Science and Technology, ICAST
BT - 2018 IEEE 7th International Conference on Adaptive Science and Technology, ICAST 2018
PB - IEEE Computer Society
T2 - 7th IEEE International Conference on Adaptive Science and Technology, ICAST 2018
Y2 - 22 August 2018 through 24 August 2018
ER -