TY - JOUR
T1 - Computational geometry-based methodology for identification of potential islanding initiators in high solar PV penetration distribution feeders
AU - Vyas, Shashank
AU - Kumar, Rajesh
N1 - Publisher Copyright:
© The Institution of Engineering and Technology 2017.
PY - 2018/3/19
Y1 - 2018/3/19
N2 - Accidental disconnection of a live feeder section is a major concern accompanying large distributed solar photovoltaic (PV) generation integration. High localised penetration can alter power flows leading to anomalous occurrences. Load-inverter power balance during grid-side disturbances, for different load models, May cause unique situations that can trigger the interconnection point protective devices. One such phenomenon, identified as a potential cause of unintentional islanding on radial feeder models (a modified IEEE feeder in simulation and verified on a laboratory-hardware network), has been used in this work. A pre-emptive detection strategy has been implemented to identify such islanding initiators among other power system transients. Computational geometry concepts have been utilised to create an optimisation-derived feature extraction methodology for effective training of a classifier module realised in a Raspberry Pi microcomputer. This module predicts the class labels of test data points transmitted from simulations carried on a personal computer for the feeder model. The proposed pre-emptive islanding detection strategy can trigger an appropriate change in a PV inverter's operating mode before a feeder protective device is tripped by such island initiating anomalies. The online classification accuracy and speed indicate a possible integration of the proposed methodology and strategy with the inverter's control circuitry.
AB - Accidental disconnection of a live feeder section is a major concern accompanying large distributed solar photovoltaic (PV) generation integration. High localised penetration can alter power flows leading to anomalous occurrences. Load-inverter power balance during grid-side disturbances, for different load models, May cause unique situations that can trigger the interconnection point protective devices. One such phenomenon, identified as a potential cause of unintentional islanding on radial feeder models (a modified IEEE feeder in simulation and verified on a laboratory-hardware network), has been used in this work. A pre-emptive detection strategy has been implemented to identify such islanding initiators among other power system transients. Computational geometry concepts have been utilised to create an optimisation-derived feature extraction methodology for effective training of a classifier module realised in a Raspberry Pi microcomputer. This module predicts the class labels of test data points transmitted from simulations carried on a personal computer for the feeder model. The proposed pre-emptive islanding detection strategy can trigger an appropriate change in a PV inverter's operating mode before a feeder protective device is tripped by such island initiating anomalies. The online classification accuracy and speed indicate a possible integration of the proposed methodology and strategy with the inverter's control circuitry.
UR - http://www.scopus.com/inward/record.url?scp=85043581433&partnerID=8YFLogxK
U2 - 10.1049/iet-rpg.2017.0111
DO - 10.1049/iet-rpg.2017.0111
M3 - Article
AN - SCOPUS:85043581433
SN - 1752-1416
VL - 12
SP - 456
EP - 462
JO - IET Renewable Power Generation
JF - IET Renewable Power Generation
IS - 4
ER -