Computational geometry‐based methodology for identification of potential islanding initiators in high solar PV penetration distribution feeders

Abstract

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.