Synchrophasor based islanding detection for microgrids using moving window principal component analysis and extended mathematical morphology

• Published in: IET renewable power generation 2020-09, Vol.14 (12), p.2089-2099
• Main Authors: Radhakrishnan, Rohikaa Micky, Sankar, Ashok, Rajan, Sunitha
• Format: Article
• Language: English
• Subjects: control centre; data dimensionality; data‐intensive grid‐connected MG; distributed power generation; enhanced grid security; extended mathematical morphological filter; extended mathematical morphology; extensive deployment; fault diagnosis; frequent fault events; IEs; islanding detecting factor; islanding events; line trips; mathematical morphology; microgrids; moving window principal component analysis; MWPCA; nondetection zone; nondispatchable renewable energy sources; nonlinear characteristics; nonlinear filter; phasor measurement; phasor measurement units; power distribution faults; power distribution reliability; power grids; precise islanding detection method; principal component analysis; reliability; reliable islanding detection method; renewable energy sources; Special Issue: Power Quality and Protection in Renewable Energy Systems and Microgrids; transient conditions; voltage phasors
• ISSN: 1752-1416; 1752-1424
• DOI: 10.1049/iet-rpg.2019.1240

Extensive deployment of non-dispatchable renewable energy sources in microgrids (MGs) has led to frequent fault events, DG and line trips and is arduous to discriminate islanding events (IEs) from other transient conditions. For enhanced grid security, time synchronised data from phasor measurement units is utilised. However, data handling at the control centre is tedious as system variables monitored often exhibit non-linear characteristics that impose a problem to the operator in discriminating between islanding and non-IEs. To cope with this, a zero non-detection zone, reliable and precise islanding detection method for data-intensive grid-connected MG, is proposed. Voltage phasors, frequency and rate of change of frequency from various locations are processed through moving window principal component analysis (MWPCA) cascaded with extended mathematical morphological filter (EMMF). MWPCA reduces data dimensionality and Q statistics obtained are passed to EMMF, which acts as a non-linear filter. Further, the islanding detecting factor identified all IEs within the prescribed time limit with minimum false alarms. Accuracy, precision and reliability demonstrated by using a case study model using DIgSILENT are encouraging. It can be adopted by operators to run MG securely. Further, testing of the proposed method on a typical utility feeder shows promising results.