Open AccessIslanding and power quality disturbance monitoring in microgrid using adaptive cross variational mode decomposition and reduced kernel ridge regression
Author(s) -
Dash Pradipta Kishore,
Satapathy Prachitara,
Nayak Pravati,
Sahani Mrutyunjaya
Publication year - 2020
Publication title -
international transactions on electrical energy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.428
H-Index - 42
ISSN - 2050-7038
DOI - 10.1002/2050-7038.12364
Subject(s) - islanding , microgrid , control theory (sociology) , photovoltaic system , computer science , robustness (evolution) , engineering , distributed generation , electronic engineering , voltage , artificial intelligence , electrical engineering , control (management) , biochemistry , chemistry , renewable energy , gene
Summary This article presents the detection and classification of islanding and power quality (PQ) disturbances for a multiple distributed generation based using an adaptive cross variational mode decomposition (XVMD) with reduced kernel ridge regression (RKRR). This article considers photovoltaic as the primary DG and studied the effect of the solar irradiation variation. Further the considered microgrid is subjected to different operating conditions pertaining to both islanding and non‐islanding disturbances. The proposed XVMD is an extension of VMD and with RKRR, it is a new contribution to the earlier studies to deal dynamic detection threshold requirement. The XVMD based RKRR needs simple features to classify the disturbances in less time as compared to the VMD based methods. The features to classify the events are calculated from the optimal mode selected by the hybrid firefly algorithm. For the performance analysis, voltage signals across the PV based DGs are obtained by initiating various power qualities and islanding disturbances. A validation case study through TMS 320C6713 Starter Kit (DSK) is presented for capacitor switching. The robustness of the proposed XVMD‐RKRR is observed from a large number of numerical experimentation by comparing it with other well‐known approaches.