Islanding and non-islanding disturbance detection in microgrid using optimized modes decomposition based robust random vector functional link network

摘要

This paper presents detection and classification of islanding and non-islanding disturbances in a PV based microgrid scenario. To design effective pattern recognition the microgrid is conditioned to operate in grid synchronous mode (i.e. according to IEEE 1547) as well as islanding mode. Various non-islanding disturbances such as sag, swell, harmonics, load switching along with islanding event (during grid synchronous operation, according to UL 1741, section 46) are generated in the microgrid and the non-stationary voltage signal samples for each event has been extracted. To cope with the requirement for the dynamic detection threshold, parameter adaptive Variational Mode Decomposition (PAVMD) with Robust Regularized Random Vector Functional Link Network (RRVFLN) has been introduced in this paper. These extracted waveforms are subjected to the proposed novel PAVMD algorithm where firefly algorithm has been used for parameter optimization. Distinguishable features are extracted from the output of the proposed method PAVMD. Applicability of PAVMD with RRVFLN has been tested for different disturbances in grid-connected mode as well as islanding mode condition which is completely a new contribution to the existing literature. The proposed algorithm has been tested for noisy condition as well. Classification accuracy achieved with the proposed method is satisfactory and acceptable.