This study presents a new islanding detection technique based on islanding discrimination factor which is derived from the superimposed components of voltages. These voltages are calculated by utilising the acquired voltage signals from the terminal of distributed generators. Various islanding and non-islanding events with varying power mismatches have been simulated on two different widely used networks namely (i) IEEE 34 bus network and (ii) IEC61850-7-420 Micro-Grid model in real time digital simulator (RTDS/RSCAD) environment. The authenticity of the presented scheme has been verified on diversified islanding and non-islanding cases, generated from the above two models. The results indicate that the proposed scheme is able to distinguish islanding situation with non-islanding events accurately. Moreover, it senses islanding condition quickly (within one and half cycle) even with perfect power balance situation and hence, eliminates non-detection zone. In addition, it also provides better stability against nuisance tripping which is initiated due to various types of non-islanding events including reconfiguration of the network. In the end, comparative assessment of the presented scheme with the scheme just published in the literature shows its domination in distinguishing islanding condition with non-islanding events.
This study present a new islanding detection technique based on relevance vector machine (RVM) containing various types of distributed generations (DGs). The proposed scheme is based on utilising negative sequence component of current (I2), acquired at the terminal of the target DG. Various islanding and non‐islanding events with variable real and reactive power, change in network topology and diverse X/R ratio have been generated by modelling IEEE 34 bus system in real time digital simulator (RTDS®/RSCAD) environment. The samples of I2 for one cycle duration from the inception of islanding/non‐islanding events are given as input to the proposed RVM classifier. The proposed classifier is able to discriminate between islanding and non‐islanding events with an accuracy of 98.62% by utilising only 40% of the total dataset in training. Moreover, it is capable to classify islanding condition rapidly and correctly even with perfect power balance situation. Furthermore, it remains immune to change in network configuration/entirely different network and X/R ratio of various types of DGs. Comparative analysis of the proposed scheme in terms of accuracy, testing time and number of relevance vectors (RVs) with the existing schemes shows its superiority in discriminating islanding situation with non‐islanding events.
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