Islanding Detection in Microgrid Compromising Missing Values Using NI Sensors

Islanding detection has become an antiquated topic in microgrid, but during the shabby network condition, the conventional islanding detection methodologies become impractical. This article develops a new islanding detection technique considering interrupted communication during islanding detection. This article has experimented a new predictive model (PM)-based method for islanding detection in the laboratory-based multiple distributed generation (DG) system (system with more than one DG). Microphasor measurement units ($\mu $PMUs) are incorporated for measuring phasors from the real-time system. The measurements are analyzed by the artificial neural network (ANN) for initiating PM, which specifies the occurrence of islanding in terms of probability. The following two subalgorithms: evaluation and decoding are simultaneously working to estimate probability of the islanding during sound network condition. The posterior probability evaluation comes into action when communication is interrupted. The accuracy of the algorithm is tested under UL 1741 loading conditions. The performance of the algorithm is analyzed under the simultaneous manifestation of communication interruption and several islanding or nonislanding condition. Nondetection zone has been evaluated for different scenarios of missing frames. The algorithm has also been compared with existing methodologies.

Automated summary

The article proposes a new islanding detection technique that takes into account interrupted communication, using an artificial neural network to analyze measurements and initiate predictive modeling to indicate islanding probability. The accuracy and performance of the algorithm under different network conditions are tested and evaluated, with posterior probability evaluation activated when communication is interrupted.