μPMU‐based intelligent island detection – the first crucial step toward enhancing grid resilience with MG

Abstract

With the increased climatic change and modern grid complexity, extreme grid power outage events caused by natural calamity and human interruptions have led to an urgency to enhance the grid resiliency. Microgrids (MGs) have proved to be a concrete solution to these situations. However, these events are quite uncertain, leading to the unintentional island of MGs that has adverse effects. Thus, as a first step toward increasing grid resiliency with MG, informing the distributed generations about the unintentional island is a critical task. Hence, there is a need to develop a quick and reliable unintentional island detection scheme. Micro phasor measurement units (μPMUs) are becoming popular in MG. Given this, this study proposes an inadvertent island detection scheme in an MG using an intelligent μPMU. With the μPMU, the voltage at solar generator bus is measured, three features are extracted through spectral kurtosis and random forest classifier is employed for island detection. After island detection, a control methodology is proposed to circumvent the post-effects. The method has zero non-detection zone, 99.83% accuracy and a detection time of 20 ms. The reliability of the algorithm is ascertained using the analytical hierarchical approach and software fault-tree analysis.

A comprehensive review on intelligent islanding detection techniques for renewable energy integrated power system

“…Also, in severe conditions, an intelligent μPMU is needed to mitigate the reliance on the communication channel which will not only measure but also through signal processing, data analysis, and classification of learning a vital decision can be taken. 124 Like PMU, the μPMU receives the voltage and current signals through the potential transformer or current transformer, assesses phasors with a digital signal processor, time stamps the phasors with the reference time provided by the GPS system, and then transfers them to the control center. 125 The advantages of the new smart μPMUs-based approach are that this can be easily integrated in the distribution network since no additional equipment is required for islanding detection, as it could be integrated into the digital relays with PMU connectivity that are currently in use.…”

“…Table 2 presents the comparison of various IDMs based on their merits and demerits 3,19,32,44,63,127,167 and Table 3 shows a feature‐based comparison between different IDMs 3,16,19,32,35,63,127 . Table 4 illustrates the comparison of various IDMs based on their detection/operating time and computational burden 3,21,32,35,104,117,124,128,144,156,171,188 …”

“…illustrates the comparison of various IDMs based on their detection/operating time and computational burden 3,21,32,35,104,117,124,128,144,156,171,188. 6 | CONCLUSION AND FUTURE SCOPEA comprehensive review of numerous IDMs along with classification and a brief introduction of each method are outlined in this paper that covers the research gap of ongoing research.…”

Islanding detection by phase difference method using a low cost quasi‐PMU

“…Alternatives to ROCOF and VS relays have been proposed in literature, although they have not seen significant application in practice [4,[11][12][13][14]. The alternatives can be roughly categorised as those which require telecommunications and those which do not.…”

“…The islanded is formed when tie-line 5-10, highlighted in blue, is opened FIGURE 13 Voltage waveforms simulated by "PowerFactory" in EMT mode. At t = 1 s, Bus 10 islands from the 9-bus system FIGURE 14 Frequency and ROCOF resulting when the tie-line opens. The island had been exporting 100 kW to the system FIGURE 15 Phase angle of the "quasi-PMU" for island (red) and system (blue) calculated from sampled value data.…”