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Optimal Blocking Device Placement for Geomagnetic Disturbance Mitigation
Author(s) -
Yi Liang,
Di He,
Hao Zhu,
Deming Chen
Publication year - 2019
Publication title -
ieee transactions on power delivery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.57
H-Index - 188
eISSN - 1937-4208
pISSN - 0885-8977
DOI - 10.1109/tpwrd.2019.2930998
Subject(s) - geomagnetically induced current , transformer , electric power system , computer science , computational complexity theory , mathematical optimization , blocking (statistics) , simulated annealing , reliability (semiconductor) , control theory (sociology) , earth's magnetic field , reliability engineering , power (physics) , engineering , mathematics , electrical engineering , geomagnetic storm , voltage , algorithm , control (management) , magnetic field , computer network , physics , quantum mechanics , artificial intelligence
Geomagnetically induced currents (GICs) introduced by geomagnetic disturbances (GMDs) can damage transformers, increase reactive power losses, and cause reliability issues in power systems. Finding an optimal strategy to place blocking devices (BDs) at transformer neutrals is essential to mitigating the negative impact of GICs. In this paper, we study and solve the problem of optimal BD placement (OBP), aiming at minimizing the damages of GMDs, subject to various practical constraints. In particular, our approach accounts for the potential impact of BD placement to neighboring interconnected systems; we also consider the time-varying nature of the geoelectric field in BD placement. To the best of our knowledge, neither has been considered in the existing works. Under these constraints, the combined complexity of solving the OBP problem on a large-scale system poses a big challenge. To address this, we developed a simulated annealing-based algorithm that can achieve near-optimal solutions for the OBP problem at a reduced computational complexity, while taking the above constraints into account. More importantly, this paper provides a general mathematical framework that can be used to solve various OBP problems, with different objective functions and constraints. We demonstrated the effectiveness and the efficiency of our method by using power systems of various sizes.

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