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Weight Calculation Alternative Methods in Prime’s Algorithm Dedicated for Power System Restoration Strategies

doi: 10.3390/en13226063
In self-healing grid systems, high utility in the use of greedy algorithms is observed. One of the most popular solutions is based on Prim’s algorithm. In the computation, the power grid is represented as a weighted graph. This paper presents a few possibilities of calculation of the numerical weight of a branch of the graph. The proposition of a modified edge weight calculation based on active power belongs to this group. The other solutions are novel subalgorithms bounded by real power, reactive power, and normalized factor. This factor is a mathematical combination of active and reactive power multiplied by influence coefficients. Requirements necessary for a power system are applied in the considered algorithms. Each of these proposed algorithms includes the power source limits, voltage level at busbars, and power system transmission features, such as transmission lines rated currents and power losses. All mentioned methods were compiled into separate algorithms, which can be used to compute graph model parameters. A simulation model based on Prim’s algorithm was prepared to compare the suitability of presented concepts. All weights of the subalgorithms were compared to each other. That is why different power system restoration strategies may require various methods of calculating weights of the graph’s branches.
spanning tree, Technology, graph theory, T, restoration strategy, greedy algorithms, Prim’s algorithm
spanning tree, Technology, graph theory, T, restoration strategy, greedy algorithms, Prim’s algorithm
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).4 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
