Phase-shifting transformer application to power-flow adjustment for large-scale PV penetration

Kawaura, Yuki; Yamanouchi, Sho; Ichihara, Miki; Iwamoto, S.; Suetsugu, Yo; Higashitani, Tomoyuki

doi:10.1109/tencon.2016.7848668

Cited by 5 publications

(1 citation statement)

References 3 publications

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“…Phase-shifting transformer, as a kind of power flow control equipment, has the characteristics of flexible control and large-range adjustment angle. In addition, it can respond quickly under the help of high-power electronic devices and is expected to be a daily regulation means for the economic operation of the system (Verboomen et al, 2005;Kawaura et al, 2016;Morrell and Eggebraaten., 2019). However, although the phase-shifting transformer (PST) has many advantages, it also increases the complexity of the power grid structure.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Genetic Algorithm for Power Flow Optimization in the Power System With Phase Shifting Transformer

Liu

et al. 2022

Front. Energy Res.

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Phase-shifting transformer (PST) is one of the flexible AC transmission technologies to solve the problem of uneven power transmission. Considering that PST can also be used as a regulation means for the economic operation of the system, it is necessary to study the power flow optimization of power systems with PST. In order to find a more efficient power flow optimization method, an improved genetic algorithm including a data-driven module is proposed. This method uses the deep belief network (DBN) to train the sample set of the power flow and obtains a high-precision proxy model. Then, the calculation of the DBN model replaces the traditional adaptation function calculation link which is very time-consuming due to a great quantity of AC power flow solution work. In addition, the sectional power flow reversal elimination mechanism in the genetic algorithm is introduced and appropriately co-designed with DBN to avoid an unreasonable power flow distribution of the grid section with PST. Finally, by comparing with the traditional model-driven genetic algorithm and traditional mathematical programming method, the feasibility and the validity of the method proposed in this paper are verified on the IEEE 39-node system.

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Section: Introductionmentioning

confidence: 99%