A Data-Driven Genetic Algorithm for Power Flow Optimization in the Power System With Phase Shifting Transformer - Li, Zuohong; Li, Feng; Liu, Ruoping; Yu, Mengze; Chen, Zhiying; Xie, Zihao; Du, Zhaobin (Frontiers in Energy Research, 2022)
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 the 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 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 validity of the method proposed in this paper are verified on the IEEE 39-node system.