Abstract
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.
Highlights
With the formation of interconnection of regional power systems, the power transmission is often done through multiple parallel channels
This paper proposes a power flow optimization method based on data-driven technology and an improved genetic algorithm for power systems with phase-shifting transformer (PST)
The method proposed in this paper reduces the difficulty of power flow calculation for power systems with PST and improves the speed of power flow optimization
Summary
With the formation of interconnection of regional power systems, the power transmission is often done through multiple parallel channels. Some methods to improve the power flow optimization efficiency of power systems with the phase-shifting transformer are proposed. In literature (Cui et al, 2013), matrix block technology is used to reduce the calculation scale of the nonlinear primal dual interior point method, which improves the efficiency of optimization of power systems with phase-shifting transformer. Lei et al (2021) proposed a data-driven optimal power flow method based on stacked extreme learning machine framework, which can directly obtain the optimal scheduling decision scheme of the system without the iterative process. The method of the second type is to obtain a more accurate linearized power flow calculation model based on the measured data (Liu et al, 2019). This paper proposes a power flow optimization method based on data-driven technology and an improved genetic algorithm for power systems with PST. The main findings of this study are summarized with some prospects for future studies in the conclusion section
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