Intelligent transformer area design combining load gravity clustering and deep Q-network
With the deepening reform of the power grid,higher requirements have also been put forward for the pre-cise investment and rational operation of power grid enterprises.The traditional planning and design of substation area lacks a scientific calculation model,which leads to a series of problems such as long power supply distance and large wire tortuosity coefficient in the design stage.In order to better promote the scientific formulation of the planning and design scheme of the distribution station area,this paper proposes an intelligent integrated planning and design meth-od for the station area based on reinforcement learning.The paper firstly uses the user's historical load data,adopts the density clustering algorithm considering the gravitational force between loads to divide the user area reasonably,and determines the load center.In the divided area,deep reinforcement learning algorithm is used to model the user points,load center points and obstacles,and the loss function of the optimal route is set,and the topology of the low-voltage line in the township transformer area is reasonably planned and designed.Finally,based on the topology of the planning and design and the user's historical power consumption data,the minimum calculation of the node neu-tral current is carried out,and the single-phase user-to-phase calculation is given to achieve the lowest neutral loss of the low-voltage power grid.We used the actual situation of a region in Anhui province for verification,and used the method proposed in this paper to transform the low-voltage platform area.The transformer layout offset load center point was only 17 m,and the line loss rate of the platform area was decreased to 1.5%,lower than 2.0%.
intelligent planning and design of transformer areaload gravitydensity clusteringdeep reinforce-ment learning
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