Power distribution network investment decision-making method based on deep transfer learning

Abstract

The invention discloses a power distribution network investment decision-making method based on deep transfer learning, and the method comprises the steps: collecting and screening data, describing the data distribution characteristics of a power grid through an edge distribution probability, representing the network relation characteristics of the power grid through a conditional distribution probability, and completing the characteristic transfer from a source domain power grid to a target power grid, so that adaptive learning under a small sample of power distribution network investment is realized, and finally an input-output nonlinear mapping model based on a target power distribution network is established to make a decision on power distribution network investment. According to the method, a power grid investment input-output association relationship is constructed through a deep learning network, a power grid investment decision problem is analyzed from the perspective of pure data, a transfer learning process is introduced, and data distribution characteristics and network relationship characteristics are migrated from other similar power grids through a small number of samples of the transfer learning process by utilizing the self-adaptive characteristic of the transfer learning process, so that the problem that training samples are insufficient in the association mining process of an existing data driving method is solved.

Description

technical field [0001] The invention relates to the technical field of power grid investment decision-making, in particular to a distribution network investment decision-making method based on deep transfer learning. Background technique [0002] In the field of power grid investment decision-making, with the rapid development of clean energy and the deepening of the interaction between supply and demand, investment transformation measures have become increasingly complex and diverse. Traditional investment decision-making methods have the following problems: (1) The upgrading and transformation measures of the distribution network are complex and diverse, and the benefits obtained by different investment projects are significantly different. Taking the addition of smart meters as an example, it is usually difficult to quantify the impact of smart meters on the grid through the establishment of mathematical models. Impact. (2) The traditional investment decision-making sche...

AI Technical Summary

Problems solved by technology

Traditional investment decision-making methods have the following problems: (1) The upgrading and transformation measures of the distribution network are complex and diverse, and the benefits obtained by different investment projects are significantly different. Taking the addition of smart meters as an example, it is usually difficult to quantify the impact of smart meters on the grid through the establishment of mathematical models. Impact

(2) The traditional investment decision-making scheme involves a series of nonlinear power flow calculations and safety constraints, but it is often difficult to obtain a clear grid structure before the actual decision-making

(3) The grid investment cycle is usually based on years, and there is no grid data sample support for the new round of investment

The applicability of existing methods is greatly reduced when the grid structure is unknown and the operating data is incomplete.

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