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A Power Flow Calculation Method Based on Power Flow Embedding Technology

A technology of power flow calculation and power flow, which is applied in the field of power flow calculation based on power flow embedding technology, can solve problems such as slow calculation speed, increased number of iterations, non-convergence, etc., and achieve the effect of simple method and fast calculation speed

Active Publication Date: 2022-06-24
ZHEJIANG UNIV CITY COLLEGE +1
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Problems solved by technology

[0005] Applying deep learning technology to power flow calculation is a supplement to the existing traditional power flow calculation method. Under the new situation of energy Internet, the power grid structure involved in power flow calculation is becoming more and more complex, which has a great impact on the algorithm's rapidity and convergence. The requirements are more stringent. The principle of the Gauss-Seidel iteration method is simple and takes up less computer memory. However, when it is applied to a large-scale power system, the number of iterations will increase and it will not converge. The Newton-Raphson method has fast convergence and accuracy. High, but the Jacobian matrix needs to be recalculated in each iteration process, which causes problems such as taking up too much computer memory and slow calculation speed; the fast decoupling method improves the calculation speed and memory usage, but when Some ill-conditioned conditions may lead to non-convergence

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  • A Power Flow Calculation Method Based on Power Flow Embedding Technology
  • A Power Flow Calculation Method Based on Power Flow Embedding Technology
  • A Power Flow Calculation Method Based on Power Flow Embedding Technology

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Embodiment Construction

[0064] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0065] The following will take N as 14 as an example, in conjunction with the relevant drawings in the embodiments of the present invention, to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention. not all examples. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0066] The invention provides a power flow calculation method of a variable topology power grid in an N node based on deep learning, which is simple and convenient, has a fast calculation speed, can be used for online power flow calculation, and has no convergence problem. The me...

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Abstract

The invention discloses a power flow calculation method based on power flow embedding technology, comprising the following steps: determining the maximum number of nodes N, constructing a training set K, a verification set V, and a test set T; for the training data in step 1), constructing the corresponding Positive sample K + and negative sample K ‑ ; Based on the training sample K and positive sample K in step 2) + , Negative sample K ‑ , using triplet-based twin neural network, after full training, the power flow embedding layer P is obtained. The calculation method of the present invention is a direct method. When it is finally used, it only needs to arrange the known parameters according to the rules and use them as the input of the deep neural network. Through the multiplication of several matrices and the nonlinear operation of neurons, The final power flow value can be obtained, so this method is relatively simple and has a fast calculation speed, and can be used for online power flow calculation without convergence problems.

Description

technical field [0001] The invention relates to the technical field of power systems, in particular to a power flow calculation method based on a power flow embedding technology. Background technique [0002] With the continuous development of new energy technologies, a new energy utilization system, the Energy Internet, has emerged. The role of the Energy Internet is to integrate a series of power grid operation data with the support of artificial intelligence technologies such as big data and machine learning to predict various situations, and finally enable all machines, equipment, and systems to be dynamically adjusted in real time to improve the power grid. overall operational efficiency. [0003] With the advent of the era of big data and the improvement of computer technology, neural networks, especially deep learning, have greatly outperformed other machine learning models in the field of artificial intelligence, and have been used in speech recognition, image class...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H02J3/00H02J3/06
CPCH02J3/00H02J3/06
Inventor 李艳君叶倩莹潘树文
Owner ZHEJIANG UNIV CITY COLLEGE
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