A Power Flow Calculation Method Based on Power Flow Embedding Technology

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...

AI Technical Summary

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

Images