<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Based on the theory of fractional differential equations, this paper proposes a simple recursive, iterative scheme for power flow calculation in pure radial networks. The paper determines the network hierarchy formed by the ADT stack through breadth theory. This helps us define the branch sequence of the forward and backward generation in the power flow calculation of the smart distribution network. We ensure that the Jacobian matrix remains unchanged in the smart distribution grid power flow calculation. The interval model is more practical and computationally simpler than the point model. The research results show that the power flow calculation method is efficient based on the fractional differential equation.</p>
</abstract>

Based on the theory of fractional differential equations, this paper proposes a simple recursive, iterative scheme for power flow calculation in pure radial networks. The paper determines the network hierarchy formed by the ADT stack through breadth theory. This helps us define the branch sequence of the forward and backward generation in the power flow calculation of the smart distribution network. We ensure that the Jacobian matrix remains unchanged in the smart distribution grid power flow calculation. The interval model is more practical and computationally simpler than the point model. The research results show that the power flow calculation method is efficient based on the fractional differential equation.

Power Flow Calculation in Smart Distribution Network Based on Power Machine Learning Based on Fractional Differential Equations

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Based on the theory of fractional differential equations, this paper proposes a simple recursive, iterative scheme for power flow calculation in pure radial...

Power Flow Calculation in Smart Distribution Network Based on Power Machine Learning Based on Fractional Differential Equations

Data publikacji: 15 lip 2022

Zakres stron: 1037 - 1044

Otrzymano: 20 lut 2022

Przyjęty: 10 kwi 2022

DOI: https://doi.org/10.2478/amns.2022.2.0187

Słowa kluczowe
Fractional differential equation, Power distribution network, Power flow calculation, Smart grid, Machine learning

© 2023 Yuan Ji et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Power Flow Calculation in Smart Distribution Network Based on Power Machine Learning Based on Fractional Differential Equations

Data publikacji: 15 lip 2022

Zakres stron: 1037 - 1044

Otrzymano: 20 lut 2022

Przyjęty: 10 kwi 2022

DOI: https://doi.org/10.2478/amns.2022.2.0187

Słowa kluczoweFractional differential equation, Power distribution network, Power flow calculation, Smart grid, Machine learning

© 2023 Yuan Ji et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Fractional differential equation, Power distribution network, Power flow calculation, Smart grid, Machine learning