Browsing by Author "Akash, Al-Mahdi"

Filter results by typing the first few letters
Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    Power Flow Analysis Using Neural Networks
    (Department of Electrical and Electronic Engineering (EEE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh, 2025-10-25) Sayad, Rafsan; Akash, Al-Mahdi; Rahman, Tahmid
    Solving power flow equations is a crucial part of power system state estimation. Due to the complexity of the relationship among the power system variables, power flow equations are solved mainly through iterative approaches. But due to the slow nature of the iterative approaches, it is only natural to examine alternatives for solving power flow equations. In this study, first, we have proceeded to solve power flow equations using a classical neural network based approach. The obvious shortcoming of a classical neural network is the amount of data required to properly train a model. So, secondly, we have proceeded to solve power flow equations using physics guided neural network based approaches in order to address the shortcoming of the classical neural network. We developed and examined two different physics guided neural networks models. Finally, we examined the feasibility of hybrid quantum classical neural networks in solving power flow equations and explored the applications of quantum neural networks in the field of power systems. Our study shows that classical neural networks surpasses iterative approaches in terms of speed by a big margin with reasonable accuracy. Our study also suggests that classical neural networks perform the best in terms of accuracy among the three neural network based approaches tried. Physics guided neural networks solve the problem of availability of data with decent accuracy. And the hybrid quantum classical neural network shows decent accuracy and a speed up in the training process.