Quantum Neural Networks for Solving Power System Transient Simulation Problem

TL;DR

This paper introduces two novel quantum neural networks designed to simulate power system transients efficiently, demonstrating promising accuracy and optimization strategies, and pioneering quantum computing applications in power system simulation.

Contribution

The study develops and applies two new quantum neural network models specifically for power system transient simulation, expanding quantum computing's role in complex engineering problems.

Findings

Successful simulation of small power systems using QNNs

Optimization of training configurations for better accuracy

Demonstration of quantum computing's potential in power system analysis

Abstract

Quantum computing, leveraging principles of quantum mechanics, represents a transformative approach in computational methodologies, offering significant enhancements over traditional classical systems. This study tackles the complex and computationally demanding task of simulating power system transients through solving differential algebraic equations (DAEs). We introduce two novel Quantum Neural Networks (QNNs): the Sinusoidal-Friendly QNN and the Polynomial-Friendly QNN, proposing them as effective alternatives to conventional simulation techniques. Our application of these QNNs successfully simulates two small power systems, demonstrating their potential to achieve good accuracy. We further explore various configurations, including time intervals, training points, and the selection of classical optimizers, to optimize the solving of DAEs using QNNs. This research not only marks a pioneering effort in applying quantum computing to power system simulations but also expands the potential of quantum technologies in addressing intricate engineering challenges.