Power system fault diagnosis with quantum computing and efficient gate decomposition.

Area of Science:

• Electrical Engineering
• Quantum Computing
• Optimization Algorithms

Background:

• Accurate power system fault diagnosis is essential for grid stability and operational efficiency.
• Classical fault diagnosis methods face scalability challenges, including high time consumption and computational complexity.
• Quantum computing offers potential advantages in solving complex optimization problems relevant to power systems.

Purpose of the Study:

• To propose a novel quantum computing-based method for power system fault diagnosis.
• To leverage the quantum approximate optimization algorithm (QAOA) for enhanced diagnostic speed and accuracy.
• To address the limitations of classical methods in large-scale power system analysis.

Main Methods:

• Reformulated the fault diagnosis problem into a Hamiltonian using the Ising model, preserving component and relay interactions.
• Employed symmetric equivalent decomposition of multi-z-rotation gates to improve efficiency on current quantum hardware.
• Utilized the low probability of power system events to reduce qubit requirements.

Main Results:

• The proposed quantum method achieved optimal fault diagnosis results comparable to classical solvers.
• Demonstrated a significant reduction in computational time compared to classical higher-order solvers.
• Validated the effectiveness of the quantum approximate optimization algorithm for power system fault diagnosis.

Conclusions:

• Quantum computing, specifically QAOA, presents a promising, faster alternative for power system fault diagnosis.
• The developed Hamiltonian formulation and qubit reduction techniques enhance practical applicability.
• This research paves the way for more efficient and scalable power grid management solutions.