Quantum Neutrino Optimization Algorithm and Its Application in Fault Diagnosis of All-Electric Trawler Power Systems

The all-electric trawler power system exhibits complex characteristics such as multi-device coupling, multitimescale dynamic responses, and extensive heterogeneous data processing. Traditional fault diagnosis methods face challenges including slow convergence and susceptibility to local optima. To address these issues, this paper proposes a Quantum Neutrino Optimization Algorithm (QNOA) that integrates quantum computing and neutrino entanglement characteristics, aiming to enhance the accuracy and efficiency of fault diagnosis. QNOA employs quantum state array encoding to represent the solution space, leveraging quantum superposition states for parallel searches of high-dimensional data. By incorporating a neutrino spin regulation factor to dynamically adjust qubit probability amplitudes, the algorithm simulates the global entanglement behavior of neutrino weak interactions, thereby strengthening its global search capability and effectively balancing exploration and exploitation. Experimental results demonstrate that QNOA achieves over $30 \%$ reduction in average fitness values and more than $25 \%$ improvement in average convergence speed compared to traditional algorithms (e.g., Genetic Algorithm and Particle Swarm Optimization) on benchmark functions such as Sphere and Rosenbrock, particularly excelling in multimodal function optimization. Further application to fault diagnosis in all-electric trawler power systems-through equipment state encoding, objective function modeling, and simulation case studiesvalidates that QNOA can accurately locate diverse faults, including transformer short circuits, circuit breaker failures, and load overcurrents, with convergence to the optimal solution within 20-30 iterations. The findings confirm the high efficiency and robustness of QNOA in complex power system fault diagnosis, providing innovative technical support for intelligent ship operation and maintenance.