Ship short-circuit fault diagnosis method based on improved ga-pso-bp

Abstract

The present invention provides a ship short-circuit fault diagnosis method based on the improved GA-PSO-BP, comprising steps: S1, collecting three-phase voltage signals when the ship power system is short-circuited, and establishing a training data set and a test data set; S2, establishing three layers BP neural network model; S3, establish a particle swarm representing the BP neural network model; S4, assign the particle position to the BP neural network model, input the training data set into the BP neural network for ship short-circuit fault diagnosis, and obtain the diagnosis result to calculate the error of the diagnosis result value, when the error value is greater than ε or the number of iterations does not reach g max , increase the number of iterations by 1 and enter S5, otherwise end the iteration and enter S7; S5, update the particle velocity and particle position; S6, cross-mutate the particle position, update the particle to the next generation particle; repeat steps S4-S6; The global optimal value of is given to the BP neural network model as the optimal particle; S8, input the test data set into the BP neural network model model, and diagnose the short-circuit fault of the ship.

Description

technical field [0001] The invention relates to the field of intelligent control, in particular to a ship short-circuit fault diagnosis method based on the improved GA-PSO-BP. Background technique [0002] When the ship's power fails, it will seriously endanger the safety of ship navigation. With the increase of voyage mileage and years, the insulation damage of ship power system lines becomes more and more serious, and short-circuit faults become the most important type of faults affecting ship power safety. In order to ensure the safety and quality of power supply, it is necessary to diagnose and remove the fault in the shortest possible time at the initial stage of the fault, so it is necessary to establish an efficient diagnosis system to deal with the complex ship power system. [0003] The current shipbuilding technology is advancing by leaps and bounds, the scale of ships is getting larger and larger, and the scale of navigation equipment and electrical equipment is ...

AI Technical Summary

Problems solved by technology

However, as the number of iterations increases, the diversity of the particle population is destroyed, and it is easy to make the particles tend to be unified, and it is easy to fall into a local optimum.

Based on GA-PSO to optimize the BP neural network, the inertia weight and learning factor of the particle swarm are fixed values, which cannot make the particles search for the target better

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