Ship short-circuit fault diagnosis method based on improved GA-PSO-BP

Abstract

The invention provides a ship short-circuit fault diagnosis method based on improved GA-PSO-BP which comprises the following steps: S1, acquiring a three-phase voltage signal when a ship power system is short-circuited, and establishing a training data set and a test data set; S2, establishing a three-layer BP neural network model; S3, establishing a particle swarm representing the BP neural network model; s4, endowing the BP neural network model with the particle position, inputting the training data set into the BP neural network to carry out ship short-circuit fault diagnosis, obtaining a diagnosis result, calculating an error value of the diagnosis result, when the error value is greater than or equal to gmax and the number of iterations does not reach gmax, adding 1 to the number of iterations, entering S5, otherwise, ending iteration, and entering S7; s5, updating the particle speed and the particle position; s6, performing cross mutation on particle positions, and updating the particles into particles of the next generation; repeating the steps S4-S6; s7, using the global optimal value of the particle swarm as an optimal particle to endow the BP neural network model; and S8, inputting the test data set into the BP neural network model, and diagnosing the ship short-circuit fault.

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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