Abstract
BP neural network (Back-Propagation Neural Network, BP-NN) is one of the most widely neural network models and is applied to fault diagnosis of power system currently. BP neural network has good self-learning and adaptive ability and generalization ability, but the operation process is easy to fall into local minima. Genetic algorithm has global optimization features, and crossover is the most important operation of the Genetic Algorithm. In this paper, we can modify the crossover of traditional Genetic Algorithm, using improved genetic algorithm optimized BP neural network training initial weights and thresholds, to avoid the problem of BP neural network fall into local minima. The results of analysis by an example, the method can efficiently diagnose network fault location, and improve fault-tolerance and grid fault diagnosis effect.
Highlights
The faults of power transmission and distribution system bring a huge loss on power companies and users [1]
Literature [7,8] describe the specific application of BP neural network in the grid fault diagnosis: first, the action information of protection and circuit breaker is regarded as the input of network, and the fault area may occur as the output, constituting the fault decision table in order to establish a diagnostic model
We can see by comparing the simulation results of the two methods that the method of improved Genetic optimized BP neural networks can effectively locate the position of grid fault
Summary
The faults of power transmission and distribution system bring a huge loss on power companies and users [1]. With the development of diagnostic techniques, expert systems of artificial intelligence and genetic algorithms, artificial neural network, Bayesian networks, fuzzy sets [3] as well as other methods were introduced to the power grid fault diagnosis These methods have their limitations, for example, the fault-tolerance of expert system is poor, and it is difficult to give a correct diagnosis; the operation of genetic algorithm is more complex, the problem should first be encoded, and genetic algorithm finds the optimal solution and decodes, and the genetic algorithm is easy to fall into the “premature” [4]; neural networks are easy to fall into local minimum [5], and so on. This article based on the advantages and disadvantages of genetic algorithm, by modifying the crossover of Genetic Algorithm, and applied to BP neural network, optimizes the initial weights and thresholds of BP neural network This method can solve the problem falling into local minima and improve fault-tolerance and the accuracy of grid fault diagnosis
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
