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Recent Advances in Electrical & Electronic Engineering

Editor-in-Chief

ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

Research Article

Prediction of Equivalent Salt Deposit Density of Insulators Using Adaptive Quantum Particle Swarm Optimization Algorithm

Author(s): Jiatang Cheng*, Li Ai and Yan Xiong

Volume 10, Issue 3, 2017

Page: [202 - 208] Pages: 7

DOI: 10.2174/2352096510666170601120624

Price: $65

Abstract

Background: The Equivalent Salt Deposit Density (ESDD) is the basis of determining pollution classes and mapping grid pollution areas. The influence of environmental factors on the ESDD is complex, and it is difficult to establish an accurate mathematical model to fit the nonlinear relationship between them.

Methods: In order to predict effectively the ESDD, a model of Adaptive Quantum Particle Swarm Optimized BP neural network (AQPSO-BP) was proposed. In this algorithm, the encoding mechanism based on probability amplitude of quantum bits was used to expand the ergodicity of population. The position and velocity information of each particle was applied to adaptively adjust the inertia factor. At the same time, the non-linear dynamic adjustment strategy of acceleration factors and mutation operation were introduced to reduce the probabilities of trapping in the local optima solution.

Results: The prediction results show that the average relative error, the mean absolute error, the mean squared error and the coefficient of determination are 0.1393%, 1.27E-04, 2.33E-06 and 0.9830, respectively; the average relative variance is 0.0171.

Conclusion: Compared with the Particle Swarm Optimized BP network (PSO-BP) and Quantum Particle Swarm Optimized BP network (QPSO-BP) models, the AQPSO-BP algorithm has higher prediction accuracy and stronger generalization ability, and is suitable for evaluating the contamination level to prevent flashover on polluted insulators.

Keywords: Insulator, Equivalent Salt Deposit Density (ESDD), prediction, Quantum Particle Swarm Optimization (QPSO), algorithm, adaptive, BP neural network.

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