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

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

Journal Name: Recent Advances in Electrical & Electronic Engineering
Formerly Recent Patents on Electrical & Electronic Engineering

Volume 10 , Issue 3 , 2017

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Graphical 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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Article Details

Year: 2017
Page: [202 - 208]
Pages: 7
DOI: 10.2174/2352096510666170601120624
Price: $25

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