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2014 | OriginalPaper | Chapter

Improved Ant Colony Optimization Algorithm in Inverter Fault Diagnosis

Authors : Qinyue Zhu, Ying Wang, Xitang Tan, Yahui Zhao

Published in: Unifying Electrical Engineering and Electronics Engineering

Publisher: Springer New York

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Abstract

In this chapter, improved Ant Colony Optimization Neural Network (ACONN) is used to achieve inverter fault diagnosis. As the neural network is detachable, this characteristic is used to improve the neural network training efficiency of single ACONN. Matlab/m-file program is written to implement the improved algorithm. Improved ACONN is applied as the method of neutral network training to identify the 22 modes of inverter power semiconductor’s open-circuit fault. The results show that improved ACONN can reduce the computation amount and identify the fault correctly in comparison with that of single ACONN. Thus improved ACONN can achieve inverter fault diagnosis quickly and correctly.

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Title: Improved Ant Colony Optimization Algorithm in Inverter Fault Diagnosis
Authors: Qinyue Zhu
Ying Wang
Xitang Tan
Yahui Zhao
Publisher: Springer New York
Book: Unifying Electrical Engineering and Electronics Engineering
Print ISBN: 978-1-4614-4980-5

Electronic ISBN: 978-1-4614-4981-2

Copyright Year: 2014
DOI: https://doi.org/10.1007/978-1-4614-4981-2_69