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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Zhu, Q., Wang, Y., Tan, X., Zhao, Y. (2014). Improved Ant Colony Optimization Algorithm in Inverter Fault Diagnosis. In: Xing, S., Chen, S., Wei, Z., Xia, J. (eds) Unifying Electrical Engineering and Electronics Engineering. Lecture Notes in Electrical Engineering, vol 238. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4981-2_69
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DOI: https://doi.org/10.1007/978-1-4614-4981-2_69
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