Fault diagnosis of the constant current remote power supply system in CUINs based on the improved water cycle algorithm

Abstract

Cabled Underwater Information Networks (CUINs) is an important platform for ocean observation where the Constant Current Remote Power Supply System (CCRPSS) guarantees the safe and normal operation of CUINs. The CCRPSS is mainly composed of the main node and underwater cables which require frequent fault diagnosis but how to improve the fault diagnosis rate is a difficult problem. This paper proposed a fault diagnosis method for the CCRPSS based on the Improved Water Cycle Algorithm (IWCA) and the multi-classifier group based on the Least Squares Support Vector Machine (LSSVM). Firstly, the multi-feature extraction method is used to obtain the characteristic information in the time and frequency domain; secondly, IWCA is established by combining the traditional Water Cycle Algorithm (WCA) with the chaotic mutation strategy, the elite memory strategy and the population reconstruction strategy. By applying 13 typical test functions to performance test, it can be found that the IWCA can effectively improve the global search ability and balance of the WCA algorithm. At last, IWCA is used to optimize the parameters of the LSSVM classifier and improve the classification efficiency. The comparisons of simulated results with traditional methods show that the proposed diagnostic model can not only obtain complete fault feature information, but also obtain the optimal classification parameters of LSSVM faster. Therefore, the proposed diagnostic method is verified to be suitable for the fault diagnosis of the constant current remote power supply system in CUINs.