Research and Application of Fault Diagnosis and Localization Techniques for Power Communication Optical Cables Based on Multimodal Data Analysis

Aiming at the problems of low accuracy of power cable fault pattern recognition and large error of fault location, a method of power cable fault diagnosis and location based on Beluga Whale Optimization-Extreme Learning Machine (BWO-ELM) algorithm and GIS system is proposed. First, the beluga whale optimization (BWO) algorithm is used to optimize the initial parameters of the extreme learning machine (ELM), and the BWO-ELM multi classification Optical Time Domain Reflectometry (OTDR) curve analysis fault pattern recognition method is constructed, obtain the straight-line distance and type of the optical fiber fault point to lay the foundation for fault location. Secondly, the accurate positioning method of optical cable fault based on GIS is proposed, which converts the straight-line distance of the optical fiber fault point into the optical cable distance, and matches it with the actual geographical location to obtain the coordinates of the actual fault point, which are intuitively displayed in the GIS system. Finally, the application effect of the proposed method is verified by simulation experiments. The results show that the proposed method achieves a fault pattern recognition accuracy of about 98.66%, the fault location error is floating up and down 3M, and the average error is 1.481%. It has higher performance and accuracy than other recognition models and fault location methods.