State Recognition Method of Power Equipment in Smart Grid Based on Machine Learning

In this paper, machine learning is used to solve the problem of power equipment state recognition, aiming to improve the accuracy of equipment operation state recognition. With the ever-expanding size of the power system, an increasing number of devices are becoming more intricate, posing significant challenges to the security of the electricity network. Consequently, enhancing equipment operational reliability is imperative to ensure the reliability of power system operations. This study focuses on the identification of the operating status of power equipment, which integrates intelligent optimization algorithms and machine learning techniques, utilizing an ameliorative FOA to solve the parameter setting problem of support vector machine (SVM). Subsequently, the new diagnosis model is utilized to distinguish the fault types of the equipment. Through simulation and measured data verification, it shows that the new power equipment state recognition model by using machine learning has high diagnostic accuracy. The method effectively enhances equipment status identification capabilities and equipment fault diagnosis proficiency.