Data Dimensionality Reduction and Risk Identification of Equipment Defects based on Principal Component Analysis

With the development of power grid enterprises, the number of equipment defects grows rapidly, which brings serious challenges to the safe operation of power grid equipment. This paper studies data dimensionality reduction and risk identification of equipment defects based on principal component analysis, aiming to solve the problems of traditional equipment defect data analysis methods. Through the introduction of principal component analysis technology, the equipment defect data is downgraded to extract the main data features and simplify the data analysis process. At the same time, the results of principal component analysis are used for risk identification to identify the risk factors that may lead to equipment failure, and to improve the prediction accuracy and risk management effect of equipment failure. It was found that the variance contribution rate of equipment defect data dimensionality reduction based on principal component analysis reached up to 84.4%. This study aims to provide more accurate and reliable decision support for equipment management and maintenance, and to improve the reliability and stability of equipment.