A microgrid alarm processing method based on equipment fault prediction and improved support vector machine learning

Abstract

Microgrids are important parts of modern strong intelligent power system. In recent years, new energy sources have developed rapidly in China, and research on microgrid technologies is in the ascendant. The typical microgrid has obvious advantages such as flexible network structure and small circuit loss. With its increasing scale and increasing application in the power system, it needs to be dealt with the complex operation mode. In order to achieve efficient operation and management, microgrid control still needs to be equipped with energy management system (EMS). One of the main problems that EMS needs to solve is the generation of massive alarm information within a short period of time after equipment failure. To this end, this paper proposes a microgrid alarm processing method based on equipment fault prediction and improved support vector machine learning. Based on historical operation risk and health state evaluation, the equipment fault prediction is carried out. Then we optimize and select kernel function for machine learning and have an improved SVM model. With this model, fault equipment sets are classified and verified so we can get the accurate source alarm and fault equipment. Finally, the validity and accuracy of this method are verified by a simple example.