การผสมผสานของโครงข่ายประสาทเทียมและเทคนิคช่วงความชันบนพื้นฐานวิธีการตรวจจับความผิดพร่องที่มีค่าอิมพีแดนซ์สูงในระบบจำหน่ายไฟฟ้า

Abstract

High Impedance Faults (HIFs) are events that, when they occur in a distribution system, often have significant impacts on life and property. Generally, traditional protection systems cannot detect the presence of high impedance faults because they have low short circuit currents that are close to the load and have various characteristics that change depending on the situation or the material the conductor contacts. This research study has studied the theory and principles of high impedance fault signals and the detection of high impedance fault signals to create a model of high impedance faults and to develop a signal detection method using a combination of artificial neural networks and interval slope pattern techniques. This method involves detecting patterns of signals to determine if they resemble prototype signals. The simulation will demonstrate the operation of high impedance fault detection in the real distribution system, and when the system detects a high impedance fault event, it will generate a notification signal to alert the system administrator of the presence of the high impedance fault.