IoT Supervised PV-HVDC Combined Wide Area Power Network Security Scheme Using Wavelet-Neuro Analysis

Abstract

Power system networks are one of the most widely used methods in the real world for trans- ferring large amounts of electrical energy from one location to another. At present, High Voltage Direct Current Transmission is preferred for long distances over hundreds of miles due to minimal power loss and transmission cost of transmission.Due to an increase in power demand, integration of renewable sources to minimise the voltage uctuations and compensate for power loss is necessary. This is a mandatory re- quirement to produce sophisticated protection methods for mainly smart systems under various balanced and unbalanced fault conditions. The system protection scheme must respond as quickly as possible to protect the connected devices in a smart environment. The network must be monitored and protected under var- ious weather conditions as well as electrical paramet- ric problems. The proposed research work is carried on the basis of physical monitoring with the aid of the Internet-of-Things and electrical parameters cali- brated with the help of wavelet analysis. A wavelet is a mathematical tool to investigate the behaviour of transient signals at di erent frequencies, which pro- vides important information related to the detailed analysis of faults in power networks. The ma- jor goals of this research are to analyse faults us- ing detailed coe cients of current signals through the bior-1.5 mother wavelet for fault identi cation and arti cial neural network analysis for fault localiza- tion. This proposed approach furnishes an IoT su- pervised Photovoltaic - High Voltage Direct Current (HVDC) combined wide area power network secu- rity scheme using wavelet detailed coe cients under various types of faults with Fault-Inception-Angles.