Intelligent Fault Detection and Diagnostic System for Smart Meter

Abstract

The energy requirements of the growing population and industries are driving up energy demand in the twenty-first century. Fulfilling the increasing demand for environmental solutions is also a major challenge due to the use of fossil fuels. As a result, all existing infrastructure must be upgraded to create an efficient system. Smart Grid is a step toward more efficient power utilization. A smart meter is an important component of a smart grid that deals directly with the consumer, as well as the option of net metering. Net metering is a critical execution nowadays and a smart meter is the core component of this execution. Smart meters design is currently not fully fault free and there is a need for detection and diagnostic mechanism for a smart meter to accurately identify the faults. Therefore, design and development of a reliable smart meter is the need of the hour to cater for the said requirements. The research goal of this work is to create a system that detects and diagnoses faults in smart meters. In this study, an intelligent prototype leads to problems such as power theft, overvoltage, and long service interruptions. The presented work can intelligently identify common faults in smart meters such as overbilling, subassembly malfunction, power failure, calibration error, and subassembly failure. The proposed methodology consists of an integration of a system that collects data, analyses it, identifies the fault using mathematical models and fault identification logic and takes corrective action using modern control techniques such as State Estimators followed by ANFIS based decision making. Both modular and system testing of the protype was performed and all faults were detected and diagnosed accurately. The strategy adopted in this work will be very beneficial in modern applications, in particular, the system will provide quick and efficient treatment of equipment/system malfunctions, which will result in increased production and reduced downtime.