Health Monitoring of Critical Power System Equipments using Identifying Codes

TL;DR

This paper proposes using Identifying Codes to monitor power transformers by analyzing SNR signals, enabling early failure detection with minimal sensors, thus improving power grid reliability.

Contribution

It introduces a novel application of Identifying Codes for transformer failure detection and determines the minimal sensor deployment needed for effective monitoring.

Findings

Identifying Code can effectively detect transformer failures.

Minimal sensor configurations are sufficient for unique failure identification.

The method enhances early warning capabilities in power systems.

Abstract

High voltage power transformers are one of the most critical equipments in the electric power grid. A sudden failure of a power transformer can significantly disrupt bulk power delivery. Before a transformer reaches its critical failure state, there are indicators which, if monitored periodically, can alert an operator that the transformer is heading towards a failure. One of the indicators is the signal to noise ratio (SNR) of the voltage and current signals in substations located in the vicinity of the transformer. During normal operations, the width of the SNR band is small. However, when the transformer heads towards a failure, the widths of the bands increase, reaching their maximum just before the failure actually occurs. This change in width of the SNR can be observed by sensors, such as phasor measurement units (PMUs) located nearby. Identifying Code is a mathematical tool that enables one to uniquely identify one or more {\em objects of interest}, by generating a unique signature corresponding to those objects, which can then be detected by a sensor. In this paper, we first describe how Identifying Code can be utilized for detecting failure of power transformers. Then, we apply this technique to determine the fewest number of sensors needed to uniquely identify failing transformers in different test systems.