Multiprocessor Architecture for Monitoring and Control of Power Transformer

Abstract

The failure of a power transformer is an area of significant concern since it can result in large capital loss as well as possible interruption of power. Therefore, it is desirable to detect the existence of abnormal or anomalous changes in the transformer’s internal condition which could indicate an incipient failure. This thesis proposes and tests an anomaly detection scheme that is based on both spatial and temporal information and ultimately integrates intelligence into the detection process. It was developed from experience gained from the field-deployed system. Results indicate that nuisance or false alarms are virtually eliminated while the sensitivity to anomalous changes is preserved. The interest in transformer monitoring has accelerated over the last few years due to structural changes in the electricity industry. This thesis examines the existing and potential incentives for the acquisition, utilization, and commercial development of transformer monitoring systems. Potential benefits are calculated based on capital cost avoidance, environmental cost avoidance, and operational benefits. The cost of a monitoring system is estimated using three scenarios. From a commercial standpoint , both transformer monitoring system as a product and a fee-based transformer monitoring service appear to be viable business opportunities.