Learning from Power Signals: An Automated Approach to Electrical Disturbance Identification within a Power Transmission System

Abstract

As power quality becomes a higher priority in the electric utility industry, the amount of disturbance event data continues to grow. Utilities do not have the required personnel to analyze each event by hand. This work presents an automated approach for analyzing power quality events recorded by digital fault recorders and power quality monitors operating within a power transmission system. The automated approach leverages rule-based analytics to examine the time and frequency domain characteristics of the voltage and current signals. Customizable thresholds are set to categorize each disturbance event. The events analyzed within this work include various faults, motor starting, and incipient instrument transformer failure. Analytics for fourteen different event types have been developed. The analytics were tested on 160 signal files and yielded an average accuracy of 99%. Continuous nominal signal data analysis was performed using an approach called the cyclic histogram. The cyclic histogram process is intended to be integrated into the digital fault recorders themselves in order to facilitate the detection of subtle signal variations that are too small to trigger a disturbance event and that can occur over hours or days. In addition to reducing memory requirements by a factor of 320, it is anticipated that cyclic histogram processing will aid in identifying incipient events and identifiers. This project is expected to save engineers time by automating the classification of disturbance events and increasing the reliability of the transmission system by providing near real-time detection and identification of disturbances as well as prevention of problems before they occur.

Keywords

• Digital Fault Recorder (DFR)
• Power Quality (PQ)
• Electrical Disturbance
• identification
• machine learning