Bivariate optimization technique for tuning sprt parameters to facilitate prognostic surveillance of sensor data from power plants

Abstract

We present a system that performs prognostic surveillance operations based on sensor signals from a power plant and critical assets in the transmission and distribution grid. The system obtains signals comprising time-series data obtained from sensors during operation of the power plant and associated transmission grid. The system uses an inferential model trained on previously received signals to generate estimated values for the signals. The system then performs a pairwise differencing operation between actual values and the estimated values for the signals to produce residuals. The system subsequently performs a sequential probability ratio test (SPRT) on the residuals to detect incipient anomalies that arise during operation of the power plant and associated transmission grid. While performing the SPRT, the system dynamically updates SPRT parameters to compensate for non-Gaussian artifacts that arise in the sensor data due to changing operating conditions. When an anomaly is detected, the system generates a notification.