Towards Intelligent Power-System Monitoring: Segmentation, Feature Extraction, and Identification of Underlying Causes

Licentiate thesis, 2011

The increase in size of modern power systems together with the concept smart grid requires advanced monitoring to ensure system availability, reliability, and power quality. The huge amount of available data no longer permits analysis to be implemented manually and centrally. Automatic methods are desirable to extract useful information contained in the data to help system operators follow the condition of individual devices as well as the whole system. This thesis proposes a monitoring system structure where data is analyzed at distributed levels depending on the monitoring purpose. The system focuses on analysis of power-system events and variations captured in voltages and current waveforms. A segmentation scheme using both causal and anti-causal segmentation is developed. A method to find the optimal threshold for the detection index in the segmentation algorithm based on detection theory is also introduced. The proposed segmentation scheme and statistically-based threshold setting method are applied to a Kalman filter-based segmentation algorithm where both semi-synthetic data and real measurement data are tested. The results show that the location in time of underlying transitions in the power system is accurately estimated. The proposed segmentation method is integrated in an event monitoring system where both voltage and current waveforms are used to find the underlying cause and the location of event origin. A case study is performed on a large-scale wind park to analyze several events including faults and switching events. Analysis of power-system data employs a number of signal-processing estimation techniques. Most of the estimation techniques are based on the assumption that the noise embedded in the observed signal is white which is not the case for power-system noise. An evaluation method is thus proposed to observe the performance of these estimation techniques under real power-system noise. The application of the evaluation method to a number of estimation techniques is shown to be feasible.