Phasor Measurement Unit (PMU) – based system for event detection on synchronous generators

Author: Nicholas Mark Randall

Year: 2017

Supervisors: Joseph Timothy, Ragnar Kristjánsson, Guðjón Hugberg Björnsson

Abstract

Unusual generator events are sometimes seen in the Phasor Measurement Unit (PMU) data in the Icelandic power system, probably due to the small size. The use of PMU data with event detection algorithms, along with modeling, could be a method of capturing these events for analysis. The goals of this thesis are to develop an event detection algorithm and methods of modeling the power system, using events found in the PMU data, to simulate generator behavior under the relevant operating conditions. There are two approaches used: Event Detection Analysis and Parameter Estimation. Event Detection Analysis employed three methods. Method One used a digital filter, and fast Fourier transforms for analysis. Method Two used a matched filter algorithm. Method Three worked on the principals of machine learning, using the Gaussian Process Classifier (GPC) for identification of events in the PMU data. The Parameter Estimation approach used the method of time series analysis for finding the necessary parameters of the generators and modeling them.These parameters were: the inertia of the generators, the speed regulation constant, and the time constant of the turbine-governor system. Axiomatic Design was used for developing the design protocol for Event Detection Analysis software. The results from the Event Detection Analysis showed that out of the three methods tried the machine learning gave the best results for the Event Detection approach. Results from Parameter Estimation revealed that this method works but requires a lot of fine-tuning for better parameter estimates. The results from the two approaches showed that when tested they would meet the requirements of processing PMU data in real time. Therefore, these approaches have relevance for the Icelandic power system and will need further research.