Examenspresentation av Børge Lund

Conducted at RISE supervised by Mattias Persson

Opponent: Mahantha Ampavatina Kambagiri

Abstract
Electrification plays an important part in reducing greenhouse gas emissions to halt climate change. Stability issues in the power system are likely to arise as an increasing share of power comes from converter-based generators which do not contribute to the inertia of the power system. One stability issue which may become bigger in a low-inertia power system is inter-area power oscillations, which arise when synchronous generators in different geographical areas start to swing against each other. Inter-area oscillations already limit the transmission capacity of certain transmission corridors in the Nordic power system.

Dynamic mode decomposition (DMD) is an algorithm for oscillation detection which has been noted as promising for power system monitoring in several studies. However, a lot of research remains on the parameterisation of this algorithm and analysis of its estimation capabilities. This project provided validation of the algorithm’s ability to recognise known structures in data. Statistical tests were performed to chart the noise-sensitivity of DMD with respect to changes in sampling rate, data shift-stacking, the number of data channels, and the length of the analysis window.

DMD was implemented with a randomised sampling technique and a rank selection method which have not been combined earlier. The algorithm’s performance was first tested on dynamic simulation data from the power-systems simulation software PSS/E. A user-defined load model was written to introduce stochastic consumption variations as a random walk in order to simulate ambient power-systems behaviour. DMD mode estimates were compared with results from stochastic subspace identification (SSI) and the multivariate autoregressive method (MAR), two algorithms which have already been in use by Nordic TSOs. DMD was found to give similar results to SSI and MAR both under very noisy ambient conditions and on undamped oscillations after a disturbance.

DMD, SSI and MAR were applied to tracking a mode in real-life PMU data from the Nordic power system under ambient conditions. Again DMD was found to give comparable results to SSI and MAR. A possible limitation in DMD’s ability to estimate damping when using long analysis windows was identified.

Welcome!

Børge, Mattias & Peiyuan