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Title: Adaptive parameter estimation of power system dynamic models using modal information
Author: Guo, Song
ISNI:       0000 0004 5352 4676
Awarding Body: Durham University
Current Institution: Durham University
Date of Award: 2014
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Knowledge of the parameter values of the dynamic generator models is of paramount importance for creating accurate models for power system dynamics studies. Traditionally, power systems consists of a relatively limited numbers of large power stations and the values of generator parameters were provided by manufacturers and validated by utilities. Recently however, with the increasing penetration of distributed generation, the accuracy of these models and parameters cannot be guaranteed. This thesis addresses the above concerns by developing a methodology to estimate the parameter values of a power system dynamic model online, employing dynamic system modes, i.e. modal frequencies and damping. The dynamic modes are extracted from real-time measurements. The aim of the proposed methodology is to minimise the differences between the observed and modelled modes of oscillation. It should be emphasised that the proposed methodology does not aim to develop the dynamic model itself but rather modify its parameter using WAMS measurements. The developed methodology is general and can be used to identify any generator parameters., However, thesis concentrates on the estimation of generator inertia constants. The results suggest that the proposed methodology can estimate inertias and replicate the dynamic behaviour of the power system accurately, through the inclusion of pseudo-measurements in the optimisation process. The pseudo-measurements not only improves the accuracy of the parameter estimation but also the robustness of it. Observability, a problem when there are fewer numbers of measurements than the numbers of parameters to be estimated, has also been successfully tackled. It has been shown that the damping measurements do not influence the accuracy and robustness of generator inertia estimation significantly.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available