Maintaining power system stability has become a challenging task for engineers. A consequence of such factors is that power system damping of electro-mechanical oscillations is often reduced which could sometimes destabilise the power systems if the electro-mechanical oscillations are poorly/lightly damped. Decentralised power system stabilisers are often used to increase damping of local oscillation modes and to some extent to increase damping of inter-area oscillation modes. However, damping of inter-area oscillations modes by the local PSS is not quite effective as for local oscillation modes. Therefore, it is necessary to implement a new control strategy to enhance damping of inter-area oscillation modes. With today’s advancement of phasor measurement units (PMUs), global information, which contains valuable information about the existence of inter-area oscillation modes, could be possibly used as inputs to mitigate inter-area oscillations as the literature suggests. The aim of this thesis is to propose supervisory power system stabilisers (SPSS) which are based on Fuzzy Logic to improve damping of such electro-mechanical oscillations in particular inter-area oscillation modes. Three different structures of supervisory power system stabilisers are presented in this thesis. The inputs to the proposed supervisory damping controllers will use global signals, speeds and their derivatives; however, the structure of the inputs to the supervisory power system stabilisers is different depending on the structural design of SPSS. The uniqueness of each SPSS type proposed is that they all make use of the same control rules regardless to which power system is applied. The robustness of such control rules and proposed SPSS are verified on three different test systems as the results of the case studies demonstrate. The time domain simulations will highlight the impact of each SPSS type on the system response.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:641489 |
| Date | January 2008 |
| Creators | Behbehani, Hussain M. |
| Publisher | University of Edinburgh |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/1842/10778 |
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