Modern power systems operate in a deregulated environment. Unpredictable behavior of participants, increased consumption and reduced investments are forcing power systems to operate under conditions they were not designed for.
To ensure a safe and reliable operation of power systems a lot of resources are being invested in better power system supervision and control. The fact that power systems are operating close to stability limits, makes power system stability one of such topics.This thesis deals with power system stability for small disturbances in the network, also known as small system stability (SSS).
Small system stability can be assessed using two main approaches. The first one is eigenvalue analysis which relies on the linearization of nonlinear equations describing power system. The alternative approach is to analyze the signals gathered directly from a real power system.
Not only equations, but also a lot of data from power grid elements, e.g. their impedances and parameters of their regulators, are needed to perform eigenvalue analysis. SSS calculations, especially for bigger power systems, are very complicated and time consuming, therefore use of dedicated software packages is essential. For the needs of this masters thesis PSS® NEVA, a part of PSS® NETOMAC from SIEMENS AG, was used.
The analysis of several different signals, which can be gathered from power systems, was performed by so called Prony algorithm programmed as a Matlab function. This function is the key part of the code developed in Matlab. The whole program enables an automatic analysis of measured signal in time domain and presenting its frequency spectrum with the help of figures and tables.
The thesis consists of several chapters. In the introduction, the issues involving electro-mechanical oscillations are briefly discussed. The Prony algorithm is explained in detail in the second chapter as well as an example of the whole analysis. Possible issues connected with the whole procedure are also discussed. In the third chapter, all stages in development of the masters thesis are presented. Despite the work being done using several different programs, the focus is on the developed program in Matlab. In the fourth chapter, four cases of frequency analysis using different Matlab scripts were used. Each of those are suitable for a particular structure of raw data and diferent requirements of the user. The last chapter is the conclusion.
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