06-05-2011, 09:16 AM
Abstract:
Excitation system is key element in the dynamic performance of electric power systems,accurate excitation models are of great importance in simulating and investigating the power systemtransient phenomena. Parameter identification of the Brushless excitation system was presented. First ablock diagram for the EXS parameter was proposed based on the documents and maps in the powerstation. To identify the parameters of this model, a test procedure to obtain step response, waspresented. Using the Genetic Algorithm with the Matlab-software it was possible to identify all thenecessary parameters of the model. Using the same measured input signals the response from thestandard model showed nearly the same behavior as the excitation system.
Key words: Excitation system, parameter estimation, genetic algorithm method, model validation
INTRODUCTION
Power system small signal, transient and dynamicstability studies are only as accurate as the underlyingmodels used in the computer analysis. The validity ofthe results of these studies depends heavily on theaccuracy of the model parameters of the systemcomponents. In practice, the parameters commonlyused in stability studies are manufacturer specifiedvalues, or “typical” values. These typical values may begrossly inaccurate, as various parameters may drift overtime or with operating condition. Thus, to avoid suchproblems and to obtain more realistic simulation results,the identification of the system parameters on basedfield test is recommended.Several attempts have been made to obtain EXSmodels from field tests. A second order static excitationsystem has been discussed in[1]. In[2] generalized leastsquare approach is used to model an excitation system.Parameter estimation of a pumped storage power plantusing stochastic approaches is discussed in[3].Identification of exciter constants using Prediction errorMethod (PEM) is addressed in[4]. In[5], the necessity torepresent the EXS in full and close to the practicalimplementations for accurate and reliable results hasbeen addressed. The feasibility and necessity of anonlinear structure for EXS is discussed in[6].In this study, the genetic algorithm is introducedinto parameter identification of excitation systemmodel. It is shown by Simulation results that the geneticalgorithm-based model identification method is oneapplied method and satisfactory identification resultscan be got with it. It should be emphasized that thismethod is not model-dependent and therefore, it isreadily applicable to a variety of model types anddifferent test procedures.
System Description and the test procedure:
The unitunder study is a 400 [MW], 13[KV] steam turbinegenerator set at the power plant and a Brushless EXS(IEEE ESAC2A type exciter mode) is used. ABrushless EXS is popular since it eliminatescommentators, brushes and slip rings. It was developedto avoid problems with the use of brushes that wereperceived to exist when supplying the high field currentof very large generators.All components in these systems are static orstationary. Static rectifiers, controlled or uncontrolled,supply the excitation current directly to the field of themain synchronous generator through slip rings. Thesupply of power to the rectifiers is from assistantexciter[7].
System description1. Real power plant model:
The power stationprovides the parameter of generator and the excitationsystem model as shown in Fig. 1.
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