Physical Parameters Identification of Synchronous Generators by a

Abstract—this paper presents a novel dynamic state estimation methodology for the estimation of the parameters of a synchronous generator. The synchronous generator model is a physically based model, that is, the model is in terms of the actual self and mutual inductances of the generator windings as a function of rotor position. No transformations are applied or model reduction techniques. The dynamic state estimation uses measurements of terminal voltages and currents of phases A, B, C of stator windings and field winding, along with the mechanical shaft speed. The proposed approach is applied to a 18 kV, 825 MVA synchronous generator. The results are validated by numerical experiments. Index Term— Generator Modeling, Dynamic State Estimation, Parameters Identification.

I.I NTRODUCTION

NOWLEDGE of the operational parameters of synchronous machines is vital for reliable stability studies and “post mortem” analyses. Having accurate models of dynamic elements (including synchronous machines and other power plant controllers) is very important for optimal decision making, planning, and efficient operation of the power grid. Parameters of generators may differ from those in the utility’s database due to aging processes, magnetic saturation, or changes of temperature during machine operation. As a matter of fact, many utilities around the world still use machine model parameters calculated during generator commissioning, leading to substantial differences between the actual and simulated dynamic behavior.

Traditional methods had been developed under the guidance of IEEE Standards and some National Electrical Standards from many countries to measure the parameters [1]. However, all of these methods are conducted under off-line conditions. Different methods have also been developed in recent years to estimate the parameters of synchronous machines from online measurements [2-6]. Zhengming et al. [2] propose a parameter estimator that uses the rotor angle as measurement and performs a linearization of the machine equations. The work presented in [3-5] introduces a nonlinear parameter estimator, where a Kalman-filter-based

R. Huang is with the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA (e-mail: rhang6@).

E. Farantatos is with the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA (e-mail: vfarantatos@) and with the Electric Power Research Institute (EPRI), Knoxville, TN, 37932, (e-mail:efarantatos@).

G. J. Cokkinides is with the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA (e-mail: george.cokkinides@).

A. P. Meliopoulos is with the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA (e-mail: ameliopo@). estimator is proposed [3-4] while [5] is using a nonlinear least squares algorithm. Paper [6] applies the unscented Kalman filter for the nonlinear estimation of synchronous machine parameters by applying online operation data. However, most of the online parameters identification methodologies use measurements from the normal operation conditions of the synchronous machine and they can only estimate the magnetizing reactance of the generator and cannot identify the transient and sub-transient parameters. Another disadvantage for these methodologies is that the estimation accuracy of the parameters are heavily dependent on whether the measurement of the rotor angle of the generator is available and if the rotor angle measurement is not available, all these methods need to have a pre-estimation procedure for the rotor angle estimation. The estimation of the rotor angle proposed in these methods is accurate only for round rotor generator while accuracy is reduced for the salient pole machine under the assumption that the d and q axis reactance are unknown.

Furthermore, most of these studies mentioned above are mainly conducted based on conventional Park’s transformation and d-q-o frame of reference models of synchronous generators. In spite of its simple structure, d-q-o models are not capable of simulating unbalanced and rectifier type loading conditions. Also, due to simplifying assumptions associated with these models, it is difficult to include the higher order harmonics, which exist in the real machine. A more accurate approach to this problem is to simulate the synchronous generators with physically existing parameters, that is, the actual self and mutual inductances of the generator windings as a function of rotor position. By this way, various loading conditions such as sudden application and removal of balanced and unbalanced loads, rectifier loads, symmetrical and asymmetrical faults can be easily investigated. In papers [7-8] a quasi-dynamic state estimation methodology was proposed for the accurate estimation of the rotor angle for both round rotor and salient pole synchronous generators. In this paper, we extend the concept of quasi-dynamic state estimation to a full time dynamic state estimation approach, which considers both the mechanical and electrical dynamics of the synchronous generator. Furthermore, in this paper, apart from estimation of the rotor angle, the dynamic generator state is extended to include the physically existing generator parameters, or in other words, the dynamic state estimator proposed in this paper estimates the rotor angle and the physical parameters of the synchronous generator together at the same time. We emphasize that there are no simplification assumptions for the proposed dynamic generator model, such as the park’s transformation or dynamic order reduction.

Physical Parameters Identification of Synchronous Generators by a

Dynamic State Estimator

Renke Huang, Student Member, IEEE, Evangelos Farantatos, Student Member, IEEE,

George J. Cokkinides, Senior Member, IEEE, and A. P. Meliopoulos, Fellow, IEEE

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978-1-4799-1303-9/13/$31.00 ©2013 IEEE