D.A. Selin

Synchronous machine parameter estimation using the Hartley series

This paper presents a novel alternative to estimate armature circuit parameters of large utility generators using real time operating data. The proposed approach uses the Hartley series for fitting operating data (voltage and currents measurements). The essence of the method is the use of linear state estimation to identify the coefficients of the Hartley series. The approach is tested for noise corruption likely to be found in measurements. The method is found to be suitable for the processing of digital fault recorder data to identify synchronous machine parameters.

Synchronous generator model identification and parameter estimation from operating data

A novel technique to estimate and model parameters of a 460-MVA large steam turbine generator from operating data is presented. First, data from small excitation disturbances are used to estimate linear model armature circuit and field winding parameters of the machine. Subsequently, for each set of steady state operating data, saturable inductances L/sub ds/ and L/sub qs/ are identified and modeled using nonlinear mapping functions-based estimators. Using the estimates of the armature circuit parameters, for each set of disturbance data collected at different operating conditions, the rotor body parameters of the generator are estimated using an output error method (OEM). The developed nonlinear models are validated with measurements not used in the estimation procedure.

An algebraic approach for identifying operating point dependent parameters of synchronous machines using orthogonal series expansions

This paper presents a method for identifying armature and field parameters of synchronous machines from digital fault recorder (DFR) data. The method uses operational properties of orthogonal series expansions such as the Hartley, Walsh and Fourier series to transform a set of differential equations into linear algebraic equations. The algebraic formulation and use of operational calculus reduce the problem of identifying parameters to the manipulation of matrices that may be easily performed in such computational packages as Matlab. The variation of machine parameters with operating point is considered.

Identification of armature, field, and saturated parameters of a large steam turbine-generator from operating data

This paper presents a step by step identification procedure of armature, field and saturated parameters of a large steam turbine-generator from real time operating data. First, data from a small excitation disturbance is utilized to estimate armature circuit parameters of the machine. Subsequently, for each set of steady state operating data, saturable mutual inductances L/sub ads/ and L/sub aqs/ are estimated. The recursive maximum likelihood estimation technique is employed for identification in these first two stages. An artificial neural network (ANN) based estimator is used to model these saturated inductances based on the generator operating conditions. Finally, using the estimates of the armature circuit parameters, the field winding and some damper winding parameters are estimated using an output error method (OEM) of estimation. The developed models are validated with measurements not used in the training of ANN and with large disturbance responses.

Methodology development for estimation of armature circuit and field winding parameters of large utility generators

This paper presents a methodology to estimate armature circuit and field winding parameters of large utility generators using the synthetic data obtained by the machine natural abc frame of reference simulation. First, a one-machine infinite bus system including the machine and its excitation system is simulated in abc frame of reference by using parameters provided by the machine manufacturer. A proper data set required for estimation is collected by perturbing the field side of the machine in small amounts, The recursive maximum likelihood (RML) estimation technique is employed for the identification of armature circuit parameters. Subsequently, based on the estimates of armature circuit parameters, the field winding and some damper parameters are estimated using an output error estimation (OEM) technique. For each estimation case, the estimation performance is also validated with noise corrupted measurements. Even in case of remarkable noise corruption, the agreement between estimated and actual parameters is quite satisfactory.

Neural network based modeling of a large steam turbine-generator rotor body parameters from on-line disturbance data

A novel technique to estimate and model rotor-body parameters of a large steam turbine generator from real time disturbance data is presented. For each set of disturbance data collected at different operating conditions, the rotor body parameters of the generator are estimated using an output error method (OEM). Artificial neural network (ANN)-based estimators are later used to model the nonlinearities in the estimated parameters based on the generator operating conditions. The developed ANN models are then validated with measurements not used in the training procedure. The performance of estimated parameters is also validated with extensive simulations and compared against the manufacturer values.

Shaft torque monitoring using conventional digital fault recorders

A software package that utilizes a conventional digital fault recorder (DFR) and a personal computer (PC) to perform the important functions of a power station turbogenerator torsional monitor has been developed. This is referred to as MOST (monitoring of shaft torque). MOST was developed to take advantage of the current technology to provide simulated shaft torque monitoring that is simple, reliable and requires no new hardware. The authors provide a description of the theory and features of MOST. MOST uses outputs from a conventional DFR. MOST results have been validated by three independent methods. MOST is currently being applied at three nuclear units and five coal-fired units in the Arizona Public Service Companys system. >

Apparent impedance measuring systems (AIMS)

For the analysis of subsynchronous resonance, computer models of an electric system have generally been based on a modified 60 Hz model with short-circuit equivalents for power sources and with loads neglected. Due to the importance of validating the electric systems model, AIMS has been developed to measure the apparent impedance as a function of frequency seen at the terminals of the study generator. AIMS has been successfully applied to an extensive power system. A description is given of AIMS and the significant modeling improvement realized by it application. >