Identification of synchronous machine parameters using a multipleinput multiple output approach

This paper presents an alternative method for the identification of the d-axis parameters of a synchronous machine. The first part of the paper describes a multiple input multiple output (MIMO) broadband excitation and measurement method which is more time efficient than the standard standstill frequency response (SSFR) method. The second part describes a MIMO frequency domain identification procedure which estimates the d-axis parameters in 3 steps. The proposed identification procedure is self starting. It does not require starting values or other prior information. The measurement method and the identification procedure are tested on a 20 kVA salient pole synchronous machine     

Short-circuit test based maximum likelihood estimation of stabilitymodel of large generators

This paper presents a time-domain statistical identification method for synchronous-machine linear parameters from the standard line-to-line short-circuit test. The measurements are recorded on a 13.75-MVA hydrogenerator at Hydro-Quebec's Rapide-des-Quinze generating station. A complete mathematical model for synchronous machine asymmetrical test analysis is proposed. An efficient algorithm is built to accurately calculate the standard equivalent circuit from time-constants and operational inductances. The maximum likelihood estimator derived from the generalized least-squares method is then used for parameter identification. Validation of the estimated model response against the measured running-time domain data confirms the effectiveness of the proposed estimation technique     

Neural network based modeling of round rotor synchronous generatorrotor body parameters from operating data

It is generally accepted that in order to account for the effect of eddy currents in the solid rotor-iron of a round-rotor synchronous machine, two or more fictitious rotor-circuits are to be used in each axis of the d- and q-axis equivalent circuit representations of the machine model. This paper presents a novel technique to estimate the parameters of these rotor-circuits (hereinafter referred to as rotor body parameters) from measurements collected online at several operating conditions. The effects of generator saturation, rotor position and loading are included in the estimation process. Tests conducted on a round-rotor synchronous generator reveal that certain rotor-body parameters are nonlinear functions of generator operating condition. A novel artificial neural network (ANN) based technique is used to map variables representative of generator operating condition to each parameter being modeled. The developed ANN models are validated with measurements not used in the modeling process     

Hybrid-State-Model-Based Time-Domain Identification of Synchronous Machine Parameters From Saturated Load Rejection Test Records

This paper presents a suitable method for time-domain identification of synchronous machine parameters from the hybrid state model recently introduced by the authors in a compact matrix form. The saturated version of this model is developed in terms of generator equivalent-circuit parameters. The load rejection test of a combined resistive/inductive load is performed for the parameter identification while the online symmetrical three-phase short-circuit test is carried out for the model cross-validation. For weak power factor initial loads connected to the generator, the rotor speed is quite constant during the full load rejection test. Thus, the mechanical transients do not have any influence on estimated electrical parameters since they are decoupled from the electrical model of the machine. The method is successfully applied for the parameter identification of 380 V, 3 kVmiddotA, four-pole, 50 Hz saturated synchronous generator.     

Online Synchronous Machine Parameter Extraction From Small-Signal Injection Techniques

This paper proposes using a novel line-to-line voltage perturbation as a technique for online measurement of synchronous machine parameters. The perturbation is created by a chopper circuit connected between two phases of the machine. Using this method, it is possible to obtain the full set of four complex small-signal impedances of the synchronous machine $d$– $q$ model over a wide frequency range. Typically, two chopper switching frequencies are needed to obtain one data point. However, it is shown herein that, due to the symmetry of the machine equations, only one chopper switching frequency is needed to obtain the information. A 3.7-kW machine system is simulated, and then constructed for validation of the impedance measurement technique. A genetic algorithm is then used to obtain IEEE standard model parameters from the $d$ –$q$ impedances. The resulting parameters are shown to be similar to those obtained by a series of tests involving synchronous reactance measurements and a standstill frequency response.      

On-line synchronous machine parameter estimation from smalldisturbance operating data

This paper presents a step-by-step system identification approach to estimate the parameters of a three-phase salient-pole synchronous machine rated at 5 kVA from online small disturbance responses. The machine equivalent circuit model linear parameters and the nonlinear saturated parameters are estimated. The estimation is performed using the maximum likelihood algorithm. Simulation studies based on the online measured small and large dynamic disturbances are performed to validate the accuracy of the identified machine model including the saturation     

Methodology development for estimation of armature circuit andfield 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     

Tuning of power system stabilizers using an artificial neuralnetwork

A new approach using an artificial neural network is proposed to adapt power system stabilizer (PSS) parameters in real time. A pair of online measurements i.e., generator real-power output and power factor which are representative of the generator's operating condition, are chosen as the input signals to the neural net. The outputs of the neural net are the desired PSS parameters. The neural net, once trained by a set of input-output patterns in the training set, can yield proper PSS parameters under any generator loading condition. Digital simulations of a synchronous machine subject to a major disturbance of a three-phase fault under different operating conditions are performed to demonstrate the effectiveness of the proposed neural network     

Time-domain identification of synchronous machine parameters fromsimple standstill tests

A set of standstill measurements that allow time-domain identification of linear model parameters for direct and quadrature axes of a synchronous machine is proposed. The advantages of this method over conventional standstill frequency response testing include the simplicity of test equipment, the higher current levels achieved, the speed with which the measurements can be obtained, and the fact that the models are obtained directly in parametric form (in per-unit or measured units). The proposed method is illustrated by results measured on a 3 kVA, 220 V microalternator     

Neural network observers for on-line tracking of synchronousgenerator parameters

This paper presents a methodology for implementing artificial neural network (ANN) observers in estimating and tracking synchronous generator parameters from time-domain online disturbance measurements. Data for training the neural network observers are obtained through offline simulations of a synchronous generator operating in a one-machine-infinite-bus environment. Nominal values of parameters are used in the machine model. After training, the ANN observer is tested with simulated online measurements to provide estimates of unmeasurable rotor body currents and in tracking simulated changes in machine parameters     

Implementation of indirect vector control on an integrated digitalsignal processor-based system

This paper presents an implementation of indirect vector control of an induction machine on an integrated DSP (digital signal processor) system manufactured by dSPACE GmbH. The system integrates into a single board the computational power of a TMS320C31 DSP with extra peripherals needed in vector control application, and therefore requires minimal hardware development. The induction machine parameters required for the vector control operation are obtained through an offline parameters identification using the maximum likelihood estimation technique with DC voltage source excitation. It is shown through extensive experimental study that the offline identified parameters yield in a reliable field orientation of the induction machine     

Maximum likelihood estimation of synchronous machine parametersfrom standstill time response data

This paper presents a systematic approach for identification of a three-phase salient-pole synchronous machine rated at 5 kVA from standstill time-domain data. Machine time constant models and the equivalent circuit models are identified and their parameters are estimated. The initialization of the estimated parameters is achieved by the Laplace transformation of the recorded standstill time-response data and the derivation of the well-known operational inductances. The estimation is performed using the Maximum Likelihood algorithm. Based on the best estimated equivalent circuit models, simulation studies using the measured on-line dynamic responses are performed to validate the identified machine models     

A dynamic on-line parameter identification and full-scale systemexperimental verification for large synchronous machines

An on-line parameter identification and full-scale experimental verification for large synchronous machines (>50 MVA) is presented in this paper. A step change of excitation is imposed to a generator when the machine is in normal operation. The transient voltages, currents and the power angle are recorded. Based on the large disturbance equations and using the measured power angle as an observation argument in an identification algorithm, the synchronous machine electrical parameters (x_d, x_d', x_d", T_do', T_do", x_q, x_q", T_qo") and mechanical parameters (H,D) are obtained. In addition, the system parameters (equivalent infinite bus voltage V_bus and line reactance x_e) are identified as well. The proposed method has been repetitively applied to turbogenerators and hydrogenerators with capacities up to 300 MVA. In particular, a field test has been conducted on a system with a capacity of 15000 MVA. The experimental results from all of the full-scale tests are consistent and the effectiveness of the proposed on-line identification method is verified. The plant experiences indicate that by adopting the identified parameters, the stability margin of the generator can be improved by up to 5%, resulting in 30-50 MVA more power generation     

Identification of armature, field, and saturated parameters of alarge 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_ads and L_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     

Genetic‐based algorithm for identification of synchronous machine parameters using short‐circuit tests

In this paper a new method for the digital identification of synchronous machine parameters from short‐circuit tests is presented. The method is based on the genetic algorithm optimization technique. Genetic algorithms (GAs) are adaptive search procedures based on the mechanism of natural selection and genetics. This kind of algorithm can search for a global solution using multiple paths. The proposed method uses a digital set of measurements for the short‐circuit current for estimating direct axis reactance and time constant. A set of over‐determined system of equations is constructed using the digital current samples. The identification problem is then solved using the proposed GAs‐based method. Different fitness functions that evaluate the solutions are suggested. A practical case study is presented in this work to evaluate the proposed method. Results are reported and conclusions are drawn. Copyright © 2000 John Wiley & Sons, Ltd.     

A Synchronous Machine Model With Saturation and Arbitrary Rotor Network Representation

This paper addresses equivalent circuit and magnetic saturation issues associated with synchronous machine modeling. In the proposed synchronous machine model, the rotor equivalent circuits are replaced by arbitrary linear networks. This allows for elimination of the equivalent circuit parameter identification procedure since the measured frequency response may be directly embedded into the model. Magnetic saturation is also represented in both the$q$- and$d$-axis. The model is computationally efficient and suitable for dynamic time-domain power system studies.     

ANN based pattern classification of synchronous generator stabilityand loss of excitation

The paper presents a novel artificial intelligence-based neural network (ANN) pattern classification and online detection scheme for a single machine infinite bus system. The proposed online relay and dynamic pattern classifier utilizes specific frequency spectra of the hyperplane discriminant vector of machine rotor angle, speed, accelerating power, instantaneous power, voltage, and current using either a perceptron single layer detection scheme or a two layer feedforward ANN for online classification and detection of fault condition causing first swing transient stability or loss of excitation. Other relay binary outputs include fault type and allowable clearing time identification. The detection accuracy is improved by utilizing the cross spectra of discriminant vector input variables correlations. The proposed pattern classification technique can be extended to interconnected multimachine power systems by using relative rotor angles, frequency deviations, tie-line powers, and their cross spectra variables     

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     

Design of augmented fuzzy logic power system stabilizers to enhancepower systems stability

This paper presents an augmented fuzzy logic power system stabilizer (PSS) for stability enhancement of multimachine power systems. In order to accomplish a satisfactory damping characteristic over a wide range of operating points, speed deviation (Δω) and acceleration (Δω) of a synchronous generator were taken as the input signals to the fuzzy controller. It is well known that these variables have significant effects on damping the generators' shaft mechanical oscillations. A modification of the terminal voltage feedback signal to the excitation system as a function of the accelerating power on the unit, is also used to enhance the stability of the system. The stabilizing signals are computed using the standard fuzzy membership function depending on these variables. The performance of the proposed augmented fuzzy controller is compared to an optimal controller and its effectiveness is demonstrated by a detailed digital computer simulation of a single machine infinite bus and a multimachine power systems     

Theory and performance of series connected self-excited synchronousgenerators

The series connected self-excited synchronous generator is a slip ring type induction machine with stator and rotor windings connected in series along with excitation capacitors. The machine, when self-excited, will yield an output voltage with a frequency equal to half the rotor angular speed. This paper presents analytical as well as experimental investigations into machine performance. The analysis is based on a deduced phasor diagram. The suggested method of analysis is simple and makes it possible to study the effect of machine parameters on its performance. Useful conclusions are given showing proper design considerations to be accounted for to allow the machine to develop acceptable output levels