Synchronous machine parameter identification in frequency and time domain

Abstract

This paper presents the results of a frequency and time-domain identification procedure to estimate the linear parameters of a salient-pole synchronous machine at standstill. The objective of this study is to use several input signals to identify the model structure and parameters of a salient-pole synchronous machine from standstill test data. The procedure consists to define, to conduct the standstill tests and also to identify the model structure. The signals used for identification are the different excitation voltages at standstill and the flowing current in different windings. We estimate the parameters of operational impedances, or in other words the reactance and the time constants. The tests were carried out on synchronous machine of 1.5 kVA 380V 1500 rpm

Identification of a synchronous generator parameters using recursive least squares and Kalman filter

“…In order to carry out the comparative study between the two techniques of identification machine parameters, it must be taken into account that the problem has been approached according to its different variants, on one side the researches carried out in [3,4,5,6,7,8,9], focus their job on estimate circuit parameters of the synchronous generator which have been object of abundant study. Whereas in [10,11,12,13,14,15,16], have been estimated excitation parameters by different techniques, and similarly there are variety of publications recorded.…”

“…On the other hand, to obtain the estimation of either the circuit or excitation parameters of the synchronous generator, it is based on obtaining a model that depends on the nature of the machine used. This model can be linear as shown in [4,5,11,10,17,14,9], or no linear as used in [18,6,12,7,13,8,19,16,20]. Also, the various investigations in their phase of data collection used different types of measurements to carry out the parameters identification, these measurements were made using data time [18,3,4,5,6,10,7,8,17,14,21], frequency data [19,15,9], or with phasor measurement units (PMU) [11,12,13,16,20].…”

“…This model can be linear as shown in [4,5,11,10,17,14,9], or no linear as used in [18,6,12,7,13,8,19,16,20]. Also, the various investigations in their phase of data collection used different types of measurements to carry out the parameters identification, these measurements were made using data time [18,3,4,5,6,10,7,8,17,14,21], frequency data [19,15,9], or with phasor measurement units (PMU) [11,12,13,16,20].…”

“…In [18,5,10] recursive least squares was chosen as the method for estimating the synchronous generator parameters and [14] used nonlinear least squares, these methods generate few computational costs but yield high prediction if the noise affects the measurement. Other particular methods as Levenberg-Marquadrdt [19], output error method (OEM) [4] and error prediction [15] were carried out to estimate the machine parameters, or the case in [9] where the frequency and time response methods [8] were applied independently. In addition, some authors of recent studies carry out joint estimation of parameters and states using the unscented Kalman filter (UKF) technique [16,13], extended Kalman fileter (EKF) [12], or Kalman square root [7], these techniques start from a non-linear model and can estimate at most 5 parameters of the synchronous generator, while in [20] the unknown input Kalman filter (UIKF) only made the estimation of four dynamic states of the machine.…”

Enhanced flux-weakening control algorithm for permanent magnet synchronous machines

“…The following sections will explain the procedure to obtain each parameter individually. Several articles and reports have been reviewed to define the procedure to obtain them [158][159][160][161][162][163][164][165].…”