Electric drive with a four-pole squirrel-cage induction motor

Model description: 

The motor dynamics are mapped by aset of five highly coupled nonlinear differential equations as given by

$$\begin{align*} \dfrac{{\mathrm d}i_{qs}^r}{{\mathrm d}t} &= \dfrac{1}{L_{\Sigma}} \left[-\dfrac{L_{RM}^2r_s+M^2r_r}{L_{RM}}i_{qs}^r + \dfrac{r_rM}{L_{RM}}\Psi_{qr}^r - (L_\Sigma i_{ds}^r + M \Psi_{dr}^r) \omega_r + L_{RM}u_{qs}^r\right] \\ \dfrac{{\mathrm d}i_{ds}^r}{{\mathrm d}t} &= \dfrac{1}{L_{\Sigma}} \left[-\dfrac{L_{RM}^2r_s+M^2r_r}{L_{RM}}i_{ds}^r + \dfrac{r_rM}{L_{RM}}\Psi_{dr}^r + (L_\Sigma i_{qs}^r + M \Psi_{qr}^r) \omega_r + L_{RM}u_{ds}^r\right] \\ \dfrac{{\mathrm d}\Psi_{qr}^r}{{\mathrm d}t} &= \dfrac{r_r M}{L_{RM}}i_{qs}^r - \dfrac{r_r}{L_{RM}}\Psi_{qr}^r \\ \dfrac{{\mathrm d}\Psi_{dr}^r}{{\mathrm d}t} &= \dfrac{r_r M}{L_{RM}}i_{ds}^r - \dfrac{r_r}{L_{RM}}\Psi_{dr}^r \\ \dfrac{{\mathrm d}\omega_r}{{\mathrm d}t} &= -\dfrac{B_m}{J}\omega_r + \dfrac{P}{2J}\left[\dfrac{P}{2}\dfrac{M}{L_{RM}}(i_{qs}^r\Psi_{dr}^r - i_{ds}^r-\Psi_{qr})-T_L\right], \end{align*}$$

where $L_{\Sigma} = L_{SM}L_{RM}-M^2$. For the load torque we assume the expression $T_L=c_2\omega_r^2 + c_3\omega_r^3$.

The state vector is given by

$x(t) = [i_{qs}^r, i_{ds}^r, \Psi_{qr}^r, \Psi_{dr}^r, \omega_r]^{\mathrm T} = [x_1, x_2, x_3, x_4, x_5]^{\mathrm T}.$

Type: 

Form: 

Model order: 

5

Time domain: 

Linearity: 

Publication details: 

TitleNonlinear identification of induction motor parameters
Publication TypeConference Paper
Year of Publication1999
AuthorsPappano, V., Lyshevski S.E., and Friedland B.
Conference NameProceedings of the 1999 American Control Conference, 1999.
Date Published01/1999
PublisherIEEE
Conference LocationSan Diego, CA
ISBN Number0-7803-4990-3
Accession Number6402981
Keywordsdynamics, identification, multivariable systems, Nonlinear dynamical systems, squirrel cage motors, state-space methods, transients
AbstractIn this paper, a nonlinear mapping identification concept is applied to identify the unknown parameters of induction motors using transient dynamics. The developed identification algorithm has significant advantages due to computational efficiency, robustness and convergence, reliability and feasibility. The reported model-based state-space identification can be applied to a wide class of nonlinear multivariable continuous-time dynamic systems. To illustrate the analytical results and to demonstrate the practical capabilities, the unknown motor parameters are found for a squirrel-cage induction motor, under the assumption that all the state vector is available for measurement
DOI10.1109/ACC.1999.782431