Order reduction of a wind turbine energy system via the methods of system balancing and singular perturbations

Al-Iedani, Intessar; Gajić, Zoran

doi:10.1016/j.ijepes.2019.105642

Cited by 26 publications

(13 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each component (i,j) of the induction matrix L mc ij in an eccentric IM depends not only on the angular separation between conductors i and j, but also on their absolute position and on the position of the rotor center, whose coordinates (g 0 • δ r , Θ r ) are, in turn, functions of the rotor angular position (18), (19). The analytical expression of L mc ij , for a given rotor position…”

Section: The Primitive Inductance Tensor Of An Eccentric Immentioning

confidence: 99%

“…The primitive inductance tensor (15) of the eccentric IM has been computed using (22), using δ se = 0.3 and δ de = 0.3 in (18) and (19). Contrary to the case of the healthy machine, the primitive inductance tensor depends now on the rotor position.…”

Section: Primitive Inductance Tensor Of the Eccentric Immentioning

confidence: 99%

“…In [16], a novel approach is proposed without employing the equivalent output error injection technology, to overcome the problem of the traditional SMO in application to Markovian jump systems, and in [15], a new proposal is made for avoiding the sliding surface switching problem. Another diagnostic technique is to run an electromechanical model of the machine [17][18][19][20] and compare the simulation outputs (currents and voltages) with the quantities measured at the machine terminals. Divergences between the predicted and the measured values are an indicator of a possible fault, especially if these differences increase over time.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Winding Tensor Approach for the Analytical Computation of the Inductance Matrix in Eccentric Induction Machines

Martínez-Román

Puche-Panadero

Sapena-Bañó

et al. 2020

Sensors

View full text Add to dashboard Cite

Induction machines (IMs) are critical components of many industrial processes, what justifies the use of condition-based maintenance (CBM) systems for detecting their faults at an early stage, in order to avoid costly breakdowns of production lines. The development of CBM systems for IMs relies on the use of fast models that can accurately simulate the machine in faulty conditions. In particular, IM models must be able to reproduce the characteristic harmonics that the IM faults impress in the spatial waves of the air gap magneto-motive force (MMF), due to the complex interactions between spatial and time harmonics. A common type of fault is the eccentricity of the rotor core, which provokes an unbalanced magnetic pull, and can lead to destructive rotor-stator rub. Models developed using the finite element method (FEM) can achieve the required accuracy, but their high computational costs hinder their use in online CBM systems. Analytical models are much faster, but they need an inductance matrix that takes into account the asymmetries generated by the eccentricity fault. Building the inductance matrix for eccentric IMs using traditional techniques, such as the winding function approach (WFA), is a highly complex task, because these functions depend on the combined effect of the winding layout and of the air gap asymmetry. In this paper, a novel method for the fast and simple computation of the inductance matrix for eccentric IMs is presented, which decouples the influence of the air gap asymmetry and of the winding configuration using two independent tensors. It is based on the construction of a primitive inductance tensor, which formulates the eccentricity fault using single conductors as the simplest reference frame; and a winding tensor that converts it into the inductance matrix of a particular machine, taking into account the configuration of the windings. The proposed approach applies routine procedures from tensor algebra for performing such transformation in a simple way. It is theoretically explained and experimentally validated with a commercial induction motor with a mixed eccentricity fault.

show abstract

Section: The Primitive Inductance Tensor Of An Eccentric Immentioning

confidence: 99%

Section: Primitive Inductance Tensor Of the Eccentric Immentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Winding Tensor Approach for the Analytical Computation of the Inductance Matrix in Eccentric Induction Machines

Martínez-Román

Puche-Panadero

Sapena-Bañó

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Time scale analysis and optimal control of wind energy systems have been widely explored [9][10][11][12][13][14]. Using time scale analysis, in [9] the linear quadratic regulator (LQR) and LQG controllers were designed for deterministic and stochastic wind energy systems with permanent magnet synchronous generators (PMSG).…”

Section: Introductionmentioning

confidence: 99%

“…The LQR controller is used in [11] for the pitch angle control, and in [12] to control the oscillatory behavior towards improving the stability, of the DFIG connected to the grid. In comparison to the balanced reduction methods in [13], time scale and singular perturbation analysis provide better results in reducing the order of the DFIG-based WT system. A LQG-based power oscillation damping controller is designed in [14] to provide the supplementary reference signals for the active and reactive power of the DFIG wind generator and to enhance the damping of poorly damped modes.…”

Section: Introductionmentioning

confidence: 99%

Optimal Control of Wind Turbine Systems via Time-Scale Decomposition

Al-Iedani

Gajić

2020

Energies

Self Cite

View full text Add to dashboard Cite

In this paper, we design an optimal controller for a wind turbine (WT) with doubly-fed induction generator (DFIG) by decomposing the algebraic Riccati equation (ARE) of the singularly perturbed wind turbine system into two reduced-order AREs that correspond to the slow and fast time scales. In addition, we derive a mathematical expression to obtain the optimal regulator gains with respect to the optimal pure-slow and pure-fast, reduced-order Kalman filters and linear quadratic Gaussian (LQG) controllers. Using this method allows the design of the linear controllers for slow and fast subsystems independently, thus, achieving complete separation and parallelism in the design process. This solves the corresponding ill-conditioned problem and reduces the complexity that arises when the number of wind turbines integrated to the power system increases. The reduced-order systems are compared to the original full-order system to validate the performance of the proposed method when a wind turbulence and a large-signal disturbance are applied to the system. In addition, we show that the similarity transformation does not preserve the performance index value in case of Kalman filter and the corresponding LQG controller.

show abstract

Dynamic stability analysis of a reduced order micro‐grid bymulti‐timescale theory

Ahmadi

Kazemi

2021

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

In the islanded micro-grids the power cannot be exchanged by the upstream network and the system inertia level is low. Therefore, the network sensitivity to disturbances and load changes is more than the connected mode. In such situation, the system states can behave with more degree of freedom and have less dependency to total system dynamic. The variety of equipment with different dynamics and the load level uncertainty leads to continuous changes in the islanded micro-grid operating point.These changes are more considerable in the islanded micro-grid due to lack of inertia and limited stability margins. Therefore, the determination of appropriate model order for system in such way that the reduced order model keeps the essential dynamic behavior and neglects the less effective modes, is critical. Therefore, in this paper a three-step method based on multi-time scale theory and singular perturbation method is proposed for optimum model order reduction in islanded micro-grid. The proposed method is applied to a typical micro-grid with different types of DGs and loads. The load level uncertainty is modeled by probability density function (PDF) and reduced order model of system is validated in different operating scenarios by sensitivity analysis, participation factor investigation and time domain simulation.

show abstract

Order reduction of a wind turbine energy system via the methods of system balancing and singular perturbations

Cited by 26 publications

References 19 publications

Winding Tensor Approach for the Analytical Computation of the Inductance Matrix in Eccentric Induction Machines

Winding Tensor Approach for the Analytical Computation of the Inductance Matrix in Eccentric Induction Machines

Optimal Control of Wind Turbine Systems via Time-Scale Decomposition

Dynamic stability analysis of a reduced order micro‐grid bymulti‐timescale theory

Contact Info

Product

Resources

About