Comparison of 5th order and 3rd order machine models for doubly fed induction generator (DFIG) wind turbines

Ekanayake, Janaka; Holdsworth, L.; Jenkins, Nick

doi:10.1016/s0378-7796(03)00109-3

Cited by 203 publications

(99 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For obtaining a good balance between the accuracy and simplification, the th DFIG nonlinear model is chosen as a third-order model [15], where the stator dynamic has been neglected.…”

Section: Dfig Model With Stochastic Disturbancesmentioning

confidence: 99%

See 1 more Smart Citation

Neural Adaptive Decentralized Coordinated Control with Fault‐Tolerant Capability for DFIGs under Stochastic Disturbances

Zhang

Cao

et al. 2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

At present, most methodologies proposed to control over double fed induction generators (DFIGs) are based on single machine model, where the interactions from network have been neglected. Considering this, this paper proposes a decentralized coordinated control of DFIG based on the neural interaction measurement observer. An artificial neural network is employed to approximate the nonlinear model of DFIG, and the approximation error due to neural approximation has been considered. A robust stabilization technique is also proposed to override the effect of approximation error. A 2 controller and a ∞ controller are employed to achieve specified engineering purposes, respectively. Then, the controller design is formulated as a mixed 2 / ∞ optimization with constrains of regional pole placement and proportional plus integral (PI) structure, which can be solved easily by using linear matrix inequality (LMI) technology. The results of simulations are presented and discussed, which show the capabilities of DFIG with the proposed control strategy to fault-tolerant control of the maximum power point tracking (MPPT) under slight sensor faults, low voltage ride-through (LVRT), and its contribution to power system transient stability support.

show abstract

“…For obtaining a good balance between the accuracy and simplification, the th DFIG nonlinear model is chosen as a third-order model [15], where the stator dynamic has been neglected.…”

Section: Dfig Model With Stochastic Disturbancesmentioning

confidence: 99%

“…The objective of the ANN controller is defined as The gradient descent method is employed to minimize the objective shown in (15). Then, the output layer weighting matrix of the ANN controller ( ) can be updated as follows:…”

Section: Neural Adaptive Weightingmentioning

confidence: 99%

Neural Adaptive Decentralized Coordinated Control with Fault‐Tolerant Capability for DFIGs under Stochastic Disturbances

Zhang

Cao

et al. 2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…1 [16,17]. In this paper, a third-order model of IG is represented for a good compromise between simplicity and accuracy [18,19]. …”

Section: Models Of the Rps Systemmentioning

confidence: 99%

Adaptive Multiple MPC for a Wind Farm with DFIG: a Decentralized-Coordinated Approach

Li¹,

Zhang²,

Lin³

et al. 2016

Journal of Electrical Engineering and Technology

View full text Add to dashboard Cite

-For low investment and flexible control, doubly fed induction generator (DFIG) is becoming the dominant type that is been used in the wind farms (WFs) today. The report researches about the rotor-side controller of DFIG where all design is based on the single machine infinite bus (SMIB) model. The interactions between the different generators have not been considered in the SMIB model, and the desired performance cannot be obtained by using the controller based on this model. In this situation, an adaptive decentralized-coordinated multiple model predictive control (ADM-MPC) is proposed. First, the interaction measurement method is developed to obtain the interaction measurement model of DFIG, where the interactions between the different generators have been considered. Next, an adaptive multiple MPC based on the obtained interaction measurement method of DFIG is employed to control the rotor-side converter of DFIG. In order to cope with the stochastic disturbance of wind turbine, the augment state structure is employed to improve the tracking control performance. An artificial neural network (ANN) trained online is employed as a weighting controller to cope with the nonlinearities and large operating range of DFIG. A simple, generic renewable power system (RPS) is used to demonstrate contributions. The results of both dominant eigenvalue analysis and time response simulations are represented to illustrate contributions to system damping and transient stability that the DFIG based WF can make with the proposed ADM-MPC controller.

show abstract

“…The induction generator which is rotor wound is modelled using a third order model [17,18]. This model considers a reference frame with both the axis (direct and quadrature) and rotating at synchronous speed with stators fields peak flux aligned to the direct axis position.…”

Section: Generation and Control Systemmentioning

confidence: 99%

Feasibility Study of Centralized Multi-agent Self-healing Power System with Superconducting Fault Current Limiter

Sujil

Agarwal

Kumar

2016

Technol Econ Smart Grids Sustain Energy

View full text Add to dashboard Cite

Recently, self-healing power systems have evolved towards centralized instructions and control functions. This paper delineates the feasibility analysis of self-healing power systems using Multi-Agent System (MAS) with Superconducting Fault Current Limiter (SFCL). For a complex power system included various microgrid, it is conceivably doubtful to expect that centralizing the aggregate system control function is practical. Hence, this paper is especially keen on settling on online decision by using smart microgrid control agents that collaborate during normal and fault situations. The control decision is taken by all agents which are deployed at different parts of the circuit. These agents collect their respective circuit information and send to the main central agent. Super conducting fault current limiter is an innovative electric equipment which has the ability to limit peak value of fault current within the first cycle of the fault current. However, a scarcity of research regarding the implementation of SFCL with the multi-agent system in a micro grid is felt. The centralized MAS with SFCL exposed in this paper help to support an enabling technology of future self-healing

show abstract

Comparison of 5th order and 3rd order machine models for doubly fed induction generator (DFIG) wind turbines

Cited by 203 publications

References 3 publications

Neural Adaptive Decentralized Coordinated Control with Fault‐Tolerant Capability for DFIGs under Stochastic Disturbances

Neural Adaptive Decentralized Coordinated Control with Fault‐Tolerant Capability for DFIGs under Stochastic Disturbances

Adaptive Multiple MPC for a Wind Farm with DFIG: a Decentralized-Coordinated Approach

Feasibility Study of Centralized Multi-agent Self-healing Power System with Superconducting Fault Current Limiter

Contact Info

Product

Resources

About