Modeling and Control of Permanent-Magnet Synchronous Generators Under Open-Switch Converter Faults

Hackl, Christoph M.; Pecha, Urs; Schechner, Korbinian

doi:10.1109/tpel.2018.2855423

Cited by 22 publications

(10 citation statements)

References 33 publications

(42 reference statements)

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“…In [34], the use of heat flux sensors to monitor the condition of a wind power converter, mainly for the failure and aging of electronic power devices, i.e., insulated-gate bipolar transistors (IGBTs) in the converter, where models are developed for the implementation of condition monitoring is proposed. In [35], a generic mathematical model of a two-level converter with open-switch faults is described. The impact of open-switch fault on the current control system of isotropic permanent-magnet synchronous generator (PMSG) was investigated.…”

Section: Model-based Methodsmentioning

confidence: 99%

Deep Learning Method for Fault Detection of Wind Turbine Converter

et al. 2021

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The converter is an important component in wind turbine power drive-train systems, and usually, it has a higher failure rate. Therefore, detecting the potential faults for prediction of its failure has become indispensable for condition-based maintenance and operation of wind turbines. This paper presents an approach to wind turbine converter fault detection using convolutional neural network models which are developed by using wind turbine Supervisory Control and Data Acquisition (SCADA) system data. The approach starts with the selection of fault indicator variables, and then the fault indicator variables data are extracted from a wind turbine SCADA system. Using the data, radar charts are generated, and the convolutional neural network models are applied to feature extraction from the radar charts and characteristic analysis of the feature for fault detection. Based on the analysis of the Octave Convolution (OctConv) network structure, an improved AOctConv (Attention Octave Convolution) structure is proposed in this paper, and it is applied to the ResNet50 backbone network (named as AOC–ResNet50). It is found that the algorithm based on AOC–ResNet50 overcomes the issues of information asymmetry caused by the asymmetry of the sampling method and the damage to the original features in the high and low frequency domains by the OctConv structure. Finally, the AOC–ResNet50 network is employed for fault detection of the wind turbine converter using 10 min SCADA system data. It is verified that the fault detection accuracy using the AOC–ResNet50 network is up to 98.0%, which is higher than the fault detection accuracy using the ResNet50 and Oct–ResNet50 networks. Therefore, the effectiveness of the AOC–ResNet50 network model in wind turbine converter fault detection is identified. The novelty of this paper lies in a novel AOC–ResNet50 network proposed and its effectiveness in wind turbine fault detection. This was verified through a comparative study on wind turbine power converter fault detection with other competitive convolutional neural network models for deep learning.

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Section: Model-based Methodsmentioning

confidence: 99%

Deep Learning Method for Fault Detection of Wind Turbine Converter

et al. 2021

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“…can be obtained [13]. In order to find a feasible solution, and to fully utilize the feasible modulation space, a proper saturation of the reference vector must be applied which limits the magnitude of the vector to the cube's surface while keeping its original orientation [17]. For the exemplary reference, it can be seen that its projection onto the (α, γ)-plane in Fig.…”

Section: B 3-dimensional Space Vector Modulation (3d-svm)mentioning

confidence: 99%

“…where Q and R denote the weighting matrices of states and input, which are parameterized according to Bryson's Rule for the initial iteration [18]. K p is designed similarly to [12], but for the discrete system, as follows In order to alleviate overshooting and oscillations, an antiwindup strategy is applied based on the dynamic saturation detected in modulation: the error integrator stops once the reference amplitude exceeds the modulation cube [17].…”

Section: State-feedback Current and Voltage Controllermentioning

confidence: 99%

3D-Modulation and PI State-Feedback Control for Voltage Source Inverters with Split DC-Link

Hackl¹,

Krüner²,

Song³

2021

Preprint

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In this work, a novel three-phase grid inverter topology with split DC-link and LC-filter is discussed. A proportionalintegral (PI) state-feedback current and voltage controller is designed based on linear quadratic regulator (LQR) theory. The midpoint current ripple is suppressed by introducing the filter capacitor voltage as control target. Moreover, a 3-dimensional Space Vector Modulation (3D-SVM) for this topology is implemented, which allows for larger voltage amplitudes compared to conventional Pulse Width Modulation (PWM). Realistic simulation results show that the combination of PI state-feedback controller and 3D-SVM achieves faster and more accurate closedloop dynamics with less oscillations and better decoupling under varying and unbalanced grid conditions.

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“…In [18], different control schemes have been proposed for torque ripple reduction. Another converter topology and control approach are proposed in [41] for rectifier fault tolerance with d‐axis current injection and a modified PI anti‐windup strategy. However, the topology does not consider BTB operation, and the reconfigured currents remain at zero for a short period, resulting in poor control performance.…”

Section: Introductionmentioning

confidence: 99%

“…The postfault operation is ensured by comparing both cost functions and giving priority to control of each converter in each sampling period. A TSR topology without additional devices or system oversizing is adopted for MSC fault tolerance. As opposed to the direct and vector control techniques in [17, 18, 34, 39–41] that require control modifications for selecting the optimum VV of the TSR topology, a simpler MPFTCC is proposed and analysed herein, where the optimum VV is obtained from cost function minimisation without extra control modifications.…”

Section: Introductionmentioning

confidence: 99%

Open‐circuit fault‐tolerant operation of permanent magnet synchronous generator drives for wind turbine systems using a computationally efficient model predictive current control

Jlassi

Cardoso

2021

IET Electric Power Appl

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Model predictive fault-tolerant current control (MPFTCC) of permanent magnet synchronous generator (PMSG) drives can make a valuable contribution to improving the reliability and availability levels of wind turbines, because back-to-back (BTB) converters are prone to failure. However, MPFTCC suffers from excessive computational burden, because the BTB converter is treated as one system where all feasible voltage vectors (VVs) are used for prediction and evaluation. Accordingly, a computationally efficient MPFTCC algorithm for a PMSG drive is developed and proposed with the ability to handle insulatedgate bipolar transistor open-circuit faults. The candidate VVs of both machine-and grid-side converters are separately predicted and evaluated, which significantly reduces calculation effort. The proposed reconfigurable converter is a five-leg power converter with a common leg that connects the generator first phase to the grid three-phase, ensuring proper postfault reconfiguration of the grid-side inverter. Moreover, a three-switch rectifier is adopted to achieve fault tolerance of the PMSG-side rectifier. Performance of the considered MPFTCC strategies is evaluated by experimental means. 1 | INTRODUCTION Modern wind turbine systems based on direct-drive permanent magnet synchronous generators (PMSGs) with back-to-back (BTB) power converters have become a standard for wind turbine manufacturers, where the development of finite control set model predictive control techniques for both generator and gridconnection control has gained substantial interest in recent years. Although wind turbines have been exploited for more than 4 decades, their reliability and availability levels remain lower than expected [1]. The power converter of a wind turbine is usually associated with a failure rate higher than that of other applications [2]. Power switches are one of the main causes of critical failures [2,3]. In general, these can be divided into open-circuit (O-C) and short-circuit faults. Standard power converters include hardware protection against short-circuit failures, whereas there is no protection in the case of O-C failures. Numerous reliable fault-diagnosis techniques requiring no additional hardware have been proposed in the literature [4-13] for power switch O-C faults in BTB converters. The methods can be classified as current-based or voltage-based. Recently, diagnosis through adaptive thresholds was proposed in [10,11] and later improved in [12,13] with added sensor diagnostic capabilities. These approaches proved that compared with conventional fixed thresholds, an adaptive threshold enables very high immunity to false diagnostics under strong current transients and fast speed variations. Therefore, fault diagnostics are not the topic of this paper. Several topologies have been proposed to endow the gridside converter (GSC) with fault-tolerant capabilities. Redundant topologies have been proposed for creating a different paths for current flow by using TRIACs to connect an extra leg to converter legs [14] or to th...

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Modeling and Control of Permanent-Magnet Synchronous Generators Under Open-Switch Converter Faults

Cited by 22 publications

References 33 publications

Deep Learning Method for Fault Detection of Wind Turbine Converter

Deep Learning Method for Fault Detection of Wind Turbine Converter

3D-Modulation and PI State-Feedback Control for Voltage Source Inverters with Split DC-Link

Open‐circuit fault‐tolerant operation of permanent magnet synchronous generator drives for wind turbine systems using a computationally efficient model predictive current control

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