Decoupled-DFIG fault ride-through strategy for enhanced stability performance during grid faults

This paper proposes a decoupled fault ride-through strategy for a doubly fed induction generator (DFIG) to enhance network stability during grid disturbances. The decoupled operation proposes that a DFIG operates as an induction generator (IG) with the converter unit acting as a reactive power source during a fault condition. The transition power characteristics of the DFIG have been analyzed to derive the capability of the proposed strategy under various system conditions. The optimal crowbar resistance is obtained to exploit the maximum power capability from the DFIG during decoupled operation. The methods have been established to ensure proper coordination between the IG mode and reactive power compensation from the grid-side converter during decoupled operation. The viability and benefits of the proposed strategy are demonstrated using different test network structures and different wind penetration levels. Control performance has been benchmarked against existing grid code standards and commercial wind generator systems, based on the optimal network support required (i.e., voltage or frequency) by the system operator from a wind farm installed at a particular location.

Optimization of FRT Active Power Performance of a DFIG during Transient Grid Faults

Optimal fault ride-through (FRT) conditions for a doubly-fed induction generator (DFIG) during a transient grid fault are analyzed with special emphasis on improving the active power generation profile. The transition states due to crowbar activation during transient faults are investigated to exploit the maximum power during the fault and post-fault period. It has been identified that operating slip, severity of fault and crowbar resistance have a direct impact on the power capability of a DFIG, and crowbar resistance can be chosen to optimize the power capability. It has been further shown that an extended crowbar period can deliver enhanced inertial response following the transient fault. The converter protection and drive train dynamics have also been analyzed while choosing the optimum crowbar resistance and delivering enhanced inertial support for an extended crowbar period.

Analysis of VSC-based HVDC System under DC Faults

This paper analyzes the behavior of a Voltage Source Converter Based HVDC system under DC cable fault conditions. The behavior of the HVDC system during a permanent line-to-earth fault is analyzed, outlining the systems configuration and behavior at each stage of the fault timeline. Operation of the proposed system under a single earthing configurations i.e. Converter (solid) earthed/AC transformer unearthed, was analyzed and simulated, with particular attention paid to the converters operation. It was observed that the development of potential earth loops within the system as a result of DC line- toearth faults leads to substantial overcurrent and results in system configuration oscillation.

A predictive maintenance system for integral type faults based on support vector machines:An application to ion implantation

In semiconductor fabrication processes, effective management of maintenance operations is fundamental to decrease costs associated with failures and downtime. Predictive Maintenance (PdM) approaches, based on statistical methods and historical data, are becoming popular for their predictive capabilities and low (potentially zero) added costs. We present here a PdM module based on Support Vector Machines for prediction of integral type faults, that is, the kind of failures that happen due to machine usage and stress of equipment parts. The proposed module may also be employed as a health factor indicator. The module has been applied to a frequent maintenance problem in semiconductor manufacturing industry, namely the breaking of the filament in the ion-source of ion-implantation tools. The PdM has been tested on a real production dataset. © 2013 IEEE.

Identifying PV module mismatch faults by a thermography-based temperature distribution analysis

Photovoltaic (PV) solar power generation is proven to be effective and sustainable but is currently hampered by relatively high costs and low conversion efficiency. This paper addresses both issues by presenting a low-cost and efficient temperature distribution analysis for identifying PV module mismatch faults by thermography. Mismatch faults reduce the power output and cause potential damage to PV cells. This paper first defines three fault categories in terms of fault levels, which lead to different terminal characteristics of the PV modules. The investigation of three faults is also conducted analytically and experimentally, and maintenance suggestions are also provided for different fault types. The proposed methodology is developed to combine the electrical and thermal characteristics of PV cells subjected to different fault mechanisms through simulation and experimental tests. Furthermore, the fault diagnosis method can be incorporated into the maximum power point tracking schemes to shift the operating point of the PV string. The developed technology has improved over the existing ones in locating the faulty cell by a thermal camera, providing a remedial measure, and maximizing the power output under faulty conditions.

Mitigating the Impact of Faults in Unreliable Memories for Error-Resilient Applications

Inherently error-resilient applications in areas such as signal processing, machine learning and data analytics provide opportunities for relaxing reliability requirements, and thereby reducing the overhead incurred by conventional error correction schemes. In this paper, we exploit the tolerable imprecision of such applications by designing an energy-efficient fault-mitigation scheme for unreliable data memories to meet target yield. The proposed approach uses a bit-shuffling mechanism to isolate faults into bit locations with lower significance. This skews the bit-error distribution towards the low order bits, substantially limiting the output error magnitude. By controlling the granularity of the shuffling, the proposed technique enables trading-off quality for power, area, and timing overhead. Compared to error-correction codes, this can reduce the overhead by as much as 83% in read power, 77% in read access time, and 89% in area, when applied to various data mining applications in 28nm process technology.

Clustering-Based Ensemble Method as an Alternative to Stacking

One of the most popular techniques of generating classifier ensembles is known as stacking which is based on a meta-learning approach. In this paper, we introduce an alternative method to stacking which is based on cluster analysis. Similar to stacking, instances from a validation set are initially classified by all base classifiers. The output of each classifier is subsequently considered as a new attribute of the instance. Following this, a validation set is divided into clusters according to the new attributes and a small subset of the original attributes of the instances. For each cluster, we find its centroid and calculate its class label. The collection of centroids is considered as a meta-classifier. Experimental results show that the new method outperformed all benchmark methods, namely Majority Voting, Stacking J48, Stacking LR, AdaBoost J48, and Random Forest, in 12 out of 22 data sets. The proposed method has two advantageous properties: it is very robust to relatively small training sets and it can be applied in semi-supervised learning problems. We provide a theoretical investigation regarding the proposed method. This demonstrates that for the method to be successful, the base classifiers applied in the ensemble should have greater than 50% accuracy levels.

Transformers on-load exciting current Park`s vector approach as a tool for winding faults diagnostics

This paper presents the development of a new approach for diagnosing the occurrence of inter-turn short-circuits in the windings of three-phase transformers, which is based on the on-line monitoring of the on-load exciting current Park's Vector patterns. Experimental and simulated results demonstrate the effectiveness of the proposed technique for detecting winding inter-turn insulation faults in operating three-phase transformers.

Parameters determination for the coupled electromagnetic transformer model, with particular reference to winding faults studies

In the past few years, a considerable research effort has been devoted to the development of transformer digital models in order to simulate its behaviour under transient and abnormal operating conditions. Although many three-phase transformer models have been presented in the literature, there is a surprisingly lack of studies regarding the incorporation of winding faults. This paper presents a coupled electromagnetic transformer model for the study of winding inter-turn short-circuits. Particular attention will be given to the model parameters determination, for both healthy and faulty operating conditions. Experimental and simulation test results are presented in the paper, demonstrating the adequacy of the model as well as the methodologies for the parameters determination.