Determining the magnitude of particular fault signature components (FSCs) generated by wind turbine (WT) faults from current signals has been used as an effective way to detect early abnormalities. However, the WT current signals are time-varying due to the constantly varying generator speed. The WT frequently operates with the generator close to synchronous speed, resulting in FSCs manifesting themselves in the vicinity of the supply frequency and its harmonics, making their detection more challenging. To address this challenge, the detection of rotor electrical asymmetry in WT doubly-fed induction generators (DFIGs), indicative of common winding, brush gear or high resistance connection faults, has been investigated using a test-rig under three different driving conditions, and then an effective extended Kalman filter (EKF) based method is proposed to iteratively estimate the FSCs and track their magnitude. The proposed approach has been compared with a continuous wavelet transform (CWT) and an iterative localized discrete Fourier-transform (IDFT). The experimental results demonstrate that the CWT and IDFT algorithms fail to track the FSCs at low load operation near synchronous speed. In contrast, the EKF was more successful in tracking the FSCs magnitude in all operating conditions, unambiguously determining the severity of the faults over time and providing significant gains in both computational efficiency and accuracy of fault diagnosis.

Abstract-This paper investigates the impact of power electronics converter when attempting wind turbine condition monitoring system and fault diagnosis by the analysis of fault signatures in the electrical output of the turbine. A wind turbine model has been implemented in the MATLAB/Simulink environment. Fault signature analysis for electrical signals is presented. A signal processing algorithm based on a fast fourier transform is then used to potentially identify fault signatures. The results obtained with this model are validated with experimental data measured from a physical test rig. Through comparison between simulation data and experimental data it is concluded that the power converter has significantly reduced fault signatures from the electrical signal though not entirely extinguished them. It may still be possible to extract some fault information after the converter though this is much more challenging than upstream. Further work is needed to see whether it may be possible to modify the power converter particularly the filter design and the switching elements to avoid removing fault signatures from electrical signals without adding significant cost or compromising performance.

Quality management is a maintenance and control process in which product and service quality are scrutinized for conformity with stated requirements. Digital quality management systems (QMS) is designed for agile, consistent and customer-centric enterprises. Digital QMS has been widely used by world-class business enterprises to attain competitive advantage in terms of performance, innovation and quality product and service delivery. Unfortunately, the extant literature is devoid of a definitive conclusion highlighting the benefits and challenges digital QMS. To fill this void, this paper reviewed the benefits and challenges digital QMS to provide a comprehensive framework. This paper concludes that digital QMS is an essential component for modern business enterprises, as it guarantees homogeneous and cost-efficient products / services at maximum returns and minimum complaints. Implications are discussed..

Abstract-Wind turbine manufacturers have introduced to the market a variety of innovative concepts and configurations for generators to maximize energy capture, reduce costs and improve reliability of wind energy. For the purpose of improving reliability and availability, a number of diagnostic methods have been developed. Stator current signature analysis (SCSA) is potentially an effective technique to diagnose faults in electrical machines, and could be used to detect and diagnose faults in wind turbines. In this study, an investigation was conducted into the application of SCSA to detect stator inter-turn faults in an excited synchronous generator and a permanent magnet synchronous generator. It was found from simulation results that, owing to disruption of magnetic field symmetry and imbalance between the current flowing in the shorted turn and the corresponding diametrically opposite turn in the winding, certain harmonic components in the stator current clearly increased as the number of shorted turns increased. The findings are helpful to detect faults involving only a few turns without ambiguity, in spite of the difference in the configuration of the generators. As expected, because of the different type, configuration and operational condition of the two generators studied, detecting faults through the generator current signature requires a particular approach for each generator type.