Yazar "Djurovic, Sinisa" seçeneğine göre listele

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    DFIG current and controller signals' angular shaft misalignment signature - an experimental case study
    (Ieee, 2020) Wang, Yingzhao; Sarma, Nur; Mohammed, Anees; Djurovic, Sinisa
    with the continuous increase in wind turbine generator size the monitoring and diagnosis of drive train shaft misalignment is of growing importance, as this condition contributes to as much as 30% of turbine's downtime. Machine current signature analysis has been widely investigated in conventional machinery as a non-invasive diagnostic tool for shaft misalignment, via spectrum analysis of the stator current signal. This paper reports a practical case study of misalignment fault signature manifestation in the doubly-fed induction generator (DFIG) controller signals spectra, with a view to assessing the feasibility of low cost and non-invasive controller signal analysis based misalignment diagnosis. The study employs a laboratory test rig to undertake a series of tests to analyze the sensitivity of controller signals to a specific angular misalignment condition, and hence evaluate and characterize the manifestation of its spectral signatures.
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    Double Fed Induction Generator Shaft Misalignment Monitoring by FBG Frame Strain Sensing
    (Ieee-Inst Electrical Electronics Engineers Inc, 2020) Wang, Yingzhao; Mohammed, Anees; Sarma, Nur; Djurovic, Sinisa
    This study explores the potential for using FBG strain sensing to enable recognition of the shaft misalignment condition in electric machine drivetrains through observation of machine frame distributed relative strain. The sensing principles, design and installation methods of the proposed technique are detailed in the paper. The scheme was applied on a purpose built wind turbine generator representative laboratory test rig and its performance evaluated in an extensive experimental study involving a range of healthy and misaligned shaft operating conditions. The obtained experimental data demonstrate the reported method's capability to enable recognition of generator shaft misalignment conditions and thus its health monitoring. Finally, it is shown that the thermal variation of the generator frame structure inherent to its operation, combined with the FBG sensor intrinsic thermo-mechanical cross sensitivity, has no detrimental impact on the fidelity and usability of the observed strain measurements.
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    Electrical & mechanical diagnostic indicators of wind turbine induction generator rotor faults
    (Pergamon-Elsevier Science Ltd, 2019) Zappala, Donatella; Sarma, Nur; Djurovic, Sinisa; Crabtree, Cristopher J.; Mohammad, Anees; Tavner, P.J.
    In MW-sized wind turbines, the most widely-used generator is the wound rotor induction machine, with a partially-rated voltage source converter connected to the rotor. This generator is a significant cause of wind turbine fault modes. In this paper, a harmonic time-stepped generator model is applied to derive wound rotor induction generator electrical & mechanical signals for fault measurement, and propose simple closed-form analytical expressions to describe them. Predictions are then validated with tests on a 30 kW induction generator test rig. Results show that generator rotor unbalance produces substantial increases in the side-bands of supply frequency and slotting harmonic frequencies in the spectra of current, power, speed, mechanical torque and vibration measurements. It is believed that this is the first occasion in which such comprehensive approach has been presented for this type of machine, with healthy & faulty conditions at varying loads and rotor faults. Clear recommendations of the relative merits of various electrical & mechanical signals for detecting rotor faults are given, and reliable fault indicators are identified for incorporation into wind turbine condition monitoring systems. Finally, the paper proposes that fault detectability and reliability could be improved by data fusion of some of these electrical & mechanical signals. (C) 2018 The Authors. Published by Elsevier Ltd.
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    Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review
    (Mdpi, 2021) Benbouzid, Mohamed; Berghout, Tarek; Sarma, Nur; Djurovic, Sinisa; Wu, Yueqi; Ma, Xiandong
    Modern wind turbines operate in continuously transient conditions, with varying speed, torque, and power based on the stochastic nature of the wind resource. This variability affects not only the operational performance of the wind power system, but can also affect its integrity under service conditions. Condition monitoring continues to play an important role in achieving reliable and economic operation of wind turbines. This paper reviews the current advances in wind turbine condition monitoring, ranging from conventional condition monitoring and signal processing tools to machine-learning-based condition monitoring and usage of big data mining for predictive maintenance. A systematic review is presented of signal-based and data-driven modeling methodologies using intelligent and machine learning approaches, with the view to providing a critical evaluation of the recent developments in this area, and their applications in diagnosis, prognosis, health assessment, and predictive maintenance of wind turbines and farms.
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    Modeling, Analysis, and Validation of Controller Signal Interharmonic Effects in DFIG Drives
    (Ieee-Inst Electrical Electronics Engineers Inc, 2020) Sarma, Nur; Tuohy, Paul Michael; Djurovic, Sinisa
    This paper presents the development of a doubly fed induction machine (DFIG) harmonic model in matlab/Simulink, which is used to examine the spectral content of DFIG controller signals and improve the understanding of their behavior and spectral nature. The reported DFIG harmonic model has the capability of representing the effects of higher order time and space harmonics and thus, allows detailed analysis of the controller signals embedded spectral effects. The model consists of a wound rotor induction machine (WRIM) harmonic model coupled with a stator flux oriented controller model. The WRIM space harmonic effects are represented using the conductor distribution function approach to enable the calculation of winding inductances as a harmonic series. In addition, analytical expressions are derived to define the possible spectral content in the controller signals of DFIGs. Both the reported DFIG harmonic model and the analytical expressions are validated by comparison with measurements taken from a purpose built vector-controlled DFIG laboratory test rig. The findings confirm the capability of the developed DFIG harmonic model in representing the controller signals embedded spectral effects, as well as the accuracy of the reported analytical expressions, and enables a much improved understanding of the spectral nature of the DFIG controller signals.
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    Rotor Electrical Fault Detection in DFIGs Using Wide-Band Controller Signals
    (Ieee-Inst Electrical Electronics Engineers Inc, 2021) Sarma, Nur; Tuohy, Paul M.; Mohammed, Anees; Djurovic, Sinisa
    This paper presents a novel study of the wide-band spectral signatures in the controller signals of doubly fed induction generators (DFIGs) for the identification of rotor electrical faults. The aim is to advance the understanding of diagnostic information obtainable from the readily available DFIG controller signals. Analytical equations defining the controller signals possible spectral contents are derived to enable characterization of spectral signatures and their correlation to operating conditions and rotor faults. The equations are verified in a DFIG harmonic model study and also validated by undertaking a range of experiments on a laboratory DFIG test-rig. It is shown that the calculated, simulated and experimental results are in good agreement with regards to representing fault induced signatures in the examined DFIG controller signals spectra. Furthermore, it is shown that wide-band rotor electrical fault related spectral signatures in the controller signals carry considerable diagnostic potential for recognition of rotor electrical faults.
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    Stator Electrical Fault Detection in DFIGs Using Wide-Band Analysis of the Embedded Signals From the Controllers
    (Ieee-Inst Electrical Electronics Engineers Inc, 2021) Sarma, Nur; Tuohy, Paul M.; Djurovic, Sinisa
    This article presents a novel study of stator electrical faults detection using the wide-band spectral signatures in the controller signals of doubly fed induction generators (DFIGs). The possible wide-band spectral content of the controller signals are first defined with a set of analytical equations in order to characterize the wide-band spectral signatures and their correlation to operating conditions including stator electrical faults. A DFIG harmonic model and laboratory test-rig are used to validate the derived equations. The calculated, simulated and experimental results are in good agreement. Furthermore, it is shown that the wide-band stator electrical fault related spectral signatures carry considerable diagnostic potential for recognition of stator electrical faults, and therefore, can potentially be used to advance controller embedded fault detection techniques.