Influence of various transmission line models on the wavelet transformation based fault location methods

Ngu, Eng Eng; Montaño, Raúl; Ramar, K.; Cooray, Vernon

doi:10.1109/icsipa.2009.5478702

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Wavelet analysis can decompose any signal through a wavelet family basis, expanded from a wavelet basis function and locally refinement the high and low-frequency details while retaining the characteristics of the original signal in time domain. So wavelet analysis has good time-frequency proprieties and can effectively identify non-stationary signals for fault diagnosis purposes [19]. On the other hand, as the variety capabilities of handling nonlinear and self-learning and parallel computing, the neural network has great advantages for fault diagnosis for nonlinear systems.…”

Section: Wavelet Neural Networkmentioning

confidence: 99%

New prognostic neural method by discrete wavelet transforms for electromechanical flight controls affected by progressive faults

2018

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Progressive failures affecting onboard electromechanical actuators (EMA), especially if related to primary flight commands, could be a critical issue for the aircraft reliability and, in the worst cases, could compromise its safety. In the last years strong interest is expected by the development of prognostic algorithms able to provide an early identification of the precursors of EMA progressive failures. In this work authors proposes a new prognostic method based on two artificial neural networks (ANN), a basic and an enhanced feedforward neural network, performing the fault detection and identification of two critical progressive faults often affecting the EMA brushless motor (i.e. turn-to-turn short circuit of a stator coil and rotor static eccentricity); in order to identify a suitable data set able to guarantee an affordable ANN classification, the said failures precursors are properly pre-processed by a Discrete Wavelet Transform, extracting several features used as input of the proposed prognostic algorithm.

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Section: Wavelet Neural Networkmentioning

confidence: 99%

New prognostic neural method by discrete wavelet transforms for electromechanical flight controls affected by progressive faults

2018

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“…It must be noted that, with respect to other EM models available in literature, the numerical model shown in the previous sections is able to calculate the instantaneous value of each current phase (I a , I b , I c ) also in case of unbalanced electromagnetic system (e.g. partial short circuit on a stator branch or rotor static eccentricity); then, it is possible to correlate the progressive faults with the dynamic response of these signals (used as failure precursors) by means of an algorithm, based on the Fourier spectral analysis, that evaluates the filtered phase currents; for this purpose, each phase current is filtered by three low pass signal filter, in order to attenuate noise and disturbances [9]. It must be noted that, according to [ESREL 2015], the aforesaid spectral analysis is based on the Fourier Transform (FT), a mathematical instrument that changes the time domain representation into a frequency domain representation, and which has many applications in physics and engineering.…”

Section: Figurementioning

confidence: 99%

Electromechanical actuators affected by multiple failures: Prognostic method based on spectral analysis techniques

Belmonte

Vedova

Ferro

et al. 2017

AIP Conference Proceedings

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Abstract. The proposal of prognostic algorithms able to identify precursors of incipient failures of primary flight command electromechanical actuators (EMA) is beneficial for the anticipation of the incoming failure: an early and correct interpretation of the failure degradation pattern, in fact, can trig an early alert of the maintenance crew, who can properly schedule the servomechanism replacement. An innovative prognostic model-based approach, able to recognize the EMA progressive degradations before his anomalous behaviors become critical, is proposed: the Fault Detection and Identification (FDI) of the considered incipient failures is performed analyzing proper system operational parameters, able to put in evidence the corresponding degradation path, by means of a numerical algorithm based on spectral analysis techniques. Subsequently, these operational parameters will be correlated with the actual EMA health condition by means of failure maps created by a reference monitoring model-based algorithm. In this work, the proposed method has been tested in case of EMA affected by combined progressive failures: in particular, partial stator single phase turn to turn short-circuit and rotor static eccentricity are considered. In order to evaluate the prognostic method, a numerical testbench has been conceived. Results show that the method exhibit adequate robustness and a high degree of confidence in the ability to early identify an eventual malfunctioning, minimizing the risk of fake alarms or unannounced failures.

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Distance protection and automatic reclosing on 400 kV transmission line with classic and compact towers

Alibašić

Stojkov

Nikolovski

2017

2017 International Conference on Smart Systems and Technologies (SST)

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Influence of various transmission line models on the wavelet transformation based fault location methods

Cited by 3 publications

References 14 publications

New prognostic neural method by discrete wavelet transforms for electromechanical flight controls affected by progressive faults

New prognostic neural method by discrete wavelet transforms for electromechanical flight controls affected by progressive faults

Electromechanical actuators affected by multiple failures: Prognostic method based on spectral analysis techniques

Distance protection and automatic reclosing on 400 kV transmission line with classic and compact towers

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