Harmonic order tracking analysis: A novel method for the diagnosis of induction generators

Abstract

Improved fault diagnostic techniques in wind turbines is a field of growing interest, given the negative impact that unexpected breakdowns have on the profitability of wind farms. New diagnostic techniques based on generator currents monitoring have recently been developed, but their use is still irrelevant despite the advantages that current monitoring presents versus monitoring vibrations. In part, this situation can be due to the needs of relatively high computing power not available in the wind-groups and also, to the use of signals that generate volumes of data difficult to transfer to control centers, where they could be processed. This paper introduces a methodology that aims to solve these problems.In this paper a novel diagnostic method based on monitoring the generator currents is proposed. This approach is based on the tracking analysis of the fault components using the harmonic order as independent variable. This approach can be implemented in low cost digital devices; the resultant patterns are very simple, since their shape is the same no matter the changes in the fundamental frequency and thus, are easily interpretable even by non-qualified persons. Moreover these patterns are characterized by a very low number of parameters, which make easy their transmission to remote control centers. This new approach is theoretically justified and validated by laboratory tests.

The Harmonic Order Tracking Analysis Method for the Fault Diagnosis in Induction Motors Under Time-Varying Conditions

“…Thus, it can be stored or transmitted using just a very small number of data. In [26] the HOTA approach was proposed and successfully tested as a suitable technique for fault diagnostic in wind generators. Nevertheless, the HOTA method as proposed in [25], [26] is only suitable for induction machines working in steady state conditions, which makes this approach not suitable for many applications.…”

“…In [26] the HOTA approach was proposed and successfully tested as a suitable technique for fault diagnostic in wind generators. Nevertheless, the HOTA method as proposed in [25], [26] is only suitable for induction machines working in steady state conditions, which makes this approach not suitable for many applications. A first step toward the application of HOTA in non-stationary conditions is proposed in [27], where the bases of transient HOTA methodology are introduced and applied to the diagnosis of rotor asymmetries through the sampling of rotor current in wound rotor induction machines (WRIM) working as generators connected to the network and undergone to slow speed variations.…”

“…Unlike the previous commented works [25], [26], [27], this paper introduces for first time the transient HOTA methodology generalized for cage machines working under non-stationary conditions and supplied through variable speed drives, with different control strategies. Unlike [27], the approach is based on measuring stator currents (instead rotor currents), where the frequencies of the fault related components and of the main component are substantially greater than those the rotor currents.…”

Broken Bar Diagnosis for Squirrel Cage Induction Motors Using Frequency Analysis Based on MCSA and Continuous Wavelet Transform

“…The results of these studies provide evidence for the problems in accuracy of spectral analysis, since the distortion induced by the noise during data acquisition leads to signal spectrum changes. To improve fault diagnosis procedure, the analysis of asymmetric components of unbalanced electric system was studied in [42,43]. The signal harmonics of Zero-Sequence Current (ZSC) analysis were proposed in [42] Math.…”

“…However, this method does not show reliable results when motors with non-load are analyzed, since the fault components are hidden in the motor operating frequency. To have efficient fault detection, an automatic harmonic component analysis was presented in [43], where the analysis was performed by periodic monitoring of the magnitude changes that identify the current motor state. The main advantage of this technique is to simplify the analysis through graphical interpretation of harmonic components in the frequency spectrum, which is done using a bar chart, allowing quick and easy failure detection.…”