AC Drive Observability Analysis

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.

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“…4) The current's phasor is projected onto the rotor's frame (16). 5) The spectrum of the projected current is computed (24).…”

Section: Harmonic Order Tracking Analysis Of Generators Faultsmentioning

confidence: 99%

“…First, it is based on an algorithm (Harmonic order tracking analysis) that can be implemented in low-cost equipment [16], [17], [18], [19], such as DSP's [20], [21], PLC's, [22], FPGA's [23]. These devices are often installed in wind groups as embedded devices or as basic elements of in-situ control.…”

Section: Introductionmentioning

confidence: 99%

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

Sapena-Bañó

Pérez-Cruz

Pineda-Sánchez

et al. 2014

2014 International Conference on Electrical Machines (ICEM)

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“…Load torque T L can be considered to be a measured disturbance. In this case it is usually estimated using a torque observer [15]- [17]. The other possibility is to consider T L to be unknown and to neglect it in the model.…”

Section: Fig 2 Stator Voltage Vectorsmentioning

confidence: 99%

PMSM model discretization for Model Predictive Control algorithms

Václavek

Blaha

2013

Proceedings of the 2013 IEEE/SICE International Symposium on System Integration

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Drives based on permanent magnet synchronous machines become more and more popular in many industrial applications. In most high-performance applications classical vector control is currently used. While this control method is usually reliable it has some limitations especially in controllers tuning and constraints handling. New control methods like Model Predictive Control become feasible in connection with increasing computational power of controller hardware. The paper deals with enhanced discrete time PMSM model which can be used for efficient predictive control implementation. The other objective of the paper is discussion of prediction horizon length impact on the drive control performance.

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“…Modern hardware can operate at sampling rates above 100 kHz [8], [27], [30], and has the computing power to perform real time digital filtering, so that high sampling rates are being increasingly used: 20 kHz in [31], 25 kHz in [32], 50 kHz in [33], and 100 kHz in [34]. • The diagnostic procedure must be simple, fast, and with low memory requirements, to be executed on line by embedded devices operating in real time [2], [11] such as DSPs [1], [6], [22], FPGAs [28], [35], portable devices [16], [36], small wireless sensors [33], [37], low cost processors [38], or energy-constrained sensor modules [39]. A growing trend is to implement the diagnostic algorithms in the same device that controls the power electronics in VSDs [40], [41].…”

Section: Introductionmentioning

confidence: 99%

“…As [22] states, these requirements bring an overhelming burden to a DSP. This specific problem has been highlighted repetitively in the technical literature by [2], [8], [9], [15], [16], [22], [23], [27], [28], [33], [35]- [38], [40], [42], among many others.…”

Section: Introductionmentioning

confidence: 99%

Condition monitoring of electrical machines using low computing power devices

Sapena-Bañó

Pérez-Cruz

Pineda-Sánchez

et al. 2014

2014 International Conference on Electrical Machines (ICEM)

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The diagnosis of electrical motors through the detection of fault frequency signatures in the current's spectrum has become an established standard in the field of industrial maintenance systems. Nevertheles, its implementation on devices with low computing power remains a practical challenge. Industrial controllers, such as programmable logic controllers, or modern, low cost controller hardware, such as the Arduino or the Raspberry Pi open source hardware proposals, lack both the on-board memory and the high speed data acquisition hardware to perform an accurate spectral analysis of the machine's current, in order to identify the spectral components produced by each type of fault. In this paper, a signal conditioning unit, based on a novel downsampling method of the current, is presented. This unit reduces the amount of current samples that must be processed by the diagnostic unit to a mere sample per current cycle, maintains the sub-hertz accuracy needed to resolve fault, and converts the mains component into a constant value that can be easily eliminated without using any additional filter. Besides, it is implemented using low cost devices, just resistors and operational amplifiers. The proposed method is theoretically developed in this paper, and it has been validated using induction motors with broken bars fed directly by the mains or through variable speed drives.

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AC Drive Observability Analysis

Cited by 130 publications

References 39 publications

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

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

PMSM model discretization for Model Predictive Control algorithms

Condition monitoring of electrical machines using low computing power devices

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