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This work presents an innovative application of Frequency Response Analysis (FRA) in order to detect early degradation of Metal Oxide Surge Arresters (MOSAs). This technique has been widely used in power transformers, but has never been applied to MOSAs. It consists in comparisons of spectra, measured at different instants of the lifetime of the arrester. Differences between these spectra are an indicator that some electrical properties of the arrester have changed. An incremental deterioration test has been performed on arrester samples (with controlled circulation of leakage current, which increases the energy dissipation over the device), and the FRA spectra correctly identified the progression of damage. Although preliminary, the FRA results seemed promising, and it is expected that this technology could be used as another diagnostic tool for arresters. [ABSTRACT FROM AUTHOR]

Permanent magnet synchronous machines (PMSMs) drives are attractive and have been used in several applications, because of their noteworthy advantages. In some applications, the continuous operation is necessary, and then the PMSM drive outage is unacceptable or may cause great losses. Thus, several studies have been accomplished in order to detect incipient faults in PMSMs. In this context, the electrical signature analysis (ESA) technique is highlighted, because of the feasibility and non-invasive features. ESA allows the fault detection by only analyzing the electrical machine quantities. This paper proposes a study of ESA for PMSM condition monitoring. The review of some fault patterns is presented as well as the development of a scale model laboratory to simulate faults in a real PMSM in operation. The PMSM is used to drive a fluid pumping system, and different types of fluids are tested, being different load conditions from the PMSM point of view. The presented results are promising, encouraging ESA based methodologies for PMSM fault detection. [ABSTRACT FROM AUTHOR]

Electrical signature analysis (ESA) has been successfully applied to predictive maintenance of synchronous machines. The fault diagnosis is performed by analyzing failure patterns in the current or voltage spectra, which allow the discrimination of a healthy and a faulty condition on the monitored machine. Generally, the failure patterns are a function of the line frequency, rotor rotation frequency, and some structure features of the machine. The rotor rotation frequency pattern, for instance, is indicative of rotor mechanical problems and rotor winding interturn short-circuit. An increase of this frequency component magnitude may indicate the incipience of a rotor fault but may not discriminate the nature of the fault (electrical or mechanical). Thus, it is necessary to distinguish the effects of electrical and mechanical faults in these components in order to get a reliable diagnosis of the machine. No works have been found in the literature approaching this specific issue. This paper proposes a simple and innovative methodology to distinguish the effect of electrical and mechanical faults in the mentioned ESA failure patterns based on the method of symmetrical components. Its effectiveness is validated by experiments performed on a synchronous generator test rig. The proposed condition monitoring system is simple, low cost, and low intrusive, as it only relies on stator electrical quantities. Moreover, it is in operation in a Brazilian 404-MW thermal power station. [ABSTRACT FROM PUBLISHER]

Motor current signature analysis (MCSA) enables non-invasive monitoring, without interruption of machine operation in a remote and online way, allowing the identification of various types of faults of electrical and mechanical nature without the need of accessing the motor itself, but only its supply cables. Despite its advantages, it has limitations in accurately diagnosing incipient roller bearing faults. For the detection of incipient roller bearing faults, envelope analysis of vibration signals is a well-known and stablished technique used by motor condition monitoring experts for a long time, overcoming MCSA for that purpose. Thus, it is proposed in this paper, that the fault characteristic frequencies of roller bearings are identified in the current spectrum with the aid of envelope analysis on the bearing vibration signal. After this aided identification, the fault related spectral components in the current spectrum can be correctly tracked over time for trending evaluation and decision-making. This approach can represent a significant economic value in a motor condition monitoring program, since vibration envelope analysis is performed only at a first step and, after that, its results can be applied for the MCSA monitoring of all same-model motor drivers in an industrial site. This approach is even more valuable considering the concept of the Self-Supplied Wireless Current Transducer (SSWCT) also proposed in this paper. The SSWCT is an Industrial Internet of Things (IIOT) device for MCSA application in an Industry 4.0 environment. This proposed device has wireless communication interface and wireless/battery less power supply, being supplied by the energy harvested from the magnetic field of the same currents it is transducing. So, it is a completely galvanic isolated monitoring device, without batteries and without any electric connections to the industry electric system, easily installable to the motor cables, not using precious space in the electric panels of the motor control centers and not having any physical contact to the monitored asset. [ABSTRACT FROM AUTHOR]

The condition of synchronous generators (SGs) is a matter of great attention, because they can be seen as equipment and also as fundamental elements of power systems. Thus, there is a growing interest in new technologies to improve SG protection and maintenance schemes. In this context, electrical signature analysis (ESA) is a non-invasive technique that has been increasingly applied to the predictive maintenance of rotating electrical machines. However, in general, the works applying ESA to SGs are focused on isolated machines. Thus, this paper presents a study on the condition monitoring of SGs in bulk electric systems by using ESA. The main contribution of this work is the practical results of ESA for fault detection in in-service SGs interconnected to a power system. Two types of faults were detected in an SG at a Brazilian hydroelectric power plant by using ESA, including stator electrical unbalance and mechanical misalignment. This paper also addresses peculiarities in the ESA of wound rotor SGs, including recommendations for signal analysis, how to discriminate rotor faults on fault patterns, and the particularities of two-pole SGs. [ABSTRACT FROM AUTHOR]

Induction motors consume a great portion of the generated electrical energy. Moreover, most of them work at underloaded conditions, so they have low efficiencies and waste a lot of energy. Because of this, the efficiency estimation of in-service induction motors is a matter of great importance. This efficiency estimation is usually performed through indirect methods, which do not require invasive measurements of torque or speed. One of these methods is the modified Air-Gap Torque (AGT) method, which only requires voltage and current data, the stator resistance value, and the mechanical losses. This paper approaches the computation of a modified stator resistance including the mechanical losses effect to be applied in the AGT method for torque and efficiency estimation of induction motors. Some improvements are proposed in the computation of this resistance by using a direct method, as well as the possibility to estimate this parameter directly from the nameplate data of the induction motor. The proposed methodology only relies on line voltages, currents, and nameplate data and is not intrusive. The proposed methodology is analyzed through simulation and validated through experimental results with three-phase induction motors. Also, a comparison of methods for in-service induction motors efficiency estimation is presented for the tested motors. [ABSTRACT FROM AUTHOR]

This work proposes a new statistical index, based on hypothesis tests, to be used on frequency response analysis (FRA) of electric machines. This index is, particularly, interesting on the detection of early failures (which appear as small deviations from the baseline spectrum), even when the measurements have high variability (such as the case of online measurements in rotating machines). The majority of the indexes proposed so far in the FRA literature only compares two spectra at a time and mix data from the measurements at different frequencies. The index proposed in this paper calculates one hypothesis test for each frequency (in order to assure randomness, independence and normality) in the spectra of several measurements (in order to take into account the variability on the measurements) and performs the normalized sum of the individual tests. Experimental results are presented on a synchronous generator (both offline and online) to validate the proposed index. [ABSTRACT FROM AUTHOR]

This work presents an application of Frequency Response Analysis (FRA) on the detection of insulation failures on electric generators. An equipment for FRA of electric machines has been developed and its online connection to an induction generator through a C–L–C T-type coupling is presented. It is shown that the coupling circuit implies in a design trade-off between the attenuation of the power signal at fundamental frequency (which may damage the electronics of the analyzer) and the useful area obtained in the FRA spectra. It is shown that a C–L–C coupling greatly improves the performance of a regular capacitive coupling, improving its attenuation at fundamental frequency while maintaining similar frequency response at high frequencies. Experimental results are presented to validate the proposed equipment. [ABSTRACT FROM AUTHOR]

This work presents some guidelines in order to achieve a more reliable diagnosis using the frequency response analysis (FRA) technique. Conventionally, this technique relies on comparisons between spectra obtained at different stages of the machine lifespan. A difference between the spectra may indicate the onset of a failure. However, these comparisons are very subjective to the experience of the analyst. To achieve a more objective diagnosis, statistical indexes have been introduced in the literature of FRA. This work presents a survey on these indexes and also compares them using experimental data. Another contribution of this work is the proposition of a trend based diagnostics. Thus, even if the FRA measurements are obtained at different temperatures/humidity or other external factors, the trend curve of the indexes is able to provide a more reliable diagnosis. [ABSTRACT FROM AUTHOR]

This work presents an investigation on the influence of rotor position on the Frequency Response Analysis (FRA) of electric machines. Different types of machines have been analyzed. Contrary to common belief, not only the salient-pole machine suffered from rotor position influence on the FRA. This can have severe impact on the repeatability of the tests and, consequently, their ability to identify early damage in the insulation system of the machine. This paper is intended to warn practitioners of FRA that care should be taken while analyzing the results, in order to avoid false positives in their measurements. Recommendations are made aiming to avoid the influence of rotor position on the results. Also, the use of statistical techniques is proposed, in order to improve the diagnosis, even when there is some lack of repeatability. [ABSTRACT FROM AUTHOR]