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Voltage unbalance and mechanical overload generate negative effects on induction motors, producing thermal damages in the stator insulation and a reduction of the motor lifetime. In this regard, the development of protection devices is crucial as they can help to maintain the integrity of the motor and avoid irreversible damages such as insulation system breakdown, short-circuits, and so on. In this work, a methodology to obtain a time-protection model for induction motors from voltage unbalance is presented. The methodology is based on the thermal impact on the motor produced by mechanical overload and voltage unbalance conditions. In general, it consists of the following steps: i) induce in the motor different overloads and voltage unbalance levels and monitor their thermal profiles at the stator winding, ii) obtain the time–overload curve of the motor, iii) determine both the thermal level according to both the time-overload curve and the overload thermal profile, and iv) estimate the parameters of the time-unbalance model using both the thermal level obtained and the unbalance thermal profiles. The model is validated through its implementation in an online protection scheme, the results show a similar behavior between overload and unbalance protections in the safe thermal level defined by the protection models. The protection scheme is carried out over a 750 W three-phase induction motor under different overload and voltage unbalance operating conditions. [ABSTRACT FROM AUTHOR]

Overheating is a negative effect that induction motors suffer under either mechanical or electrical anomalous conditions, which can decrement the motors' useful life or produce catastrophic damage. In this sense, a new approach for assisting in the analysis and solution of problems related to overheating in induction motors is the correlation of thermal effects between the abovementioned conditions. In this study, a methodology for the experimental extraction of mathematical models that correlate the thermal effect on the induction motor stator due to voltage unbalance and mechanical overload is developed. In general, the proposed methodology is based on thermal gradients at different points of an induction motor stator. These thermal gradients are generated from either voltage unbalance or mechanical overload. For voltage unbalance estimation, the definitions of IEEE standard 141, IEEE standard 1159, and NEMA norm are considered. The correlation models are carried out and validated experimentally on a three-phase induction motor of 746 W (1 hp). Results demonstrate effectiveness in the correlation at different stator points of both variables: unbalance voltage and mechanical overload. [ABSTRACT FROM AUTHOR]

Diverse conditions in power systems, such as massive use of nonlinear loads, continuous switching and operation of large electrical loads, and the integration of renewable energies, among others, have adversely affected the power quality (PQ) because they produce undesirable distortions in the waveforms of voltage and current. The conventional way to quantify the PQ is using the PQ indices (PQIs). Yet, the nonstationary properties of voltage and current signals degrade the PQIs estimation whenever classical techniques are used. In this paper, a methodology based on single-sideband modulation method and the Wavelet and Hilbert transforms for the estimation of instantaneous PQIs is proposed. It is shown that the proposal yields better tracking of transitory changes in the voltage/current signals than classical techniques such as the short-time Fourier transform. The PQIs used are the root-mean-square values, frequency, total harmonic distortion, active power, reactive power, apparent power, distortion power, power factor, and total power factor. PQIs performance is validated using synthetic and real signals. [ABSTRACT FROM PUBLISHER]

Accurate and fast characterization of voltage variations helps to evaluate their severity on equipment and activate protections. In this paper, a methodology for tracking and characterization of voltage variations, sample to sample, is presented. It consists of a Hilbert transform to estimate the voltage of the signal’s envelope, a fuzzy logic system to track down the type of voltage variation, and a rule-based method for the final identification and decision making according to IEEE Std 1159-2009. Unlike some techniques presented in the literature for tracking voltage variations such as the Kalman filter and adaptive linear network techniques, the proposed methodology requires neither a harmonic model nor an algorithm to adjust the model parameters, which in many cases increases the computational burden and time tracking. It is worth mentioning that the proposed classification stage does not need a training stage; therefore, its development is easier and its efficiency does not depend on a data training set. The performance of the proposed methodology is validated and tested using synthetic signals as well as real measurements of voltage variations. In addition, an implementation of our methodology into an field-programmable gate array based system is performed in an effort to offer a low-cost and portable system-on-a-chip solution for online and real-time monitoring of voltage variations. [ABSTRACT FROM PUBLISHER]