The development and application of techniques and methodologies for the analysis of power quality (PQ) signals that offer a more efficient and reliable analysis in terms of processing and performance are still issues for industrial and academic fields, mainly considering the quick growing of the PQ data in modern power systems. In this regard, an iterative downsampling stage fused to the empirical mode decomposition (EMD) method is proposed. It is validated and tested using synthetic and real signals. In general, the aim of the proposed method is to extract the fundamental component as the first intrinsic mode function (IMF) to simplify the remaining decomposition. The proposed method is compared to the classical EMD and the ensemble EMD (EEMD). Advantages of the proposed method include a more adequate IMF extraction than the EMD technique, and a noticeable reduction of computational burden compared to the EEMD. The results obtained from the synthetic and real signals demonstrate the reliability and efficiency of the proposed method.

Nowadays, many industrial applications require online systems that combine several processing techniques in order to offer solutions to complex problems as the case of detection and classification of multiple faults in induction motors. In this work, a novel digital structure to implement the empirical mode decomposition (EMD) for processing nonstationary and nonlinear signals using the full spline-cubic function is presented; besides, it is combined with an adaptive linear network (ADALINE)-based frequency estimator and a feed forward neural network (FFNN)-based classifier to provide an intelligent methodology for the automatic diagnosis during the startup transient of motor faults such as: one and two broken rotor bars, bearing defects, and unbalance. Moreover, the overall methodology implementation into a field-programmable gate array (FPGA) allows an online and real-time operation, thanks to its parallelism and high-performance capabilities as a system-on-a-chip (SoC) solution. The detection and classification results show the effectiveness of the proposed fused techniques; besides, the high precision and minimum resource usage of the developed digital structures make them a suitable and low-cost solution for this and many other industrial applications.

Transient motor current signature analysis has become a mature technique for fault detection in induction motors. By using start-up transients, the whole range of slip in the machine is exploited to generate well-defined fault frequency patterns. However, in the inverter-fed motor case, the fault-patterns are always close to the supply frequency and often of low amplitude. Therefore, it is difficult to distinguish and localize the fault-patterns. In this paper, a novel method is proposed to create a new fault-pattern; the proposed technique can concentrate the fault-harmonic in a specific frequency bandwidth and avoid the spectral leakage by reducing the supply frequency amplitude. The methodology has been validated through experimental tests carried out to detect broken rotor bar in an induction motor started through a voltage source inverter.

One of the most critical devices in an electrical system is the transformer. It is continuously under different electrical and mechanical stresses that can produce failures in its components and other electrical network devices. The short-circuited turns (SCTs) are a common winding failure. This type of fault has been widely studied in literature employing the vibration signals produced in the transformer. Although promising results have been obtained, it is not a trivial task if different severity levels and a common high-level noise are considered. This paper presents a methodology based on statistical time features (STFs) and support vector machines (SVM) to diagnose a transformer under several SCTs conditions. As STFs, 19 indicators from the transformer vibration signals are computed; then, the most discriminant features are selected using the Fisher score analysis, and the linear discriminant analysis is used for dimension reduction. Finally, a support vector machine classifier is employed to carry out the diagnosis in an automatic way. Once the methodology has been developed, it is implemented on a field-programmable gate array (FPGA) to provide a system-on-a-chip solution. A modified transformer capable of emulating different SCTs severities is employed to validate and test the methodology and its FPGA implementation. Results demonstrate the effectiveness of the proposal for diagnosing the transformer condition as an accuracy of 96.82% is obtained.