The development of monitoring systems for rotating machines is the ability to accurately detect different faults in an incipient state. The most popular rotating machine in industry is the squirrel-cage induction motor, and the failure on such motors may have severe consequences in costs, product quality, and safety. Most of the condition-monitoring techniques for induction motors focus on a single specific fault. The identification of two or more combined faults has been rarely considered, in spite of being a very usual situation in real rotary machines. On the other hand, information entropy is a signal processing technique that has recently proved its suitability for fault detection on induction motors, and fuzzy logic analysis has extensively been used in combination with several processing techniques in improving the diagnosis of a single isolated fault. The contribution of this paper is a novel methodology that is suitable for hardware implementation, which merges information entropy analysis with fuzzy logic inference to identify faults like bearing defects, unbalance, broken rotor bars, and combinations of faults by analyzing one phase of the induction motor steady-state current signal. The proposed methodology shows satisfactory results that prove its suitability for online detection of single and multiple combined faults in an automatic way through its hardware implementation in a field programmable gate array device.

In electric power systems, there are always power quality disturbances (PQDs). Usually, noise contamination interferes with their detection and classification. Common methods perform frequency or time-frequency analyses on the power distribution signal for detecting and classifying a limited number of PQDs with some difficulties at low signal-to-noise ratio (SNR). In this regard, recently proposed methodologies for PQD detection estimate several parameters and apply distinct signal processing techniques to improve the detection of PQD. In this work, a novel methodology that merges empirical mode decomposition (EMD), the moments of a random variable, and an artificial neural network (ANN) is proposed for detecting and classifying different PQD. The proposed method estimates skewness, kurtosis, and Shannon entropy from the EMD of one-phase voltage/current signal. Then, an ANN is in charge of classifying the input signal into one of nine different classes for PQD, receiving these parameters as inputs. The effectiveness of the proposed method was verified through computer simulations and experimentation with real data. Obtained results demonstrate its high effectiveness reaching an outstanding 100% of accuracy in detecting and classifying all treated PQD through a few number of parameters, outperforming most of previously proposed approaches.

This paper presents an experimental study of one broken-rotor-bar time-frequency evolution effects, when the bar is partially broken in an induction motor by gradually drilling one hole, each three millimeters, from healthy until the rotor bar is broken. The one phase current startup transient at each of three partially-broken conditions (0 mm, 3 mm, 6 mm and 10 mm) are analyzed, using the short-time multiple signal classification (MUSIC) method that provides a high-resolution and a time-frequency pseudo-representation of the signals, where fault-related frequencies and how they evolve in the time-frequency spectrum are obtained. Results show how the gradual drilling of one rotor bar produces a shifting in the fault frequencies as the defect increases. The sideband components around the supply frequency are shifted with respect to those from a healthy rotor bar condition obtaining a regular fault evolution from a healthy rotor bar until the bar is completely broken.

Power quality disturbances (PQD) in electric distribution systems can be produced by the utilization of non-linear loads or environmental circumstances, causing electrical equipment malfunction and reduction of its useful life. Detecting and classifying different PQDs implies great efforts in planning and structuring the monitoring system. The main disadvantage of most works in the literature is that they treat a limited number of electrical disturbances through personal computer (PC)-based computation techniques, which makes it difficult to perform an online PQD classification. In this work, the novel contribution is a methodology for PQD recognition and classification through discrete wavelet transform, mathematical morphology, decomposition of singular values, and statistical analysis. Furthermore, the timely and reliable classification of different disturbances is necessary; hence, a field programmable gate array (FPGA)-based integrated circuit is developed to offer a portable hardware processing unit to perform fast, online PQD classification. The obtained numerical and experimental results demonstrate that the proposed method guarantees high effectiveness during online PQD detection and classification of real voltage/current signals.

In this study is approached the design problem of a control law for an asymmetric nine-level multilevel cascade inverter when it is implemented as active filter for harmonic current mitigation purposes. Two are the main features of the proposed controller, namely, its structure is defined considering at a fundamental level the precise establishment, in terms of mathematical expressions, of the steady-state converter behaviour required to achieve the control objective and its stabilisation properties, that render this desired steady-state behaviour attractive (asymptotically stable), are obtained by exploiting the energy-dissipation (passivity) properties of the circuit. These features lead to a simple controller structure that is easy to tune. The stability, high performance and robustness properties of the presented control scheme are experimentally evaluated.