Harmonic distortion is one of the disturbances that most affects the quality of the electrical system. The widespread use of power electronic systems, especially power converters, has increased harmonic and interharmonic emission in a wide range of frequencies. Therefore, there are new needs in the measurement of harmonic distortion in modern electrical systems, such as measurement in the supra-harmonic range (>2 kHz) and the measurement of interharmonics. The International Electrotechnical Commission (IEC) standards define new total harmonic distortion (THD) rates based on the concept of frequency groupings. However, the rates defined in the IEC standards have shortcomings when measuring signals such as those present in the outputs of power systems with abundant interharmonic content and presence of components in the supra-harmonic range. Therefore, in this work, a comparison is made between the different THD factors currently defined, both in the literature and in the standards, to show which of them are the most suitable for assessing harmonic and interharmonic contamination in power system signals such as those present at the output of inverters.

Intelligent fault detection in induction motors (IMs) is a widely studied research topic. Various artificial-intelligence-based approaches have been proposed to deal with a large amount of data obtained from destructive laboratory testing. However, in real applications, such volume of data is not always available due to the effort required in obtaining the predictors for classifying the faults. Therefore, in realistic scenarios, it is necessary to cope with the small-data problem, as it is known in the literature. Fault-related instances along with healthy state observations obtained from the IM compose datasets that are usually imbalanced, where the number of instances classified as the faulty class (minority) is much lower than those classified under the healthy class (majority). This paper presents a novel supervised classification approach for IM faults based on the adaptive boosting algorithm with an optimized sampling technique that deals with the imbalanced experimental dataset. The stator current signal is used to compose a dataset with features both from the time domain and from the frequency domain. The experimental results demonstrate that the proposed approach achieves higher performance metrics than others classifiers used in this field for the incipient detection and classification of faults in IM.

The diagnosis of electric machines, such as induction motors, is one of the key tasks that needs to be performed to guarantee their right operation as electromechanical energy converters in most industrial facilities. The ability to reliably identify a mechanical fault occurrence, before it becomes catastrophic, can reduce risks related to the productive chain. Recently, different intelligent approaches have been proposed to develop feature-based methods for automatic rotor fault diagnosis of induction motors. This paper provides an experimental comparative evaluation of different machine learning techniques for rotor fault identifications. The classifiers are tested with data obtained under different operating conditions of the ones used to train them, as it is usual in industry. The input information is obtained from current signals of an induction motor with two states of rotor bar degradation under two pre-established load levels.

Despite the complex mathematical models and physical phenomena on which it is based, the simplicity of its construction, its affordability, the versatility of its applications and the relative ease of its control have made the electric induction motor an essential element in a considerable number of processes at the industrial and domestic levels, in which it converts electrical energy into mechanical energy. The importance of this type of machine for the continuity of operation, mainly in industry, is such that, in addition to being an important part of the study programs of careers related to this branch of electrical engineering, a large number of investigations into monitoring, detecting and quickly diagnosing its incipient faults due to a variety of factors have been conducted. This bibliographic research aims to analyze the conceptual aspects of the first discoveries that served as the basis for the invention of the induction motor, ranging from the development of the Fourier series, the Fourier transform mathematical formula in its different forms and the measurement, treatment and analysis of signals to techniques based on artificial intelligence and soft computing. This research also includes topics of interest such as fault types and their classification according to the engine, software and hardware parts used and modern approaches or maintenance strategies.

In this paper, we present the results of an ongoing investigation relating to the detection of static and dynamic eccentricity in cage induction motors fed by a pulsewidth-modulation frequency converter using line-current spectral analysis. Two different motors fed by different supply sources (utility voltage and two different voltage converters at different assigned frequencies) were tested. A statistical analysis of the results obtained was carried out. These results allow us to present practical conclusions relating to the detection of mixed eccentricity.