Carla Tassoni

Bio: Carla Tassoni is an academic researcher from University of Parma. The author has contributed to research in topics: Induction motor & Rotor (electric). The author has an hindex of 22, co-authored 65 publications receiving 4690 citations.

Advances in Diagnostic Techniques for Induction Machines

Abstract: This paper investigates diagnostic techniques for electrical machines with special reference to induction machines and to papers published in the last ten years. A comprehensive list of references is reported and examined, and research activities classified into four main topics: 1) electrical faults; 2) mechanical faults; 3) signal processing for analysis and monitoring; and 4) artificial intelligence and decision-making techniques.

Quantitative evaluation of induction motor broken bars by means of electrical signature analysis

Abstract: The paper reports the comparison and performance evaluation of different diagnostic procedures that use input electric signals to detect and quantify rotor breakage in induction machines supplied by the mains. Besides the traditional current signature analysis based on one-phase current spectrum lines at frequencies (1/spl plusmn/2s)f, the procedures based on analysis of the line at frequency 2sf in the spectrum respectively of electromagnetic torque, space vector current modulus and instantaneous power are considered. These last procedures have similar features and the comparison is developed on the basis of instantaneous torque. It is addressed that the speed ripple introduces two further terms in the instantaneous torque, decreasing the accuracy of the diagnosis. It is shown that there is a link between the angular displacement of the current sideband components at frequencies (1/spl plusmn/2s)f. This allows a more correct quantitative evaluation of the fault and shows the superiority of the side band current components diagnostic procedure over the other proposed methods.

Recent developments of induction motor drives fault diagnosis using AI techniques

Abstract: This paper presents a review of the developments in the field of diagnosis of electrical machines and drives based on artificial intelligence (AI). It covers the application of expert systems, artificial neural networks (ANNs), and fuzzy logic systems that can be integrated into each other and also with more traditional techniques. The application of genetic algorithms is considered as well. In general, a diagnostic procedure starts from a fault tree developed on the basis of the physical behavior of the electrical system under consideration. In this phase, the knowledge of well-tested models able to simulate the electrical machine in different fault conditions is fundamental to obtain the patterns characterizing the faults. The fault tree navigation performed by an expert system inference engine leads to the choice of suitable diagnostic indexes, referred to a particular fault, and relevant to build an input data set for specific AI (NNs, fuzzy logic, or neuro-fuzzy) systems. The discussed methodologies, that play a general role in the diagnostic field, are applied to an induction machine, utilizing as input signals the instantaneous voltages and currents. In addition, the supply converter is also considered to incorporate in the diagnostic procedure the most typical failures of power electronic components. A brief description of the various AI techniques is also given; this highlights the advantages and the limitations of using AI techniques. Some applications examples are also discussed and areas for future research are also indicated.

AI techniques in induction machines diagnosis including the speed ripple effect

Abstract: Various applications of artificial intelligence (AI) techniques (expert systems, neural networks, and fuzzy logic) presented in the literature prove that such technologies are well suited to cope with on-line diagnostic tasks for induction machines. The features of these techniques and the improvements that they introduce in the diagnostic process are recalled, showing that, in order to obtain an indication on the fault extent, faulty machine models are still essential. Moreover, by the models, that must trade off between simulation result effectiveness and simplicity, it is possible to overcome crucial points of the diagnosis. With reference to rotor electrical faults of induction machines, a new and simple procedure based on a model which includes the speed ripple effect is developed. This procedure leads to a new diagnostic index, independent of the machine operating condition and inertia value, that allows the implementation of the diagnostic system with a minimum configuration intelligence.

Battery choice and management for new-generation electric vehicles

Abstract: Different types of electric vehicles (EVs) have been recently designed with the aim of solving pollution problems caused by the emission of gasoline-powered engines. Environmental problems promote the adoption of new-generation electric vehicles for urban transportation. As it is well known, one of the weakest points of electric vehicles is the battery system. Vehicle autonomy and, therefore, accurate detection of battery state of charge (SoC) together with battery expected life, i.e., battery state of health, are among the major drawbacks that prevent the introduction of electric vehicles in the consumer market. The electric scooter may provide the most feasible opportunity among EVs. They may be a replacement product for the primary-use vehicle, especially in Europe and Asia, provided that drive performance, safety, and cost issues are similar to actual engine scooters. The battery system choice is a crucial item, and thanks to an increasing emphasis on vehicle range and performance, the Li-ion battery could become a viable candidate. This paper deals with the design of a battery pack based on Li-ion technology for a prototype electric scooter with high performance and autonomy. The adopted battery system is composed of a suitable number of cells series connected, featuring a high voltage level. Therefore, cell equalization and monitoring need to be provided. Due to manufacturing asymmetries, charge and discharge cycles lead to cell unbalancing, reducing battery capacity and, depending on cell type, causing safety troubles or strongly limiting the storage capacity of the full pack. No solution is available on the market at a cheap price, because of the required voltage level and performance, therefore, a dedicated battery management system was designed, that also includes a battery SoC monitoring. The proposed solution features a high capability of energy storing in braking conditions, charge equalization, overvoltage and undervoltage protection and, obviously, SoC information in order to optimize autonomy instead of performance or vice-versa.

Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review

Abstract: Recently, research has picked up a fervent pace in the area of fault diagnosis of electrical machines. The manufacturers and users of these drives are now keen to include diagnostic features in the software to improve salability and reliability. Apart from locating specific harmonic components in the line current (popularly known as motor current signature analysis), other signals, such as speed, torque, noise, vibration etc., are also explored for their frequency contents. Sometimes, altogether different techniques, such as thermal measurements, chemical analysis, etc., are also employed to find out the nature and the degree of the fault. In addition, human involvement in the actual fault detection decision making is slowly being replaced by automated tools, such as expert systems, neural networks, fuzzy-logic-based systems; to name a few. It is indeed evident that this area is vast in scope. Hence, keeping in mind the need for future research, a review paper describing different types of faults and the signatures they generate and their diagnostics' schemes will not be entirely out of place. In particular, such a review helps to avoid repetition of past work and gives a bird's eye view to a new researcher in this area.

A review of induction motors signature analysis as a medium for faults detection

Abstract: This paper is intended as a tutorial overview of induction motors signature analysis as a medium for fault detection. The purpose is to introduce in a concise manner the fundamental theory, main results, and practical applications of motor signature analysis for the detection and the localization of abnormal electrical and mechanical conditions that indicate, or may lead to, a failure of induction motors. The paper is focused on the so-called motor current signature analysis which utilizes the results of spectral analysis of the stator current. The paper is purposefully written without "state-of-the-art" terminology for the benefit of practising engineers in facilities today who may not be familiar with signal processing.

Power Electronics and Motor Drives in Electric, Hybrid Electric, and Plug-In Hybrid Electric Vehicles

Abstract: With the requirements for reducing emissions and improving fuel economy, automotive companies are developing electric, hybrid electric, and plug-in hybrid electric vehicles. Power electronics is an enabling technology for the development of these environmentally friendlier vehicles and implementing the advanced electrical architectures to meet the demands for increased electric loads. In this paper, a brief review of the current trends and future vehicle strategies and the function of power electronic subsystems are described. The requirements of power electronic components and electric motor drives for the successful development of these vehicles are also presented.