scholarly journals On-line Detection and Classification of PMSM Stator Winding Faults Based on Stator Current Symmetrical Components Analysis and the KNN Algorithm

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1786
Author(s):  
Przemyslaw Pietrzak ◽  
Marcin Wolkiewicz
Keyword(s):  
High Efficiency ◽  
Dynamic Properties ◽  
Stator Winding ◽  
Electric Motors ◽  
Permanent Magnet Synchronous Motors ◽  
K Nearest Neighbors ◽  
Short Circuits ◽  
Drive Systems ◽  
The Impact

The significant advantages of permanent magnet synchronous motors, such as very good dynamic properties, high efficiency and power density, have led to their frequent use in many drive systems today. However, like other types of electric motors, they are exposed to various types of faults, including stator winding faults. Stator winding faults are mainly inter-turn short circuits and are among the most common faults in electric motors. In this paper, the possibility of using the spectral analysis of symmetrical current components to extract fault symptoms and the machine-learning-based K-Nearest Neighbors (KNN) algorithm for the detection and classification of the PMSM stator winding fault is presented. The impact of the key parameters of this classifier on the effectiveness of stator winding fault detection and classification is presented and discussed in detail, which has not been researched in the literature so far. The proposed solution was verified experimentally using a 2.5 kW PMSM, the construction of which was specially prepared for carrying out controlled inter-turn short circuits.

scholarly journals Comparison of Selected Methods for the Stator Winding Condition Monitoring of a PMSM Using the Stator Phase Currents

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1630
Author(s):  
Przemyslaw Pietrzak ◽  
Marcin Wolkiewicz
Keyword(s):  
Permanent Magnet ◽  
Early Stage ◽  
Permanent Magnet Synchronous Motor ◽  
Experimental Studies ◽  
Discrete Wavelet ◽  
Stator Winding ◽  
Permanent Magnet Synchronous Motors ◽  
Synchronous Motors ◽  
Phase Current ◽  
Short Circuits

Stator winding faults are one of the most common faults of permanent magnet synchronous motors (PMSMs), and searching for methods to efficiently detect this type of fault and at an early stage of damage is still an ongoing, important topic. This paper deals with the selected methods for detecting stator winding faults (short-circuits) of a permanent magnet synchronous motor, which are based on the analysis of the stator phase current signal. These methods were experimentally verified and their effectiveness was carefully compared. The article presents the results of experimental studies obtained from the spectral analysis of the stator phase current, stator phase current envelope, and the discrete wavelet transform. The original fault indicators (FIs) based on the observation of the symptoms of stator winding fault were distinguished using the aforementioned methods, which clearly show which symptom is most sensitive to the incipient fault of the stator winding of PMSMs.

scholarly journals Development of Magnetic Material Testing Equipment

2020 ◽  
Vol 3 (1) ◽  
pp. 33-37
Author(s):  
András Nagy ◽  
Imre Némedi
Keyword(s):  
Magnetic Material ◽  
Magnetic Properties ◽  
Thin Plate ◽  
High Frequency ◽  
Small Volume ◽  
High Efficiency ◽  
Amorphous Structure ◽  
Material Testing ◽  
Electric Motors

AbstractThis paper deals with the development of equipment that can accurately determine the magnetic properties of small volume thin plate samples. The alloys to be tested are sheets of amorphous structure, such as Finemet alloy, which has excellent high frequency magnetic properties, making it a good candidate for the construction of high efficiency electric motors. This article discusses the components and operation of the equipment under development, whilst giving a brief overview of the efficiency classification of electric motors and the importance of the emerging efficiency class.

scholarly journals Pulse Width Modulation Analysis of Five-Level Inverter- Fed Permanent Magnet Synchronous Motors for Electric Vehicle Applications

2021 ◽  
Vol 1 (4) ◽  
pp. 477-487
Author(s):  
Omokhafe J. Tola ◽  
Edwin A. Umoh ◽  
Enesi A. Yahaya
Keyword(s):  
Permanent Magnet ◽  
Electric Vehicle ◽  
Pulse Width ◽  
Pulse Width Modulation ◽  
High Efficiency ◽  
Performance Enhancement ◽  
Time Synchronization ◽  
Stator Winding ◽  
Permanent Magnet Synchronous Motors ◽  
Synchronous Motors

In recent times, intense research has been focused on the performance enhancement of permanent magnet synchronous motors (PMSM) for electric vehicle (EV) applications to reduce their torque and current ripples. Permanent magnet synchronous motors are widely used in electric vehicle systems due to their high efficiency and high torque density. To have a good dynamic and transient response, an appropriate inverter topology is required. In this paper, a five-level inverter fed PMSM for electric vehicle applications, realized via co-simulation in an electromagnetic suite environment with a reduced stator winding current of PMSM via the use of in-phase disposition (PD) pulse width modulation (PWM) techniques as the control strategy is presented. The proposed topology minimizes the total harmonic distortion (THD) in the inverter circuit and the motor fed and also improves the torque ripples and the steady-state flux when compared to conventional PWM techniques. A good dynamic response was achieved with less than 10A stator winding current, zero percent overshoot, and 0.02 second settling time synchronization. Thus, the stator currents are relatively low when compared to the conventional PWM. This topology contribution to the open problem of evolving strategies that can enhance the performance of electric drive systems used in unmanned aerial vehicles (UAV), mechatronics, and robotic systems.

scholarly journals Convolutional Neural Network-Based Stator Current Data-Driven Incipient Stator Fault Diagnosis of Inverter-Fed Induction Motor

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1475 ◽  
Author(s):  
Maciej Skowron ◽  
Teresa Orlowska-Kowalska ◽  
Marcin Wolkiewicz ◽  
Czeslaw T. Kowalski
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Induction Motor ◽  
Current Data ◽  
Operating Conditions ◽  
Stator Winding ◽  
Stator Current ◽  
Short Circuits ◽  
The Impact

In this paper, the idea of using a convolutional neural network (CNN) for the detection and classification of induction motor stator winding faults is presented. The diagnosis inference of the stator inter-turn short-circuits is based on raw stator current data. It offers the possibility of using the diagnostic signal direct processing, which could replace well known analytical methods. Tests were carried out for various levels of stator failures. In order to assess the sensitivity of the applied CNN-based detector to motor operating conditions, the tests were carried out for variable load torques and for different values of supply voltage frequency. Experimental tests were conducted on a specially designed setup with the 3 kW induction motor of special construction, which allowed for the physical modelling of inter-turn short-circuits in each of the three phases of the machine. The on-line tests prove the possibility of using CNN in the real-time diagnostic system with the high accuracy of incipient stator winding fault detection and classification. The impact of the developed CNN structure and training method parameters on the fault diagnosis accuracy has also been tested.

scholarly journals Analysis of the Impact of Stator Inter-Turn Short Circuits on PMSM Drive with Scalar and Vector Control

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 153
Author(s):  
Mateusz Krzysztofiak ◽  
Maciej Skowron ◽  
Teresa Orlowska-Kowalska
Keyword(s):  
Vector Control ◽  
Control Structure ◽  
Frequency Control ◽  
Permanent Magnet Synchronous Motor ◽  
Operating Conditions ◽  
Closed Field ◽  
Stator Winding ◽  
State Variables ◽  
Short Circuits ◽  
The Impact

Permanent Magnet Synchronous Motor (PMSM) failures are currently widely discussed in the literature, but the impact of these failures on the operation of control systems and the ability to detect selected failures despite the compensating effect of control algorithms being relatively rarely analyzed. The article presents the impact of damage to the stator winding of a PMSM motor on the operation of two frequency control structures, scalar and vector control. The mathematical model of PMSM that takes into account the influence of a different number of shorted turns in the stator winding phase was presented, and its experimental verification was performed. Then, the influence of various degrees of damage to the stator winding on the waveforms of the motor state variables in an open scalar control structure and in a closed field-oriented control structure was analyzed. Based on the analysis of phase currents and rotational speed of the motor as well as the influence of the PMSM motor operating conditions, the basic techniques of extracting the symptoms of stator winding inter-turn short-circuits were analyzed, and the failure indicators were developed, which enable simple diagnostics of the stator winding.

scholarly journals TAKING INTO ACCOUNT THE PARAMETERS OF AEROSOL EMISSIONS IN THE DEVELOPMENT TECHNOLOGICAL SOLUTIONS TO REDUCE THE IMPACT ON THE ENVIRONMENT

2021 ◽  
pp. 3-10
Author(s):  
Ivan Koziy ◽  
Leonid Plyatsuk ◽  
Larysa Hurets ◽  
Inna Trunova
Keyword(s):  
High Efficiency ◽  
Selection Process ◽  
Morphological Characteristics ◽  
Literary Sources ◽  
Reasonable Choice ◽  
Aerosol Emissions ◽  
Physical And Chemical ◽  
The Impact ◽  
Selection Of

The article discusses the issues of studying the parameters of various origins aerosol emissions in order to make a reasonable choice of appropriate technological solutions to reduce the impact on the environment. Based on the analysis of literary sources, a classical approach to the existing classification of aerosols considered: by the nature of the formation; by dispersion; by the state of aggregation of the dispersed phase; by morphological characteristics of particles; by particle concentration; by the nature of the impact on a person. Refinement of existing classifications was conducted based on the most important physical and chemical characteristics such as cohesiveness of particles, hygroscopicity, and ability to absorb additional substances from the environment which in turn is an important factor in the selection process of environmental solutions. Based on the analysis of aerosols classifications concluded possible solutions of the problem of selection of high-efficiency and reliable equipment capable for trapping fine dust with various physical and physicochemical parameters

Vector Magnetic Characteristic Technology for Developing High Efficiency Machines in Oita National Project

2014 ◽  
Vol 792 ◽  
pp. 207-214
Author(s):  
Masato Enokizono
Keyword(s):  
High Efficiency ◽  
Minimum Energy ◽  
Magnetic Characteristic ◽  
Performance Standards ◽  
Energy Performance ◽  
Electric Motors ◽  
New Class ◽  
Draft Version ◽  
The Eu

In this paper we will discuss about the development of IE4 level motor proposed by IEC, which is new target for international efficiencies. Efficiency classes IE1-IE4 are defined in international standard IEC 60034-30. The draft version of IEC 60034-31 defines the new class IE4. The IE-codes replaces the former voluntary Eff classification of electric motors as shown in Table 1. The Eff classes are based on a voluntary agreement between the EU and the CEMEP in 1998.Table 1 Minimum energy performance standards for electric motors.

scholarly journals Ranked classification of environmental conditions

2021 ◽  
pp. 129-136
Author(s):  
A.V. Skatkov ◽  
◽  
A.A. Bryukhovetskiy ◽  
D.V. Moiseev ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Environmental Conditions ◽  
High Efficiency ◽  
Environmental Indicators ◽  
Artificial Neural ◽  
Intelligent Technology ◽  
Evaluation Matrix ◽  
The Impact

This paper discusses the main features associated with the development and research of the device based on the methods of intelligent technology for assessing the state of the natural environment. It should be noted that natural and technical objects, as well as the processes occurring in them, are characterized by high complexity and dynamism, and a significant part of these processes has not yet been fully studied and formalized. Therefore, one of the most important areas of data analysis in this area is the use of artificial neural networks in information and measurement systems. In the works of scientists from various countries, the high efficiency of the use of artificial neural networks in solving individual data processing problems in the classification of environmental conditions is shown. The proposed approach is based on methods of nonparametric statistics using rank criteria and will allow for intelligent analysis of data on key environmental indicators, such as hydrometeorological data on the level of pollution and composition of air, soil, maximum permissible emissions of harmful substances, environmental monitoring of anomalies, and others. Static, dynamic, integral, and generalized models of classification of environmental conditions are presented. Further research plans suggest evaluating the impact of sample size on statistical sensitivity, statistical stability, and areas of confident/uncertain recognition, as well as building a decision support system for detecting the G-effect, and considering an adaptive approach to constructing an evaluation matrix.

Piezoelectric β-polymorph formation of new textiles by surface modification with coating process based on interfacial interaction on the conformational variation of poly (vinylidene fluoride) (PVDF) chains

2020 ◽  
Vol 91 (3) ◽  
pp. 31301
Author(s):  
Nabil Chakhchaoui ◽  
Rida Farhan ◽  
Meriem Boutaldat ◽  
Marwane Rouway ◽  
Adil Eddiai ◽  
...  
Keyword(s):  
High Efficiency ◽  
Vinylidene Fluoride ◽  
Research Work ◽  
Research Area ◽  
Interfacial Interaction ◽  
Tetraethyl Orthosilicate ◽  
Poly Vinylidene Fluoride ◽  
Carbon Nanofillers ◽  
The Impact ◽  
Advanced Applications

Novel textiles have received a lot of attention from researchers in the last decade due to some of their unique features. The introduction of intelligent materials into textile structures offers an opportunity to develop multifunctional textiles, such as sensing, reacting, conducting electricity and performing energy conversion operations. In this research work nanocomposite-based highly piezoelectric and electroactive β-phase new textile has been developed using the pad-dry-cure method. The deposition of poly (vinylidene fluoride) (PVDF) − carbon nanofillers (CNF) − tetraethyl orthosilicate (TEOS), Si(OCH2CH3)4 was acquired on a treated textile substrate using coating technique followed by evaporation to transform the passive (non-functional) textile into a dynamic textile with an enhanced piezoelectric β-phase. The aim of the study is the investigation of the impact the coating of textile via piezoelectric nanocomposites based PVDF-CNF (by optimizing piezoelectric crystalline phase). The chemical composition of CT/PVDF-CNC-TEOS textile was detected by qualitative elemental analysis (SEM/EDX). The added of 0.5% of CNF during the process provides material textiles with a piezoelectric β-phase of up to 50% has been measured by FTIR experiments. These results indicated that CNF has high efficiency in transforming the phase α introduced in the unloaded PVDF, to the β-phase in the case of nanocomposites. Consequently, this fabricated new textile exhibits glorious piezoelectric β-phase even with relatively low coating content of PVDF-CNF-TEOS. The study demonstrates that the pad-dry-cure method can potentially be used for the development of piezoelectric nanocomposite-coated wearable new textiles for sensors and energy harvesting applications. We believe that our study may inspire the research area for future advanced applications.

Sign in / Sign up

Export Citation Format

Share Document