Squirrel Cage Induction Machine Research Articles

Bearing Faults Diagnosis by Current Envelope Analysis under Direct Torque Control Based on Neural Networks and Fuzzy Logic—A Comparative Study

Diagnosing bearing defects (BFs) in squirrel cage induction machines (SCIMs) is essential to ensure their proper functioning and avoid costly breakdowns. This paper presents an innovative approach that combines intelligent direct torque control (DTC) with the use of Hilbert transform (HT) to detect and classify these BFs. The intelligent DTC allows precise control of the electromagnetic torque of the asynchronous machine, thus providing a quick response to BFs. Using HT, stator current is analyzed to extract important features related to BFs. The HT provides the analytical signal of the current, thus facilitating the detection of anomalies associated with BFs. The approach presented incorporates an intelligent DTC that adapts to stator current variations and characteristics extracted via the HT. This intelligent control uses advanced algorithms such as neural networks (ANN-DTCs) and fuzzy logic (FL-DTCs). In this paper, a comparison between these two algorithms was performed in the MATLAB/Simulink environment for a three-phase asynchronous machine to evaluate their effectiveness under the proposed approach. The results obtained demonstrated a high ability to detect and classify BFs, confirming the effectiveness of each algorithm. In addition, this comparison highlighted the specific advantages and disadvantages of each approach. This information is valuable in choosing the most suitable algorithm according to the constraints and specific needs of the application.

Comparison of Hyper-Reduction Methods Combined With POD: Model Order Reduction of a Squirrel Cage Induction Machine in Nonlinear Case

Comparison of Hyper-Reduction Methods Combined With POD: Model Order Reduction of a Squirrel Cage Induction Machine in Nonlinear Case

Thermal finite element modeling and simulation of a squirrel-cage induction machine

Finite element models of electrical machines allow insights in electrothermal stresses which endanger the insulation system of the machine. This paper presents a thermal finite element model of a 3.7 kW squirrel-cage induction machine. The model resolves the conductors and the surrounding insulation materials in the stator slots. A set of transient thermal scenarios is defined and measured in the machine laboratory. These data are used to assess the finite element model.

Design and Performance Analysis of Single-Phase Squirrel-Cage Induction Machine with High Efficiency

Design and Performance Analysis of Single-Phase Squirrel-Cage Induction Machine with High Efficiency

Image-Based Approach Applied to Load Torque Estimation in Three-Phase Induction Motors.

This paper presents a novel method for load torque estimation in three-phase induction motors using air gap flux measurement and the conversion of this type of time-domain signal into grayscale images for further processing as inputs for an inception-type convolutional neural network. The magnetic flux was measured employing a Hall effect sensor installed inside the machine, near the stator slots, and above the stator windings. In this case, the sensor was able to measure a resultant magnetic flux density, having both rotor and stator magnetic flux contributions. The present methodology does not require motor parameters for torque prediction. The proposed approach successfully estimated load torque using three optimizers across almost the entire motor load operational range, spanning from 1.5% to 93.9% of the rated load. Four model configurations achieved a mean absolute percentage error (MAPE) less than or equal to 3.7%. Specifically, two models for a 40 × 50 pixel image achieved MAPE of 3.7% and 3%, one model for a 40 × 25 pixel image achieved a MAPE of 3.5%, and one model for a 50 × 80 pixel image achieved a MAPE of 3.3%. This research has been experimentally validated with a 7.5 kW squirrel cage induction machine.

Experimental Assessment of Predictive Current Control Applied to Induction Machine Drive Systems Operating Under Single-Phase Open-Circuit Fault


 Predictive Current Control (PCC) has been widely applied in several applications. However, the literature has not discussed its use as a fault tolerance control algorithm in induction drive systems. In this way, this paper discusses the PCC method in two faulttolerant squirrel-cage induction machine drive systems operating under single-phase open-circuit faults. PCC’s postfault performance is compared to Field Oriented Control (FOC) for different steady- and transient-state scenarios, analyzing harmonic distortion, torque ripple, and the transition from healthy to postfault operation. Also, experiments tested the robustness of postfault PCC to low-speed operation, parametric variation, and a step change in reference rotor speed, showing that PCC also presents fault-tolerant operation under these conditions.

Finite Element Analysis of a Three Speed Induction Machine

This paper deals with the Finite Element (FE) analysis of a special Three Speed Squirrel Cage Induction Machine (TSSCIM) with pole changing stator windings. Compared to classical multi-speed induction machines, the studied TSSCIM is designed so as to change in the same time the number of pole pairs (p) and the number of stator phases (m) so as to keep constant the product p x m. The numerical model of the machine used in this study is based on a 2D plane-parallel finite element approach and aims at finding some of the main steady state operation characteristics of the machine working both as motor and generator for three different running speeds. A comparison of the proposed TSSCIM with a classical inverter fed squirrel cage induction machine is also presented in order to underline some advantages and disadvantages of each solution.

High Efficiency Squirrel Cage Induction Machines

This paper highlights the benefits of replacing the classical cast aluminum cage with a cast copper cage in the manufacture of future generation of high efficiency induction machines used as motors or generators. The numerical analysis carried out in the paper is based on a 2D plane-parallel finite element approach of the induction machine, the numerical results being discussed and compared with experimental measurements.

Semianalytical modeling of squirrel-cage induction machines with consideration of iron permeability and eddy-currents using elementary subdomain technique

PurposeThis paper aims to propose a semianalytical model of a squirrel-cage induction machine (SCIM), considering local magnetic saturation and eddy-currents induced in the rotor bars.Design/methodology/approachThe regions of the rotor and stator are divided into elementary subdomains (E-SDs) characterized by general solutions at the first harmonic of the magneto-harmonic Maxwell’s equations. These E-SDs are connected in both directions (i.e., along the r- and θ-edges).FindingsThe calculation of the magnetic field has been validated for various values of slip and iron permeability. All electromagnetic quantities were compared with those obtained using a two-dimensional finite-element method. The semianalytical results are satisfactory compared with the numerical results, considering both the amplitude and waveform.Originality/valueExpansion of the recent analytical model (E-SD technique) for the full prediction of the magnetic field in SCIMs, considering the local saturation effect and the eddy-currents induced in the rotor bars.

Enhancing the Efficiency of Failure Recognition in Induction Machines through the Application of Deep Neural Networks

The objective of the investigation was to increase the effectiveness of damage detection in the stator of the squirrel-cage induction machine. The analysis aimed to enhance the operational trustworthiness of the squirrel-cage induction machine by employing nonintrusive diagnostic methods based on a current signal and modern artificial intelligence methods. The authors of the study introduced a diagnostic technique for identifying multiphase interturn short circuits of stator winding. These short circuits are one of the most common faults in induction machines. The proposed method focusses on deriving a diagnostic signal from the phase-current waveforms of the machine. The noninvasive nature of the diagnostic technique presented is attributed to the application of the field model of electromagnetic phenomena to determine the diagnostic signal. For this purpose, a field model of a squirrel-cage machine was developed. The waveforms of phase currents obtained from the field model were used as input into an elaborated machine failure neural classifier. A deep neural network was used to develop a neural classifier. The effectiveness of the developed classifier has been experimentally verified, and the obtained results have been presented, concluded, and discussed. The scientific novelty presented in the article is the presentation of research results on the use of a neural classifier to detect damage in all phases of the stator winding at an early stage of its appearance. The features of this type of damage are very difficult to observe in signal waveforms such as a phase current or torque.

Torque fluctuation and critical clearing time in wind power generation by induction machines.

Squirrel cage induction machines represent a common solution widely adopted in wind generation. The induction machine is simple, reliable, cheap and requires very little maintenance. Some problems are, however, connected with the use of induction machines in wind power generation systems. In particular two effects have to be taken into account: the torque fluctuations at the generator shaft and the critical clearing time (CCT). Both problems represent a serious limitation for weak networks in which the first effect can cause flicker effect while the CCT is very short. In the paper a full modelling of a wind energy plant constituted by n machines is presented. Using the set-up model the behaviour of the plant for a different number of connected machines is analysed. In particular, the system stability connected with voltage sags and network faults is studied

Analysis of Squirrel Cage Induction Machine Based on the Magductance Modulator

Analysis of Squirrel Cage Induction Machine Based on the Magductance Modulator

Increasing efficiency of induction motor by predictive control system

Standard squirrel-cage induction machine is widely used in industrial enterprises due to its advantages in terms of reliability, durability and economic efficiency. However, one of its disadvantages is that it suffers from a power dissipation factor due to its inability to balance active and reactive power consumption. Especially when the SCIM is running at no load condition or during the initial start-up, the power factor or useful operation and performance are drastically reduced. Therefore, improving the power factor of the induction machine is an actual topic for the following article, and has had some interesting solutions for several years. In this thesis, two different induction motor control algorithms (frequency converter and Predictive torque control) are included and a clear comparison between them is given. The main focus of this article is to design an induction motor control system using the two algorithms mentioned above, analyse the performance of different control methods, and experimentally verify these algorithms by comparing simulations and experimental results.

Dynamic Modelling of Wind Farms: a Comparative Study Between Two Modelling Approaches.

World’s energy needs have encouraged wind power sources. An increasing number of wind farms are being connected to the power grid and accurate wind farm models need to be developed to study the dynamic behaviour of them. This paper reports an investigation to compare two different modelling approaches of a real wind farm. This study is applied to the dynamic model of a real wind farm located at El Perdón, in Navarre, (Northern Spain). It consists of 30 Squirrel Cage Induction Machine (SCIM) wind generators of 660 kW and a nominal voltage of 690V. The wind farm feeds three local loads at 66 kV and is connected through two transformers to the network of 220KV.

Grid Integration of Livestock Biogas Using Self-Excited Induction Generator and Spark-Ignition Engine

This study developed a grid-connected biogas power generation system for a rural community using a 3-phase 2.2 kW squirrel-cage induction machine as the self-excited induction generator. The generator was driven by a 196 cc single-cylinder spark-ignition engine fueled by biogas. We developed a back-to-back converter that consists of a 3-phase PWM rectifier as the generator-side converter and a single-phase LCL-filtered grid-connected inverter. The generator-side converter transferred the active power to the grid-side converter and supplied the reactive power control back to the generator. The notch filter-based bus voltage control on the generator side mitigated the inter-harmonics in the generator current. The injected grid current complied with the IEEE 1547 standard because of the multi-frequency unbalanced synchronous reference frame control. The proposed system was validated with biogas produced from pig manure at a pig farm in central Thailand, which found a maximum output of 1.2 kW with a thermal system efficiency of 10.7%. The proposed system was scheduled to operate at 1.2 kW for 8 h per day with a levelized cost of 0.07 US$/kWh, 42% cheaper than the retail electricity price, and a payback period of 2.76 years. The proposed system is suitable for a farm with a minimum of 34 pigs.

Parameter identification of squirrel cage induction machine using Linear Kalman filter

In the industrial environment, the induction machine is generally the most used, given its low cost, its efficiency, and its reliability. To obtain optimal operation, it is essential to collect values of its parameters (stator and rotor resistances and inductions). This data can be used to prevent breakdowns and ensure these machines’ rational use. In this context, this article proposes a new method to estimate the internal parameters of these machines using the KALMAN FILTER. Simulations under the MATLAB environment have been developed. Their results show that the estimated parameters are close to the nominal ones.

Cascaded H-Bridge Multilevel Converter Applied to a Wind Energy Conversion System with Open-End Winding

With the growing expansion of renewable sources around the world, wind energy is among those that stand out. With the advances of technology, wind turbine projects have considerably increased their power, reaching higher power, mainly for offshore installations. One of the main challenges is the power converters, more specifically the semiconductor components, which have limited voltage and current capabilities. Thus, the concept of multilevel converters emerged, increasing the voltage levels and thus carrying higher power levels. In addition to the application of multilevel converters, it is possible to increase the voltage and power levels employing an open-end winding (OEW) connection to the generator. In this context, the present work investigated the application of a multilevel converter (three-level cascaded H-bridge back-to-back) driving a squirrel-cage induction machine in an open-end winding configuration, connected to a wind energy conversion system (WECS). The analysis of the proposed system was developed through dynamic simulation of a 1.67 MW WECS, using PLECS software, including the modeling of the main system components: generator, power converters, system control, filter, and grid connection. The results show that the objective of obtaining a 5-level behavior in the output voltage is achieved by using the OEW connection. Furthermore, a low harmonic content is achieved in the machine current as in the current injected into the grid. In addition, it is possible to verify the power distribution between the converters, demonstrating that converters with smaller power can be combined to reach higher WECS power.

Development of a Novel High Strength Aluminum-Cerium Based Rotor Alloy for Electric Vehicle Induction Motor Applications

<div class="section abstract"><div class="htmlview paragraph">To increase vehicle range, light weighting of electric vehicles has been extensively researched and implemented by using aluminum intensive solutions. With regards to traction motors, aluminum alloys that have a desired combination of high electrical conductivity and strength are required for high power output and efficiency. In this research, a novel Al-Ce based alloy, with minor additions of Si and Mg for strengthening, was investigated in different heat treatment tempers to maximize mechanical properties while maintaining a high electrical conductivity. This new alloy system appears to have addressed the classic conundrum of the inverse relationship of mechanical performance verses electrical conductivity for traditional aluminum alloy systems. The results suggest that the Al-Ce-Si-Mg alloy had yield strength in excess of 120 MPa and electrical conductivity of at least 50 %IACS in the T5 and T6 conditions. Simulations of a squirrel cage induction machine show that this combination of properties resulted in similar motor performance to other rotor Al alloys. As a result, the Al-Ce-Si-Mg alloy is a promising alternative to the near pure Al or Cu-based alloys that are currently used in induction motor rotor castings.</div></div>

Nonintrusive Motor Current Signature Analysis

Motor current signature analysis (MCSA) techniques measure slip-related current signals in the supply lines of a squirrel-cage induction machine (SCIM) for slip estimation, fault detection, and motor diagnostics. In a traditional setup for MCSA, current sensors directly measure the machine currents and are usually installed physically close to the machine of interest, e.g., current transducers clamped about the machine stator leads. For grid-connected SCIMs, the slip-related current signals propagate through the power system. Thus, a single aggregate power monitor can potentially perform MCSA for a collection of SCIMs powered by a common electrical service, referred to in this article as nonintrusive MCSA. Current division and system impedances (source impedance, load impedances, and machine parameters) determine the applicability of nonintrusive MCSA. This article demonstrates measurement hardware, modeling, and experimental results for nonintrusive MCSA. Analysis and results investigate the current-source model of a grid-connected SCIM and its interaction with the system impedances. Applied to a distribution system with multiple machines, the techniques inform when nonintrusive MCSA is possible for collections of SCIMs and equivalent to traditional MCSA.

An Improved Dual DTC of Double-Inverter-Fed WRIM Drive With Reduced Torque Ripple by Emulating Equivalent 3L NPC VSC

Wound rotor induction machine (WRIM) connected to stator and rotor voltage source converters (VSCs), known as the double-inverter-fed WRIM drive, can be viewed as two virtual squirrel cage induction machine drives, when seen from air-gap. In this article, this feature of WRIM is explored to develop an improved dual direct torque vontrol (DDTC) technique such that, two individual and independent conventional DTC controllers can be directly employed to control the stator and rotor VSCs. The improved technique is inculcated with all the benefits of conventional DTC controller, especially the benefit of zero dependence on rotor position. Furthermore, the increased torque ripple with the conventional DTC controllers is mitigated by modifying the switching tables (STs) such that, the voltage vectors emulated in the air-gap are equivalent to a 3-level neutral point clamped VSC. The STs are developed by analyzing the WRIM as an open-end winding induction machine (OEWIM). To demonstrate the performance of the proposed DDTC technique, the experimental results obtained from 3 kW WRIM are presented, under various transients and steady-state conditions. The reduction in the torque ripple is demonstrated by estimated torque ripple waveforms, at different speeds.