Design of Online Vision Detection System for Stator Winding Coil

The quality of the stator winding coil directly affects the performance of the motor.A dual-camera online machine vision detection method to detect whether the coil leads and winding regions were qualified was designed.A vision detection platform was designed to capture individual winding images,and an image processing algorithm was used for image pre-processing,template matching and positioning of the coil lead area to set up a coordinate system.After eliminating image noise by Blob analysis,the improved Canny algorithm was used to detect the location of the coil lead paint stripped region,and the time was reduced by about half compared to the Canny algorithm.The coil winding region was trained with the ShuffleNet V2-YOLOv5s model for the dataset,and the detect file was converted to the Open Neural Network Exchange(ONNX)model for the detection of winding cross features with an average accuracy of 99.0%.The software interface of the detection system was designed to perform qualified discrimination tests on the workpieces,and the detection data were recorded and statistically analyzed.The results showed that the stator winding coil qualified discrimination accuracy reached 96.2%,and the average detection time of a single workpiece was about 300 ms,while YOLOv5s took less than 30 ms.

Creation and Development of Turbo-generators with Water-Cooled Stator and Rotor Windings in Shanghai Electrical Machinery Mfg Works

1. An Overview of Manufacture and Operation A turbine generator utilizing a new technology of electrical machinery industry, i.e. the windings of its stator and rotor all being inner water-cooled, was first successfully created in China and was known afterwards as a turbine generator with watercooled stator and rotor windings (Abbrev, TGWSR). The teachers from Zhejiang University came to Shanghai between

GENERATOR VIBRATION FAULT DIAGNOSIS METHOD BASED ON ROTOR VIBRATION AND STATOR WINDING PARALLEL BRANCHES CIRCULATING CURRENT CHARACTERISTICS

Rotor vibration characteristics are first analyzed, which are that the rotor vibration of fundamental frequency will increase due to rotor winding inter-turn short circuit fault, air-gap dynamic eccentricity fault, or imbalance fault, and the vibration of the second frequency will increase when the air-gap static eccentricity fault occurs. Next, the characteristics of the stator winding parallel branches circulating current are analyzed, which are that the second harmonics circulating current will increase when the rotor winding inter-turn short circuit fault occurs, and the fundamental circulating current will increase when the air-gap eccentricity fault occurs, neither being strongly affected by the imbalance fault. Considering the differences of the rotor vibration and circulating current characteristics caused by different rotor faults, a method of generator vibration fault diagnosis, based on rotor vibration and circulating current characteristics, is developed. Finally, the rotor vibration and circulating current of a type SDF-9 generator is measured in the laboratory to verify the theoretical analysis presented above.

Calculation and analysis of unbalanced magnetic pulls of different stator winding setups in static eccentric induction motor

In large-scale electric machines, unbalanced magnetic pull （UMP） caused by eccentricity usually results in stator-rotor rub, so it is necessary to investigate the amplitude and the influencing factors. This paper takes the squirrel-cage induction motor as an example. A magnetic loop model of an induction motor is established by an analytical method. The impact of stator winding setup （parallel branch and pole pairs） on each magnetomotive force （MMF） and unbalanced magnetic pull is analyzed. Using the finite element simulation method, the spatial and time distribution of flux density of the rotor outer circle under static eccentricity is obtained, and the unbalanced magnetic pull calculation caused by static eccentricity is completed. The conclusion of the influence of stator winding on the size of unbalanced magnetic pull provides reliable gist for motor noise and vibration analysis, and especially provides an important reference for large induction motor design.

Stator Winding Turn Faults Diagnosis for Induction Motor by Immune Memory Dynamic Clonal Strategy Algorithm

Quick detection of a small initial fault is important for an induction motor to prevent a consequent large fault.The mathematical model with basic motor equations among voltages,currents,and fluxes is analyzed and the motor model equations are described.The fault related features are extracted.An immune memory dynamic clonal strategy(IMDCS)system is applied to detecting the stator faults of induction motor.Four features are obtained from the induction motor,and then these features are given to the IMDCS system.After the motor condition has been learned by the IMDCS system,the memory set obtained in the training stage can be used to detect any fault.The proposed method is experimentally implemented on the induction motor,and the experimental results show the applicability and effectiveness of the proposed method to the diagnosis of stator winding turn faults in induction motors.

Analysis of electromagnetic characteristics of typical faults in permanent magnet wind generators

Due to the harsh actual operating environment of the permanent magnet wind turbine,it is easy to break down and difficult to monitor.Therefore,the electromagnetic characteristics identification of major fault types of large-scale permanent magnet wind turbines is studied in this paper.The typical faults of rotor eccentricity,stator winding short circuit and permanent magnet demagnetization of permanent magnet wind turbines are analyzed theoretically.The wavelet analysis algorithm is used to decompose and reconstruct the abnormal electromagnetic signal waveform band,and the characteristic frequency of the electromagnetic signal is obtained when the fault occurs.In order to verify the effectiveness of the proposed method,a 3.680MW permanent magnet wind turbine was taken as the research object.Its physical simulation model was established,and an external circuit was built to carry out field co-simulation.The results show that the motor fault type can be determined by detecting the change rule of fault characteristic frequency in the spectrum diagram,and the electromagnetic characteristic analysis can be applied to the early monitoring of the permanent magnet wind turbine fault.

风力发电机定子绕组雷电过电压分布的仿真(英文)

This paper analyzes lightning surge on the stator windings of wind turbine generators.The path of lightning in the wind turbines was analyzed.An equivalent circuit model for megawatt direct-driven wind turbine system was developed,in which high-frequency distributed parameters of the blade conducts,tower,power cables and stator windings of generator were calculated based on finite element method,and the models of converter,grounding,loads, surge protection devices and power grid were established.The voltage distribution along stator windings,when struck by lightning with 10/350μs wave form and different amplitude current between 50 kA and 200 kA,was simulated using electro-magnetic transient analysis method.The simulated results show that the highest coil-to-core voltage peak appears on the last coil or near the neutral of stator windings,and the voltage distribution along the windings is nonuniform initially.The voltage drops of each coil fall from first to last coil,and the highest voltage drop appears on the first coil.The insulation damage may occur on the windings under lightning overvoltage.The surge arresters can restrain the lightning surge in effect and protect the insulation.The coil-to-core voltage in the end of windings is nearly 19.5 kV under the 200 kA lightning current without surge arresters on the terminal of generator,but is only 2.7 kV with arresters.

Fault Detection and Identification Using Deep Learning Algorithms in Induction Motors

Owing to the 4.0 industrial revolution condition monitoring maintenance is widely accepted as a useful approach to avoiding plant disturbances and shutdown.Recently,Motor Current Signature Analysis(MCSA)is widely reported as a condition monitoring technique in the detection and identification of individual andmultiple Induction Motor(IM)faults.However,checking the fault detection and classification with deep learning models and its comparison among them selves or conventional approaches is rarely reported in the literature.Therefore,in this work,wepresent the detection and identification of induction motor faults with MCSA and three Deep Learning(DL)models namely MLP,LSTM,and 1D-CNN.Initially,we have developed the model of Squirrel Cage induction motor in MATLAB and simulated it for single phasing and stator winding faults(SWF)using Fast Fourier Transform(FFT),Short Time Fourier Transform(STFT),and Continuous Wavelet Transform(CWT)to detect and identify the healthy and unhealthy conditions with phase to ground,single phasing and in multiple fault conditions using Motor Current Signature Analysis.The faults impact on stator current is presented in the time and frequency domain(i.e.,power spectrum).The simulation results show that the scalogram has shown good results in time-frequency analysis for fault and showing its impact on the energy of current during individual fault and multiple fault conditions.This is further investigated with three deep learning models(i.e.,MLP,LSTM,and 1D-CNN)for checking the fault detection and identification(i.e.,classification)improvement in a three-phase induction motor.By simulating the three-phase induction motor in various healthy and unhealthy conditions in MATLAB,we have collected current signature data in the time domain,labeled them accordingly and created the 50 thousand samples dataset for DL models.All the DL models are trained and validated with a suitable number of architecture layers.By simulation,the multiclass confusion matrix,precision,recall,and F1-score are obtained in several conditions.The result shows that the stator current signature of the motor can be used to detect individual and multiple faults.Moreover,deep learning models can efficiently classify the induction motor faults based on time-domain data of the stator current signature.In deep learning(DL)models,the LSTM has shown better accuracy among all other three models.These results show that employing deep learning in fault detection and identification of induction motors can be very useful in predictive maintenance to avoid shutdown and production cycle stoppage in the industry.

Impact of Motor Stator Winding Faults on Motor Differential-mode Impedance and Mode Transformation

Motor impedance and mode transformation have significant effects on the electromagnetic interference(EMI)generated in motor drive systems.Stator winding faults commonly cause motor failure;however,in their early stages,they may not affect the short-term operation of the motor.To date,EMI noise under the influence of premature stator winding faults has not been adequately studied,particularly the differential-mode(DM)noise due to the common-mode(CM)-to-DM transformation.This study investigates and quantifies the influence of stator winding faults on the motor DM impedance and mode transformation.First,the transmission line model of an induction motor is described based on the scattering(S)parameter measurements of each phase of the motor.It offers the flexibility to emulate different types of stator winding faults at specific locations and various severities,such that the impacts of the faults on the motor DM impedance can be easily estimated.Second,a test setup is proposed to quantify the CM-to-DM transformation due to the stator winding faults.The findings of this study reveal that even the early stages of stator winding faults can result in significant changes in the DM noise.

Modeling Common-Mode Current Due to Asymmetric Aging of Machine Winding Insulation

Machine stator winding insulation degradation is one of the main results of machine aging.It is non-negligible once this degradation process becomes asymmetric between phases.The traditional way to determine the insulation state of health is a partial discharge test.However,this method requires the system offline,which causes production loss and extra administrative burden.This paper presents an idea for better characterizing the insulation machine’s state of health using common-mode(CM)behavior in the machine-drive system.With the help of circuit decomposition methods and modeling tools,the CM quantities due to asymmetric aging show a unique characteristic that distinguishes itself from other differential-mode(DM)quantities in the equivalent circuit.It is shown effective to represent the asymmetric aging effect from the detection of system leakage current.This paper provides an analytical method to quantify this characteristic from mathematical approaches,and a proper approximation has been made on the CM equivalent model(CEM)such that the CM behavior is accurately characterized.The proposed method will serve the purpose of predicting machine abnormal behavior using the simple RLC circuit.Researchers can adapt this method to quantify and characterize the machine insulation state of health(SOH).