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Öğe Analysis of Effects of Prime Mover Speed And Exciting Capacitor Value on Output Voltage of Generator of Self-Excited Squirrel Cage Induction Generator(IEEE, 2014) Arabaci, HayriIn this study, impedance model of the induction generator was used. Steady state self-excitation voltages were calculated, taking into account the rotor parameter variations with the frequency. The minimum value of capacitance were obtained to initiate self-excitation by simulation results. The effects of speed and exciting capacitor on output voltage were analyzed.Öğe Analysis of Rotor Faults Effects on Submersible Induction Motor' Efficiency(INT ASSOC ENGINEERS-IAENG, 2013) Arabaci, Hayri; Bilgin, OsmanThis paper analyzes effects of squirrel cage faults on submersible induction motors efficiency at steady-state condition. There are a lot of studies about effects of the cage faults on motor performance. Especially, the effects of the cage faults on the motor parameters such as current, torque and speed are well known. Unlike the literature, cage fault effects on efficiency are analyzed in this study. Furthermore, fluctuations and mean value changes resulting from the rotor faults are ranked according to size of these faults. Healthy and five different faults were investigated by using 10 HP, 25 HP, 30 HP and 50 HP submersible induction motors in both simulations and experiments. Time stepping finite element method solution was used to compute motor quantities in the simulation. Good agreement was achieved between simulation and experimental results. The effects of rotor faults on motor efficiency were clearly ranked according to size of faults.Öğe An artificial neural network approach for sensorless speed estimation via rotor slot harmonics(TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY, 2014) Arabaci, HayriIn this paper, a sensorless speed estimation method with an artificial neural network for squirrel cage induction motors is presented. Motor current is generally used for sensorless speed estimation. Rotor slot harmonics are available in the frequency spectrum of the current. The frequency components of these determined harmonics are used to estimate the speed of the motor in which the number of rotor slots is given. In the literature, individual algorithms have been used to calculate the speed from the slot harmonics. Unlike the literature, in the proposed method, an artificial neural network is used to extract the speed from the rotor slot harmonic components in the spectrum. This experimental study is carried out to prove the method under steady-state conditions. The experimental results show that the proposed method is suitable for speed estimation and its average error is below 1.5 rpm.Öğe Detection of Rotor Bar Faults by Using Stator Current Envelope(INT ASSOC ENGINEERS-IAENG, 2011) Arabaci, Hayri; Bilgin, OsmanThe paper presents detection of rotor bar faults at steady state operation in squirrel cage induction motor by using stator current envelope. Three different rotor faults and healthy motor conditions were investigated in experiments. One of the stator currents has been used in the investigation of effects of rotor faults on the current envelope. The ratios of fluctuation of the envelope were used as feature of fault conditions for diagnosis. In the literature a lot of studies are available about diagnosis and detection rotor many different analysis and feature extraction methods such as motor current signature analysis (MCSA), fast Fourier transform (FFT). Unlike the literature, in the present study the stator current envelopes are used. The use of the current envelope does not affect the performance of the fault detection, while requiring much less computation and low cost in implementation, which would make it easier to implement in embedded systems for condition monitoring of motor.Öğe The detection of rotor faults by using Short Time Fourier Transform(IEEE, 2007) Arabaci, Hayri; Bilgin, OsmanIn this paper an experimental study detecting of rotor faults in three-phase squirrel cage induction motors by means of Short Time Fourier Transform (STFT) is presented. The frequency spectrum of motor line current is exploited for the detection. By obtaining a number of frequency spectrums from a current data with STET and averaging these spectrums, faults are diagnosed instead of Fast Fourier Transform frequently applied at the detection of broken rotor faults in the literature. Five different faulted rotors are investigated. These faults are one bar with high resistance of the rotor, one broken bar of the rotor, two broken bars of the rotor, three broken bar of the rotor and broken end ring of the rotor. Artificial Neural Network is used for classification of faults. Test results show that this method increase the accuracy of the fault diagnose.Öğe The detection of rotor faults in the manufacturing of submersible induction motor(IEEE, 2007) Arabaci, Hayri; Bilgin, Osman; Urkmez, AbdullahIn this study, rotor faults detection in submersible induction motors which is used at deep well submersible pumps is presented by analyzing stator current. In some production squirrel cage rotor bars are welded to end rings by argon welding. While the welding sometimes some bars are not connected to end rings ore bad connection have been occurred. This affects the motor performance. For not preventing the production speed motor tests should be made quickly. In this study practical results are taken from POLMOT factory which produce submersible induction motors. When the motor construction is finished its robustness is tested with no load test. Their stator current time frequency domain is made and its current spectrum is investigated. According to current spectrum analysis its fault and robustness is determined. For classification Artificial Neural Network (ANN) is used. A decision mechanism that uses ANN result matrixes is occurred to detect faulted rotors.Öğe The detection of rotor faults using artificial neural network(IEEE, 2006) Arabaci, Hayri; Bilgin, OsmanThe detection of broken rotor bars in tree-phase squirrel cage induction motors by means of current signature analysis is presented. In order to diagnose faults, a Neural Network approach is used. At first the data of different rotor faults are achieved. The effects of different rotor faults on current spectrum, in comparison with other fault conditions, are investigated via calculating Power Spectrum Density (PSD). Training the Neural Network discern between "healthy" and "faulty" motor conditions by using experimental data in case of healthy and faulted motor. The test results clearly illustrate that the stator current signature can be used to diagnose faults of squirrel cage rotor.Öğe Diagnosis of Broken Rotor Bar Faults by Using Frequency Spectrum of Stator Current Envelope(IEEE, 2012) Arabaci, Hayri; Bilgin, OsmanThe paper presents squirrel cage induction motor rotor bar faults detection and classification by using stator current envelope at steady state operation. One of the stator currents has been used in the investigating of effects of rotor faults on the current envelopes. Fluctuations of the envelope were used as features of faults conditions for diagnosis. For feature extraction, frequency spectrum of the envelope of current was obtained by fast Fourier Transform (FFT). Significant picks in the spectrum were used to discern "healthy" and "faulty" motor conditions. The motor conditions were classified by Artificial Neural Network (ANN). In experiments three different rotor faults and healthy motor conditions were investigated by 30 HP, 8 '', with 18 bars, 380V, 2 poles and 50 Hz squirrel cage submersible induction motor. The proposed decision structure detects and classifies rotor bar faults with 100% accuracy.Öğe Efficiency Analysis of Submersible Induction Motor with Broken Rotor Bar(SPRINGER, 2014) Arabaci, Hayri; Bilgin, OsmanThis study analyzes effects of squirrel cage faults on submersible induction motors efficiency at steady-state condition. There are a lot of studies about effects of the cage faults on motor performance. Especially, the effects of the cage faults on the motor parameters such as current, torque and speed are well known. Unlike the literature, cage fault effects on efficiency are analyzed in this study. Furthermore, fluctuations and mean value changes resulting from the rotor faults are ranked according to size of these faults. Healthy and five different faults were investigated by using 10, 25, 30 and 50 HP submersible induction motors in both simulations and experiments. Time stepping finite element method solution was used to compute motor quantities in the simulation. Good agreement was achieved between simulation and experimental results. The effects of rotor faults on motor efficiency were clearly ranked according to size of faults.Öğe A Genetic Algorithm Approach for Sensorless Speed Estimation by using Rotor Slot Harmonics(INT ASSOC ENGINEERS-IAENG, 2015) Arabaci, HayriIn this paper a sensorless speed estimation method with genetic algorithm for squirrel cage induction motors is presented. Sensorless speed estimation methods generally are based on motor current. Frequency spectrum of the current has components representing rotor slot harmonics. These frequency components are used to estimate the speed of the motor given the number of rotor slots. In the literature, individual algorithms have been used to calculate the speed from the rotor slot harmonics. Unlike the literature, genetic algorithm is used to extract the motor speed from rotor slot harmonics components in the proposed method. The experimental study was carried out to prove this method under steady state conditions. The experimental results show that the proposed method is suitable for sensorless speed estimation and its average error is the 0.29 rpm at 50 Hz.Öğe Neural Network Classification and Diagnosis of Broken Rotor Bar Faults by Means of Short Time Fourier Transform(INT ASSOC ENGINEERS-IAENG, 2009) Arabaci, Hayri; Bilgin, OsmanIn this paper an experimental study of classification and diagnosis of different number of broken rotor bars and broken end-ring in the three-phase squirrel cage induction motors is presented. Six different faulted rotors are investigated. These faults are one, two, three broken bars, broken end-ring, a bar with high resistance and healthy rotor. The base structure of the study consist of current signal analysis (CSA), feature extraction, Artificial Neural Network (ANN) and diagnosis algorithm. The motor current signal is used for obtaining of effects of broken bars and end-ring in the rotor. To get sight of the effects the current signal that is in the time domain is transformed time-frequency domain via Short Time Fourier Transform (STFT). And the spectrums are averaged and normalized on the time axis. The rotor cage faults are classified with ANN by using these spectrums. And result matrixes of ANN are considered improved decision structure. Thus the faulted rotors are diagnosed at 100% accuracy and classified 98,33% accuracy.Öğe A novel motor speed calculation method using square wave speed sensor signals via fast Fourier transform(TUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEY, 2012) Arabaci, Hayri; Bilgin, OsmanThis paper presents a novel motor speed measurement method and experimental results using the fast Fourier transform (FFT). Motor speed is obtained using the square wave output signal of a speed sensor. In the proposed method, the speed can be measured in a wide range, and transient speed changes can also be clearly observed. The experiments were conducted during start-up and in steady state. The sampled speed data were transformed to frequency spectrums using the FFT. The frequency, which corresponds to the maximum amplitude in the spectrum, was used to calculate the motor speed. The test results show that the proposed method is very useful.Öğe Rotor Bar Fault Diagnosis by Using Power Factor(INT ASSOC ENGINEERS-IAENG, 2011) Arabaci, Hayri; Bilgin, Osman; Urkmez, AbdullahThe paper presents detection and classification of rotor bar faults at steady state operation in squirrel cage induction motor by using power factor. One phase current and voltage of the stator coils were used to calculate the power factor. To investigate effects of rotor faults on the power factor, its frequency spectrum was obtained by fast Fourier Transform (FFT). Significant picks in the spectrum were used to discern "healthy" and "faulty" motor conditions. The motor conditions were classified by Artificial Neural Network (ANN). In experiments three different rotor faults and healthy motor conditions were investigated by 30 HP, 8", with 18 bars, 380V, 2 poles and 50 Hz squirrel cage submersible induction motor. The proposed decision structure detects and classifies rotor bar faults with 100% accuracy.
