Analysis of voltage unbalance effects on induction motors with open and closed slots

Donolo, Pablo; Bossio, Guillermo R.; Angelo, Cristian H. De

doi:10.1016/j.enconman.2010.10.045

Cited by 34 publications

(28 citation statements)

References 19 publications

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“…Figure 4a reveals the no-load air-gap magnetic field density (B m ) and fundamental no load magnetic field density (B m1 ) in Figure 4b, utilized from Labels (21) and (27). The waveforms for a range of 360 θ e (electrical degree) rotor displacement are shown in Figure 4.…”

Section: Analytical Concept Of the Iron Loss Model With Harmonic Lossmentioning

confidence: 99%

“…The remaining harmonic component waveform of B m can be calculated through the difference between B m1 and B m [28]. Comparison of no-load air-gap magnetic field density waveforms using Equations (21) and (27), for (a) fundamental no-load magnetic induction (Bm1), and (b) no-load airgap magnetic induction (Bm).…”

Section: Analytical Concept Of the Iron Loss Model With Harmonic Lossmentioning

confidence: 99%

“…In addition, the location of search coils at armature tooth tips and at various parts of the rotor and the location of the d-axis and the d-axis pickup in the tested generator. The used methodology is validated in [27].…”

Section: Analytical Concept Of the Iron Loss Model With Harmonic Lossmentioning

confidence: 99%

“…Regarding the armature reaction air-gap magnetic induction, the equivalent factors are affiliated with the machines' parameters like Equations (27) and (28), which are coupled with the d-q axis equivalent circuit as:…”

Section: Analytical Concept Of the Iron Loss Model With Harmonic Lossmentioning

confidence: 99%

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Iron Loss Prediction Using Modified IEM-Formula during the Field Weakening for Permanent Magnet Synchronous Machines

2017

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During field weakening operation time (FWOT), the total iron loss rises and affects the accuracy of loss prediction and efficiency, especially if a large range of FWOT exists due to a large voltage drop that was rooted from the resistance of the used material. Iron loss prediction is widely employed in investigations for a fast electrical machine analysis using 2D finite element analysis (FEA). This paper proposes harmonic loss analytically by a steady-state equivalent circuit with a novel procedure. Consideration of skin effects and iron saturation are utilized in order to examine the accuracy through the relative error distribution in the frequency domain of each model from 50 to 700 Hz. Additionally, this comparative study presents a torque-frequency-field density calculation over each single term of the modified institute of electrical machines formula (IEM-Formula). The proposed analytical calculation is performed using 2D FEA for a classic and modified IEM-Formula along with experimental verifications on a surface-mounted permanent magnet synchronous generator (PMSG) for a wind generation application.

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Section: Analytical Concept Of the Iron Loss Model With Harmonic Lossmentioning

confidence: 99%

Section: Analytical Concept Of the Iron Loss Model With Harmonic Lossmentioning

confidence: 99%

Section: Analytical Concept Of the Iron Loss Model With Harmonic Lossmentioning

confidence: 99%

Section: Analytical Concept Of the Iron Loss Model With Harmonic Lossmentioning

confidence: 99%

See 2 more Smart Citations

Iron Loss Prediction Using Modified IEM-Formula during the Field Weakening for Permanent Magnet Synchronous Machines

2017

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“…Also they determined that the financial losses were caused by unbalanced voltage of the power supply using an analytical-statistical method. Donolo et al [14] investigated the influence of voltage unbalance on the performance of induction motors. They use sequence equivalent circuits to determine the increase in losses of the induction motor.…”

Section: Introductionmentioning

confidence: 99%

Efficiency improvement in induction motor by slitted tooth core design

Yetgin

Turan

2017

Teh. vjesn.

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Original scientific paper In this study, a new design was suggested in order to improve the performance of induction motors. In the proposed design, slits were applied in the middle of stator and rotor teeth. In these slitted models, the depth and width of slits were optimized with Finite Element Method Magnetics (FEMM) software using Finite Elements Method (FEM). To show performance improvement, suggested motor model and a reference motor model were compared at the rated operating point for the values such as input and output power, input current, power factor, efficiency and losses. Because of the decreases in both iron and copper losses, total losses in motor were reduced and the efficiency for rated operating point was improved by 1,869%. In the modelling, 3 kW squirrel-cage induction motor was used. Keywords: induction motor efficiency; motor losses; slitted tooth core design Porast učinkovitosti asinkronih motora pomoću dizajna jezgre s procijepljenim zubomIzvorni zanstveni članak U ovom istraživanju predložen je novi dizajn kako bi se poboljšala učinkovitost rada asinkronih motora. U predloženom dizajnu primjenjeni su procijepi u sredini zubi statora i rotora. U ovim procijepljenim modelima dubina i širina procijepa su optimizirani uz pomoć programa Magnetska metoda konačnih elemenata (FEMM) i koristeći Metodu konačnih elemenata (FEM). Za prikaz poboljšanja izvedbe, predloženi model motora i referentni model motora su uspoređeni u definiranim pogonskim tačkama za vrijednosti kao što su ulazna i izlazna snaga, ulazna struja, faktor snage, efikasnost i gubitci. Zbog smanjenja gubitaka željeza i bakra, ukupni gubitak u motoru je smanjen, a povećana je efikasnost za definiranu pogonsku tačku za 1,869%. U modeliranju korišten je 3 kW kavezni asinkroni motor.

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Enhancing the response of thyristor‐controlled reactor using neural network

Ragab

Ghaeb

Al‐Naimi

2019

Int Trans Electr Energ Syst

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Summary In this paper, a neural network controller is proposed to retrieve the voltage balancing conditions in three‐phase power systems. The neural network is suggested to calculate the required set of firing angles for the thyristor‐controlled reactor accurately to balance the three‐load voltages quickly. The proposed controller is fed by different parameters within different feeding techniques, namely, root mean square (RMS) values of the three load voltages, RMS value of the space vector signal calculated from the three load voltages, and the RMS values of both the three load voltages and their associated space vector. The intentions of the proposed techniques are to combine between reducing the number of measured parameters and providing the controller with qualitative data about system status. The influence of the measured parameters on the neural network performance is examined by calculating the regression coefficients through several test cases. Accordingly, only the effective parameters are utilized to reduce the neural network complexity and to enhance the controller response time. Additionally, the calculations of the controller input parameters are made in terms of space vector cycle, which is half of system sinusoidal cycle. Consequently, the calculation time is reduced significantly. The Aqaba‐Qatrana‐South Amman power system is considered and modeled as a real case study. In addition, several test cases have been conducted to test and validate the ability of the proposed neural network controller in retrieving the voltage balance conditions precisely and quickly. The results have revealed the ability of the proposed neural network controller to calculate the firing angles quickly within 10 milliseconds and achieve very low voltage unbalance factor.

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Analysis of voltage unbalance effects on induction motors with open and closed slots

Cited by 34 publications

References 19 publications

Iron Loss Prediction Using Modified IEM-Formula during the Field Weakening for Permanent Magnet Synchronous Machines

Iron Loss Prediction Using Modified IEM-Formula during the Field Weakening for Permanent Magnet Synchronous Machines

Efficiency improvement in induction motor by slitted tooth core design

Enhancing the response of thyristor‐controlled reactor using neural network

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