A high performance generalized discontinuous PWM algorithm

Hava, Ahmet M.; Kerkman, R.J.; Lipo, T.Α.

doi:10.1109/apec.1997.575750

Cited by 130 publications

(146 citation statements)

References 18 publications

(26 reference statements)

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“…However, the difference in motor loss between SPWM, SVPWM and DPWM is within 0.5 percent of the total motor loss. These results imply that the negative effect of DPWM on output waveform quality has negligible regarding impact on machine efficiency: DPWM does not generate higher machine loss than the other PWM schemes, even though its output waveform quality is theoretically lower [7]. …”

Section: A Operation At Rated Frequency (50hz)mentioning

confidence: 76%

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Investigation and Comparison of Inverter-Fed Induction Machine Loss

Zhan

Knight

et al. 2008

2008 IEEE Industry Applications Society Annual Meeting

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This paper presents an investigation into the losses in a three-phase induction motor under different pulse width modulation (PWM) excitation conditions. The impacts of Sinusoidal PWM, Space Vector PWM and Discontinuous PWM on machine loss are compared and studied. Finite element analysis simulations are employed to predict the machine losses with the loss breakdown analysis under different PWM schemes. Direct Calorimetric measurements are utilized to verify the finite element modeling and provide direct quantifications of machine loss under modern PWM techniques.

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Section: A Operation At Rated Frequency (50hz)mentioning

confidence: 76%

“…Among these three, DPWM has been shown to be capable of reducing inverter loss by up to 20 percent compared to the other PWM schemes [7], [8], [9]. Fourier analysis of the switching frequency content shows that the harmonic content of DPWM is higher than SPWM and SVPWM [3].…”

Section: Introductionmentioning

confidence: 96%

Investigation and Comparison of Inverter-Fed Induction Machine Loss

Zhan

Knight

et al. 2008

2008 IEEE Industry Applications Society Annual Meeting

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“…In other words just by changing the zero state at any interval γ = α ranging from 0 0 60 0 to different DPWM methods can be generated. All the existing DPWM methods use clamping sequences 012 and 721 [5]- [7]. The so called DPWM methods popularly by the names DPWM0, DPWM1, DPWM2, and DPWM3 can be generated with this method by choosing a proper value of γ [5].…”

Section: B Proposed Adpwm Algorithmmentioning

confidence: 99%

“…Though conventional space vector pulse width modulation technique (CSVPWM) could able to make the switching frequency of the PWM inverter constant [3], [4], steady state ripple in torque, flux and current are still high predominantly when the drive is operating at high speeds. To improve the performance of the drive during high speed regions and to reduce the inverter switching losses, several discontinuous PWM methods (DPWM) are proposed in [5] - [6]. In this paper an ADPWM algorithm is proposed for reduced switching losses.…”

Section: Introductionmentioning

confidence: 99%

Minimum Switching Loss ADPWM Algorithm Based DTC Induction Motor Drive Operating at Near Rated Speeds

Gowri¹,

Reddy

Babu

2009

2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies

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In this paper a high performance advanced discontinuous PWM (ADPWM) algorithm for direct torque controlled (DTC) induction motor drive is proposed. The proposed ADPWM method uses a special category of DPWM sequences (0121 and 7212), because of which the performance of the drive improves in terms of the inverter switching losses. Also, significant reduction in line current distortion during high speed operations is achieved.

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“…The main power converters' losses terms are switching and conduction ones. The latter can be significantly reduced, once the switching frequency of the converter is fixed, by adopting suitable pulsewidth modulation (PWM) techniques, such as the discontinuous (D-PWM) ones [14][15][16]. The electric generator losses terms depend on the kind of machine, and correspondingly any electrical loss minimisation technique (ELMT) [17][18][19] does.…”

mentioning

confidence: 99%

Induction machines sensors‐less wind generator with integrated intelligent maximum power point tracking and electric losses minimisation technique

Pucci

2015

IET Control Theory & Applications

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This study presents a high-performance wind generation system with induction machine (IM), specifically devised with the target of maximising the efficiency of the electromechanical conversion, and contemporary minimising the number of the system sensors and their cost. To this aim, the control system has been integrated, from one side, with an intelligent maximum power point tracking (MPPT) technique, so to make the generator track the power available in the wind, from the other side with techniques for the minimisation of the electrical losses (ELMT). Particularly, the power converters' switching losses have been reduced adopting a discontinuous pulsewidth modulation, while the IM overall losses have been reduced by a suitable electric losses minimisation technique. Contemporary, to reduce costs and increase the reliability of the system, the system has been devised as a fully sensors-less generation unit, meaning that both the wind speed and the machine speed sensors are not present. The anemometer has been substituted by the wind speed estimator integrated in the MPPT, based on the growing neural gas (GNG) network. The encoder has been substituted with an intelligent IM speed estimator, the so called MCA EXIN + reduced order observer (ROO). The performance of the adopted technique has been verified experimentally on a suitably devised test set-up. IntroductionNowadays, wind energy is becoming an ever more exploited renewable energy source [1,2]. Modern solutions range from permanent magnet synchronous machines, to doubly-fed induction machine or squirrel cage induction machine (IM), each supplied by a suitable power converter [3,4]. Whatever is the machine chosen, from the control point of view, the most challenging issues to be solved are described in the following. First, suitable maximum power point techniques (MPPT) should be developed, able to quickly track any variation of the wind speed, and correspondingly of its power, inside the variable speed range of the wind turbine, avoiding perturb and observer (P&O) [5][6][7] or hill and climb (H&C) algorithms, inevitably requiring very long times to converge in systems characterised by high inertias, as wind generators typically are, and causing persistent undesired oscillations around the MPPs. Even other methods, for example those exploiting proper wind turbine modelling [8][9][10][11] to compute the optimal power reference speed of the drive, can be problematic since they often require the knowledge of a wind speed sensor to work, whose presence can significantly increase the cost and reduce the reliability of the drive system, Some intelligent neural based MPPTs have been proposed in [12,13], respectively, adopting a two radial basis function NNs and a multi-layer perceptron trained by a backpropagation (BPN) algorithm as a virtual anemometer. Both these approaches present the following significant drawbacks: (1) with such approaches, no evidence of the correct learning of inverse characteristic of the wind turbine exists and, (2) a very complex and compu...

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A high performance generalized discontinuous PWM algorithm

Cited by 130 publications

References 18 publications

Investigation and Comparison of Inverter-Fed Induction Machine Loss

Investigation and Comparison of Inverter-Fed Induction Machine Loss

Minimum Switching Loss ADPWM Algorithm Based DTC Induction Motor Drive Operating at Near Rated Speeds

Induction machines sensors‐less wind generator with integrated intelligent maximum power point tracking and electric losses minimisation technique

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