Hardware implementation of neural network controlled optimal PWM inverter using TMS320C30 board

Mohaddes, M.; Gole, A.M.; McLaren, P.G.

doi:10.1109/wescan.1997.627133

Cited by 9 publications

(3 citation statements)

References 6 publications

(2 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, in [25], a clonal selection algorithm is introduced to find optimum solution with a random disturbance selection operation; in [26], sliding mode variable structure control is proposed based on closed loop algorithm for better performance in harmonics elimination; in [27], Homotopic fixed-point approach is utilized to find the initial values of the roots and conduct cubic iterations to refine the roots; and in [28], a feed forward artificial neural network is applied for selected harmonics elimination. In [29], m dimensional space is introduced to eliminate m harmonics.…”

Section: V Dc Mπmentioning

confidence: 99%

A Universal Selective Harmonic Elimination Method for High-Power Inverters

Ahmadi

Zou

et al. 2011

IEEE Trans. Power Electron.

153

View full text Add to dashboard Cite

In medium-/high-power inverters, optimal pulsewidth modulation (OPWM) is often used to reduce the switching frequency and at the same time, realize selective harmonic elimination (SHE). For both two-level and multilevel inverters, most selective harmonic elimination (SHE) studies are based on solving multiple variable high-order nonlinear equations. Furthermore, for multilevel inverters, SHE has been often studied based on the assumption of balanced dc levels and single switching per level. In this paper, the authors further developed harmonics injection and equal area criteria-based four-equation method to realize OPWM for two-level inverters and multilevel inverters with unbalanced dc sources. For the cases, where only small number of voltage levels are available, weight oriented junction point distribution is utilized to enhance the performance of the four-equation method. A case study of multilevel inverter at low-modulation index is used as an example. Compared with existing methods, the proposed method does not involve complex equation groups and is much easier to be utilized in the case of large number of switching angles, or multiple switching angles per voltage level in multilevel inverters.

show abstract

Section: V Dc Mπmentioning

confidence: 99%

A Universal Selective Harmonic Elimination Method for High-Power Inverters

Ahmadi

Zou

et al. 2011

IEEE Trans. Power Electron.

153

View full text Add to dashboard Cite

show abstract

“…By switching the IGBTs at these angles the desired harmonics will disappear in the output voltage and the amplitude of the fundamental will be at the ordered leve1 [6,7].…”

Section: The Artificial Neural Network (Ann)mentioning

confidence: 99%

A neural network controlled optimal pulse-width modulated STATCOM

Mohaddes¹,

Gole

McLaren

1999

IEEE Trans. Power Delivery

Self Cite

View full text Add to dashboard Cite

The paper describes a technique to control the harmonic output of a Static Synchronous Compensator (STAT-COM) using a Pulse Width Modulation (PWM) scheme with a minimal number of additional switchings. A neural network algorithm is developed to define the switching instants. This technique offers an alternative to the multi-pulse techniques that require complex magnetic circuit arrangements as well as to the conventional P W M technology that requires a large number of switchings. &?wort& Static Synchronous Compensator, STATCOM, Optimal P W M , Artificial Neural Networks, ANN, harmonic reduction, reactive power.

show abstract

“…Particular function of neural network is modified by adjusting the weighting factors among the training algorithm to converge to the variation between target and its output (threshold). In this approach Adaptive online drive process via neural network is proposed to generate PWM signal [24], [10] for regulating the outputs voltages [23] and input current of IUT in regard of load and grid disturbances. Weights and polarizations are adaptively adjusted per the control process.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Universal Transformers Online Adaptive Control via Artificial Neural Network

Sadeghi

Gholami

2012

AMR

View full text Add to dashboard Cite

Adaptive control is a novel methodology introducing for dynamic identification and control of nonlinear system in case of unknown parameters and absence of precise mathematical model. The Artificial Neural Networks (ANNs) suggest the parallel algorithm in resolving paradigms and result on a robust control fashion in which learning algorithm resembling to the biological brain. Back propagation algorithm is proposed for updating the ANN weighting factors through the on line learning procedure. This research is carry out to investigate the ANN trained algorithm to elaborate the switching angle signals for controlling the Intelligent Universal transformer (IUT) in input and output stages. IUT motive the Advanced Distribution Automation (ADA) with the new invention in automation, management and control. ANN online adaptive scheme is developed for controlling the input current and output voltages of IUT with the major benefits and service option advantages, comprising from a voltage regulation in real time operation, capability on providing three phase power outputs in case of one phase input, energy storage capability,48V DC output option, harmonic Filtering, reliable divers power 240V AC 400HZ for communication usage together with two 240 V AC 60 HZ outputs, automatic sag correction, dynamic system monitoring and system robustness in term of input and load disturbances.

show abstract

Hardware implementation of neural network controlled optimal PWM inverter using TMS320C30 board

Cited by 9 publications

References 6 publications

A Universal Selective Harmonic Elimination Method for High-Power Inverters

A Universal Selective Harmonic Elimination Method for High-Power Inverters

A neural network controlled optimal pulse-width modulated STATCOM

Intelligent Universal Transformers Online Adaptive Control via Artificial Neural Network

Contact Info

Product

Resources

About