Improved neural network current regulator for VS-PWM inverters

Kaźmierkowski, Marian P.; Sobczuk, D.L.

doi:10.1109/iecon.1994.397970

Cited by 11 publications

(8 citation statements)

References 15 publications

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“…The parallel processing of data, the ability of learning, sturdiness, and generality are the main advantages of using a neural network as a controlling tool for PWM. All these abilities of NN are effective for a current controller [121][122][123][124][125].…”

Section: Neural Network (Nn) Based Current Controllermentioning

confidence: 99%

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Review on Current Control Techniques of Grid Connected PWM-VSI Based Distributed Generation

2019

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Nowadays, new trends in the industry of electricity generation are to enhance the power generation by employing distributed generation (DG) system which is mostly based on renewable generation sources such as wind, solar, etc. However, many power quality problems could arise on existing grid when DG is connected or the operation of distributed energy resources (DER) is not controlled properly. That's why, while integrating DG with the power grid, a seamless attention should be given to power generation and safe running of system. Several methods, having a diverse concept, have been divided into two main sets: linear and nonlinear controllers. The first group comes with PI controller and parameter feedback controller, and control by means of constant frequency with predictive techniques. The second group includes hysteresis current control and on-line optimization for predictive controllers. Additionally, new current control techniques with neural networks and fuzzy based controllers are also discussed. Selected methods associate the arrangement for the sake to demonstrate the described groups of the controller.

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Section: Neural Network (Nn) Based Current Controllermentioning

confidence: 99%

“…The NN is a simple technique when applied with PWM as shown in Fig.6(a), which excludes the need for on-line calculations required to implement optimal DM-CC [125], as shown in Fig. 5(b).…”

Section: Neural Network (Nn) Based Current Controllermentioning

confidence: 99%

Review on Current Control Techniques of Grid Connected PWM-VSI Based Distributed Generation

2019

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“…10(a), is shown in Fig. 10(b) [117]. The three layers of the feedforward NN with sigmoidal nonlinearity-before using as a controller-were trained using a back propagation algorithm with randomly selected data from the output pattern of the optimal controller of Fig.…”

Section: ) Nn's Controllersmentioning

confidence: 99%

Current control techniques for three-phase voltage-source PWM converters: a survey

Kaźmierkowski

Malesani

1998

IEEE Trans. Ind. Electron.

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1,814

671

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The aim of this paper is to present a review of recently used current control techniques for three-phase voltagesource pulsewidth modulated converters. Various techniques, different in concept, have been described in two main groups: linear and nonlinear. The first includes proportional integral stationary and synchronous) and state feedback controllers, and predictive techniques with constant switching frequency. The second comprises bang-bang (hysteresis, delta modulation) controllers and predictive controllers with on-line optimization. New trends in the current control-neural networks and fuzzy-logicbased controllers-are discussed, as well. Selected oscillograms accompany the presentation in order to illustrate properties of the described controller groups.

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“…In the past, ANNs, especially feed-forward ANNs, have been applied to various control, identification, and estimation schemes in power electronics and drives [1]- [6]. Recently, the interest of implementing large parallel neural networks on a single field-programmable gate array (FPGA) device using stochastic computation theory has been growing [7]- [11].…”

Section: Introductionmentioning

confidence: 99%

A Stochastic Digital Implementation of a Neural Network Controller for Small Wind Turbine Systems

Zhang

Foo

2006

IEEE Trans. Power Electron.

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This letter presents a reconfigurable hardware implementation of feed-forward neural networks using stochastic techniques. The design is based on the stochastic computation theory to approximate the nonlinear sigmoid activation functions with reduced digital logic resources. The large parallel neural network structure is then implemented on a reconfigurable field-programmable gate array (FPGA) device with high fault tolerance capability. The method is applied to a neural-network based wind-speed sensorless control of a small wind turbine system. The experimental results confirmed the validity of the developed stochastic FPGA implementation. The general design method can be extended to include other power electronics applications with different feed-forward neural network structures.Index Terms-Field-programmable gate array (FPGA), neural networks, small wind turbine, stochastic computing.

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Improved neural network current regulator for VS-PWM inverters

Cited by 11 publications

References 15 publications

Review on Current Control Techniques of Grid Connected PWM-VSI Based Distributed Generation

Review on Current Control Techniques of Grid Connected PWM-VSI Based Distributed Generation

Current control techniques for three-phase voltage-source PWM converters: a survey

A Stochastic Digital Implementation of a Neural Network Controller for Small Wind Turbine Systems

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