A novel approach to capture the maximum power from variable speed wind turbines using PI controller, RBF neural network and GSA evolutionary algorithm

Assareh, Ehsanolah; Biglari, Mojtaba

doi:10.1016/j.rser.2015.07.034

Cited by 76 publications

(32 citation statements)

References 29 publications

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“…RBFN is a type of feed-forward neural network, which uses radial basis network as an activation function [13]. The radial basis network is configured using the distance between the input and the Electronics 2018, 7, 20 7 of 17 prototype vector.…”

Section: Radial Basis Function Network Algorithmmentioning

confidence: 99%

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Neural Network Based Maximum Power Point Tracking Control with Quadratic Boost Converter for PMSG—Wind Energy Conversion System

et al. 2018

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This paper proposes an artificial neural network (ANN) based maximum power point tracking (MPPT) control strategy for wind energy conversion system (WECS) implemented with a DC/DC converter. The proposed topology utilizes a radial basis function network (RBFN) based neural network control strategy to extract the maximum available power from the wind velocity. The results are compared with a classical Perturb and Observe (P&O) method and Back propagation network (BPN) method. In order to achieve a high voltage rating, the system is implemented with a quadratic boost converter and the performance of the converter is validated with a boost and single ended primary inductance converter (SEPIC). The performance of the MPPT technique along with a DC/DC converter is demonstrated using MATLAB/Simulink.

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Section: Radial Basis Function Network Algorithmmentioning

confidence: 99%

“…Generated voltage and current is fed to the input neurons of the RBFN, which is used to compute the duty cycle as the output neuron. The basic nodes of operation are characterized by three layers, namely, input layer, a hidden layer and outer layer as shown in Figure 4 [13].…”

Section: Radial Basis Function Network Algorithmmentioning

confidence: 99%

Neural Network Based Maximum Power Point Tracking Control with Quadratic Boost Converter for PMSG—Wind Energy Conversion System

et al. 2018

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“…This capability allows them to run at wind speeds above or below their synchronous speed. Similarly, it keeps the stator voltage almost constant by control of the rotor voltage to preserve turbine synchronization with power system while wind speed varies …”

Section: Impacts Of Wt and Fcl On Current And Voltage Signals; Investmentioning

confidence: 99%

Evaluation of directional relay algorithms in presence of wind turbines and fault current limiters

Samet

Ghanbari

Ashtiani

et al. 2018

Int Trans Electr Energ Syst

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Summary Connection of distributed generations (DGs) to distribution networks changes their radial construction to mesh type and increases fault current level. In order to prevent over current relay tripping in case of out‐of‐zone faults, direction of faults must be determined. On the other hand, fault current limiters (FCLs) are a suitable technique to reduce the issue of fault current increasing. In this paper, it is confirmed that operation of FCLs affects voltage and current signals, drastically. Since fault direction is detected by directional relays using these signals, hence, FCLs can affect the performance of the relays. In the other hand, if the added DG is a wind turbine, it may affect the directional relays performance due to intricate fault behavior of wind turbines. To investigate the mentioned hypothesis, some of directional relaying algorithms are evaluated in presence of wind turbine and FCL.

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“…The radial basis network is determined by the distance between the input and the prototype vector [19]. The RBFN is similar to the multi-layer perceptron (MLP) network.…”

Section: Radial Basis Function Networkmentioning

confidence: 99%

Coordinated Control Strategies for a Permanent Magnet Synchronous Generator Based Wind Energy Conversion System

2017

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Abstract:In this paper, a novel co-ordinated hybrid maximum power point tracking (MPPT)-pitch angle based on a radial basis function network (RBFN) is proposed for a variable speed variable pitch wind turbine. The proposed controller is used to maximise output power when the wind speed is low and optimise the power when the wind speed is high. The proposed controller provides robustness to the nonlinear characteristic of wind speed. It uses wind speed, generator speed, and generator power as input variables and utilises the duty cycle and the reference pitch angle as the output control variables. The duty cycle is used to control the converter so as to maximise the power output and the reference pitch angle is used to control the generator speed in order to control the generator output power in the above rated wind speed region. The effectiveness of the proposed controller was verified using MATLAB/Simulink software.

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A novel approach to capture the maximum power from variable speed wind turbines using PI controller, RBF neural network and GSA evolutionary algorithm

Cited by 76 publications

References 29 publications

Neural Network Based Maximum Power Point Tracking Control with Quadratic Boost Converter for PMSG—Wind Energy Conversion System

Neural Network Based Maximum Power Point Tracking Control with Quadratic Boost Converter for PMSG—Wind Energy Conversion System

Evaluation of directional relay algorithms in presence of wind turbines and fault current limiters

Coordinated Control Strategies for a Permanent Magnet Synchronous Generator Based Wind Energy Conversion System

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