BRNeural ‑ Artificial Neural Networks Simulator with Topology Multilayer Perceptron Using the Encog Framework

Mathias, Ivan; Zanlorensi, Luiz A.; Matyak, Luciano Bueno; Dias, Ariangelo Hauer; Duda, Robson Fernando; Afonso, Guilhermino Marcos Silva

doi:10.1109/tla.2016.7430094

Cited by 6 publications

(3 citation statements)

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“…The resilient backpropagation training algorithm allows reducing effects of the differences of the magnitudes of the partial derivatives. Since only the derivative signal can determine the direction of the weight update and not the magnitude of the derivative [29].…”

Section: Laboratory Setupmentioning

confidence: 99%

Analysis of training techniques of ANN for classification of insulators in electrical power systems

Stefenon

Branco

Nied

et al. 2020

IET Generation, Transmission & Distribution

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Section: Laboratory Setupmentioning

confidence: 99%

Analysis of training techniques of ANN for classification of insulators in electrical power systems

Stefenon

Branco

Nied

et al. 2020

IET Generation, Transmission & Distribution

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“…An incremental conductance (INC)-based maximum power point tracking (MPPT) algorithm is also has been implemented to track the maximum power point (MPP) of solar PV system under variations in solar irradiations. A large number of MPPT techniques have been developed and reported in the literature [35][36][37][38][39][40][41]. The performance of the given system is tested through simulations in MATLAB/Simulink platform and by using hardware prototype.…”

Section: Introductionmentioning

confidence: 99%

Intelligent approach‐based hybrid control algorithm for integration of solar photovoltaic system in smart grid environment

Chaudhary

Rizwan

2019

IET Smart Grid

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Integration of solar PV as a distributed generator (DG) require efficient and coordinated control measures for the proper synchronization. In this paper, a hybrid control algorithm for single stage solar photovoltaic (PV) system integrated with low voltage (LV)/medium voltage (MV) grid is proposed. The hybrid algorithm utilizes I cos ϕ technique and quasi-Newton backpropagation (QNBP) neural network (NN). The main contributions of the present work are: i) support the utility grid by feeding power and connected loads, ii) provide harmonics elimination, reactive power compensation, load balancing, iii) also works in power factor correction (PFC) and zero voltage regulation modes, iv) provides power quality improvement. The proposed control of grid tied PV system provides very fast response during static and dynamic conditions. The obtained results are compared with other well-established algorithms available in the literature. The comparison of the proposed algorithm is done on the basis of various parameters such as DC voltage undershoot and overshoot, settling time and THD in grid currents. The developed system is demonstrated in MATLAB/SIMULINK platform. Using the proposed algorithms there is significant improvement of 1.1% in total harmonic distortion (THD) of source current. The results of proposed controllers are experimentally validated on a developed laboratory protype.

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“…Hidden layer is the intermediate layer is also called as field layer. The feedforward BP technique having the ability to deal various complex non‐linear problems is reported in the literature [29]. This control algorithm is applied for several purposes such as the identification of user faces, mapping data, system fault analysis, industrial processes, CPDs [30, 31].…”

Section: Introductionmentioning

confidence: 99%

DSTATCOM employing hybrid neural network control technique for power quality improvement

Kumar

Mangaraj

2017

IET Power Electronics

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A new hybrid control technique called gradient descent back propagation (GDBP)-based i cos ϕ for a three phase twolevel distribution static compensator (DSTATCOM) to perform the functions such as harmonic mitigation, power factor correction under reactive loads, which further reduces the DC link voltage across the self-supported capacitor of voltage source converter (VSC). The weighted value of fundamental active and reactive components of load currents are extracted using the proposed control technique to generate the reference source currents. Furthermore, these currents are used to trigger the VSC of the DSTATCOM. The effectiveness of this control technique is demonstrated through simulation using MATLAB/SIMULINK and sim power system tool boxes. The real-time implementation of DSTATCOM is also realised by real-time digital simulator. These results reveal the robustness of the proposed DSTATCOM as it is showing outstanding harmonic compensation capabilities under the various loading conditions and keeping the total harmonics distortion of the source current well <5%, the limit imposed by IEEE-519 standard.

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BRNeural ‑ Artificial Neural Networks Simulator with Topology Multilayer Perceptron Using the Encog Framework

Cited by 6 publications

References 0 publications

Analysis of training techniques of ANN for classification of insulators in electrical power systems

Analysis of training techniques of ANN for classification of insulators in electrical power systems

Intelligent approach‐based hybrid control algorithm for integration of solar photovoltaic system in smart grid environment

DSTATCOM employing hybrid neural network control technique for power quality improvement

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