Fast multilayer perceptron neural network‐based control algorithm for shunt compensator in distribution systems

Ahmad, Md. Tausif; Kumar, Narendra; Singh, Bhim

doi:10.1049/iet-gtd.2016.0328

Cited by 18 publications

(18 citation statements)

References 18 publications

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“…As the structure of MLP is flexible, we need to define the architecture of the MLP before training it for optimal weights/biases. [19] MLP can be used for classification and function approximation, and the performance can be superior to support vector machine, if the architecture is defined properly. …”

Section: Classifier-multilayer Perceptronmentioning

confidence: 99%

A Fruit Sensing and Classification System by Fractional Fourier Entropy and Improved Hybrid Genetic Algorithm

Lu²,

Wang³

et al. 2017

The Proceedings of the 5th International Conference on Industrial Application Engineering 2017

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It remains a challenge to classify different categories of fruits because of the similarities of shape, color, and texture among them. We presented a novel approach in order to classify fruits accurately and efficiently based on computer vision techniques. We obtained the coefficients using fractional Fourier transform. The entropies extracted from the coefficients were fed into the classifier as the features. A multilayer perceptron optimized by an improved hybrid genetic algorithm was used as the classifier. The experiment results on 1653 fruit images demonstrated that the proposed method achieved an overall accuracy of 89.59%, which was superior to the state-of-art approaches. Our method is efficientive in identifying fruit categories.

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Section: Classifier-multilayer Perceptronmentioning

confidence: 99%

A Fruit Sensing and Classification System by Fractional Fourier Entropy and Improved Hybrid Genetic Algorithm

Lu²,

Wang³

et al. 2017

The Proceedings of the 5th International Conference on Industrial Application Engineering 2017

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“…Several papers have discussed the mitigation of PQ problems [10][11][12][13][14][15][16][17][18][19][20][21]. Khadkikar et al [10,11] have discussed conventional algorithms such as synchronous reference frame theory (SRFT) and instantaneous reactive power theory for shunt power compensation.…”

Section: Introductionmentioning

confidence: 99%

“…These conventional techniques require phase locked loop (PLL) based synchronisation and transformations (αβ & dq) . Newer and adaptive control techniques based on the ability of networks to learn, organise, train and perform better successively have also been suggested recently by many researchers [12][13][14][15][16][17][18]. Badoni et al [12] have discussed adaptive neuro fuzzy inference least mean square (LMS) based control algorithm, Hannan et al have discussed FLC-based control algorithm [13].…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of Legendre‐based neural network controller in grid‐connected PV systems

Arora

Singh

2019

IET Renewable Power Generation

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This study presents the development of Legendre-based functional neural network algorithm for shunt compensation in photovoltaic (PV)-based grid-connected system. The controller is developed for improving power quality (PQ) and the compensator is controlled to work in current control mode. It injects the requisite compensating current depending on the nature of the load current. The compensator is also interfaced with PV source and the controller design incorporates its contribution too. Some of the PQ problems studied include curtailment of harmonics, providing necessary reactive power, power factor improvement and so on. Results under distorted grid, varying solar irradiation and variety of loads have been presented. The proposed algorithm is designed using non-linear functional Legendre expansion of load current and has not been used for compensation or PQ problem alleviation till date. Both simulation and experimental results verify that the proposed algorithm performs far better than the adaptive popular backpropagation multilayer perceptron neural network, recurrent neural network and non-adaptive conventional synchronous reference frame theory based techniques.

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“…However, the non-linearity of source currents caused by load currents at the distribution level has been mitigated by injecting current components at point of common coupling (PCC) by the DSTATCOM [5][6][7]. The compensation current components at PCC using DSTATCOM are provided through the control algorithms reported in [8][9][10][11]. Therefore, a substantial research is carried out by researchers for improvement in the performance of DSTATCOM with the help of control algorithms on the aspects of computation time, reliability and simplicity.…”

Section: Introductionmentioning

confidence: 99%

“…ANN-based algorithms reported in the literature, have used BP training with slow convergence and conductance estimation to improve the control functions of DSTATCOM [3][4][5][6][7][8][9][10][11][12]. However, the fast convergence of ANN trained network has improved the performance of DSTATCOM more effectively compared to conventional and adaptive methods [18].…”

Section: Introductionmentioning

confidence: 99%

Generalised neural network‐based control algorithm for DSTATCOM in distribution systems

Ahmad

Kumar

Singh

2017

IET Power Electronics

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This study presents a new concept to control a distribution static compensator (DSTATCOM) based on generalised neural network in a three-phase power distribution system. Artificial neural network (ANN)-based controllers play the vital role in the performance improvement of DSTATCOM. However, their application is limited by the increase in complexity as well as the computational time. The proposed generalised neural network algorithm is a combination of Gaussian, sigmoidal and linear transfer functions within a layer to improve the DSTATCOM control strategy. This algorithm estimates the amplitude of the wattful and wattless current components of the load currents for harmonics elimination and reactive power compensation by the DSTATCOM. The algorithm is developed in MATLAB. The case studies validate its superiority over ANN-based control algorithms. The proposed method needs a less number of training patterns and unknown weights compared to other algorithms which reduces the complexity and the computational time. It also improves the performance of DSTATCOM estimating the weights and its learning online which is of the main merits of this algorithm. Its other inherent advantages are ease in design, robustness and its adaptivity with dynamics of load at utility end.

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Fast multilayer perceptron neural network‐based control algorithm for shunt compensator in distribution systems

Cited by 18 publications

References 18 publications

A Fruit Sensing and Classification System by Fractional Fourier Entropy and Improved Hybrid Genetic Algorithm

A Fruit Sensing and Classification System by Fractional Fourier Entropy and Improved Hybrid Genetic Algorithm

Design and implementation of Legendre‐based neural network controller in grid‐connected PV systems

Generalised neural network‐based control algorithm for DSTATCOM in distribution systems

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