Optimization of harmonics with active power filter based on ADALINE neural network

Sujith, Mini; Padma, S.

doi:10.1016/j.micpro.2019.102976

Cited by 18 publications

(10 citation statements)

References 12 publications

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“…In order to quantify the results obtained with the ANN-APF, two essential indices were used: the total harmonic distortion of the supply current, ITHD, defined in (16), and the power factor, PF, defined in (17), measured before and after compensation.…”

Section: Results In Practical Casesmentioning

confidence: 99%

“…However, in these adaptive cases, the appropriate choice of the learning parameter for each problem is very important in order to avoid convergence problems; this is one of its weaknesses. However, recent works [15][16][17] illustrate that researchers continue to propose advances in the application of the Adaline. Other ANN types have also been applied to the APF, but with less relevance.…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear Loads Compensation Using a Shunt Active Power Filter Controlled by Feedforward Neural Networks

2021

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The shunt active power filter (SAPF) is a widely used tool for compensation of disturbances in three-phase electric power systems. A high number of control methods have been successfully developed, including strategies based on artificial neural networks. However, the typical feedforward neural network, the multilayer perceptron, which has provided effective solutions to many nonlinear problems, has not yet been employed with satisfactory performance in the implementation of the SAPF control for obtaining the reference currents. In order to prove the capabilities of this simple neural topology, this work describes a suitable strategy of use, based on the accurate estimation of the Fourier coefficients corresponding to the fundamental harmonic of any distorted voltage or current. An effective training method has been developed, consisting of the use of many distorted patterns. The new generation procedure uses random combinations of multiple harmonics, including the possible nominal frequency deviations occurring in real power systems. The design of the generation of reference signals through computations based on the Fourier coefficients is presented. The objectives were the harmonic mitigation and power factor correction. Practical cases were tested through simulation and also by using an experimental platform, showing the feasibility of the proposal.

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Section: Results In Practical Casesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonlinear Loads Compensation Using a Shunt Active Power Filter Controlled by Feedforward Neural Networks

2021

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“…The proposed ADNN has a good agreement with the similar established work. Recently, Sujith and Padma (2020) utilized ADNN to estimate the harmonics for Pulse Width Modulation. The proposed ADNN optimized the crucial parameter of the system such as load voltage, current and reactive power.…”

Section: Introductionmentioning

confidence: 99%

Logic Learning in Adaline Neural Network

Norani¹,

Kasihmuddin²,

Mansor³

et al. 2021

JST

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In this paper, Adaline Neural Network (ADNN) has been explored to simulate the actual signal processing between input and output. One of the drawback of the conventional ADNN is the use of the non-systematic rule that defines the learning of the network. This research incorporates logic programming that consists of various prominent logical representation. These logical rules will be a symbolic rule that defines the learning mechanism of ADNN. All the mentioned logical rule are tested with different learning rate that leads to minimization of the Mean Square Error (MSE). This paper uncovered the best logical rule that could be governed in ADNN with the lowest MSE value. The thorough comparison of the performance of the ADNN was discussed based on the performance MSE. The outcome obtained from this paper will be beneficial in various field of knowledge that requires immense data processing effort such as in engineering, healthcare, marketing, and business.

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“…The effective wind speed horizon has been forecasted with higher level of correctness as shown in the work of Madhiarasan [ 18 ]. Some works leveraged the Adaline neural network approach in various forecasting tasks such as in power filter optimization [ 19 ] and interior permanent magnet synchronous motor (IPMSM) parameter prediction [ 20 ]. Both work of Sujith and Padma [ 19 ] and Wang et al [ 20 ] utilized an Adaline neural network as a classifier for the parameters involved in industrial control problem.…”

Section: Introductionmentioning

confidence: 99%

“…Some works leveraged the Adaline neural network approach in various forecasting tasks such as in power filter optimization [ 19 ] and interior permanent magnet synchronous motor (IPMSM) parameter prediction [ 20 ]. Both work of Sujith and Padma [ 19 ] and Wang et al [ 20 ] utilized an Adaline neural network as a classifier for the parameters involved in industrial control problem. The deep convolutional neural network (DCNN) is a variant of powerful ANN, with multi-layer hidden neurons that play important role for the data prediction.…”

Section: Introductionmentioning

confidence: 99%

Energy Based Logic Mining Analysis with Hopfield Neural Network for Recruitment Evaluation

Mohd Jamaludin,

Mohd Kasihmuddin,

Md Ismail

et al. 2020

Entropy

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An effective recruitment evaluation plays an important role in the success of companies, industries and institutions. In order to obtain insight on the relationship between factors contributing to systematic recruitment, the artificial neural network and logic mining approach can be adopted as a data extraction model. In this work, an energy based k satisfiability reverse analysis incorporating a Hopfield neural network is proposed to extract the relationship between the factors in an electronic (E) recruitment data set. The attributes of E recruitment data set are represented in the form of k satisfiability logical representation. We proposed the logical representation to 2-satisfiability and 3-satisfiability representation, which are regarded as a systematic logical representation. The E recruitment data set is obtained from an insurance agency in Malaysia, with the aim of extracting the relationship of dominant attributes that contribute to positive recruitment among the potential candidates. Thus, our approach is evaluated according to correctness, robustness and accuracy of the induced logic obtained, corresponding to the E recruitment data. According to the experimental simulations with different number of neurons, the findings indicated the effectiveness and robustness of energy based k satisfiability reverse analysis with Hopfield neural network in extracting the dominant attributes toward positive recruitment in the insurance agency in Malaysia.

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Optimization of harmonics with active power filter based on ADALINE neural network

Cited by 18 publications

References 12 publications

Nonlinear Loads Compensation Using a Shunt Active Power Filter Controlled by Feedforward Neural Networks

Nonlinear Loads Compensation Using a Shunt Active Power Filter Controlled by Feedforward Neural Networks

Logic Learning in Adaline Neural Network

Energy Based Logic Mining Analysis with Hopfield Neural Network for Recruitment Evaluation

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