A self-synchronized ADALINE network for on-line tracking of power system harmonics

Sarkar, Apurba; Choudhury, Saurav Roy; Sengupta, Samarjit

doi:10.1016/j.measurement.2011.01.009

Cited by 44 publications

(17 citation statements)

References 17 publications

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“…A modified ADALINE structure has been used in the paper [9] for online tracking of harmonics. Self-synchronized ADALINE network for power system harmonics estimation relies on the Levenberg Gradient Descent method for updating the system parameters.…”

Section: Literature Reviewmentioning

confidence: 99%

Kalman Filters for Parameter Estimation of Nonstationary Signals

Nanda¹

2018

Kalman Filters - Theory for Advanced Applications

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An adaptive Taylor-Kalman filter with PSO tuning for tracking nonstationary signal parameters in a noisy environment with primary focus on time-varying power signals has been presented in this piece of work. In order to deal with the dynamic envelope of the power signal, second-order Taylor expansion has been used such that the Taylor coefficients are updated with the PSO-tuned Taylor-Kalman Filter algorithm. In addition to this, for fast convergence, a self-adaptive particle swarm optimization technique has been used for obtaining the optimal values of model and measurement error covariances of the Kalman filter. The proposed algorithm is linear and therefore has less computational burden, which is easier to be implemented on a hardware platform like DSP processor or FPGA. The proposed PSO-tuned Taylor-Kalman filter exhibits robust tracking capabilities even under changing signal dynamics, immune to critical noise conditions, harmonic contaminations, and also reveals excellent convergence properties.

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Section: Literature Reviewmentioning

confidence: 99%

Kalman Filters for Parameter Estimation of Nonstationary Signals

Nanda¹

2018

Kalman Filters - Theory for Advanced Applications

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“…The ADALINE neural network [18,19], is a linear combiner that uses the LMS algorithm for its operation. Fig.…”

Section: Adaline Neural Network Algorithmmentioning

confidence: 99%

“…Artificial neural networks are successfully applied to power systems, especially for harmonic extraction [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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A comparative study of harmonic currents extraction by simulation and implementation

Merabet¹,

Saad²,

Abdeslam³

et al. 2013

International Journal of Electrical Power & Energy Systems

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a b s t r a c tThe aim of the present work is to obtain a perfect compensation by extracting accurate harmonic currents. The objective is to avoid the consequences due to the presence of disturbances in the power system. A comparative study of harmonic currents extraction by simulation and implementation is carried out for two different techniques. The first technique is based on the instantaneous powers, taking advantage of the relationship between current and the power transformed from the supply source to the loads. The second is based on ADALINE neural network. The neural method can estimate the harmonic terms individually and online, therefore, the APF can realise a selective compensation. The developed architectures are validated by computer simulation and experimental tests. The algorithms are implemented in the dSPACE Board in order to show the effectiveness and capability of each technique. The results have demonstrated that the speed and the accuracy of the ADALINE can improve greatly the performances of active power filters.

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“…A recent study proposed a fast adaptive linear neural network (ADALINE) based time-domain approach for extracting the fundamentals from the distorted current [3]- [6]. The weights of ADALINE are generally updated using the least mean square (LMS) recursive algorithm [14]- [16].…”

Section: Introductionmentioning

confidence: 99%

Harmonic Elimination and Reactive Power Compensation with a Novel Control Algorithm based Active Power Filter

Garanayak¹,

Panda²

2015

Journal of Power Electronics

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This paper presents a power system harmonic elimination using the mixed adaptive linear neural network and variable step-size leaky least mean square (ADALINE-VSSLLMS) control algorithm based active power filter (APF). The weight vector of ADALINE along with the variable step-size parameter and leakage coefficient of the VSSLLMS algorithm are automatically adjusted to eliminate harmonics from the distorted load current. For all iteration, the VSSLLMS algorithm selects a new rate of convergence for searching and runs the computations. The adopted shunt-hybrid APF (SHAPF) consists of an APF and a series of 7 th tuned passive filter connected to each phase. The performance of the proposed ADALINE-VSSLLMS control algorithm employed for SHAPF is analyzed through a simulation in a MATLAB/Simulink environment. Experimental results of a real-time prototype validate the efficacy of the proposed control algorithm.

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A self-synchronized ADALINE network for on-line tracking of power system harmonics

Cited by 44 publications

References 17 publications

Kalman Filters for Parameter Estimation of Nonstationary Signals

Kalman Filters for Parameter Estimation of Nonstationary Signals

A comparative study of harmonic currents extraction by simulation and implementation

Harmonic Elimination and Reactive Power Compensation with a Novel Control Algorithm based Active Power Filter

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