Several variants of Kalman Filter algorithm for power system harmonic estimation

Singh, Santosh Kumar; Sinha, Nilotpal; Goswami, Arup Kumar; Sinha, Nidul

doi:10.1016/j.ijepes.2015.12.028

Cited by 56 publications

(34 citation statements)

References 20 publications

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“…To obtain a continuous prediction, the optimal filters are used: the Wiener-Hopf filter [15] for predicting stationary processes and the Kalman filter [16] for non-stationary processes. The principal difficulties in applying these filters are the cumbersomeness of computational procedures and the need for representative statistical data.…”

Section: Research Of Existing Solutions Of the Problemmentioning

confidence: 99%

Forecasting of the performance of the shipboard electric power system on the basis of the artificial neural network

Гвоздева

Lukovtsev

Tierielnyk

2017

TAPR

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Section: Research Of Existing Solutions Of the Problemmentioning

confidence: 99%

Forecasting of the performance of the shipboard electric power system on the basis of the artificial neural network

Гвоздева

Lukovtsev

Tierielnyk

2017

TAPR

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“…For the LMS-based harmonic identification method, the harmonic estimation accuracy, especially for the harmonic phase, descended along with the harmonic order [12]. The Kalman filter based method firstly modeled a state space model of the acceleration response which contains higher harmonics [13,14]. By estimating the state of the model, the harmonic amplitudes and phases can be computed.…”

Section: Introductionmentioning

confidence: 99%

Acceleration Harmonic Estimation in a Hydraulic Shaking Table Using Water Cycle Algorithm

Yao

Wan

2018

Shock and Vibration

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Hydraulic shaking table is mainly used to stimulate the desired vibration environment and evaluate the shock resistance of structure. However, due to the inherent nonlinearities of the hydraulic shaking table, the acceleration response displays amplitude attenuation and phase delay for sinusoidal excitation signal. The distorted response degrades the control performance and even leads to an increase in system instability. In this paper, the water cycle algorithm (WCA), a recently developed metaheuristic method, is developed to estimate the harmonic information such as amplitude and phase. The basic idea of the proposed algorithm is inspired from nature and based on the observation of water cycle process and how rivers and streams flow to the sea in the nature world. The estimation process based on WCA is sequentially updating the weight vector of the signal. The amplitude and phase of fundamental as well as each harmonic can be achieved when the objective function is minimized. Simulation and experimental results demonstrate that the proposed harmonic estimation algorithm has good real-time performance, fast convergence, and excellent accuracy.

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“…Hence, due to the significantly increasing use of power electronic devices, power quality has become an important issue in the power system operation. Power system harmonics are one of the important indices of the quality of power, and thus, it is necessary to estimate the power system harmonic components to provide high-quality of power [5][6][7][8]. In many power system applications, fast Fourier transform (FFT) and discrete Fourier transform (DFT) have been extensively used to estimate the harmonic components of voltage or current signals owing to its fast computation and simplicity [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…To this end, the selection of the process and measurement noise covariance matrices is a critical issue for the use of the Kalman filter. In order to solve these problems, modified Kalman filter-based harmonics estimation techniques have been proposed [2,4,8,[16][17][18][19][20][21][22][23]. In [16], it was demonstrated that the estimation result depends more on the ratio of the process and measurement noise covariance matrices than on each of their values.…”

Section: Introductionmentioning

confidence: 99%

Robust Estimation and Tracking of Power System Harmonics Using an Optimal Finite Impulse Response Filter

2018

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In this paper, a robust estimation method for estimating the power system harmonics is proposed by using the optimal finite impulse response (FIR) filter. The optimal FIR filter is applied to the state space representation of the noisy current or voltage signal and estimates the magnitude and phase-angle of the harmonic components. Due to the FIR structure, the FIR filter is more robust against model uncertainty than the Kalman filter. Hence, the FIR filter-based method will give a more robust solution for the power system harmonic estimation than the previous Kalman filter-based approaches. The performance and robustness of the proposed method are verified through simulation. Moreover, the proposed method is employed in the power conditioning system to estimate the harmonic components and total harmonic distortions.

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Several variants of Kalman Filter algorithm for power system harmonic estimation

Cited by 56 publications

References 20 publications

Forecasting of the performance of the shipboard electric power system on the basis of the artificial neural network

Forecasting of the performance of the shipboard electric power system on the basis of the artificial neural network

Acceleration Harmonic Estimation in a Hydraulic Shaking Table Using Water Cycle Algorithm

Robust Estimation and Tracking of Power System Harmonics Using an Optimal Finite Impulse Response Filter

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