A New Transient Power Quality Disturbances Detection Using Strong Trace Filter

He, Shaoming; Li, Kaicheng; Zhang, Ming

doi:10.1109/tim.2014.2326762

Cited by 23 publications

(18 citation statements)

References 20 publications

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“…Step 2: Weights calculation. As illustrated in equation (14), particle weights can be recursively computed. Once we have the particle weights v (i) k , we can calculate the normalized weights using the following equation…”

Section: Proposed Pf Algorithmmentioning

confidence: 99%

A rank correlation coefficient based particle filter to estimate parameters in non-linear models

Meng

2019

International Journal of Distributed Sensor Networks

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Particle filtering algorithm has found an increasingly wide utilization in many fields at present, especially in non-linear and non-Gaussian situations. Because of the particle degeneracy limitation, various resampling methods have been researched. The estimation process of particle filtering algorithm is a series of weighted calculation processes, which can be regarded as weighted data fusion. This article proposed an improved particle filtering algorithm combining with different rank correlation coefficients to overcome the shortcomings of degeneracy. By simulating iteration operation in MATLAB, it discovers that the proposed algorithm provides better accuracy in comparison with particle filtering, Gaussian sum particle filter, and Gaussian mixture sigma-point particle filter in Gaussian mixture noise. A practical seven-dimensional harmonic model is also implemented in the simulation. After comparing the performances of different algorithms, we found that the proposed method had more accuracy than the widely used extended Kalman filtering algorithm.

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Section: Proposed Pf Algorithmmentioning

confidence: 99%

A rank correlation coefficient based particle filter to estimate parameters in non-linear models

Meng

2019

International Journal of Distributed Sensor Networks

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“…Using the filter model in [8], and the time-varying amplitude, phase, and frequency of the i th component can be obtained by A i k , i ω k , and θ i n − i ω k k t, respectively.…”

Section: Strong Trace Filter For the Detection Of Transient Disturmentioning

confidence: 99%

“…The STF was proved useful in transient power quality disturbance (PQD) detection [8]. Interharmonic is a stationary power disturbance, which cannot raise a fading factor change of STF.…”

Section: Introductionmentioning

confidence: 99%

A Parameterization Power Data Compress Using Strong Trace Filter and Dynamics

Zhang

Tian

et al. 2015

IEEE Trans. Instrum. Meas.

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This paper proposed a parameterization power data compress using strong trace filter (STF) and dynamics (Dyn). The STF estimates the fundamental and harmonic component parameters. When there are only fundamental and harmonic components, the power data frame is compressed in parameter form. When there are other disturbances, the fading factors of STF and Dyn are used to recognize possible transient disturbances and interharmonics, respectively. By subtracting the fundamental and harmonic components, the residue of disturbances is coded by lifting wavelet transform (LWT). Then, the estimated parameters and LWT coefficients are used to reconstruct the power signal. The proposed method places emphasis on two important steps of parameterization power data compress and has a better performance than that of the related methods when the compress ratio is the same.

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“…PQ issues in the power systems can be divided into steady-state and transient problems [5]. Transient PQ disturbances (PQDs), including voltage sag, voltage swell, voltage interruption, oscillation transient, and impulsive transient [5], have strong impacts on both the grid and customers, with potentially serious consequences [6]. Therefore, significant researches on transient PQD analysis are expected to increase in the immediate future.…”

Section: Introductionmentioning

confidence: 99%

Transient power quality disturbance denoising and detection based on improved iterative adaptive kernel regression

Wang

Zhou

2018

J. Mod. Power Syst. Clean Energy

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The denoising and detection of transient disturbances are two important subjects for power quality monitoring and analysis. To effectively denoise and detect transient disturbances under noisy conditions, an improved iterative adaptive kernel regression method is proposed in this paper. The proposed method has advantages in that it does not need to estimate the noise variance or a filter threshold, and has both denoising and detection capabilities for transient disturbances. Simulation results demonstrate that the proposed method provides excellent denoising effects, which can not only suppress noise effectively but also preserve disturbance features of sudden change points well. Additionally, it provides good detection and location performance for single and combined transient disturbances, even under strong noise conditions. Finally, the effectiveness of the proposed method is further verified by using real disturbance data.

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A New Transient Power Quality Disturbances Detection Using Strong Trace Filter

Cited by 23 publications

References 20 publications

A rank correlation coefficient based particle filter to estimate parameters in non-linear models

A rank correlation coefficient based particle filter to estimate parameters in non-linear models

A Parameterization Power Data Compress Using Strong Trace Filter and Dynamics

Transient power quality disturbance denoising and detection based on improved iterative adaptive kernel regression

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