An improved complex ICA based method for wind farm harmonic emission levels evaluation

Zhao, Jinshuai; Hong-geng, Yang; Pan, Aiqiang; Xu, Fangwei

doi:10.1016/j.epsr.2019.106105

Cited by 22 publications

(13 citation statements)

References 19 publications

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“…In the multi-wind farm, the utility side contains other wind farm harmonic sources, which lead to the instability of the utility side harmonic sources. 22 Besides, the filters installed on the wind farm side result in the decrease of the customer side harmonic impedance. In this case, the accuracy of harmonic contributions evaluation can be improved only that both utility and customer side harmonic impedances are estimated.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Typical non-intrusive methods include fluctuation method, 13,14 linear regression method, [15][16][17] random vectors covariance method, 12 Cauchy mixed model-based method, 18 and independent component analysis (ICA) method. [19][20][21][22][23] The fluctuation method and linear regression method are poor in terms of resisting the variation of background harmonic inherently, which usually requires background harmonic source keep constant, and the customer side harmonic source is the dominant harmonic source at PCC. The method based on covariance characteristics of random vectors, which measures the utility harmonic impedance based on the utility harmonic source and the harmonic current at PCC are weakly correlated or independent, it can suppress the influence of background harmonic to some degree.…”

Section: Introductionmentioning

confidence: 99%

“…Harmonic load fluctuation is composed of fast-varying components and slow-varying components, and the fastvarying components are independent. 19 Therefore, the independent component analysis methods are proposed [20][21][22][23] based on this principle. These methods are free from the assumption that the customer side harmonic impedance is much large than that of the utility side, which can estimate the harmonic impedance accurately when the background harmonic fluctuates acutely.…”

Section: Introductionmentioning

confidence: 99%

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An impedance matrix constrained‐based method for harmonic emission level estimation

Zheng

Xiao

Wang

2020

Int Trans Electr Energ Syst

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Summary The harmonic impedances are important for both utility companies and their customers. The data have many uses, such as estimating harmonic emission levels, designing filters. Complex independent component analysis is a well‐accepted method for harmonic impedance estimation. However, a lack of data points for each calculation leads to wrong results. To increase the feasibility of this method, we proposed a constrained complex independent component analysis‐based method by adding the essential feature of the harmonic impedance matrix. Thus, the recovered source signals, which are highly consistent with the original source signals can be found when the data quantity for each calculation is little. The method has been verified using simulation studies and extensive field data. The results show that the proposed method can serve as a useful tool to support the harmonic impedance calculation task of utility companies and their customers.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An impedance matrix constrained‐based method for harmonic emission level estimation

Zheng

Xiao

Wang

2020

Int Trans Electr Energ Syst

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“…Existing methods for calculating utility-side harmonic impedance based on harmonic data at the PCC [9]- [23] primarily include the fluctuation method [9], linear regression method [10]- [12], independent random vector method [13]- [14], independent component analysis [15]- [20], and modern class method [21]- [23].…”

Section: Introductionmentioning

confidence: 99%

Novel Method for Estimating Utility Harmonic Impedance for a Direct-Drive Permanent Magnet Synchronous Generator Wind Farm

Zhou

et al. 2022

IEEE Access

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Accurate estimation of the utility harmonic impedance is significant for harmonic control and responsibility division. Traditional methods of estimating the utility harmonic impedance are valid on the premise that the harmonic impedance on the wind-farm side is significantly greater than that on the utility side. However, with the installation of filters and reactive power compensation devices in wind farms, traditional estimation methods are no longer applicable. The accuracy of most existing estimation methods depends only on the accuracy of the model. This can cause large errors in the estimation of the utility harmonic impedance on a wind farm. To address this challenge, this paper proposes a method for estimating the utility harmonic impedance for a direct-drive permanent magnet synchronous generator wind farm based on a modified model with measured data. The proposed method uses the measured data to modify a model to obtain a more accurate estimation of the utility harmonic impedance. The results of the simulation and case study indicated that the traditional estimation methods have large errors when the background harmonic fluctuates significantly or the wind-farm-side harmonic impedance is smaller than the utility-side harmonic impedance. In contrast, the proposed method has a higher accuracy for the above scenarios.INDEX TERMS D-PMSG wind farm, power quality, utility-side harmonic impedance， wind-farm-side harmonic impedance.

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“…Consequentially, different BSS techniques have been proposed and are well‐established in academic fields, such as telecommunications, audio signal separation, feature extraction, biomedical signal processing, pattern recognition, and financial time series analysis. 11 , 12 , 13 In EPS, more specifically in power quality issues, the BSS methods have been used for harmonic load identification, 14 , 15 , 16 in the separation of harmonic components 17 , 18 and to estimate the utility 9 , 19 , 20 , 21 and consumer 10 , 22 , 23 harmonic impedance at PCC.…”

Section: Introductionmentioning

confidence: 99%

Blind source separation methods applied to evaluate harmonic contribution

Pereira

Silva

Santos

2021

Int Trans Electr Energ Syst

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Summary One of the greatest challenges found in power quality has been, and still is, the establishing of a reliable noninvasive method for quantifying the harmonic contributions of an electric utility and a consumer at the point of common coupling (PCC). This difficulty arises from the fact that the harmonic distortions measured on the PCC are unidentifiable mixtures from both parties. Currently, some work has focused on exploring the use of blind source separation (BSS) techniques to promote the sharing of harmonic responsibilities in a noninvasive way. However, the usage of this methodology is very recent and still requires thorough evaluations, not only from the point of view of the BSS techniques available in the literature, but also from the perspective of the electrical power systems and the nature of their loads. Thus, seeking to contribute to the consolidation of the use of BSS techniques in the sharing of harmonic responsibilities, this work aimed to investigate the impact of the variation of harmonic current sources on the accuracy of BSS methods. In addition, three algorithms suitable for complex‐valued signals were evaluated computationally and experimentally; the results obtained computationally were compared with the results of the method based on the covariance characteristic of the random vectors, and the experimental results was compared to that obtained through an invasive procedure.

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An improved complex ICA based method for wind farm harmonic emission levels evaluation

Cited by 22 publications

References 19 publications

An impedance matrix constrained‐based method for harmonic emission level estimation

An impedance matrix constrained‐based method for harmonic emission level estimation

Novel Method for Estimating Utility Harmonic Impedance for a Direct-Drive Permanent Magnet Synchronous Generator Wind Farm

Blind source separation methods applied to evaluate harmonic contribution

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