PD Source Diagnosis and Localization in Industrial High-Voltage Insulation System via Multimodal Joint Sparse Representation

Wan, Liangtian; Han, Guangjie; Shu, Lei; Chan, Sammy; Feng, Naixing

doi:10.1109/tie.2016.2520905

Cited by 61 publications

(23 citation statements)

References 29 publications

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“…A similar scheme has been proposed in our previous work [ 32 , 33 ]. It is used for the localization of patients in the emergency healthcare system and partial-discharge source diagnosis and localization in an industrial high-voltage insulation system.…”

Section: A1 the Localization Methods Based On Multiple Vmimomentioning

confidence: 99%

“…It is used for the localization of patients in the emergency healthcare system and partial-discharge source diagnosis and localization in an industrial high-voltage insulation system. The effectiveness of the proposed scheme in [ 32 , 33 ] has been verified. The real test has been done for partial-discharge source localization [ 33 ].…”

Section: A1 the Localization Methods Based On Multiple Vmimomentioning

confidence: 99%

“…The effectiveness of the proposed scheme in [ 32 , 33 ] has been verified. The real test has been done for partial-discharge source localization [ 33 ]. In order to have an intuitive impression, we give the localization method of the proposed scheme as follows.…”

Section: A1 the Localization Methods Based On Multiple Vmimomentioning

confidence: 99%

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Wearable Sensor Localization Considering Mixed Distributed Sources in Health Monitoring Systems

Wan

Han

Wang

et al. 2016

Sensors

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In health monitoring systems, the base station (BS) and the wearable sensors communicate with each other to construct a virtual multiple input and multiple output (VMIMO) system. In real applications, the signal that the BS received is a distributed source because of the scattering, reflection, diffraction and refraction in the propagation path. In this paper, a 2D direction-of-arrival (DOA) estimation algorithm for incoherently-distributed (ID) and coherently-distributed (CD) sources is proposed based on multiple VMIMO systems. ID and CD sources are separated through the second-order blind identification (SOBI) algorithm. The traditional estimating signal parameters via the rotational invariance technique (ESPRIT)-based algorithm is valid only for one-dimensional (1D) DOA estimation for the ID source. By constructing the signal subspace, two rotational invariant relationships are constructed. Then, we extend the ESPRIT to estimate 2D DOAs for ID sources. For DOA estimation of CD sources, two rational invariance relationships are constructed based on the application of generalized steering vectors (GSVs). Then, the ESPRIT-based algorithm is used for estimating the eigenvalues of two rational invariance matrices, which contain the angular parameters. The expressions of azimuth and elevation for ID and CD sources have closed forms, which means that the spectrum peak searching is avoided. Therefore, compared to the traditional 2D DOA estimation algorithms, the proposed algorithm imposes significantly low computational complexity. The intersecting point of two rays, which come from two different directions measured by two uniform rectangle arrays (URA), can be regarded as the location of the biosensor (wearable sensor). Three BSs adopting the smart antenna (SA) technique cooperate with each other to locate the wearable sensors using the angulation positioning method. Simulation results demonstrate the effectiveness of the proposed algorithm.

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Section: A1 the Localization Methods Based On Multiple Vmimomentioning

confidence: 99%

Section: A1 the Localization Methods Based On Multiple Vmimomentioning

confidence: 99%

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Wearable Sensor Localization Considering Mixed Distributed Sources in Health Monitoring Systems

Wan

Han

Wang

et al. 2016

Sensors

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“…Trabalhos como (Chan et al, 2015) e (Wan and Chan, 2016) abordam alguns métodos on-line propostos pelo IEEE-Std-1434. São utilizadas ondas sonoras ou eletromagnéticas emitidas no momento da centelha elétrica da descarga para indicar sua presença e encontrar sua localização.…”

Section: Introductionunclassified

Sensores Virtuais para Detecção de Descargas Parciais em Transformadores de Potência

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et al. 2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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Partial discharge is a common type of fault in power transformers. It indicates the state of the insulation material. Identifying their existence is not an easy task. Some techniques such as chromatographic analysis of the refreshing oil have been used for offline detection of partial discharges. In this paper we present a Virtual Sensor scheme for online partial-discharge detection. The virtual sensor consists of two steps. First, the pre-processing step consists in cleaning the data, and applying wavelet transforms. Second, a neural classifier, namely, a radialbasis-function neural network (RBF), an adaptive neuro-fuzzy system (ANFIS) or an evolving neuro-fuzzy system (DENFIS) is employed. In this paper partial discharges are simulated using a lumped-parameter model. For performance analysis, disturbances are considered. System results have shown to be feasible and robust. The use of virtual sensors for online detection of partial discharges is encouraging. Resumo: Descarga parcialé um tipo de falta comum em transformadores de potência. Ela indica o estado do material isolante dos equipamentos e sua identificação nãoé uma tarefa fácil. Algumas técnicas como análise cromatográfica doóleo refrigerante têm sido utilizadas na detecção das descargas parciais off-line. Neste artigoé apresentada uma proposta de Sensor Virtual para detecção de descargas parciais on-line. O sensor virtualé constituído de duas seções. Primeiro, o pré-processamento, onde os dados de correntes do transformador são tratados e transformadas wavelet são empregadas. Segundo, o classificador inteligente, constituído por uma rede neural de base radial (RBF), um sistema neuro-fuzzy adaptativo (ANFIS) ou um sistema neuro-fuzzy evolutivo (DENFIS). Neste artigo as descargas parciais foram simuladas utilizando um modelo de parâmetros concentrados. Para análise de desempenho são consideradas situações de distúrbio. Os resultados obtidos mostram-se viáveis, assim como a utilização de sensores virtuais para detecção on-line de descargas parciais.

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“…The research of SSR algorithms has been mainly focused on two core procedures: the sparse representation model and the reconstruction algorithm. Extensive studies have shown that the performance of SSR algorithms is superior compared to the traditional subspace-based DOA estimation algorithms in the environments of low SNR or a small number of snapshots [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Weighted Block Sparse Recovery Algorithm for High Resolution DOA Estimation with Unknown Mutual Coupling

et al. 2018

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Based on weighted block sparse recovery, a high resolution direction-of-arrival (DOA) estimation algorithm is proposed for data with unknown mutual coupling. In our proposed method, a new block representation model based on the array covariance vectors is firstly formulated to avoid the influence of unknown mutual coupling by utilizing the inherent structure of the steering vector. Then a weighted 1l -norm penalty algorithm is proposed to recover the block sparse matrix, in which the weighted matrix is constructed based on the principle of a novel Capon space spectrum function for increasing the sparsity of solution. Finally, the DOAs can be obtained from the position of the non-zero blocks of the recovered sparse matrix. Due to the use of the whole received data of array and the enhanced sparsity of solution, the proposed method effectively avoids the loss of the array aperture to achieve a better estimation performance in the environment of unknown mutual coupling in terms of both spatial resolution and accuracy. Simulation experiments show the proposed method achieves better performance than other existing algorithms to minimize the effects of unknown mutual coupling.

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PD Source Diagnosis and Localization in Industrial High-Voltage Insulation System via Multimodal Joint Sparse Representation

Cited by 61 publications

References 29 publications

Wearable Sensor Localization Considering Mixed Distributed Sources in Health Monitoring Systems

Wearable Sensor Localization Considering Mixed Distributed Sources in Health Monitoring Systems

Sensores Virtuais para Detecção de Descargas Parciais em Transformadores de Potência

Weighted Block Sparse Recovery Algorithm for High Resolution DOA Estimation with Unknown Mutual Coupling

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