Direction of arrival estimation method for multiple UHF partial discharge sources based on virtual array extension

Zhou, Nan; Luo, Lingen; Sheng, Gehao; Jiang, Xiuchen

doi:10.1109/tdei.2018.007093

Cited by 18 publications

(5 citation statements)

References 26 publications

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“…Two major goals while working with PD localization problems are their precision and speed [6], [9]. Therefore, to ensure an accurate detection of PD source, a statistical simulation-based VMM is proposed in this section; whereas, for speedy detection of PD source a Multi DNN model is introduced in the next section.…”

Section: Localization Methodsmentioning

confidence: 99%

“…Therefore, to ensure an accurate detection of PD source, a statistical simulation-based VMM is proposed in this section; whereas, for speedy detection of PD source a Multi DNN model is introduced in the next section. VMM-based mathematical model used in this paper has been introduced previously by [9], which itself is an improved version of extended 2-D model presented by [10].…”

Section: Localization Methodsmentioning

confidence: 99%

“…It is noteworthy that a single set of time delay equations obeys the normal distribution. On the other hand, the latter deviation is considered to be a uniform distribution, as supposed by [10], and proved to be stable (especially for θ coordinate) by [9] also. To obtain a desired number of virtual measurements for the VMM, in addition to studying the impact of 'error in time difference of arrival' on the PD location, an error following a uniform distribution ()ε ij ∼U(−T m , T m ) is deliberately added to the measured t ij0 + ε ij .…”

Section: A Mathematical Modelling For Pd Localizationmentioning

confidence: 99%

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Partial Discharge Localization in 3-D With a Multi-DNN Model Based on a Virtual Measurement Method

Zaki

Jiang

2020

IEEE Access

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Time difference of Arrival (TDOA)-based localization method, although used widely, calls for a fast and accurate solution owing to its time inefficiency and sensitivity to time delay estimation. In order to speed up the solution for nonlinear TDOA equations, while guaranteeing the location accuracy, this paper presents a hybrid approach namely multi-deep neural network model based on a virtual measurement method (MDNNM-VMM). Data consisting of multiple time difference values, resulting from a virtual measurement method (VMM), are fed to a pretrained multi-deep neural network model (MDNNM). These 'n' number of virtually generated sequences of time delays are obtained from a single set of TDOA equations, while conforming with a uniform distribution. The multi-DNN model using these data, outputs the required partial discharge (PD) coordinates that help determine accurate PD location. While applying a measurement error of 24 ns, the average error values r, θ , , and d for the proposed method, compared to a multi-DNN method, see a significant percentage decrease of 32 %, 24 %, 39 %, and 44 %, respectively. Additionally, varying simulated error, different array designs, and certain other parameters are studied to make the PD localization process more efficient and multifaceted. INDEX TERMS Deep neural network (DNN), partial discharge (PD) localization, time difference of arrival (TDOA), ultra-high frequency (UHF), virtual measurement method (VMM).

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Section: Localization Methodsmentioning

confidence: 99%

Section: Localization Methodsmentioning

confidence: 99%

Section: A Mathematical Modelling For Pd Localizationmentioning

confidence: 99%

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Partial Discharge Localization in 3-D With a Multi-DNN Model Based on a Virtual Measurement Method

Zaki

Jiang

2020

IEEE Access

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“…In the past decade, the UHF method in particular, has become the subject of numerous research and development activities. These works cover a wide range of issues such as: the development of new designs of UHF antennas [6][7][8], combined acoustic and electromagnetic PD sensors [9,10], new calibration methods for UHF partial discharge measurement [11], the use of UHF sensors to locate PD sources using time-difference of arrival (TDOA) [12], direction of arrival (DOA) [13,14], received signal strength indication (RSSI) [15,16], electromagnetic time reversal (EMTR) [17] and electromagnetic inverse filter (EMIF) [18] techniques, development of on-line PD monitoring systems [19], and automatic recognition of insulation defects based on advanced digital signal processing algorithms and machine learning methods [20]. The last issue is an important element of reliable diagnostics of electrical power devices because effective detection and identification of a defect (source of PD) can contribute to the prevention of a serious failure.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Capability of PD-Type Recognition Based on UHF Signals Recorded with Different Antennas Using Supervised Machine Learning

2022

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The article presents research on the influence of the type of UHF antenna and the type of machine learning algorithm on the effectiveness of classification of partial discharges (PD) occurring in the insulation system of a power transformer. For this purpose, four antennas specially adapted to be installed in the transformer tank (UHF disk sensor, UHF drain valve sensor, planar inverted F-type antenna, Hilbert curve fractal antenna) and a reference log-periodic antenna were used in laboratory tests. During the research, the main types of PD, typical for oil-paper insulation, were generated, i.e., PD in oil, PD in oil wedge, PD in gas bubbles, surface discharges, and creeping sparks. For the registered UHF PD pulses, nine features in the frequency domain and four features in the wavelet domain were extracted. Then, the PD classification process was carried out with the use of selected methods of supervised machine learning. The study investigated the influence of the number and type of feature on the obtained classification results gained with the following machine-learning methods: decision tree, support vector machine, Bayes method, k-nearest neighbor, linear discriminant, and ensemble machine. As a result of the works carried out, it was found that the highest accuracies are gathered for the feature representing peak frequency using a decision tree, reaching values, depending on the type of antenna, from 89.7% to 100%, with an average of 96.8%. In addition, it was found that the MRMR method reduces the number of features from 13 to 1 while maintaining very high effectiveness. The broadband log-periodic antenna ensured the highest average efficiency (100%) in the PD classification. In the case of the tested antennas adapted to work in an energy transformer tank, the highest defect-recognition efficiency is provided by the UHF disk sensor (99.3%), and the lowest (89.7%) is by the UHF drain valve sensor.

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“…In [21] authors have introduced localisation method for a far-field situation where the source is far from the sensors, and liner wavefront model can be applied. Recently [30,31], have utilised the liner wave-front model and have applied direction of arrival (DOA)-based high-resolution subspace-based method for PD source localisation using phased array theory and virtual array extension, respectively. These subspace methods are computationally intensive.…”

Section: Introductionmentioning

confidence: 99%

Radiometric localisation of partial discharge sources inside Air‐insulated electrical substation

Mishra¹,

Bhukya²,

Koley³

et al. 2019

IET Science, Measurement & Technology

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Direction of arrival estimation method for multiple UHF partial discharge sources based on virtual array extension

Cited by 18 publications

References 26 publications

Partial Discharge Localization in 3-D With a Multi-DNN Model Based on a Virtual Measurement Method

Partial Discharge Localization in 3-D With a Multi-DNN Model Based on a Virtual Measurement Method

Investigating the Capability of PD-Type Recognition Based on UHF Signals Recorded with Different Antennas Using Supervised Machine Learning

Radiometric localisation of partial discharge sources inside Air‐insulated electrical substation

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