Quantitative comparison of partial discharge localization algorithms using time difference of arrival measurement in substation

Zhu, Ming-Xiao; Wang, Yan‐Bo; Chang, Ding-Ge; Zhang, Guanjun; Tong, Xin; Ruan, Ling

doi:10.1016/j.ijepes.2018.06.036

Cited by 13 publications

(11 citation statements)

References 33 publications

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“…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%

“…At the heart of these methods lies the concept of measuring distance amongst sensors and PD source(s). Based on angle and time, or strength information of UHF signals, UHF methods can either be based on Angle of Arrival (AOA), Time of Arrival (TOA), Time Difference of Arrival (TDOA), or Received Signal Strength Indicator (RSSI) [8]- [10]. TDOA-based method has already received more attention than other methods due to its comparatively high accuracy.…”

Section: Introductionmentioning

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: A Mathematical Modelling For Pd Localizationmentioning

confidence: 99%

Section: Introductionmentioning

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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“…Moreover, with the help of the least-squares (LS) estimation method, several closed-form solutions such as those of the Chan algorithm, spherical-interpolation, two-step weighted LS, and squared range-difference LS can be introduced [19][20][21]. The summarization of various algorithms can be found in [22].…”

Section: Coordinate Estimationmentioning

confidence: 99%

“…The e r curves of the two deployments are shown in Figure 2. In the calculation, r was set as 10 m, which is a typical distance between the PD sources and the array center in the substation, and σ t was set as 0.2 ns according to our previous experiments [22]. The objective function J is defined as…”

Section: Objective Function For Coordinate Estimationmentioning

confidence: 99%

Optimization of Antenna Array Deployment for Partial Discharge Localization in Substations by Hybrid Particle Swarm Optimization and Genetic Algorithm Method

Zhu

Chang

et al. 2018

Energies

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A radio frequency antenna array was adopted to localize partial discharge (PD) sources in an entire substation. The deployment of an antenna array is a significant factor affecting the localization accuracy, and the array needs to be carefully selected. In this work, a hybrid method of particle swarm optimization (PSO) and a genetic algorithm (GA) is proposed to optimize the array deployment. A direction-of-arrival (DOA) estimation algorithm applicable to arbitrary array configurations is firstly presented. The Cramér-Rao lower bound (CRLB) was employed to evaluate the localization accuracy of different arrays, and two objective functions characterizing the estimation errors of coordinates and the DOA are proposed. With the goal of minimizing the objective functions, the array deployments for the coordinate and DOA localizations were optimized by using the hybrid PSO/GA algorithm. Using the developed method, optimal antenna configurations for different constraint areas, aspect ratios, and numbers of sensors were investigated. The results indicate that the optimal deployments for coordinate and DOA estimations are different; specifically speaking, superior DOA performance is achieved when all antennas are placed on the outer boundary of the constraint area while part of the antennas in the optimal coordinate array are placed in the middle position.

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On-line partial discharge monitoring system for underground MV cables – Part II: Detection and location

Milioudis¹,

Ανδρέου

Labridis

2019

International Journal of Electrical Power & Energy Systems

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Quantitative comparison of partial discharge localization algorithms using time difference of arrival measurement in substation

Cited by 13 publications

References 33 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

Optimization of Antenna Array Deployment for Partial Discharge Localization in Substations by Hybrid Particle Swarm Optimization and Genetic Algorithm Method

On-line partial discharge monitoring system for underground MV cables – Part II: Detection and location

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