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

Abstract

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).

Accurate partial discharge localisation using a multi‐deep neural network model trained with a novel virtual measurement method

“…In order to test the efficiency and robustness of the MDNNM trained with a novel VMM (TNVMM), a multifaceted study has been carried out in this section. For doing so, comparisons have been made with the two previously presented methodologies for the PD localisation, for example, (1) MDNNM [9], and (2) MDNNM based on a VMM (MDNNM-VMM) [23]. First, the measurement error value is changed, and then its subsequent effects are examined.…”

“…Note:The subscripts VMMnew is the multi-deep neural network model trained with a novel virtual measurement method (MDNNM-TNVMM) being proposed in this study, VMMold is the method proposed in[9], DNN is the method proposed in[23].…”

Detection, Classification and Location of Sources of Partial Discharges Using the Radiometric Method: Trends, Challenges and Open Issues

“…Furthermore, technological advances in signal processing and acquisition methodologies have also provided the development of several works that make use of deep learning techniques for pattern recognition in various fields of study, such as image classification, segmentation, detection and tracking [89], [90] with hit rates usually higher than other classic machine learning approaches. However, the deep learning is still little explored in the area of PD detection [82], [91]- [96] mainly due to the higher computational processing time compared to the classical approaches used by the papers described throughout this article. Therefore, further studies regarding the feasibility evaluation of the application of deep learning techniques in the recognition of PD patterns are still needed, aiming to find classifiers that present high hit rates based on, desirably, a relatively low computational cost and a small number of input features.…”

Smart Care Using a DNN-Based Approach for Activities of Daily Living (ADL) Recognition

“…This study develops a recognition system for ADL using DNN and compares it with the single layer BPNN, multi-layer BPNN, and CNN methods to evaluate the feasibility of the proposed system. We start with consideration of the parameters for these neural networks, using the following suggested parameter settings [34][35][36]: (1) activation functions = sigmoid; (2) learning rate = 0.8 for BP neural networks and 0.01 for DNN; (3) optimal method = gradient correction for BP neural networks and Adam optimization for DNN; (4) five hidden layers with 5-50 neurons in each layer for DNN, e.g., DNN with a [20,20,20,20,20] classifier; (5) two fully connected classifiers for CNN to deal with 2048 dimensions, 384 neurons for the first hidden layers, 192 neurons for the second hidden layers since the kernel size of convolution layer = 5 by 5 with step = 1 by 1 and pooling layer = 3 by 3 with step = 2 by 2.…”