Antenna contactless partial discharges detection in covered conductors using ensemble stacking neural networks. (1st March 2023)

• Record Type: Journal Article
• Authors: Klein, Lukáš; Seidl, David; Fulneček, Jan; Prokop, Lukáš; Mišák, Stanislav; Dvorský, Jiří
• Is Part Of: Expert systems with applications. Volume 213:Part A(2023)
• Publication Date: 2023-03-01
• Subjects: Ensemble neural network -- Partial discharge -- Time series classification -- High impedance fault -- Covered conductor; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.118910
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: High impedance faults caused by vegetation are difficult to detect when covered conductors in medium voltage overhead power lines are used. Long-term contact of XLPE insulation with vegetation causes partial discharges (PDs) which damage the insulation. Although a cheap and easy to install, contactless detection method was developed using an antenna, there is a lack of classification algorithms for this method. Only two custom machine learning algorithms have been tested so far, and both rendered unsatisfactory results for the real application. This work investigates the use of neural network algorithms for this problem and the application of heterogeneous stacking ensembles using neural networks. We used real data collected from a number of detection stations in the Czech Republic. Also, we limited ourselves to supporting edge computing using devices such as Edge TPU. We propose the application of a heterogeneous stacking ensemble neural network to classify PDs obtained by the contactless method. The algorithm we propose is based on a stacking ensemble with a novel combination of base learners, and the Wide and Deep neural network is used as a meta-learner. We compared the results of our algorithm with other algorithms designated for time series classification. Also, an ablation study of the ensemble was conducted, and satisfactory results were obtained using the proposed algorithm. The ensemble outperformed all algorithms tested and is usable on the edge using AI HW accelerator as the ensemble is only feedforward and contains only well-used and known layers. This research improves our understanding of the classification of PDs using the contactless PD detection method and also introduces a stacking ensemble of convolutional neural network and autoencoders for a time series classification for the first time. Graphical abstract: Highlights: Detection of partial discharges using heterogeneous ensemble stacking NN. Novel combination of 1D-CNN and autoencoders in ensemble stacking NN. Proposed algorithm usable for edge detection using HW NN accelerator. Evaluated on dataset previously used for best published algorithm for this dataset. Evaluated on new unvalidated dataset and outperforms wide range of classifiers.