Usage of Antenna for Detection of Tree Falls on Overhead Lines with Covered Conductors

Mišák, Stanislav; Fulnecek, Jan; Ježowicz, Tomáš; Vantuch, Tomáš; Burianek, Tomas

doi:10.15598/aeee.v15i1.1894

Cited by 10 publications

(5 citation statements)

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“…In this work, we focus on another principal method for PD detection. PDs create in the electromagnetic field surrounding the CCs specific patterns that can be observed using an antenna [15]. Such contactless approach is an alternative to data acquisition by galvanic contact.…”

Section: A Partial Discharge Detectionmentioning

confidence: 99%

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Partial Discharge Detection by Edge Computing

Klein

Žmij²,

Krömer

2023

IEEE Access

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Edge computing is becoming a mainstream platform for practical applications of machine learning and in particular deep learning. Many systems capable of efficient execution of deep neural models in the context of edge computing are readily available or beginning to appear on the consumer market. The Jetson platform from NVIDIA, the Neural stick from Intel, and the Edge TPU designed by Google are examples of devices that enable the application of complex neural networks in edge computing. This work investigates the ability of selected edge devices to address a real-world classification problem from electrical power engineering. It consists of the detection of partial discharges (PDs) from covered conductors (CCs) on high-voltage power lines. The CCs are used in heavily forested and generally inaccessible areas where clearance zones cannot be maintained. Detection of PDs can prevent forest fires and other disasters potentially caused by prolonged contact of CCs with vegetation. The problem is suitable for an edge computing-based solution because Internet connectivity in remote areas is usually insufficient and a 2G (GSM) mobile network is available at best. Because such locations are difficult to access and usually without a suitable power supply, the proposed solution puts an emphasis also on PD detection latency and the associated power consumption. Two principal approaches to PD detection are considered. One is based on the classification of 1D time series (raw data). The second approach uses the signal transformed into a 2D spectrogram. In this case, two types of algorithms are evaluated. The first one is a novel custom stacking ensemble detector composed of 2D convolutional neural networks and a neural meta-learner on top of it. The second one uses the well-known and widely-used used ResNet deep neural model.

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Section: A Partial Discharge Detectionmentioning

confidence: 99%

“…We compare their accuracy and performance on a specific real-world problem and a variety of widely available systems. The problem is a part of a broader solution allowing the detection and classification of partial discharges using a contactless antenna-based method [15].…”

Section: Introductionmentioning

confidence: 99%

Partial Discharge Detection by Edge Computing

Klein

Žmij²,

Krömer

2023

IEEE Access

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“…However, this issue is not discussed in the literature There are a few tangentially related studies focusing on cable joints [23], workmanship defects [24], and bare conductors using simulated data [25]. None of them use an antenna-based method [26], which offers many benefits for detecting the type of fault in covered conductors in power lines. This is of interest in this study, as it involves unique types of faults.…”

Section: Motivation For the Studymentioning

confidence: 99%

Enhanced Fault Type Detection in Covered Conductors Using a Stacked Ensemble and Novel Algorithm Combination

Kabot,

Klein,

Prokop

et al. 2023

Sensors

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This study introduces an innovative approach to enhance fault detection in XLPE-covered conductors used for power distribution systems. These covered conductors are widely utilized in forested areas (natural parks) to decrease the buffer zone and increase the reliability of the distribution network. Recognizing the imperative need for precise fault detection in this context, this research employs an antenna-based method to detect a particular type of fault. The present research contains the classification of fault type detection, which was previously accomplished using a very expensive and challenging-to-install galvanic contact method, and only to a limited extent, which did not provide information about the fault type. Additionally, differentiating between types of faults in the contact method is much easier because information for each phase is available. The proposed method uses antennas and a classifier to effectively differentiate between fault types, ranging from single-phase to three-phase faults, as well as among different types of faults. This has never been done before. To bolster the accuracy, a stacking ensemble method involving the logistic regression is implemented. This approach not only advances precise fault detection but also encourages the broader adoption of covered conductors. This promises benefits such as a reduced buffer zone, improved distribution network reliability, and positive environmental outcomes through accident prevention and safe covered conductor utilization. Additionally, it is suggested that the fault type detection could lead to a decrease in false positives.

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“…When PD activity appears on the surface of CC, it creates a typical pattern of electromagnetic field in the surrounding space of a CC. This pattern can be acquired by an antenna, as it was done in our previous works [3], [4]). The main advantage of this contactless approach is its price -antenna is much cheaper than a high voltage capacitive divider or Rogowski coil [5].…”

Section: Problem Definitionmentioning

confidence: 99%

“…Afterwards, the features are aggregated across the clusters. On the cluster level, the following 10 features are computed: [3]. It was found that the factors for the identification of partial discharges are the duration of PD pulse, its magnitude, and shape.…”

Section: Features Computationmentioning

confidence: 99%

Fast Algorithm for Contactless Partial Discharge Detection on Remote Gateway Device

Martinovič

Fulnecek

2022

IEEE Trans. Power Delivery

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Detection of the high impedance fault caused by vegetation is one of the biggest challenges brought by the usage of covered conductors in medium voltage overhead power lines. One of the accompanying events of long-term contact of vegetation with the XLPE insulation is partial discharge which damages the insulation. Current systems for the detection of partial discharge have two major problems: the price and difficult installation. In this paper, an approach for the detection of the partial discharge from the data collected by the antenna is described. This approach is focused on the small computational demand and low false positive rate. Thanks to the small computational requirements, it can be run on the remote gateway devices which are collecting the data from the antenna. It is composed of four steps: outlier detection, outlier clustering, feature extraction, and classification. It is shown that this approach greatly improves the detection rate and lowers false positives compared to the previous algorithm used for partial discharge detection based on the data from an antenna, making it fit to use in the production environment.

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Usage of Antenna for Detection of Tree Falls on Overhead Lines with Covered Conductors

Cited by 10 publications

References 0 publications

Partial Discharge Detection by Edge Computing

Partial Discharge Detection by Edge Computing

Enhanced Fault Type Detection in Covered Conductors Using a Stacked Ensemble and Novel Algorithm Combination

Fast Algorithm for Contactless Partial Discharge Detection on Remote Gateway Device

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