Unsupervised Monitoring System for Predictive Maintenance of High Voltage Apparatus

Gianoglio, Christian; Ragusa, Edoardo; Bruzzone, Andrea; Gastaldo, Paolo; Zunino, Rodolfo; Guastavino, F.

doi:10.3390/en13051109

Cited by 5 publications

(3 citation statements)

References 36 publications

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“…The lower part of Table 1 reports the references for the explicit empirical models [21,[26][27][28][29][30]. In [31], a fully unsupervised approach detected changes in the life status of a specimen. The authors showed that such an approach could be combined with the method proposed [29,30] to improve the insulation lifetime predictions.…”

Section: Related Workmentioning

confidence: 99%

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Online Predictive Maintenance Monitoring Adopting Convolutional Neural Networks

et al. 2021

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Thermal, electrical and mechanical stresses age the electrical insulation systems of high voltage (HV) apparatuses until the breakdown. The monitoring of the partial discharges (PDs) effectively assesses the insulation condition. PDs are both the symptoms and the causes of insulation aging and—in the long term—can lead to a breakdown, with a burdensome economic loss. This paper proposes the convolutional neural networks (CNNs) to investigate and analyze the aging process of enameled wires, thus predicting the life status of the insulation systems. The CNNs training does not require any kind of assumption of how the factors (e.g., voltage, frequency and temperature) contribute to the life model. The experiments confirm that the proposal obtains better estimations of the life status of twisted pair specimens concerning existing solutions, which are based on strong hypotheses about the life model dependency on the factors.

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Section: Related Workmentioning

confidence: 99%

“…Problems arise only in the very first part of the lifetime of the specimen. In fact, fast changes affect the insulation material when the PD inception occurs [30,31]. Hence, one may expect the model to be less accurate in that phase.…”

Section: Algorithm 2 Evaluation Inputmentioning

confidence: 99%

Online Predictive Maintenance Monitoring Adopting Convolutional Neural Networks

et al. 2021

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“…However, in cases where individual data profiles were closely similar, the method suffered from a lack of useful discharge features, impacting the accuracy of localized discharge pattern-recognition. The literature [32] developed an HV equipment monitoring system based on embedded technology, designed specifically to monitor the occurrence of PD phenomena in the insulation layer. Nevertheless, it could not accurately classify specific discharge types using deep-learning techniques.…”

Section: Introductionmentioning

confidence: 99%

Partial Discharge Pattern-Recognition Method Based on Embedded Artificial Intelligence

Yan,

Bai,

Zhang

et al. 2023

Applied Sciences

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This paper proposes a method for detecting and recognizing partial discharges in high-voltage (HV) equipment. The aim is to address issues commonly found in traditional systems, including complex operations, high computational demands, significant power consumption, and elevated costs. Various types of discharges were investigated in an HV laboratory environment. Discharge data were collected using a high-frequency current sensor and a microcontroller. Subsequently, this data underwent processing and transformation into feature sets using the phase-resolved partial discharge analysis technique. These features were then converted into grayscale map samples in PNG format. To achieve partial discharge classification, a convolutional neural network (CNN) was trained on these samples. After successful training, the network model was adapted for deployment on a microcontroller, facilitated by the STM32Cube.AI ecosystem, enabling real-time partial discharge recognition. The study also examined storage requirements across different CNN layers and their impact on recognition efficacy. To assess the algorithm’s robustness, recognition accuracy was tested under varying discharge voltages, insulation media thicknesses, and noise levels. The test results demonstrated that the algorithm could be effectively implemented on a microcontroller, achieving a recognition accuracy exceeding 98%.

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