Health Management of Dry-Type Transformer Based on Broad Learning System

Ge, Quanbo; Wang, Mengmeng; Jiang, Yu; Lu, Zhenyu; Yao, Gang; Sun, Changyin

doi:10.1109/tie.2021.3065616

Cited by 16 publications

(2 citation statements)

References 24 publications

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“…Although many high-performance intelligent insulation materials have been developed and extensively employed [3,4], defects will still arise in dry-type transformers during their operation [5,6]. These defects could escalate into severe malfunctions, jeopardizing the safety of the power system [7]. Hence, early defect detection is paramount.…”

Section: Introductionmentioning

confidence: 99%

Localization of mechanical and electrical defects in dry-type transformers using an optimized acoustic imaging approach

Zhang,

Wang,

et al. 2023

PLoS ONE

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This paper presents an acoustic imaging localization system designed to pinpoint common defects in dry-type transformers by analyzing the unique sounds they produce during operation. The system includes an optimized microphone array and an improved multiple signal classification algorithm. Sound signal characteristics of typical defects, such as foreign object intrusion, screw loosening, and partial discharge, are investigated. A 64-element, 8-arm spiral microphone array is designed using a particle swarm optimization algorithm. The multiple signal classification algorithm enhances acoustic imaging quality in field environments by transforming the input from time-domain to preprocessed frequency-domain signals. The power spectra of subarray and main array are combined, forming the optimization algorithm’s output. Experimental results demonstrate the system’s effectiveness and accuracy.

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Section: Introductionmentioning

confidence: 99%

Localization of mechanical and electrical defects in dry-type transformers using an optimized acoustic imaging approach

Zhang,

Wang,

et al. 2023

PLoS ONE

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“…Among the various existing topologies, dry-type transformers play a significant role in the electrical system as they are widely used in substations, instrumentation systems, electrical installations, and power system protection. The cooling is performed with natural air circulation, and the insulation is composed of solid dielectric materials such as paper and epoxy resin [2].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis Applied to the Partial Discharges Identification in Dry-Type Transformers by Hall and Acoustic Emission Sensors

Castro

Santos

Lucas

et al. 2022

Sensors

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Dry-type insulated transformers stand out for their higher applicability in substations, high-voltage instrumentation systems, and electrical installations. In this machine, the insulation system is constituted of dielectric materials such as epoxy resin and Nomex paper. Some critical issues in the operation of this equipment, such as overload, moisture, or heat, can induce a slow degradation of the physical–chemical properties of the dielectric materials, which can culminate in the total failure of the transformer. However, before the transformer’s shutdown, it is common to detect discharge activity in the insulation system. Based on this issue, this work proposes an experimental and comparative analysis between acoustic emission and Hall-effect sensors, aiming at differentiating discharges in epoxy resin and Nomex paper, materials that constitute the insulation of the dry-type insulated transformers. Two signal processing techniques were studied: traditional frequency analysis and discrete wavelet transform. The objective is to develop signal processing techniques to differentiate each type of discharge since different discharges require different maintenance actions. The results obtained indicate that acoustic emission sensors and Hall sensors are promising in differentiating discharge in epoxy resin and Nomex paper. Furthermore, the pattern recognition tools presented by this work, which associated the wavelet levels energies and the energy of the full signals with the average band and the equivalent bandwidth, were effective to perform feature extraction of power transformer condition.

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Load Forecasting with LSTM and Loss of Life Estimation of Distribution Transformer Considering the Impacts of Electric Vehicle

Chandran,

Padwal,

Nair

et al. 2024

Lecture Notes in Electrical Engineering

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Health Management of Dry-Type Transformer Based on Broad Learning System

Cited by 16 publications

References 24 publications

Localization of mechanical and electrical defects in dry-type transformers using an optimized acoustic imaging approach

Localization of mechanical and electrical defects in dry-type transformers using an optimized acoustic imaging approach

A Comparative Analysis Applied to the Partial Discharges Identification in Dry-Type Transformers by Hall and Acoustic Emission Sensors

Load Forecasting with LSTM and Loss of Life Estimation of Distribution Transformer Considering the Impacts of Electric Vehicle

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