Novel trend of "l" shape in PD pattern to judge the appropriate crucial moment of replacing cast-resin current transformer

Lin, Yun; Wu, Ruay‐Nan; Chung, I‐Hua

doi:10.1109/t-dei.2008.4446763

Cited by 16 publications

(3 citation statements)

References 14 publications

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“…To observe the partial discharge data captured by the measurement device, the current signal of the raw data was split into j phase windows based on the test voltage signal period. After filtering by the wavelet method [36,37], the maximum discharge of the PD current signal was stored. In this study, 60 Hz was used as the reference frequency, and each cycle was divided into 600 phase windows.…”

Section: Data Simplification Methodsmentioning

confidence: 99%

Exploring the Impact of Parallel Architecture on Improving Adaptable Neuro-Fuzzy Inference Systems for Gas-Insulated Switch Defect Recognition

Chung¹,

Lin

2022

Energies

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Gas-insulated switchgear malfunctions during power system operation may occur due to electrical, thermal, or human errors in the manufacturing process. The leading causes of insulation deterioration of gas-insulated switchgear are discharging along the surface caused by dirt on the insulating material, internal discharge caused by impurities and cavities in the insulating material, corona discharge caused by poor assembly or construction at the site, and electric tree channel discharge caused by the intense internal discharge. Since different defects produce different partial discharge characteristics, the operating power equipment can be analyzed using measurement instruments to detect partial discharge for preventive equipment fault diagnosis, avoiding unnecessary power outages and losses; therefore, evaluating the defects in gas-insulated switchgear is essential. In this study, three gas-insulated switchgears were prefabricated with different defects before encapsulation, and the partial discharge data of each defect were measured by applying different test voltages. The adaptive neuro-fuzzy inference system (ANFIS) input data were used to evaluate the recognition effect, showing that the average recognition rate of the core for all defects was over 90%. The proposed system architecture can continuously accumulate the defect measurement database of gas-insulated switchgear and be used as a reference for constructing electrical equipment defect recognition systems.

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Section: Data Simplification Methodsmentioning

confidence: 99%

Exploring the Impact of Parallel Architecture on Improving Adaptable Neuro-Fuzzy Inference Systems for Gas-Insulated Switch Defect Recognition

Chung¹,

Lin

2022

Energies

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“…The insulating material status of these equipment acts as an important indicator of the equipment operation quality. Therefore, the insulation status monitoring of insulating materials and the assessment of aging phenomena have become one of the keys to judging the performance and life of power transmission system equipment [1,2]. Using detection technology to know the insulation state of equipment is an important key to judging the operating state of power transmission system equipment, so it is a subject worthy of indepth study.…”

Section: Literature Reviewmentioning

confidence: 99%

An Operating Condition Monitoring and Fault Diagnosis for Transformer Based on Partial Discharge and Artificial Neural Networks

Chen,

Shieh

2024

Preprint

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The operation of transformers suffers from natural or man-made disasters, subjecting the equipment to prolonged exposure to temperature, lightning strikes, transient over-voltages, and over-current, gradually producing insulation aging phenomenon, which results in partial discharges within the transformer. Without dealing with it beforehand properly, it may lead to insulation breakdown. Nowadays, with the mature development of microchip control technology and wireless communication technology, which is helpful for realizing the research for online real-time and long-term monitoring capabilities for power equipment. A transformer operating condition monitoring and diagnostic system that can rapidly and effectively prevent the breakdown of transformer insulation, which can impact the overall power supply system is developed in this paper. The system is divided into two parts based on its function. The first part is the operating condition monitoring and real-time alert unit for the transformer, primarily composed of an embedded computing module, a sensor module, and a transmission module. Its work content is to capture real-time online partial discharge ultrasonic signals and high frequency current comparator pulse signals, and analyze and determine the condition of the internal insulation and operational status whether it is fault or not. The second part is the central control unit, consisting mainly of an industrial computer and a transmission module. Its work content is to store the ultrasonic and high frequency current comparator pulse signals from the transformer operation monitoring and real-time alert unit. It uses the software to eliminate signal noise and interference, and then utilizes artificial neural networks to identify the type of fault. In this paper, the research for the phenomenon of insulation degradation in transformer equipment aims to establish aging trend profiles through the dual malfunctioning alert and aging process analysis, as well as the long-term measurement of ultrasonic signals from partial discharges and high-frequency current comparator pulse signals. This approach provides crucial reference data and a foundation for disposable on-site measurement, assessing the phenomenon of insulation aging stages, defects and damage of the transformers effectively. It allows for timely replacement or maintenance in advance, achieving the prevention of malfunctioning and predictive diagnosis of aging.

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“…Since transformer is one of electric equipments most closely associated with power consumption, the lifespan of transformer depends upon the insulating strength of insulation materials. Therefore, monitoring the aging status of the transformer will be helpful to prevent system failure (Lin, Wu, & Chung, 2008). Since digital PD detection is developed (Ward, 1992) has extremely high diagnosis accuracy for insulating medium (James & Phung, 1995;Tanaka, 1995), it is an efficient way to well identify the aging status of cast-resin transformer for maintenance or replacement to further prevent insulation breakdown (Farag et al, 1999;James & Phung, 1995;Karthikeyan, Gopal, & Venkatesh, 2008;Tanaka, 1995;Yue, Chen, Cheng, Song, & Xie, 2006).…”

Section: Introductionmentioning

confidence: 99%

Artificial identification system for transformer insulation aging

Kuo¹

2010

Expert Systems with Applications

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Novel trend of "l" shape in PD pattern to judge the appropriate crucial moment of replacing cast-resin current transformer

Cited by 16 publications

References 14 publications

Exploring the Impact of Parallel Architecture on Improving Adaptable Neuro-Fuzzy Inference Systems for Gas-Insulated Switch Defect Recognition

Exploring the Impact of Parallel Architecture on Improving Adaptable Neuro-Fuzzy Inference Systems for Gas-Insulated Switch Defect Recognition

An Operating Condition Monitoring and Fault Diagnosis for Transformer Based on Partial Discharge and Artificial Neural Networks

Artificial identification system for transformer insulation aging

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