A Non-Intrusive and Non-Destructive Technique for Condition Assessment of Transformer Liquid Insulation

Sangineni, Rohith; Nayak, Sisir Kumar; Becerra, Marley

doi:10.1109/tdei.2022.3157912

Cited by 12 publications

(6 citation statements)

References 22 publications

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“…The mapping features between partial discharge signals and electric field distribution are extracted with the SN-EMD (Sifting Natural Orthogonal Mode Function Empirical Mode Decomposition) algorithm to determine the electric field distribution on the surface of insulators [7]. The specific steps for extracting mapped features are as follows:…”

Section: Feature Extraction Of Mapping Between Partial Discharge Sign...mentioning

confidence: 99%

A method for identifying the operating state of deteriorated insulators based on partial discharge signal reconstruction algorithm and spatial electric field distribution law

Li,

Wang,

Yang

et al. 2024

J. Phys.: Conf. Ser.

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In order to improve the accuracy of identifying the working status of degraded insulators and thereby contribute to preventive maintenance and repair, this proposes a method for identifying the operating status of deteriorated insulators based on a partial discharge signal reconstruction algorithm and spatial electric field distribution law. Firstly, the partial discharge signal of the deteriorated insulator is obtained through capacitive sensor technology and clustering analysis algorithm. Then, the mapping features between the partial discharge signal and the electric field distribution are extracted using the SN-EMD algorithm to determine the spatial electric field distribution on the surface of the insulator. Finally, a state recognition model is established based on the hidden Markov model to identify the operating status of deteriorated insulators. The experimental results show that the method designed in this paper has superior performance in terms of accuracy.

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Section: Feature Extraction Of Mapping Between Partial Discharge Sign...mentioning

confidence: 99%

A method for identifying the operating state of deteriorated insulators based on partial discharge signal reconstruction algorithm and spatial electric field distribution law

Li,

Wang,

Yang

et al. 2024

J. Phys.: Conf. Ser.

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“…ASTM D924-15 [ 10 ] states that, in general,

values of insulation oils in a good condition are below 0.005. Some of the

values obtained from the scientific literature are 0.001 [ 32 ], 0.053 [ 33 ], and 0.015 [ 34 ].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…The insulation system in a transformer degrades over time and needs periodic monitoring for the uninterrupted operation of a power system network. A non-intrusive and non-destructive testing method using an S-band pyramidal horn antenna is introduced in [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Effective Electrical Properties and Fault Diagnosis of Insulating Oil Using the 2D Cell Method and NSGA-II Genetic Algorithm

Monzón-Verona

González-Domínguez

García-Alonso

2023

Sensors

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In this paper, an experimental analysis of the quality of electrical insulating oils is performed using a combination of dielectric loss and capacitance measurement tests. The transformer oil corresponds to a fresh oil sample. The paper follows the ASTM D 924-15 standard (standard test method for dissipation factor and relative permittivity of electrical insulating liquids). Effective electrical parameters, including the tan δ of the oil, were obtained in this non-destructive test. Subsequently, a numerical method is proposed to accurately determine the effective electrical resistivity, σ, and effective electrical permittivity, ε, of an insulating mineral oil from the data obtained in the experimental analysis. These two parameters are not obtained in the ASTM standard. We used the cell method and the multi-objective non-dominated sorting in genetic algorithm II (NSGA-II) for this purpose. In this paper, a new numerical tool to accurately obtain the effective electrical parameters of transformer insulating oils is therefore provided for fault detection and diagnosis. The results show improved accuracy compared to the existing analytical equations. In addition, as the experimental data are collected in a high-voltage domain, wireless sensors are used to measure, transmit, and monitor the electrical and thermal quantities.

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“…Rohit, Sisir and Marley [ 9 ] implemented a non-intrusive, non-destructive technique for the condition assessment of transformer oil as shown in Figure 12 . A horn antenna was placed over an open vessel in an anechoic chamber (a room designed to stop sound reflections or electromagnetic waves) to prevent interference, filled with oil to study its characteristics.…”

Section: Example Of Related Systemsmentioning

confidence: 99%

“…The thermal stress originates from either the windings’ copper or core iron losses. This further decomposes the oil, jointly provoking the partial discharge formation and yielding the formation of transformer oil ageing by-products (ABPs), such as moisture-dissolved gases (carbon dioxide, methane, ethane, ethylene, acetylene, propane, propylene, methanol and ethanol), acids and sludge [ 7 , 9 , 10 , 11 , 12 ]. As shown in Figure 1 , Partial discharges are incipient faults that can culminate in insulation breakdown [ 13 ] with a cyclic cause and effect relationship with ageing [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Towards Online Ageing Detection in Transformer Oil: A Review

Elele

Nekahi

Ali

et al. 2022

Sensors

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Transformers play an essential role in power networks, ensuring that generated power gets to consumers at the safest voltage level. However, they are prone to insulation failure from ageing, which has fatal and economic consequences if left undetected or unattended. Traditional detection methods are based on scheduled maintenance practices that often involve taking samples from in situ transformers and analysing them in laboratories using several techniques. This conventional method exposes the engineer performing the test to hazards, requires specialised training, and does not guarantee reliable results because samples can be contaminated during collection and transportation. This paper reviews the transformer oil types and some traditional ageing detection methods, including breakdown voltage (BDV), spectroscopy, dissolved gas analysis, total acid number, interfacial tension, and corresponding regulating standards. In addition, a review of sensors, technologies to improve the reliability of online ageing detection, and related online transformer ageing systems is covered in this work. A non-destructive online ageing detection method for in situ transformer oil is a better alternative to the traditional offline detection method. Moreover, when combined with the Internet of Things (IoT) and artificial intelligence, a prescriptive maintenance solution emerges, offering more advantages and robustness than offline preventive maintenance approaches.

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A Non-Intrusive and Non-Destructive Technique for Condition Assessment of Transformer Liquid Insulation

Cited by 12 publications

References 22 publications

A method for identifying the operating state of deteriorated insulators based on partial discharge signal reconstruction algorithm and spatial electric field distribution law

A method for identifying the operating state of deteriorated insulators based on partial discharge signal reconstruction algorithm and spatial electric field distribution law

Effective Electrical Properties and Fault Diagnosis of Insulating Oil Using the 2D Cell Method and NSGA-II Genetic Algorithm

Towards Online Ageing Detection in Transformer Oil: A Review

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