Characterization of Dielectric Oil with a Low-Cost CMOS Imaging Sensor and a New Electric Permittivity Matrix Using the 3D Cell Method

Self Cite

In this work, an exhaustive analysis of the partial discharges that originate in the bubbles present in dielectric mineral oils is carried out. To achieve this, a low-cost, high-resolution CMOS image sensor is used. Partial discharge measurements using that image sensor are validated by a standard electrical detection system that uses a discharge capacitor. In order to accurately identify the images corresponding to partial discharges, a convolutional neural network is trained using a large set of images captured by the image sensor. An image classification model is also developed using deep learning with a convolutional network based on a TensorFlow and Keras model. The classification results of the experiments show that the accuracy achieved by our model is around 95% on the validation set and 82% on the test set. As a result of this work, a non-destructive diagnosis method has been developed that is based on the use of an image sensor and the design of a convolutional neural network. This approach allows us to obtain information about the state of mineral oils before breakdown occurs, providing a valuable tool for the evaluation and maintenance of these dielectric oils.

Section: Cnn Testmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Characterization of Partial Discharges in Dielectric Oils Using High-Resolution CMOS Image Sensor and Convolutional Neural Networks

Monzón-Verona,

González-Domínguez,

García-Alonso

2024

Self Cite

“…The first was the distributed parameter model of the TDC, which is developed in Section 2.2 . The finite formulation (FF) of the electromagnetic equations [ 22 , 23 ] was used, along with the numerical CM to solve these equations [ 24 , 25 ].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…In this equation, σ and ε appear simultaneously. This is the equation that must be programmed together with the global electrode complex current vector

, which is calculated by Equation (3), where

is an incidence vector of the relative cut between the edges of the mesh of the oil volume and the surface of one of the TDC cylindrical plates [ 25 , 28 ]. The sum of all the currents in that cut is equal to the total current that enters or leaves each of the TDC electrodes.…”

Section: Theoretical Backgroundmentioning

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

Self Cite

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.

“…This electrical testing method is used to ascertain the offline age of a transformer oil sample (see Figure 5 [ 28 ]). The breakdown voltage is the voltage at which the oil sample becomes conductive (evidenced by spark) [ 17 ], usually reported in [kV/mm] units.…”

Section: Transformer Oil Ageing Characterisation Techniquesmentioning

confidence: 99%

Towards Online Ageing Detection in Transformer Oil: A Review

Elele

Nekahi

Ali

et al. 2022

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.