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.

In this work we analyse the temperature distribution in a conductor disk in transitory regime. The disk is in motion in a stationary magnetic field generated by a permanent magnet and so, the electric currents induced inside it generate heat. The system acts as a magnetic brake and is analysed using infrared sensor techniques. In addition, for the simulation and analysis of the magnetic brake, a new thermal convective matrix for the 3D Cell Method (CM) is proposed. The results of the simulation have been verified by comparing the numerical results with those obtained by the Finite Element Method (FEM) and with experimental data obtained by infrared technology. The difference between the experimental results obtained by infrared sensors and those obtained in the simulations is less than 0.0459%.

In this paper, a new method for characterizing the dielectric breakdown voltage of dielectric oils is presented, based on the IEC 60156 international standard. In this standard, the effective value of the dielectric breakdown voltage is obtained, but information is not provided on the distribution of Kelvin forces an instant before the dynamic behavior of the arc begins or the state of the gases that are produced an instant after the moment of appearance of the electric arc in the oil. In this paper, the behavior of the oil before and after the appearance of the electric arc is characterized by combining a low-cost CMOS imaging sensor and a new matrix of electrical permittivity associated with the dielectric oil, using the 3D cell method. In this way, we also predict the electric field before and after the electric rupture. The error compared to the finite element method is less than 0.36%. In addition, a new method is proposed to measure the kinematic viscosity of dielectric oils. Using a low-cost imaging sensor, the distribution of bubbles is measured, together with their diameters and their rates of ascent after the electric arc occurs. This method is verified using ASTM standards and data provided by the oil manufacturer. The results of these tests can be used to prevent incipient failures and evaluate preventive maintenance processes such as transformer oil replacement or recovery.

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.

In this paper, electrical discharge images are classified using AI with quantum machine learning techniques. These discharges were originated in dielectric mineral oils and were detected by a high-resolution optical sensor. The captured images were processed in a Scikit-image environment to obtain a reduced number of features or qubits for later training of quantum circuits. Two quantum binary classification models were developed and compared in the Qiskit environment for four discharge binary combinations. The first was a quantum variational model (QVM), and the second was a conventional support vector machine (SVM) with a quantum kernel model (QKM). The execution of these two models was realized on three fault-tolerant physical quantum IBM computers. The novelty of this article lies in its application to a real problem, unlike other studies that focus on simulated or theoretical data sets. In addition, a study is carried out on the impact of the number of qubits in QKM, and it is shown that increasing the number of qubits in this model significantly improves the accuracy in the classification of the four binary combinations studied. In the QVM, with two qubits, an accuracy of 92% was observed in the first discharge combination in the three quantum computers used, with a margin of error of 1% compared to the simulation obtained on classical computers.

This study presents an innovative bimodal approach for laboratory partial discharge (PD) analysis using a YOLOv8-based convolutional neural network (CNN). The main contribution consists, first, in the transformation of a conventional DDX-type electrical detector into a smart and autonomous data source. By training the CNN, a system capable of automatically reading and interpreting the data from the detector display—discharge magnitude and applied voltage—is developed, achieving an average training accuracy of 0.91 and converting a passive instrument into a digitalized and structured data source. Second, and simultaneously, an optical visualization system captures direct images of the PDs with a high-resolution camera, allowing for their morphological characterization and spatial distribution. For electrical voltages of 10, 13, and 16 kV, PDs were detected with a confidence level of up to 0.92. The fusion of quantitative information intelligently extracted from the electrical detector with qualitative characterization from optical analysis offers a more complete and robust automated diagnosis of the origin and severity of PDs.