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.

This paper presents a low power voltage limiter design that avoids possible damages in the circuits of the analog front-end of the RFID sensor due to voltage surges whenever reader and tag are very close. The proposed voltage limiter design takes advantage of the implemented bandgap reference and voltage regulator in order to provide low temperature and process deviation of the limiting voltage. The measured limiting voltage is 2.9V with a voltage variation of only +/-0.025V for the four measured dies. The current consumption is only 150nA when the reader and the tag are far away one to each other, not limiting the sensitivity of the tag due to an undesired consumption in the voltage limiter. The circuit is implemented on a low cost 2P4M 0.35μm CMOS technology.

There is a strong motivation for the development of new low power sensors for wireless passive applications such as RFID. In this work, a combination of a MEMS capacitive pressure transducer and a capacitive to digital converter compatible with a sensor enabled RFID tag are presented. The MEMS transducer has been designed and fabricated using a MetalMUMPs process. Its characterization showed an output variation between 9.3 pF and 13.4 pF corresponding to a pressure variation between 0 kPa and 30 kPa, with a INL of 0,2%. On the other hand, the capacitance to digital converter has been implemented in a low cost 2P4M 0.35μm CMOS standard process. The interface measurements showed a resolution of 7.8 bit and an average power consumption of 16.56 μW. Combining both devices together with a sensor enabled RFID tag, a long range passive RFID sensor for pressure measurements up to 30 kPa could be implemented.

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.