In this paper, the design and application of a circular microstrip antenna for partial discharge detection is presented. For this purpose, the truncated ground plane technique was applied for the bandwidth enhancement. In addition, aiming at an optimized structure, the computational simulations with several combinations of the antenna dimensional parameters were performed. In order to verify whether the developed antenna met the prerequisites (bandwidth and gain) for practical application in partial discharge detection, the optimized antenna was subjected to tests in an anechoic chamber. Finally, the antenna sensitivity was compared with the IEC 60270 standard method. For this purpose, the simultaneous tests were carried out in a partial discharge generator arrangement composed of an oil cell with point-to-plane and plane-to-plane electrode configurations. The developed antenna can detect apparent charge values higher than 30 pC and has an average gain greater than 2 dB along its bandwidth. The obtained results attested to the potential of the developed antenna for the development of UHF sensors for partial discharge detection in high-voltage equipment.

In this paper, the design and development of a bio-inspired UHF sensor for partial discharge detection in power transformers is presented. The UHF sensor was developed for external use in dielectric windows of power transformers. For this purpose, a microstrip antenna was designed with a radiating element shape based on the leaf of the Jatropha mollissima (Pohl) Baill plant. Then, an epoxy coating and an aluminium enclosure were developed to protect the antenna against corrosion and to provide mechanical support, external noise immunity, and a lifetime compatibility with power transformers. In order to verify the electrical parameters of the developed sensor, measurements of the gain and the reflection coefficient were performed in an anechoic chamber. Lastly, the antenna sensitivity for denominated partial discharge (PD) detection was compared with the IEC 60270 standard method. For this purpose, simultaneous tests were carried out in a partial discharge generator setup, composed of an oil cell with needle-plane electrodes. The experimental tests demonstrated the effectiveness of the sensor for detecting PD signals with apparent charge values higher than 35 pC.

This paper discusses the application of the first peak and cumulative energy methods to calculate the time difference of arrival (TDOA) of UHF signals emitted by a partial discharge source in order to locate it. We used a 200 × 200 × 97 cm3 dimensional arrangement composed of four UHF printed monopole antennas, which was bio-inspired by the Jatropha mollissima (Pohl) Baill leaf. In addition, an oil cell composed of a pair of immersed flat-tip electrodes was used as a source of partial discharge (PD). The oil cell was positioned at four different coordinates in order to determine the efficiency of source localization based on the signals detected by the antennas and the applied methods. Finally, we evaluated the effects of the addition of noise filtering techniques and the sampling error of the measuring instrument on the localization results. Both TDOA extraction methods converged to satisfactory location results considering the dimensional arrangement evaluated, resulting in absolute errors of 7.20 cm up to 9.00 cm and 5.40 cm up to 10.96 cm, regarding the real PD position, for the cumulative energy and first peak methods, respectively.

This paper presents an algorithm for the localisation of partial discharge (PD) sources in power transformers based on the electromagnetic waves radiated by a PD pulse. The proposed algorithm is more accurate than existing methods, since it considers the effects of the reflection, refractions and diffractions undergone by the ultra-high frequency (UHF) signal within the equipment tank. The proposed method uses computational simulations of the electromagnetic waves generated by PD, and obtains the time delay of the signal between each point in the 3D space and the UHF sensors. The calculated signals can be compared with the signals measured in the field, so that the position of the PD source can be located based on the best correlation between the simulated propagation delay and the measured data. The equations used in the proposed method are defined as a 3D optimisation problem, so that the binary particle swarm optimisation algorithm can be used. To test and demonstrate the proposed algorithm, computational simulations were performed. The solutions were sufficient to identify not only the occurrence of defects, but also the winding and the region (top, centre or base) in which the defect occurred. In all cases, an accuracy of greater than 15 cm was obtained for the location, in a 180 MVA three-phase transformer.

The adaptation of dielectric windows as metamaterial superstrate over a bio-inspired Printed Monopole Antenna (PMA) was evaluated in order to improve the detection sensitivity of Ultra High Frequency (UHF) sensors designed for Partial Discharge (PD) measurement. For this purpose, rectangular and circular Split Ring Resonators (SRR) structures were designed and evaluated aiming to achieve a metamaterial superstrate that improves the characteristics of the bio-inspired PMA as the gain, bandwidth, and radiation pattern. Measurements of the PMA with metamaterial superstrate were carried out in an anechoic chamber and compared to the simulations performed. The results show that the metamaterial superstrate insertion did not impact the original operating bandwidth, covering most of the characteristic frequency range of PD activity. Moreover, this insertion resulted in a mean gain enhancement of 0.7 dBi regarding the reference PMA, resulting in an antenna with better sensitivity for PD detection (mean gain of 3.61 dBi). The PMA-metamaterial set PD detection sensitivity was evaluated through laboratory tests with a point-to-plane PD generator setup and in field with measurements from a 230 kV current transformer. The developed PMA-metamaterial set was able to detect, successfully, the activity of PD for both tests, being classified as an optimized sensor for PD detection through dielectric windows.

This paper addresses the critical challenge of detecting, separating, and classifying partial discharges in substations. It proposes two solutions: the first involves developing a signal conditioning system to reduce the sampling requirements for PD detection and increase the signal-to-noise ratio. The second approach uses machine learning techniques to separate and classify PD based on features extracted from the conditioned signal. Three clustering algorithms (K-means, Gaussian Mixture Model (GMM), and Mean-shift) and the Support Vector Machine (SVM) method were used for signal separation and classification. The proposed system effectively reduced high-frequency components up to 50 MHz, improved the signal-to-noise ratio, and effectively separated different sources of partial discharges without losing relevant information. An accuracy of up to 93% was achieved in classifying the partial discharge sources. The successful implementation of the signal conditioning system and the machine learning-based signal separation approach opens avenues for more economical, scalable, and reliable PD monitoring systems.