Luis Orellana

Pollution on the surface of an insulator, combined with conditions such as fog, dew, or moisture, directly influences partial discharge (PD) activity, a key indicator of an insulator’s aging process. Using an artificial pollution chamber, three classes of insulators were subjected to saline dew across multiple test runs, during which radiofrequency emissions were recorded by an antenna. The contamination process was monitored and characterized using three indicators derived from the Cumulative Energy per Interval, the Cumulative Average Time Between Signals, and the Cumulative Distributed Energy. By employing segmented regression analysis on these variables, it was possible to detect and quantify changes in PD activity as measured in the ultra-high-frequency (UHF) band by a nearby antenna. Compared with galvanic, leakage-current based approaches and artificial intelligence (AI)-based models, the proposed scheme has lower computational cost and provides operator tunable alert states for training-free, non-contact, trend-based early warning. In every test run, the developed algorithm identified at least one trend change by 80% of the test duration. Analyzing each trend change with the three indicators enabled graded alert states, demonstrating flexibility for assessing flashover (FO) risk. In all experiments conducted on the different types of insulators, FO consistently occurred during the highest alarm state.

Insulators are critical components in the transmission of electrical energy and their correct operation is essential to guarantee the safety and continuity of the electrical supply. However, their constant exposure to environmental conditions such as pollution can affect their performance and lead to costly power outages. Proper monitoring and maintenance of this equipment are essential to avoid unexpected failures. Although significant progress has been made in the implementation of new monitoring methodologies and instruments, a solution that enables accurate and reliable online monitoring remains a challenge. This work presents a novel insulator flashover alert system that uses an acoustic sensor and an ultra-high frequency antenna to detect partial discharge activity and generate alerts in three different states. The system uses hybrid deep learning models that combine a Convolutional Neural Network and a Long Short-Term Memory network that process the information provided by the sensors and generate local alerts to produce the general decision. This system can indicate whether the monitored insulator is in a normal state, attention state or alarm state, which could help specialists take preventive measures and avoid sudden insulator failures.

Partial Discharge detection is crucial to diagnose the condition of the insulation system of High-voltage equipment. Despite the existence of many different methods to monitor the presence of PD, ultra-high frequency methods are frequently used since galvanic contact between the antenna and the equipment under test is not required during the measurement. However, the measurement of electromagnetic emissions using antennas may be influenced not only by noise from outside sources but also by reflections or changes in the signals caused by obstacles in the measurement environment. This work presents the results of a series of experimental measurements that led to characterizing the spectral behavior of the partial discharge signals captured with a Vivaldi antenna in the presence of two different configurations of obstacles located in the vicinity of the partial discharge emission source. From these results, it was possible to establish that the spectral content of the partial discharge signals in ultra-high frequency changes for certain bands or frequency intervals depending on the shape of the obstacles located in the measurement environment. Likewise, it was evidenced that the changes in the signals can modify the characteristic parameters based on the spectral behavior of the signals, generating variations in the results obtained in the processes of separation and identification of sources.

Electrical treeing is the main degradation mechanism in high voltage polymeric insulation, that leads to power system plant failure and the loss of electricity supply. Electrical trees grow under partial discharge (PD) activity, which can be measured and analyzed to understand and characterize electrical tree growth. In this work, PD measurements were analyzed for electrical trees grown in epoxy resin needleplane samples under very low frequency (VLF, 0.1 Hz) voltage excitation. VLF is interesting as it is used for testing power cables and other high capacitance insulation loads. However, more experience and new methods are needed for PD interpretation. PDs were studied using two tools: pulse sequence analysis (PSA) and nonlinear time series analysis (NLTSA) from dynamic system theory. PSA was treated here as a particular case of NLTSA since their constructions are similar in their mathematical treatment. The experimental results showed that electrical trees grown at VLF had branch-type structure and times to breakdown about fifty times larger than samples aged at industrial frequency. PSA plots were compared with 2D projections of state-space trajectories that represent the dynamics of the nonlinear system (NLTSA approach). In terms of graphical representation, NLTSA 2D projections generated more clusters than the PSA plots, thus, it was interpreted that NLTSA revealed more details about the nonlinear dynamic system associated with electrical tree growth. On the other hand, using the NLTSA approach, the correlation dimension was estimated to characterize the electrical tree growth. The results showed a different evolution obtained for VLF excitation compared to the results reported for test samples aged at industrial frequency in other studies.

Pulsed plasma discharges, such as the plasma focus, are a source of pulsed X rays, therefore it is desirable to understand the relationship between this fast transient phenomena and the electrical variables of the discharge. Parameters from the electrical diagnostic signals are typically used to characterize the plasma focus discharge and for the correlations with X rays measurements via scatter plots. To further evaluate relevant information in the electrical signals, besides the characteristic parameters, an implementation of different types of machine learning algorithms, that included deep learning, was performed. A classification of pulses associated with an X rays measurement, in terms of the electrical signals data as input, was carried out. Two approaches were compared: the selection of the characteristic parameters and the use of the entire signals so the algorithms could find additional information for the classification task. The electrical diagnostic signals corresponded to: the voltage at the electrodes of the discharge chamber measured with a resistive voltage divider; time variation of the circuit current measured with a Rogowski coil and an inductive loop sensor; and the electromagnetic burst from the circuit measured with a Vivaldi antenna. The X rays measurement corresponded to the signal obtained from a scintillatorphotomultiplier. In terms of the performance of the algorithms models in this classification problem, the results indicated that there is no significative improvements when using the entire signal or the selection of characteristic parameters. The best results were obtained when the following parameters were used: voltage at time of gas breakdown, voltage at time of pinch, current at time of pinch, time derivative of current at time of pinch, time from breakdown to pinch, and the Fast Fourier Transform of the part of the Vivaldi antenna signal related to the pinch event.

A remote and non-invasive diagnostic of the plasma focus using antennas is presented in this work. The main motivation is the application of such diagnostic in a miniaturized plasma accelerator, based on the plasma focus architecture, as a cube satellite thruster. The evaluation of this proposal was carried out measuring a hundred of joules plasma focus operation simultaneously with the inductive measurement and antennas. Three different antennas tuned in the ultra high frequency range were tested: a monopole, Vivaldi and helical. The high frequency transients detected with the antennas were time correlated to the known inductive measurement features. The initial dielectric breakdown and later plasma pinch and subsequent disruption (i.e. the source of the propulsion) were identified to be the principal phenomena to be detected. Signal parameter correlations between the inductive sensor and the antennas showed that the pinch phenomena can be correlated with the antenna signals. Good correlation results were obtained with the monopole antenna when using peak value and signal energy parameter from the antenna transient. An improvement in the correlation results, for the helical and Vivaldi antennas, was obtained when calculating the frequency band energy. In this case, the Vivaldi antenna achieved the best results. The results of the monopole antenna make it an alternative remote sensor for plasma focus, but for the application of a miniaturized plasma focus as pulsed plasma thruster, the Vivaldi antenna is a more feasible design to replace the inductive diagnostic due to its compact design in comparison to the monopole.