Lightning discharges in the atmosphere owe their existence to the combination of complex dynamic and microphysical processes. Knowledge discovery and data mining methods can be used for seeking characteristics of data and their teleconnections in complex data clusters. We have used machine learning techniques to successfully hindcast nearby and distant lightning hazards by looking at single-site observations of meteorological parameters. We developed a four-parameter model based on four commonly available surface weather variables (air pressure at station level (QFE), air temperature, relative humidity, and wind speed). The produced warnings are validated using the data from lightning location systems. Evaluation results show that the model has statistically considerable predictive skill for lead times up to 30 min. Furthermore, the importance of the input parameters fits with the broad physical understanding of surface processes driving thunderstorms (e.g., the surface temperature and the relative humidity will be important factors for the instability and moisture availability of the thunderstorm environment). The model also improves upon three competitive baselines for generating lightning warnings: (i) a simple but objective baseline forecast, based on the persistence method, (ii) the widely-used method based on a threshold of the vertical electrostatic field magnitude at ground level, and, finally (iii) a scheme based on CAPE threshold. Apart from discussing the prediction skill of the model, data mining techniques are also used to compare the patterns of data distribution, both spatially and temporally among the stations. The results encourage further analysis on how mining techniques could contribute to further our understanding of lightning dependencies on atmospheric parameters.

In this work, we present a novel technique to locate partial discharge (PD) sources based on the concept of time reversal. The localization of the PD sources is of interest for numerous applications, including the monitoring of power transformers, Gas Insulated Substations, electric motors, super capacitors, or any other device or system that can suffer from PDs. To the best of the authors’ knowledge, this is the first time that the concept of time reversal is applied to localize PD sources. Partial discharges emit both electromagnetic and acoustic waves. The proposed method can be used to localize PD sources using either electromagnetic or acoustic waves. As a proof of concept, we present only the results for the electromagnetic case. The proposed method consists of three general steps: (1) recording of the waves from the PD source(s) via proper sensor(s), (2) the time-reversal and back-propagation of the recorded signal(s) into the medium using numerical simulations, and (3) the localization of focal spots. We demonstrate that, unlike the conventional techniques based on the time difference of arrival, the proposed time reversal method can accurately localize PD sources using only one sensor. As a result, the proposed method is much more cost effective compared to existing techniques. The performance of the proposed method is tested considering practical scenarios in which none of the former developed methods can provide reasonable results. Moreover, the proposed method has the unique advantage of being able to locate multiple simultaneous PD sources and doing so with a single sensor. The efficiency of the method against the variation in the polarization of the PDs, their length, and against environmental noise is also investigated. Finally, the validity of the proposed procedure is tested against experimental observations.

In this paper, we propose and implement a novel technique to locate Electromagnetic Interference (EMI) sources using the concept of time reversal cavity. We show in an intuitive manner that reflections from the surfaces of a cavity can emulate an infinite number of sensors in the Time Reversal (TR) method. In order to locate EMI sources, the equipment under test (EUT) is placed in a rectangular metallic cavity, of suitable dimensions according to the considered frequency band and the EUT size, equipped with a simple monopole or dipole antenna. We demonstrate that, by using only one sensor, we are able to locate EMI sources by taking advantage of the focusing properties of a time reversal cavity. The entropy criterion is applied to obtain the focusing time slice in which the maximum electric field determines the location of the EMI source. Both two- and three-dimensional numerical simulation schemes are deployed to demonstrate the ability of the proposed technique. The validity of numerical simulations is tested against frequency domain measurements. Compared to conventional EMI tests in anechoic chambers and scanning methods, the proposed technique represents a simpler and cost-effective test method requiring only one sensor (a monopole or dipole antenna).

Lightning is highly destructive due to its high power density and unpredictable character. Directing lightning away would allow to protect sensitive sites from its direct and indirect impacts (electromagnetic perturbations). Up to now, lasers have been unable to guide lightning efficiently since they were not offering simultaneously terawatt peak powers and kHz repetition rates. In the framework of the Laser Lightning Rod project, we develop a laser system for lightning control, with J-range pulses of 1 ps duration at 1 kHz. The project aims at investigating its propagation in the multiple filamentation regime and its ability to control high-voltage discharges. In particular, a field campaign at the Säntis mountain will assess the laser ability to trigger upward lightning.

We implement an electromagnetic time reversal technique (EMTR) to locate lightning return strokes. The two-dimensional finite difference time domain is employed to simulate the EMTR process in both, the forward-time and the backward-time phases. Scatterers are included in the computational domain to emulate the presence of objects. Three possible criteria to find the optimum time slice of the EMTR process that includes the maximum peak field, maximum peak energy, and last local minimum of entropy are tested and it is found that only the entropy criterion can successfully locate the lightning discharge. Our analysis shows that the EMTR process in both, using an unchanged and a simplified medium for the backward time works reasonably well even with only two sensors. Furthermore, we validated the proposed method via experimental results using waveforms recorded at two sensors at distances of 14.7 and 380 km from the Säntis Tower. The results demonstrate that the EMTR back-propagation process leads to a refocusing of the radiated energy at the location of the Säntis Tower. The ambiguity in the obtained location when only two sensors are used can be resolved either by using an additional sensor or through a more accurate modeling of the terrain.

In this article, we present a machine learning-based method to locate lightning flashes using calculations of lightning-induced voltages on a transmission line. The proposed approach takes advantage of the preinstalled voltage measurement systems on power transmission lines to get the data. Hence, it does not require the installation of additional sensors such as extremely low frequency, very low frequency, or very high frequency. The proposed model is shown to yield reasonable accuracy in estimating two-dimensional geolocations for lightning strike points for different grid sizes up to 100 × 100 km2. The algorithm is shown to be robust against the distance between the voltage sensors, lightning peak current, lightning current rise time, and signal to noise ratio of the input signals.