The growth in global electricity demand has expanded the search for new energy resources. Renewable sources such as photovoltaic (PV) systems have proven to be major alternatives. PV generators connected to distribution grids have exhibited significant growth in the last decade, so it is essential to analyse the impacts resulting from this increase. This work investigated the influence of high PV system penetration in distribution grids in terms of harmonic levels. In particular, this study addressed the distortions in voltage and current waveforms in the presence and absence of PV generators connected to a distribution grid. Additionally, the total harmonic content and the individual harmonic frequencies of voltage and current were analysed. This study was performed with an IEEE 37 bus distribution system and the Open Distribution System Simulator software (OpenDSS). The results show that the voltage limits of some phases far away from a substation can exceed the limits. Furthermore, the total harmonic distortion (THD) and individual harmonic distortion (IHD) levels for voltage and current were significantly high for harmonics of the 3rd, 5th, and 7th orders, where current levels violate the standard levels.

A low-cost system for measuring lightning electric field waveforms (Lightning Detection and Waveform Storage System or LDWSS) is presented. The system has been developed for research and operational use in the Amazon region of Brazil. In order to quantify the magnitudes of recorded fields, the LDWSS was calibrated relative to the electric field measuring system operating at the Lightning Observatory in Gainesville (LOG), Florida. The various factors that can influence electric field measurements are discussed. As an example of application of the system, peak current of a return stroke in lightning triggered using the rocket-and-wire technique at Camp Blanding, Florida, was inferred using the corresponding electric field waveform recorded by LDWSS and compared with the directly measured current. Additionally, peak currents inferred from LDWSS field measurements were compared with those reported by the U.S. National Lightning Detection Network.

The importance of lightning has long been recognized from the point of view of climate‐related phenomena. However, the detailed investigation of lightning on global scales is currently hindered by the incomplete and spatially uneven detection efficiency of ground‐based global lightning detection networks and by the restricted spatio‐temporal coverage of satellite observations. We are developing different methods for investigating global lightning activity based on Schumann resonance (SR) measurements. SRs are global electromagnetic resonances of the Earth‐ionosphere cavity maintained by the vertical component of lightning. Since charge separation in thunderstorms is gravity‐driven, charge is typically separated vertically in thunderclouds, so every lightning flash contributes to the measured SR field. This circumstance makes SR measurements very suitable for climate‐related investigations. In this study, 19 days of global lightning activity in January 2019 are analyzed based on SR intensity records from 18 SR stations and the results are compared with independent lightning observations provided by ground‐based (WWLLN, GLD360, and ENTLN) and satellite‐based (GLM, LIS/OTD) global lightning detection. Daily average SR intensity records from different stations exhibit strong similarity in the investigated time interval. The inferred intensity of global lightning activity varies by a factor of 2–3 on the time scale of 3–5 days which we attribute to continental‐scale temperature changes related to cold air outbreaks from polar regions. While our results demonstrate that the SR phenomenon is a powerful tool to investigate global lightning, it is also clear that currently available technology limits the detailed quantitative evaluation of lightning activity on continental scales.

The occurrence context of compact intracloud discharges (CIDs) is examined using their electric field waveforms and corresponding NLDN data. A total of 1096 CIDs transporting negative charge upward and 8 CIDs transporting positive charge upward were analyzed. The CIDs were categorized based on whether they were isolated or were followed, preceded, or both followed and preceded by other NLDN-reported lightning events. The percentages of isolated CIDs transporting negative charge upward decreased from 92% for 5 km search radius and ±10 ms time window to 31% for 10 km and ±1000 ms, this decrease being accompanied by an increase of the percentage of CIDs preceding (initiating) normal lightning events from 6.8% to 43%. GM NLDN-reported peak currents for isolated CIDs (33 kA) were similar to those initiating normal lightning events (34 kA). Some of our isolated CIDs could be viewed as precursors, because they apparently initiated normal lightning events at essentially the same location after time intervals measured in seconds. CIDs transporting positive charge upward (a) occurred at heights ranging from 16 to 19 km vs. 6 to 16 km for CIDs transporting negative charge upward and (b) had considerably higher NLDN-reported peak currents: 113 kA vs. 33 kA (GM values).

Lightning represent a potential threat to various society activities, such as damage to telecommunication systems and the distribution of electric power, as well as injury or death of humans beings. Predicting the occurrence of lightning can help in making decisions about the actions that must be taken to minimize the risks of this natural phenomenon. In this study, data from air temperature profiles, dew point temperature and historical lightning data were used to obtain two predictive models of lightning occurrence. The models were obtained by using an artificial neural network. The first model was obtained through unbalanced data and the second one with data balanced with Synthetic Minority Over-sampling Technique (SMOTE). The model performance was tested in five different classes of lightning predictions: ABSENCE, LOW, MODERATE, VERY and SEVERE, considering five prediction periods: case 1 (one hour), case 2 (two hours), case 3 (three hours), case 4 (four hours) and case 5 (five hours). It was observed that the use of the Synthetic Minority Over-sampling Technique improved accuracy in the recognition of atmospheric patterns that lead to the incidence of lightning in the five classes used in the five prediction cases.