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An algorithm making use of hybrid features of Hilbert transform (HT) and Stockwell transform (ST) to identify the single-stage and multiple (multi-stage) power quality disturbances (PQDs) is introduced in this manuscript. A power quality index (PI) and time location index (TLI), based on the features computed from the voltage signal by the use of HT and ST are proposed for recognition of the PQDs. Four features extracted from the PI and TLI are considered for classification of the PQDs achieved using decision tree driven by rules. The algorithm is tested on the PQDs generated with the help of mathematical models (in conformity with standard IEEE-1159). Performance is evaluated on 100 data set of every disturbance computed by varying various parameters, and efficiency is found to be greater than 99%. It is established that an algorithm is effective for recognition of PQ events with an efficiency greater than 98% even in the presence of high-level noise. Algorithm is faster compared to many reported techniques and scalable for application to voltages of all range. Results are validated through comparison with the results of the algorithms reported in the literature. Performance of the algorithm is effectively validated on the practical utility network. This algorithm can be effectively implemented for designing the power quality (PQ) monitoring devices for the utility grids.INDEX TERMS Hilbert transform, rule based decision tree, power quality disturbance, power quality index, Stockwell transform, time location index.

Infection in apple leaves is typically brought on by unanticipated weather conditions such as rain, hailstorms, draughts, and fog. As a direct consequence of this, the farmers suffer a significant loss of productivity. It is essential to be able to identify apple leaf diseases in advance in order to prevent the occurrence of this disease and minimise losses to productivity caused by it. The research offers a bibliometric analysis of the effectiveness of artificial intelligence in diagnosing diseases affecting apple leaves. The study provides a bibliometric evaluation of apple leaf disease detection using artificial intelligence. Through an analysis of broad current developments, publication and citation structures, ownership and cooperation patterns, bibliographic coupling, productivity patterns, and other characteristics, this scientometric study seeks to discover apple diseases. Nevertheless, numerous exploratory, conceptual, and empirical studies have concentrated on the identification of apple illnesses. However, given that disease detection is not confined to a single field of study, there have been very few attempts to create an extensive science map of transdisciplinary studies. In bibliometric assessments, it is important to take into account the growing amount of research on this subject. The study synthesises knowledge structures to determine the trend in the research topic. A scientometric analysis was performed on a sample of 214 documents in the subject of identifying apple leaf disease using a scientific search technique on the Scopus database for the years 2011–2022. In order to conduct the study, the Bibliometrix suite’s VOSviewer and the web-based Biblioshiny software were also utilised. Important journals, authors, nations, articles, and subjects were chosen using the automated workflow of the software. Furthermore, citation and co-citation checks were performed along with social network analysis. In addition to the intellectual and social organisation of the meadow, this investigation reveals the conceptual structure of the area. It contributes to the body of literature by giving academics and practitioners a strong conceptual framework on which to base their search for solutions and by making perceptive recommendations for potential future research areas.

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The aim of this paper is to analyze the short circuit (SC) behavior and variation in fault level due to solar PV inverters in a smart distribution network. In order to investigate the issue, a generic urban distribution feeder is modeled, which supplies power to the loads of varying nature. The network is analyzed according to IEC 60909 standard and the alteration in SC current and fault level by the inverters is reported. Paper also reports that the grid strength and neutral grounding techniques significantly affect the inverter SC current. It is found that, with solar PV integration, the major change in fault level occurs due to the three phase fault then followed by double line to ground and other faults. Moreover, the varying network conditions also alter the SC current and fault level of the system. Such situation has to be managed to ensure the protection coordination and reliability of the system. The integration of more and more renewable energy sources like solar PV is expected in future power grid, therefore a proper planning study is required at the time of integration to ensure the reliable operation of the system. Keywords Single line to ground (SLG) · Double line to ground (DLG) · Line to line (LL) · 3 phase fault · Multiple faults · Solar PV · Distribution network · Weak grid · Fault MVA