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Distributed generation (DG) offers solution to the ever increasing energy needs by generating energy at the consumer end, in most cases, by means of renewable energy sources. Islanding detection is an important aspect of interconnecting a DG to the utility. This study presents comprehensive review of various islanding detection techniques along with their relative advantages and disadvantages. A broad classification of islanding detection methods (IDMs) is laid out as classical methods, signal processing (SP)‐based methods, and computational intelligence‐based methods with a focus on SP‐based methods and computational intelligence‐based methods. The evolution of SP techniques used for islanding detection is presented along with the merits and shortcomings of each technique. Furthermore, the advent of computational intelligence methods based IDMs are discussed along with their merits and demerits. An insight into various islanding methods based on quantitative measures of performance indices such as detection time, detection accuracy, and efficiency are tabulated and presented. Finally, the prospective direction of research for IDMs is also presented.

This paper provides a comprehensive and systematic review of fault diagnosis methods based on artificial intelligence (AI) in smart distribution networks described in the literature. For the first time, it systematically combs through the main fault diagnosis objectives and corresponding fault diagnosis methods for a smart distribution network from the perspective of combined signal processing and artificial intelligence algorithms. The paper provides an in-depth analysis of the advantages and disadvantages of various signal processing techniques and intelligent algorithms in different fault diagnosis tasks, focusing on the impact of different data dimensions on the effect of fault diagnosis. This paper points out that data security issues and the question of how to combine expert domain knowledge with artificial intelligence technology are essential directions for the future development of fault diagnosis in smart distribution network.

The \"23.7\" heavy rainfall event in Beijing triggered multiple geological disasters of flush flood and debris flows, resulting in 33 deaths, 18 missing persons, and significant economic losses, which has drawn widespread social attention. Currently, geological disaster monitoring and early warning systems struggle to achieve precise warning in complex environments, making the development of refined monitoring and early warning technologies a hot and challenging topic in the research of mountain disaster and engineering disaster prevention and control. Through field investigations and the analysis of continuous records from nearby seismic stations, this study determined that the debris flow at Che'erying Village at the foot of the Western Hills in Beijing occurred at 03:36 on July 31, 2023 (UTC+0, time), with the flood peak height of approximately 3.5 m. The seismic records triggered by this disaster event exhibited a spindle shape, lasting for about 100 minutes. This study employed three methods—the long-term