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This paper focuses on identifying voltage sag-sensitive loads within unknown load type or when the load is already in operation. To achieve this, a new method of sensitive load identification based on power quality monitoring data is proposed. Firstly, the active power RMS monitoring data is used as the base data. The Hodrick-Prescott filtering and sliding mean segmentation are used to divide the period of the voltage sag event. Next, based on the division result, the differences of steady-state power quality monitoring data before and after each event are calculated as the dataset. The dynamic K-means is used to divide various load action areas. Finally, the voltage tolerance curves of each action area are fitted and compared with the preset curves, then according to the constituted rules in this paper, the type of sensitive load contained by user is recognized. The feasibility and accuracy of the proposed method are verified by analyzing the simulation examples and actual power quality monitoring data.© 2017 Elsevier Inc. All rights reserved.

The development and application scope of the Internet of Things is also becoming more and more extensive. Especially in the application of power testing improved systems, great progress has been made. This article aims to study how to analyze the system detection equipment based on the Internet of Things. This article describes the basic theoretical knowledge of the Internet of Things and power detection improved systems. A clustering analysis algorithm and a support vector machine algorithm based on the Internet of Things are proposed. In the experiment of this article, the scoring items of the expert’s traditional detection system include complex technology, inconvenient use, and incomplete intelligence. Among them, the highest score for complex technology is 8.6 points, the lowest score is 7 points; the highest score for inconvenience is 8.6 points, and the lowest is 8.3 points. It can be seen that related experts believe that the traditional power detection improved system is not only very complicated in technology, very inconvenient to use but also incompletely intelligent. Therefore, it is very necessary to study the system detection equipment based on the Internet of Things.

N‐heterocyclic carbene (NHC) organocatalysis has been developed as a powerful tool in modern synthetic chemistry. NHC catalytic activation of ynals and alkynoic acid derivatives provided versatile reactions that involve acetylenic Breslow and/or acylazolium as key intermediates, and diverse transformations have been established for access to molecules with unique skeletons in efficient fashions. Herein we summarize the recent achievements in NHC‐catalyzed reactions involving acetylenic Breslow and/or acylazolium intermediates. Different reactions belonging to three catalytic modes, including (1) conjugate additions to acetylenic Breslow derived α,β‐unsaturated acylazolium intermediates, (2) β‐umpolung of ynals via acetylenic Breslow intermediates, and (3) conjugate additions to acetylenic acylazolium intermediates, are emphasized with examples and plausible mechanisms cited to guide a better understanding. [ABSTRACT FROM AUTHOR]

In the case of a weak signal from a photon counting lidar and strong noise from the solar background, the signal is completely submerged by noise, potentially resulting in the appearance of multiple peaks in the denoising algorithm of photon counting entropy. Consequently, a clear distinction between the signal and noise may become challenging, leading to significant fluctuation in the ranging error. To solve this problem, this paper proposes an improved offset parameter optimization algorithm under the framework of photon counting entropy, aiming to effectively eliminate peak interference caused by noise and enhancing ranging accuracy. The algorithm includes two aspects. First, we introduce the solar irradiance prediction of an MLP network and least squares linear conversion to accurately estimate the noise rate of the solar background noise. Then, we propose the offset parameter optimization method to effectively mitigate the interference caused by noise. In simulation and experimental analyses, the ranging error of our proposed method is within 5 and 30 cm , respectively. Compared with the denoising method of photon counting entropy, the average ranging error is increased by 81.99% and 73.76%. Furthermore, compared to other anti-noise methods, it exhibits superior ranging capability. [ABSTRACT FROM AUTHOR]