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The traceability method directly affects the reliability and accuracy of the measurement of electronic transformers. The stability and unreliable traceability performance of analog devices would affect the traditional analog differential method. Thus, we propose a digital differential method combining a time series-weighted correlation degree extension integrating Hilbert phase-shift of four-term continuous third-derivative Nuttall window function, effectively suppressing the end effect problem and realizing the high-precision addition of the differential. Results of simulation experiments showed that the differential's deviation was <10−10, much less than that (1.32 × 10−4) due to the analog differential. The voltage and current ratio difference's combined standard uncertainty was 5.01 × 10−5 and 5.08 × 10−5, respectively, and 0.1718′ in the phase error. Finally, an automatic traceability detection system was constructed. Compared with the traditional method, the accuracy and stability of the automatic system were better and could meet the traceability requirements for 0.05-accuracy electronic voltage transformer calibrator. [ABSTRACT FROM AUTHOR]

Ratio error (RE) estimation of the voltage transformers (VTs) plays an important role in modern power delivery systems. Existing RE estimation methods mainly focus on periodical calibration but ignore the time-varying property. Consequently, it is difficult to efficiently estimate the state of the VTs in real time. To address this issue, we formulate a time-varying RE estimation (TREE) problem into a large-scale multiobjective optimization problem, where the multiple objectives and inequality constraints are formulated by statistical and physical rules extracted from the power delivery systems. Furthermore, a set of TREE problems from different substations is systematically formulated into a benchmark test suite for characterizing their different properties. The formulation of these TREE problems not only transfers an expensive RE estimation task to a relatively cheaper optimization problem but also promotes the research in large-scale multiobjective optimization by providing a real-world benchmark test suite with complex variable interactions and correlations to different objectives. To the best of our knowledge, this is the first time to formulate a real-world problem into a benchmark test suite for large-scale multiobjective optimization, and it is also the first work proposing to solve TREE problems via evolutionary multiobjective optimization. [ABSTRACT FROM AUTHOR]

An automatic verification assembly line (AVAL) verifies the reliability and the accuracy of electricity smart meters using standard meters. As time goes by, the standard meters on an AVAL may experience metrological performance degradation, affecting verification results. Thus, the control over the standard meters’ error states is of great significance. Traditionally, their error states can only be acquired at regular intervals and remain unknown during the AVAL's operation. To address this issue, we propose a data-driven method to evaluate standard meters’ error states without interrupting the verification task. Instead of using an additional standard meter with a higher accuracy, a statistics method is applied to the verification data collected from an AVAL to conduct this work. The proposed method consists of four phases: Creating evaluation parameters, identifying the reference meter, calculating deviations, and recognizing error states. A case study on an AVAL verifies the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Exponential increase in photovoltaic installations arouses concerns regarding the impacts of large-scale solar power plants on dryland ecosystems. While the effects of photovoltaic panels on soil moisture content and plant biomass in arid ecosystems have been recognized, little is known about their influence on soil microbial communities. Here, we employed a combination of quantitative PCR, high-throughput sequencing, and soil property analysis to investigate the responses of soil microbial communities to solar panel installation. We also report on the responses of plant communities within the same solar farm. Our findings showed that soil microbial communities responded differently to the shading and precipitation-alternation effects of the photovoltaic panels in an arid ecosystem. By redirecting rainwater to the lower side, photovoltaic panels stimulated vegetation biomass and soil total organic carbon content in the middle and in front of the panels, positively contributing to carbon storage. The shade provided by the panels promoted the co-occurrence of soil microbes but inhibited the abundance of 16S rRNA gene in the soil. Increase in precipitation reduced 18S rRNA gene abundance, whereas decrease in precipitation led to decline in plant aboveground biomass, soil prokaryotic community alpha diversity, and dehydrogenase activity under the panels. These findings highlight the crucial role of precipitation in maintaining plant and soil microbial diversities in dryland ecosystems and are essential for estimating the potential risks of large-scale solar power plants on local and global climate change in the long term., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Liu, Peng, Ma, Qi, Song, Zhang, Li, Gao, Pu, Wang, Bi and Na.)