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Unnoticed breakdowns in capacitor voltage transformers (CVTs) result in accuracy degeneration, even explosions. Thus, detecting breakdowns in time is critical. Existing methods have made unsatisfactory progress as labeled data is insufficient. We propose an unsupervised end-to-end method, including domain knowledge-assisted feature extraction, problem formulation, and optimization. First, an estimator is proposed to obtain the extra ratio error with detailed breakdown information. Second, a new feature, i.e., distance to the clustered voltage (DCV), is introduced. Third, the detection task is reformulated into an optimization problem. Details about breakdowns are the decision variables, aiming to minimize the differences between DCV and the estimated ratio error. An optimization algorithm called the DCV-lead grid search (DCV-GS) is designed for acceleration. Experiments and practical implementations demonstrate its accuracy and efficiency. This method could detect both existing and new breakdowns timely without labeled data, which can reduce the cost and time of maintenance. • Domain knowledge is utilized in feature extraction, problem formulation, and optimization. • A feature (DCV) is developed with voltages measured by same-phase CVTs are the same. • A ratio error estimator with details of breakdowns in a CVT is proposed. • The detection task is formulated into an optimization problem. • A DCV-guided algorithm is developed to accelerate the optimization. [ABSTRACT FROM AUTHOR]

Understanding long-term monitoring steady-state errors of capacitor voltage transformers (CVTs) is critical for acquiring accurate and continuous voltage signals from the power grid. However, existing methods cannot maintain good performance over the long term because they cannot update the initial model while primary voltages fluctuate nor maintain stability while steady-state error deteriorates. To remedy this problem, this paper proposes a physics-information-enabled self-updating method. Differences in fluctuations between primary voltages and steady-state error are illustrated based on the physical structure of CVTs. This information is then used to select samples that only contain fluctuations from primary voltages and update the model. At the same time, the mapping relationship between statistics and errors is derived, which makes the evaluation of error possible. Simulations on data from real substations show that its recognition accuracy reaches 97%, and the evaluation deviations of ratio error and phase displacement are within ± 0. 085 % and ± 2. 5 ′ . • Detect CVT with abnormal long-term measurement in real-time without power outages. • Theoretical analysis of error variation characteristics caused by the CVD in the CVT. • Construct an index that describes the degree of change in complex periodic data. • Give a physics-information-enabled self-updating method to update monitoring models. [ABSTRACT FROM AUTHOR]

Uncalibrated capacitive voltage transformers (CVTs) may significantly degrade measurement accuracy, because of the undetected excessive measurement error (ME). In this article, an online detection method is proposed which combines multi-source heterogeneous data composed of CVT measurements, acceptance test errors, and error limits. By measuring the same voltage with multiple CVTs, the monitoring statistics are generated and the statistic thresholds for the excessive ME detection are set according to the acceptance test errors and the error limits. To further ensure accuracy, the monitoring statistics and acceptance test errors for the CVTs surpassing the thresholds are used to estimate the ME. This estimation is then compared with the error limits as a cross-check to the detection result. Simulation shows that the difference between the ME estimated from the proposed method, and the actual ME is less than 0.01 % and the faulty CVT recognition accuracy exceeds 99%. • Detect the CVT with abnormal measurement in real-time without power outages. • The regulation for determining abnormal CVT is its error surpass the limits. • Can apply to all voltage transformers with different accuracy levels. • Experiments verify this method by Monte Carlo method. [ABSTRACT FROM AUTHOR]

Background: Physical exercise directly stretching the peripheral nerve promotes nerve regeneration; however, its action mechanism remains elusive. Our present study aimed to investigate the effects of mechanosensitive channel of large conductance (MscL) activated by mechanical stretching on the cultured Schwann cells (SCs) and explore the possible mechanism. Methods: Primary SCs from neonatal mice at 3–5 days of age were derived and transfected with the lentivirus vector expressing a mutant version of MscL, MscL-G22S. We first detected the cell viability and calcium ion (Ca2+) influx in the MscL-G22S-expressing SCs with low-intensity mechanical stretching and the controls. Proteomic and energy metabolomics analyses were performed to investigate the comprehensive effects of MscL-G22S activation on SCs. Measurement of glycolysis- and oxidative phosphorylation-related molecules and ATP production were respectively performed to further validate the effects of MscL-G22S activation on SCs. Finally, the roles of phosphatidylinositol-3-kinase (PI3K)/AKT/mammalian target of rapamycin (mTOR) signaling pathway in the mechanism of energy metabolism modulation of SCs by MscL-G22S activation was investigated. Results: Mechanical stretching-induced MscL-G22S activation significantly increased the cell viability and Ca2+ influx into the SCs. Both the proteomic and targeted energy metabolomics analysis indicated the upregulation of energy metabolism as the main action mechanism of MscL-G22S-activation on SCs. MscL-G22S-activated SCs showed significant upregulation of glycolysis and oxidative phosphorylation when SCs with stretching alone had only mild upregulation of energy metabolism than those without stimuli. MscL-G22S activation caused significant phosphorylation of the PI3K/AKT/mTOR signaling pathway and upregulation of HIF-1α/c-Myc. Inhibition of PI3K abolished the MscL-G22S activation-induced upregulation of HIF-1α/c-Myc signaling in SCs and reduced the levels of glycolysis- and oxidative phosphorylation-related substrates and mitochondrial activity. Conclusion: Mechanical stretching activates MscL-G22S to significantly promote the energy metabolism of SCs and the production of energic substrates, which may be applied to enhance nerve regeneration via the glia-axonal metabolic coupling. [ABSTRACT FROM AUTHOR]

• Actual ratio error of phase B CVT is lower than phase A and phase C. • Only the actual phase error of phase B CVT stay the same as calibration results. • The secondary load plays a decisive role to the error characteristics of CVTs. • The actual secondary load in substations should be applied to any kind of calibration of CVTs. The capacitor voltage transformer (CVT) is one of the most important measurement equipment in the power system, and its measurement accuracy is a key factor to ensure the safety of the power system and the fairness of electricity trade. To ensure the steady-state measurement accuracy, the conventional calibration is carried out to CVTs before operating in substations, and the operating CVTs must be calibrated periodically. However, excessive measurement error often occurs to CVTs which have passed the calibrations. In this paper, the reasons for the inconsistency between calibration results and actual measurement errors are proposed. The calibration in the laboratory, on-site calibration and three-phase operating CVTs are simulated according to actual operating conditions. Both the theoretical analysis and simulation results demonstrate that significant differences exist between the actual measurement errors and the calibration results of CVTs. Some experiments corresponding to the simulations verify the conclusions above. Finally, some suggestions to improve the reliability of calibration results are given. [ABSTRACT FROM AUTHOR]

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]

Abstract: Evaporative light scattering detector (ELSD) is considered as a universal detector able to detect almost any compound. This study successfully established a HPLC-ELSD method for the simultaneous determination of phenolic acids, flavonoids and anthocyanins in muscadine grape. Twenty-four phenolics, including 5 anthocyanins, were identified by HPLC–UV-MS, ten of them were selected to calibrate the ELSD. Results from the ELSD quantification suggested that gallic acid, proanthocyanidins, and ellagic acid were the main phenolics in muscadines. In addition, antioxidant tests suggested that the extracts of muscadine grapes possessed strong antioxidant activities. All extracts had a high total phenolic content (TPC). High total anthocyanins contents (TAC) were found in Noble muscadine, and high proanthocyanidins content (TPA) in the seed portion. [Copyright &y& Elsevier]

As the key metering equipment of the high-voltage transmission network, the high-voltage potential transformer (HVPT) needs manual maintenance to ensure that its operating status complies with relevant regulations. Due to the requirement of power outages, the manual maintenance could only be conducted at regular intervals and the measurement error of HVPT remains unknown during the operation. To address the issue, this paper proposes an on-line anomaly detection method for the measurement error of HVPT with high applicability to the complex power system. By taking advantage of the multi-bus structure, the abnormal HVPTs can be detected and located based on the in-phase relation between the HVPTs connected to the same phase and the three-phase relation within the HVPT groups. This method is data-driven and will not affect the operation of the power system. The effectiveness of the proposed method is verified in a 220 kV substation. • Detect the abnormal HVPTs in real time without power outages. • Applied to any structure where one voltage is measured with at least two HVPTs. • Not affected by voltage fluctuations and active voltage adjustment. • Performance is better than the previous three-phase relation method. • Simulation results and field application results in the substation validate the method. [ABSTRACT FROM AUTHOR]