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Distributed feeder automation (FA) needs to automatically recognize the changes in the feeder topology in real-time and has to configure the application topology of the corresponding distributed functions. This paper analyzes the application topology’s requirements when implementing distributed FA for the feeder equipped with a smart terminal unit (STU) and a fault passage indicator (FPI). Based on IEC 61850, the functional logic node and topology configuration of intelligent electronic devices (IEDs) are studied. The application topology identification and data flow dynamic binding methods for fault location, fault isolation, and power supply restoration are proposed. A case study verifies the effectiveness of the proposed method.

Primary sclerosing cholangitis (PSC) is a complex bile duct disorder. Its etiology is incompletely understood, but environmental chemicals likely contribute to risk. Patients with PSC have an altered bile metabolome, which may be influenced by environmental chemicals. This novel study utilized state-of-the-art high-resolution mass spectrometry (HRMS) with bile samples to provide the first characterization of environmental chemicals and metabolomics (collectively, the exposome) in PSC patients located in the United States of America (USA) (n = 24) and Norway (n = 30). First, environmental chemical- and metabolome-wide association studies were conducted to assess geographic-based similarities and differences in the bile of PSC patients. Nine environmental chemicals (false discovery rate, FDR

Vanadium is a toxic metal listed by the IARC as possibly carcinogenic to humans. Manufactured nanosize vanadium pentoxide (V

In nonsolidly earthed distribution networks, single-phase-to-ground faults (SPGFs) significantly threaten the safety of people and equipment. Although selection and location techniques for the existing fault lines have made remarkable contributions in reducing the damage due to SPGFs, a certain amount of power loss still exists in the SPGF owing to its low efficiency in detection and maintenance. Multiple-dimensional classification of the SPGF May help reveal the nature of the fault from different perspectives; therefore, in this article, a multilabel classification model for recognizing the types of SPGF is proposed. In the proposed model, the SPGF is classified considering five dimensions, namely time-domain continuity, time-domain stability, volt-ampere characteristics of transition impedance, transition impedance size, and fault point medium. Subsequently, the corresponding features are determined. In addition, a multilabel classification model of the SPGF is constructed with an 8-D feature space and a 14-label fault-type space. Finally, a k-nearest neighbors Bayesian method is designed to solve the multilabel classification problem. The feasibility and advantages of the proposed model and methods are verified using field data and through comparison with the KNN method.

Background The incidence of primary hyperparathyroidism has increased 300% in the United States in the past 30 years, and secondary hyperparathyroidism is almost universal in patients with end-stage renal disease. We assessed the presence of environmental chemicals in human hyperplastic parathyroid tumors as possible contributing factors to this increase. Methods Cryopreserved hyperplastic parathyroid tumors and normal human parathyroids were analyzed by gas chromatography and liquid chromatography coupled to ultra-high-resolution mass spectrometry, bioinformatics, and biostatistics. Results Detected environmental chemicals included polychlorinated biphenyls, polybrominated diphenyl ethers, dichloro-diphenyl-trichloroethane derivatives, and other insecticides. A total of 99% had p,p’-dichlorodiphenyldichloroethylene. More than 50% contained other environmental chemicals, and many classified as endocrine disruptors. Polychlorinated biphenyl-28 and polychlorinated biphenyl-49 levels correlated positively with parathyroid tumor mass. Polybrominated diphenyl ether-47 concentrations in tumors were inversely correlated with patients’ serum calcium levels. Cellular metabolites in pathways of purine and pyrimidine synthesis and mitochondrial energy production were associated with tumor growth and with p,p’-dichlorodiphenyldichloroethylene in primary hyperparathyroidism tumors. In normal parathyroids, p,p'-dichlorodiphenyldichloroethylene , polychlorinated biphenyl-28, polychlorinated biphenyl-74, and polychlorinated biphenyl-153, but not p,p'-dichlorodiphenyldichloroethylene or polychlorinated biphenyl-49, were detected. Conclusion Environmental chemicals are present in human parathyroid tumors and warrant detailed epidemiologic and mechanistic studies to test for causal links to the growth of human parathyroid tumors.

The single-phase-to-ground fault (SPGF) affects the reliability and security of the distribution system greatly. Accurate online recognition of fault causes can help improve the efficiency of weak components finding and maintenance. In this article, various symptom features of the SPGF by typical causes are analyzed and a fuzzy inference system (FIS) for fault cause recognition is established for overhead lines in nonsolidly earthed distribution networks. Based on the survey of fault causes in a certain city in China, artificial grounding experiments are designed for six typical fault causes, including arrester breakdown, insulator flashover, line-to-crossbar discharge, line fallen on wet mud, line fallen on wet sand, and line fallen into the pond for waveform data collection. Through multiple time–frequency analysis on waveform data of various causes, five features are extracted and the statistical results are obtained, including self-recoverability, zero current time, transition time, degree of distortion, and randomness. Based on the above, a FIS for cause recognition for SPGFs is established. The experimental results and the comparison with the BPNN model show that the proposed method has good performance and feasibility.

This article introduces a new scheme for suburban renewable energies, hydrogen plant, and logistics (SRE-HP-LC) integration scheme and dispatching. The objective of the proposed SRE-HP-LC integration scheme aims at coordinating the operation of renewable energies, hydrogen production, and storage and logistic transportation. In order to take advantages of various energy conversion techniques to enhance the energy utilization rate and improves the financial revenue of the integration scheme, a completed dispatching and operating scheme for the proposed SRE-HP-LC integration scheme is introduced in the scheme stage. A multiple-demand response objective function is developed in the operation stage to coordinate the hydrogen energy usage in the SRE-HP-LC integration scheme. The proposed SRE-HP-LC integration scheme considers the uncertainties of renewable energy output and truck demand so that it improves the revenue of every component in the integration scheme. Case studies on a general integration system verify the effectiveness and economic value of the proposed SRE-HP-LC integration scheme.

Although polychlorinated biphenyls and polybrominated biphenyls are no longer manufactured the United States, biomonitoring in human populations show that exposure to these pollutants persist in human tissues. The objective of this study was to identify metabolic variations associated with exposure to 2,2’4,4’,5,5’-hexabromobiphenyl (PBB-153) and 2,2’4,4’,5,5’-hexachlorobiphenyl (PCB-153) in two generations of participants enrolled in the Michigan PBB Registry (http://pbbregistry.emory.edu/). Untargeted, high-resolution metabolomic profiling of plasma collected from 156 individuals was completed using liquid chromatography with high-resolution mass spectrometry. PBB-153 and PCB-153 levels were measured in the same individuals using targeted gas chromatography-tandem mass spectrometry and tested for dose-dependent correlation with the metabolome. Biological response to these exposures were evaluated using identified endogenous metabolites and pathway enrichment. When compared to lipid-adjusted concentrations for adults in the National Health and Nutrition Examination Survey (NHANES) for years 2003–2004, PCB-153 levels were consistent with similarly aged individuals, whereas PBB-153 concentrations were elevated (p

Both poor cooling methods and complex heat dissipation lead to prominent asymmetry in transformer temperature distribution. Both the operating life and load capacity of a power transformer are closely related to the winding hotspot temperature. Realizing accurate prediction of the hotspot temperature of transformer windings is the key to effectively preventing thermal faults in transformers, thus ensuring the reliable operation of transformers and accurately predicting transformer operating lifetimes. In this paper, a hot spot temperature prediction method is proposed based on the transformer operating parameters through the particle filter optimization support vector regression model. Based on the monitored transformer temperature, load rate, transformer cooling type, and ambient temperature, the hotspot temperature of a dry-type transformer can be predicted by a support vector regression method. The hyperparameters of the support vector regression are dynamically optimized here according to the particle filter to improve the optimization accuracy. The validity and accuracy of the proposed method are verified by comparing the proposed method with a traditional support vector regression method based on the real operating data of a 35 kV dry-type transformer.

Machine learning algorithms based on data mining have been continuously developed for half a century and have achieved good application effects in many fields including transformer fault diagnosis. However, mining rules only by data and using empirical risks to fit structural risks will have a certain negative impact on the generalization performance of the model and the security of the output results. When it is applied to transformer fault diagnosis, it is easy to diagnose serious fault as non-serious fault. In this paper, we introduce the characteristics of the domain experiential knowledge based approach and the machine learning model based approach, then a concept called guiding learning is introduced. On the basis of using Receiver operating characteristics curve (ROC) to verify and mine the relevant experience knowledge in transformer fault diagnosis field, combined with the extreme learning machine (ELM) model, a guiding learning model for transformer fault diagnosis is established. After that, the particle swarm optimization (PSO) algorithm is used to optimize the model, and in this process, the trust degree that changes with the number of iterations is introduced to better adapt to the training process of the model. By comparing with the ELM fault diagnosis model optimized by particle swarm optimization, it is proved that the established guiding learning model is superior to the traditional model in generalization performance and safety of output results. At the same time, the probability of serious diagnostic errors is dramatically reduced.

The power distribution internet of things (IoT) needs to solve the interconnection and interoperation problems when large-scale perception layer devices are connected. CoAP protocol is the most widely used protocol for the perception layer. The use of IEC 61850 can solve the interconnection, interoperability and interoperability issues of the power distribution IoT. After the abstract communication service interface (ACSI) subset is determined by analyzing the requirements, the direct mapping strategy is adopted to construct the method of CoAP protocol to realize the IEC 61850 services. According to the characteristics of the CoAP protocol, the resource access method is designed. By comparing the coding efficiency and resource consumption, the data coding method is determined. Based on the test platform, the real-time performance is tested. The test results show the effectiveness of the proposed method.

Integrated energy system can combine power, thermal and natural gas systems, which can effectively improve energy efficiency and maximize system benefits. The research on integrated energy system covers many aspects, among which the modeling and optimization research of system are more important. On the basis of existing research literature on integrated energy system, this paper summarizes the modeling methods and key issues of optimization research of the system respectively. At the same time, a typical integrated energy system is optimized to verify the feasibility of the relevant methods. In addition, this paper also makes a prospect for the future development of integrated energy system, and puts forward reasonable suggestions on key issues, in order to provide reference for future research.

Complementing the genome with an understanding of the human exposome is an important challenge for contemporary science and technology. Tens of thousands of chemicals are used in commerce, yet cost for targeted environmental chemical analysis limits surveillance to a few hundred known hazards. To overcome limitations which prevent scaling to thousands of chemicals, we develop a single-step express liquid extraction and gas chromatography high-resolution mass spectrometry analysis to operationalize the human exposome. We show that the workflow supports quantification of environmental chemicals in human plasma (200 µL) and tissue (≤100 mg) samples. The method also provides high resolution, sensitivity and selectivity for exposome epidemiology of mass spectral features without a priori knowledge of chemical identity. The simplicity of the method can facilitate harmonization of environmental biomonitoring between laboratories and enable population level human exposome research with limited sample volume., Humans are exposed to millions of chemicals but mass spectrometry (MS)-based targeted biomonitoring assays are usually limited to a few hundred known hazards. Here, the authors develop a workflow for MS-based untargeted exposome profiling of known and unidentified environmental chemicals.

In non-solidly earthed distribution networks, single-phase-to-ground fault (SPGF) greatly threats the safety of person and equipment. Though the existing fault line selection and location techniques have made great contributions in reducing the harm of SPGF, certain amount of power loss still exists in SPGF because of low efficiency in detecting and repairing faults of current techniques. Accurate type classification of SPGF could help reveal the fault nature from different views and further improve the fault repair efficiency. In this paper, a multi-label classification model for recognizing types of SPGF is proposed, In the proposed model, SPGF are classified respectively, and corresponding symptom features are determined according to the following five aspects: time-domain continuity, time-domain stability, volt-ampere characteristics of transition impedance, transition impedance size and fault point medium. In addition, a multi-label classification architecture for SPGF is constructed with an 8-dimension feature space and a 14-label fault type space. Finally, a KNN-Bayesian method is designed to solve the multi-label classification problem. The feasibility and advantages of the proposed model and methods are verified by the field data and the comparison with KNN method.

With the increase of the penetration rate of distributed generation, DC distribution network has gradually attracted attention due to higher power quality, lower losses, and larger distributed energy resource acceptance capability compared with AC distribution network. This paper proposes a new power flow calculation method for DC distribution network considering DC-DC converter control mode. Firstly, the steady-state models of voltage source converter (VSC) and DC-DC converter are established, and their control modes are analyzed in detail. Then power flow equations in DC distribution network considering different DC-DC converter control modes are derived, and the method for solving DC power flow problem is proposed. Finally, the modified IEEE 14-bus system is taken, and the program is implemented in MATLAB. Compared with the PSCAD simulation results, the effectiveness of the proposal is verified.

Reference standardization was developed to address quantification and harmonization challenges for high-resolution metabolomics (HRM) data collected across different studies or analytical methods. Reference standardization relies on the concurrent analysis of calibrated pooled reference samples at predefined intervals and enables a single-step batch correction and quantification for high-throughput metabolomics. Here, we provide quantitative measures of approximately 200 metabolites for each of three pooled reference materials (220 metabolites for Qstd3, 211 metabolites for CHEAR, 204 metabolites for NIST1950) and show application of this approach for quantification supports harmonization of metabolomics data collected from 3677 human samples in 17 separate studies analyzed by two complementary HRM methods over a 17-month period. The results establish reference standardization as a method suitable for harmonizing large-scale metabolomics data and extending capabilities to quantify large numbers of known and unidentified metabolites detected by high-resolution mass spectrometry methods.

Background Oxidative stress (OS) may be a key mechanism underlying the development of atrial fibrillation (AF) in experimental studies, but data in humans remain limited. Objective Systemic OS can be estimated by measurements of circulating levels of the aminothiols including glutathione, cysteine, and their oxidized products. We tested the hypothesis that the redox potentials of glutathione (E h GSH) and cysteine will be associated with prevalent and incident AF. Methods Plasma levels of aminothiols were measured in 1439 patients undergoing coronary angiography, of whom 148 (10.3%) had a diagnosis of AF. After a median follow-up of 6.3 years, 104 of 917 patients (11.5%) developed incident AF. Multivariate logistic regression and Cox regression models were used to determine whether OS markers were independent predictors of prevalent and incident AF after adjustment for traditional risk factors, heart failure, coronary artery disease, and high-sensitivity C-reactive protein level. Results For each 10% increase in E h GSH, the odds of prevalent AF was 30% higher (odds ratio [OR] 1.3; 95% confidence interval [CI] 1.1–1.7; P = .02) and 90% higher (OR 1.9; 95% CI 1.3–2.7; P = .004) when the median was used as a cutoff. The E h GSH level above the median was more predictive of chronic AF (OR 4.0; 95% CI 1.3–12.9; P = .01) than of paroxysmal AF (OR 1.7; 95% CI 1.1–2.7; P = .03). Each 10% increase in E h GSH level was associated with a 40% increase in the risk of incident AF (hazard ratio 1.4; 95% CI 1.1–1.7; P = .01). Conclusion Increased OS measured by the redox potentials of glutathione is associated with prevalent and incident AF. Therapies that modulate OS need to be investigated to treat and prevent AF.

Background Chronic ethanol (EtOH) consumption is a major cause of liver disease worldwide. Oxidative stress is a known consequence of EtOH metabolism and is thought to contribute significantly to alcoholic liver disease (ALD). Therefore, elucidating pathways leading to sustained oxidative stress and downstream redox imbalances may reveal how EtOH consumption leads to ALD. Recent studies suggest that EtOH metabolism impacts mitochondrial antioxidant processes through a number of proteomic alterations, including hyperacetylation of key antioxidant proteins. Methods To elucidate mechanisms of EtOH-induced hepatic oxidative stress, we investigate a role for protein hyperacetylation in modulating mitochondrial superoxide dismutase (SOD2) structure and function in a 6-week Lieber–DeCarli murine model of EtOH consumption. Our experimental approach includes immunoblotting immunohistochemistry (IHC), activity assays, mass spectrometry, and in silico modeling. Results We found that EtOH metabolism significantly increased the acetylation of SOD2 at 2 functionally relevant lysine sites, K68 and K122, resulting in a 40% decrease in enzyme activity while overall SOD2 abundance was unchanged. In vitro studies also reveal which lysine residues are more susceptible to acetylation. IHC analysis demonstrates that SOD2 hyperacetylation occurs near zone 3 within the liver, which is the main EtOH-metabolizing region of the liver. Conclusions Overall, the findings presented in this study support a role for EtOH-induced lysine acetylation as an adverse posttranslational modification within the mitochondria that directly impacts SOD2 charge state and activity. Last, the data presented here indicate that protein hyperacetylation may be a major factor contributing to an imbalance in hepatic redox homeostasis due to chronic EtOH metabolism.

The quality of original data is crucial to the performance of diagnosis model. To improve the performance of transformer diagnosis model based on Dissolved Gas Analysis (DGA), a new diagnosis scheme suitable for time-series dissolved gas data is proposed in this paper. After the analysis of traditional transformer diagnosis architecture, a fault data extraction step is added to the architecture to improve the quality of original fault data. The fault data extraction step is mainly composed of two parts, invalid data correction and determination of possible initial fault time based on fault early warning. Finally, the numerical results validate that the accuracy and sensitivity of DGA based fault diagnosis for the transformer are improved by extracting fault feature of time-series data.

The exposome is the cumulative measure of environmental influences and associated biological responses throughout the lifespan, including exposures from the environment, diet, behavior, and endogenous processes. A major challenge for exposome research lies in the development of robust and affordable analytic procedures to measure the broad range of exposures and associated biologic impacts occurring over a lifetime. Biomonitoring is an established approach to evaluate internal body burden of environmental exposures, but use of biomonitoring for exposome research is often limited by the high costs associated with quantification of individual chemicals. High-resolution metabolomics (HRM) uses ultra-high resolution mass spectrometry with minimal sample preparation to support high-throughput relative quantification of thousands of environmental, dietary, and microbial chemicals. HRM also measures metabolites in most endogenous metabolic pathways, thereby providing simultaneous measurement of biologic responses to environmental exposures. The present research examined quantification strategies to enhance the usefulness of HRM data for cumulative exposome research. The results provide a simple reference standardization protocol in which individual chemical concentrations in unknown samples are estimated by comparison to a concurrently analyzed, pooled reference sample with known chemical concentrations. The approach was tested using blinded analyses of amino acids in human samples and was found to be comparable to independent laboratory results based on surrogate standardization or internal standardization. Quantification was reproducible over a 13-month period and extrapolated to thousands of chemicals. The results show that reference standardization protocol provides an effective strategy that will enhance data collection for cumulative exposome research. In principle, the approach can be extended to other types of mass spectrometry and other analytical methods.

Similar to other major electrical apparatuses, the reliability and stability of the dc network is becoming the most important issue when using the voltage-source-converter-based multiterminal dc (VSC-MTDC) system for offshore wind power integration. A coordinated control strategy of VSC-MTDC named master–auxiliary is proposed by combining the advantages of the voltage margin and voltage droop control. This strategy has three advantages. First, the master converter station with the constant dc voltage control can provide reference to the system dc voltage and is helpful for the stabilization of dc voltage. Second, the integrated control of the dc voltage in both master and auxiliary converter stations are helpful for providing adequate active power control (APC) and restraining large power variation. Third, the APC converter station can serve as a backup for the dc voltage control in abnormal conditions. In order to guarantee the reliability and stability of the system under various operating conditions, this paper introduces the priority of dc voltage control to the coordination control strategy. Moreover, a parameter optimizing method of controllers for this strategy is also proposed. Finally, the effectiveness of the master–auxiliary control is verified by simulations under normal and abnormal conditions.