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In this study, the open-circuit faults diagnosis and location issue of the neutral-point-clamped (NPC) inverters are analysed. A novel fault diagnosis approach based on knowledge driven and data driven was presented for the open-circuit faults in insulated-gate bipolar transistors (IGBTs) of NPC inverter, and Concordia transform (knowledge driven) and random forests (RFs) technique (data driven) are employed to improve the robustness performance of the fault diagnosis classifier. First, the fault feature data of AC in either normal state or open-circuit faults states of NPC inverter are analysed and extracted. Second, the Concordia transform is used to process the fault samples, and it has been verified that the slopes of current trajectories are not affected by different loads in this study, which can help the proposed method to reduce overdependence on fault data. Moreover, then the transformed fault samples are adopted to train the RFs fault diagnosis classifier, and the fault diagnosis results show that the classification accuracy and robustness performance of the fault diagnosis classifier are improved. Finally, the diagnosis results of online fault diagnosis experiments show that the proposed classifier can locate the open-circuit fault of IGBTs in NPC inverter under the conditions of different loads. [ABSTRACT FROM AUTHOR]

In this study, a comprehensive online multi-period power dispatch formulation that takes AC constraints, renewable energy, and ramping constraints into account is proposed. The AC power flow equations, voltage limits, and thermal limits are respected to ensure the obtained solution causes no static violation while guaranteeing transfer capability. A four-stage solution methodology is proposed to solve the non-linear constrained optimisation problem in which the thermal limit constraints are equivalently represented by active-set-based constraints in the third stage, and an adaptive homotopy-enhanced primal-dual interior point method (PDIPM) in solving power dispatch problem is presented in the fourth stage to reliably and efficiently obtain the solution. This solution methodology enhances both the robustness and speed of the PDIPM. The proposed four-stage solution methodology has been evaluated on several testing systems, ranging from a 30-bus system to a 3012-bus system with four time periods, with promising results. [ABSTRACT FROM AUTHOR]

Years of operation experience on power systems reveal that most transmission line fault events are related to seasonal alternating meteorological disasters, which have typical temporal and spatial distribution characteristics. However, power system risk assessment lacks basically accurate descriptions of failure rate time distribution for transmission lines. In this study, a method to calculate the time-varying failure rate of transmission lines in a monthly time scale is proposed to reflect the time-dependent fault regulation. However, the failure rate during any time interval cannot be derived directly from limited historical fault samples. Therefore, a simulation method of continuous time distribution function for failure rate is proposed, which adopts Fourier, Gaussian, and Weibull function assumptions. Furthermore, the parameters of these function hypotheses are fitted and compared using the fault samples of a province power grid and an urban power grid in China, respectively. Results show that the proposed simulation model is reasonable. Finally, the time-varying failure rate simulation model is adopted to quantify the risk of the verified IEEERBTS system. The risk indices also indicate that considering the time distribution characteristics of failure rate has a more significant influence on the system risk than the conventional methods. [ABSTRACT FROM AUTHOR]

Stochastic subspace identification (SSI) methods have been widely employed for oscillatory mode identification on probing and ambient data and are reported to have good performances. This work proposes a novel SSI-based approach for identifying dominant oscillatory mode from measurement data and extends the application of SSI to ringdown condition. The proposed approach first constructs an initial cluster of eigenvalues from SSI with repetitive calculations and then utilises a novel hierarchical clustering method to extract the dominant modes from the initial cluster. The repetitive calculations within the SSI are performed through varying the model order over a range defined by a novel initial order determination process. By doing so the challenge of model order determination for SSI-based methods is resolved. Moreover, benefiting from the repetitive calculations and the clustering process, the proposed approach is highly immune to prevalent noises in the measurements. Finally, the proposed approach is applied and validated on the field-measurement data from the phasor measurement units of China Southern Power Grid (CSG) through comparisons with Prony, ARMAX, and Monte Carlo methods. Test results demonstrate that the proposed approach performs with high accuracy, robustness, and efficiency in CSG. [ABSTRACT FROM AUTHOR]

The scalable extension of the H.264/AVC video coding standard (SVC) demonstrates superb adaptability in video communications. Joint source and channel coding (JSCC) has been shown to be very effective for such scalable video consisting of parts of different significance. In this paper, a new JSCC scheme for SVC transmission over packet loss channels is proposed which performs two-dimensional optimization on the quality layers of each frame in a ratedistortion (R D) sense as well as on the temporal hierarchical structure of frames under dependency constraints. To compute the end-to-end R-D points of a frame, a novel reduced trellis algorithm is developed with a significant reduction of complexity from the existing Viterbi-based algorithm. The R-D points of frames are sorted under the hierarchical dependency constraints and optimal JSCC solution is obtained in terms of the best R-D performance. Experimental results show that our scheme outperforms the existing scheme of [13] with average quality gains of 0.26 dB and 0.22 dB for progressive and non-progressive modes respectively. [ABSTRACT FROM AUTHOR]

Helicobacter pylori persists deep in the human gastric mucus layer in a harsh, nutrient-poor environment. Survival under these conditions depends on the ability of this human pathogen to invoke starvation/stress responses when needed. Unlike many bacteria, H. pylori lacks starvation/stress-responding alternative sigma factors, suggesting an additional mechanism might have evolved in this bacterium. Helicobacter pylori produces polyphosphate; however, the role and target of polyphosphate during starvation/stress have not been identified. Here we show that polyphosphate accumulated during nutrient starvation directly targets transcriptional machinery by binding to the principal sigma factor in H. pylori, uncovering a novel mechanism in microbial stress response. A positively charged Lys-rich region at the N-terminal domain of the major sigma factor is identified as the binding region for polyphosphate (region P) in vivo and in vitro, revealing a new element in sigma 70 family proteins. This interaction is biologically significant because mutant strains defective in the interaction undergo premature cell death during starvation. We suggested that polyphosphate is a second messenger employed by H. pylori to mediate gene expression during starvation/stress. The putative ‘region P’ is present in sigma factors of other human pathogens, suggesting that the uncovered interaction might be a general strategy employed by other pathogens. [ABSTRACT FROM AUTHOR]

Neurodegenerative diseases are progressive and incurable and are becoming ever more prevalent. To study whether neural stem cells can reactivate or rescue functions of impaired neurons in the human aging and neurodegenerating brain, we co-cultured postmortem slices from Alzheimer patients and control participants with rat embryonic day 14 (E14) neural stem cells. Viability staining based on the exclusion of ethidium bromide by intact plasma membranes showed that there were strikingly more viable cells and fewer dead cells in slices co-cultured with neural stem cells than in untreated slices. The presence of Alzheimer pathology in the brain slices did not influence this effect, although the slices from Alzheimer patients, in general, contained fewer viable cells. Co-culturing with rat E14 fibroblasts did not improve the viability of neurons in the human brain slices. Since the human slices and neural stem cells were separated by a membrane during co-culturing our data show for the first time that neural stem cells release diffusible factors that may improve the survival of aged and degenerating neurons in human brains. [ABSTRACT FROM AUTHOR]