Fault analysis in four-wire distribution networks

The neutral wire in most existing power flow and fault analysis software is usually merged into phase wires using Kron's reduction method. In some applications, such as fault analysis, fault location, power quality studies, safety analysis, loss analysis etc., knowledge of the neutral wire and ground currents and voltages could be of particular interest. A general short-circuit analysis algorithm for three-phase four-wire distribution networks, based on the hybrid compensation method, is presented. In this novel use of the technique, the neutral wire and assumed ground conductor are explicitly represented. A generalised fault analysis method is applied to the distribution network for conditions with and without embedded generation. Results obtained from several case studies on medium- and low-voltage test networks with unbalanced loads, for isolated and multi-grounded neutral scenarios, are presented and discussed. Simulation results show the effects of neutrals and system grounding on the operation of the distribution feeders.

Well-conditioned robust observer for detection of family systems components

A new concept of fault detection and isolation using robust observation for systems with random noises is presented. The method selects the parameters from components that may fault during the process and constructs well conditioned robust observers, considering sensors faults. To isolate component failures via robust observation, a bank of detection observers is constructed, where each observer is only sensitive to one specified component failure while robust to all other component failures.

Liquid-phase blocking sandwich enzyme-linked immunosorbent assay for detection of antibodies against foot-and-mouth disease virus in water buffalo sera

Objective-To develop and apply the liquid-phase blocking sandwich ELISA (BLOCKING-ELISA) for the quantification of antibodies against foot-and-mouth disease virus (FMDV) strains O-1 Campos, A(24) Cruzeiro, and C-3 Indaial.Design-Antibody quantification.Sample Population-158 water buffalo from various premises of São Paulo Stale-Brazil. The sera were collected either from systemically vaccinated or nonvaccinated animals.Procedure-The basic reagents of BLOCKING-ELISA (capture and detector antibodies, virus antigens, and conjugate) were prepared and the reaction was optimized and standardized to quantify water buffalo antibodies against FMDV. An alternative procedure based on mathematical interpolation was adopted to estimate more precisely the antibody 50% competition liters in the BLOCKING-ELISA. These titers were compared with the virus-neutralization test (VNT) titers to determine the correlation between these techniques. The percentages of agreement, cutoff points, and reproducibility also were determined.Results-The antibody liters obtained in the BLOCKING-ELISA had high positive correlation coefficients with VNT, reaching values of 0.90 for O-1 Campos and C-3 Indaial, and 0.82 for the A(24) Cruzeiro (P < 0.0005). The cutoff points obtained by use of the copositivity and conegativity curves allowed determination of high levels of agreement between BLOCKLNG-ELISA and VNT antibody titers against the 3 FMDV strains analyzed.Conclusions-The results characterized by high cor relation coefficients, levels of agreement, and reproducibility indicate that the BLOCKING-ELISA may replace the conventional VNT for detection and quantification of antibodies from water buffalo sera to FMDV.

Inactivation of COX-2, HMLH1 and CDKN2A Gene by Promoter Methylation in Gastric Cancer: Relationship with Histological Subtype, Tumor Location and Helicobacter pylori Genotype

Objective: We aimed to evaluate the inactivation of COX-2, HMLH1 and CDKN2A by promoter methylation and its relationship with the infection by different Helicobacter pylori strains in gastric cancer. Methods: DNA extracted from 76 H. pylori-positive gastric tumor samples was available for promoter methylation identification by methylation-specific PCR and H. pylori subtyping by PCR. Immunohistochemistry was used to determine COX-2, p16(INK4A) and HMLH1 expression. Results: A strong negative correlation was found between the expression of these markers and the presence of promoter methylation in their genes. Among cardia tumors, negativity of p16(INK4A) was a significant finding. on the other hand, in noncardia tumors, the histological subtypes had different gene expression patterns. In the intestinal subtype, a significant finding was HMLH1 inactivation by methylation, while in the diffuse subtype, CDKN2A inactivation by methylation was the significant finding. Tumors with methylated COX-2 and HMLH1 genes were associated with H. pylori vac A s1 (p = 0.025 and 0.047, respectively), and the nonmethylated tumors were associated with the presence of the gene flaA. Conclusions: These data suggest that the inactivation of these genes by methylation occurs by distinct pathways according to the histological subtype and tumor location and depends on the H. pylori genotype. Copyright (C) 2011 S. Karger AG, Basel

Optimized Allocation of Control and Protective Devices in Electric Distribution Systems

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Well-conditioned robust observer for detection of faulty systems components

A new concept of fault detection and isolation using robust observation for systems with random noises is presented. The method selects the parameters from components that may fault during the process and constructs well conditioned robust observers, considering sensors faults. To isolate component failures via robust observation, a bank of detection observers is constructed, where each observer is only sensitive to one specified component failure while robust to all other component failures.

Production grinding burn detection using acoustic emission and electric power signals

This work uses a monitoring system based on a PC platform, where the acoustic emission and electric power signals generated during the grinding process are used to investigate superficial burning occurrence in a surface grinding operation using two types of steel, three grinding conditions and an Al203 vitrified grinding wheel. Acoustic emission signals on the workpiece and grinding power were measured during a surface plunge operation until the grinding burn happened. From the results the standard deviation of the acoustic emission signal and the maximum electric power were calculated for each grinding pass. The proposed DPO parameter is the product between the power level and acoustic emission standard deviation. The results show that both signals can be used for burning detection, and the parameter DPO is the best indicator for the burning studied in this work. This can be explained by the high dispersion of the acoustic emission RMS level associated to the high power consumption when the grinding wheel lose its sharpness.

Analise de desempenho de observadores de estado robustos na localizacao de falhas

The state observers can reconstruct and monitor unmeasurable states. A new concept of fault detection and isolation using state observers is presented. The method selects the parameters from components that may fail during the process and constructs optimized robust observers. To isolate component failures via robust observation, a bank of detection observers is organized, in which each observer is only sensitive to one specified component failure while robust to all other component failures. This paper analyzes the performance of transient and steady-state behavior of the state observer.

Defects location in power systems components through a dedicated genetic algorithm

In this work the problem of defects location in power systems is formulated through a binary linear programming (BLP) model based on alarms historical database of control and protection devices from the system control center, sets theory of minimal coverage (AI) and protection philosophy adopted by the electric utility. In this model, circuit breaker operations are compared to their expected states in a strictly mathematical manner. For solving this BLP problem, which presents a great number of decision variables, a dedicated Genetic Algorithm (GA), is proposed. Control parameters of the GA, such as crossing over and mutation rates, population size, iterations number and population diversification, are calibrated in order to obtain efficiency and robustness. Results for a test system found in literature, are presented and discussed. © 2004 IEEE.

Investigating effects of neutral wire and grounding in distribution systems with faults

In some applications like fault analysis, fault location, power quality studies, safety analysis, loss analysis, etc., knowing the neutral wire and ground currents and voltages could be of particular interest. In order to investigate effects of neutrals and system grounding on the operation of the distribution feeders with faults, in this research a hybrid short circuit algorithm is generalized. In this novel use of the technique, the neutral wire and assumed ground conductor are explicitly represented. Results obtained from several case studies using IEEE 34-node test network are presented and discussed.

Steel bars identification in reinforced concrete structures by using ANN and magnetic fields

This work proposes a methodology for non destructive testing (NDT) of reinforced concrete structures, using superficial magnetic fields and artificial neural networks, in order to identify the size and position of steel bars, embedded into the concrete. For the purposes of this paper, magnetic induction curves were obtained by using a finite element program. Perceptron Multilayered (PML) ANNs, with Levemberg-Marquardt training algorithm were used. The results presented very good agreement with the expect ones, encouraging the development of real systems based upon the proposed methodology.

Location of single line-to-ground faults on distribution feeders using voltage measurements

This paper proposes a dedicated algorithm for lation of single line-to-ground faults in distribution systems. The proposed algorithm uses voltage and current phasors measured at the substation level, voltage magnitudes measured at some buses of the feeder, a database containing electrical, operational and topological parameters of the distribution networks, and fault simulation. Voltage measurements can be obtained using power quality devices already installed on the feeders or using voltage measurement devices dedicated for fault location. Using the proposed algorithm, likely faulted points that are located on feeder laterals geographically far from the actual faulted point are excluded from the results. Assessment of the algorithm efficiency was carried out using a 238 buses real-life distribution feeder. The results show that the proposed algorithm is robust for performing fast and efficient fault location for sustained single line-to-ground faults requiring less than 5% of the feeder buses to be covered by voltage measurement devices. © 2006 IEEE.

Detection of SPM and IMP metallo-β-lactamases in clinical specimens of Pseudomonas aeruginosa from a Brazilian public tertiary hospital

Phenotypic and genotypic SPM and IMP metallo-β-lactamases (MBL) detection and also the determination of minimal inhibitory concentrations (MIC) to imipenem, meropenem and ceftazidime were evaluated in 47 multidrug-resistant Pseudomonas aeruginosa isolates from clinical specimens. Polymerase chain reaction detected 14 positive samples to either blaSPM or blaIMP genes, while the best phenotypic assay (ceftazidime substrate and mercaptopropionic acid inhibitor) detected 13 of these samples. Imipenem, meropenem and ceftazidime MICs were higher for MBL positive compared to MBL negative isolates. We describe here the SPM and IMP MBL findings in clinical specimens of P. aeruginosa from the University Hospital of Botucatu Medical School, São Paulo, Brazil, that reinforce local studies showing the high spreading of blaSPM and blaIMP genes among Brazilian clinical isolates. © 2011 Elsevier Editora Ltda.

Remote Sensing Detection of atmospheric pollutants using Lidar, Sodar and correlation with air quality data in an industrial area

Optical remote sensing techniques have obvious advantages for monitoring gas and aerosol emissions, since they enable the operation over large distances, far from hostile environments, and fast processing of the measured signal. In this study two remote sensing devices, namely a Lidar (Light Detection and Ranging) for monitoring the vertical profile of backscattered light intensity, and a Sodar (Acoustic Radar, Sound Detection and Ranging) for monitoring the vertical profile of the wind vector were operated during specific periods. The acquired data were processed and compared with data of air quality obtained from ground level monitoring stations, in order to verify the possibility of using the remote sensing techniques to monitor industrial emissions. The campaigns were carried out in the area of the Environmental Research Center (Cepema) of the University of São Paulo, in the city of Cubatão, Brazil, a large industrial site, where numerous different industries are located, including an oil refinery, a steel plant, as well as fertilizer, cement and chemical/petrochemical plants. The local environmental problems caused by the industrial activities are aggravated by the climate and topography of the site, unfavorable to pollutant dispersion. Results of a campaign are presented for a 24- hour period, showing data of a Lidar, an air quality monitoring station and a Sodar. © 2011 SPIE.

On data-centric misbehavior detection in VANETs

Detecting misbehavior (such as transmissions of false information) in vehicular ad hoc networks (VANETs) is a very important problem with wide range of implications, including safety related and congestion avoidance applications. We discuss several limitations of existing misbehavior detection schemes (MDS) designed for VANETs. Most MDS are concerned with detection of malicious nodes. In most situations, vehicles would send wrong information because of selfish reasons of their owners, e.g. for gaining access to a particular lane. It is therefore more important to detect false information than to identify misbehaving nodes. We introduce the concept of data-centric misbehavior detection and propose algorithms which detect false alert messages and misbehaving nodes by observing their actions after sending out the alert messages. With the data-centric MDS, each node can decide whether an information received is correct or false. The decision is based on the consistency of recent messages and new alerts with reported and estimated vehicle positions. No voting or majority decisions is needed, making our MDS resilient to Sybil attacks. After misbehavior is detected, we do not revoke all the secret credentials of misbehaving nodes, as done in most schemes. Instead, we impose fines on misbehaving nodes (administered by the certification authority), discouraging them to act selfishly. This reduces the computation and communication costs involved in revoking all the secret credentials of misbehaving nodes. © 2011 IEEE.