Top PDF The Fault Location Method Research of Three-Layer Network System

The Fault Location Method Research of Three-Layer Network System

The Fault Location Method Research of Three-Layer Network System

Abstract — The fault location technology research of three-layer network system structure dynamic has important theoretic value and apparent engineering application value on exploring the fault detection and localization of the complex structure dynamic system. In this article, the method of failure propagation and adverse inference are adopted, the fault location algorithm of the three-layer structure dynamic network system is established on the basis of the concept of association matrix and the calculating method are proposed, and the simulation calculation confirmed the reliability of this paper. The results of the research can be used for the fault diagnosis of the hierarchical control system、 testing of the engineering software and the analysis of the failure effects of layered network of all kinds and other different fields.
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Transient Stability Analysis of a Three-machine Nine Bus Power System Network

Transient Stability Analysis of a Three-machine Nine Bus Power System Network

A new method has been proposed for analyzing transient stability of a three-machine nine bus power system network. This method uses a set of closed loop transfer functions, one for each synchronous generator and is derived by taking Laplace transformation of the nonlinear swing equation. The dominant root is searched in the s -plane for each generator starting from a real value, which is same for all generators and a function of only the rotor oscillation frequency and the fault clearing time. The minimum average distance in the s - plane was deemed to be -10.87 for the three-machine system. A decrement of 10 was found to be quite satisfactory for searching the dominant root. In the stable cases of the system, the average distance of the dominant roots was at or further left of the minimum average distance depending upon the fault clearing time. The average location shifted more towards right with higher fault clearing time and this location was completely on the right of the minimum allowable value for the unstable cases. The simulation was carried out using CYME 5.02 power system software by considering two cases. The simulation results were compared with the proposed method and were found to 0
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Fault Detection and Classification in Power Electronic Circuits Using Wavelet Transform and Neural Network

Fault Detection and Classification in Power Electronic Circuits Using Wavelet Transform and Neural Network

Abstract: Problem statement: The identification of faults in any analog circuit is highly required to ensure the reliability of the circuit. Early detection of faults in a circuit can greatly assist in maintenance of the system by avoiding possibly harmful damage borne out of the fault. Approach: A novel method for establishing a fault dictionary using Wavelet transform is presented. The Circuit Under Test (CUT) is three phase single level inverter. The transform coefficients for the fault free circuit as well as for the simulated faults of CUT are found. The Wavelet transform is applied to the output of CUT and Standard Deviation (SD) of the transform coefficients are extracted. Using the transform coefficients, fault dictionary has been formed. In order to identify the type of fault, a neural network classifier has been utilized. Results: The compatibility of wavelet analysis with the various classification techniques for fault diagnosis has been illustrated in this study. The results of the study demonstrate the suitability and viability of wavelet analysis in fault diagnosis of power electronic circuits. Conclusion: The proposed approach is found to be more reliable in accurate identification and isolation of faults using fault dictionary. Moreover, the neural classifier improves the efficiency of the system as neural networks do not require prior knowledge as they are capable of learning and evolving through a number of learning algorithms.
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Computational Effective Fault Detection by Means of Signature Functions.

Computational Effective Fault Detection by Means of Signature Functions.

3. classification—the reduced vector is an input to a neural network to perform fault classifica- tion. As a neural network can produce not deterministic results [17, 18] due to overlearning and local extrema, often the support vector machine (SVM) is used, as in [10, 19]. The work [7] uses Bayesian network and shows its superiority over neural network or SVM in an appli- cation of monitoring a gear pump. Some works also use hidden Markov models, as in [3, 4]. The above-presented methodology is general, flexible and allows to analyse signals pro- duced by non-stationary and non-linear systems. This comprehensiveness, however, incurs computational complexity that in most cases precludes implementation in embedded devices. At the same time, there are systems that can be considered stationary and linear and whose output responses for different faults are distinguishable in the time domain. Therefore, there is no need to apply signal transform which imposes a considerable computational burden. The approach suggested in this paper does not require a complex classifier, such as a neural net- work, HMM, SVM, etc. The system’s response is projected into the space created by the so- called signature functions, which are computed from the training responses. The location of the signal in that space determines the fault type. The properties of the presented method make the approach suitable for embedded industrial solutions where hardware cost, reliability and simplicity are important factors.
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ANN based Classification and Location of Faults in EHV Transmission Line

ANN based Classification and Location of Faults in EHV Transmission Line

Abstract— The paper discusses an implemented methodology for Fault Classifier and Fault Locator for locating faults occurring on a double circuit dual feed EHV transmission line, using Artificial Neural Network. The method uses the fundamental frequency components of voltage and current at pre-fault and post-fault condition, measured in each phase at a reference end on one of the lines of the system. The proposed neural fault classifier and fault locator were trained using various sets of data available from a selected power system model by simulating different fault scenarios like - fault types, fault locations, fault resistances and fault inception angles. The proposed scheme for Fault Classifier and Fault Locator gives great promising results which satisfy for a fast and accurate classification and location.
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Fault Tolerance Mobile Agent System Using Witness Agent in 2-Dimensional Mesh Network

Fault Tolerance Mobile Agent System Using Witness Agent in 2-Dimensional Mesh Network

The main objective of our approach is to decrease the witness-dependency in 2D-Mesh. In this method, agents are in the nodes of 2D-Mesh graph. The location of each agent in the graph is shown as a pair (i, j). Assume that the owner of the actual agent hold an array, W, for the location of the witness agent. The W is empty at first and is filled during the actual agent’s itinerary with the indices number of each witness. When the actual agent travels to a new server in the 2D-mesh network, it sends a message to owner after computing and check-pointing the data. This message contains the node indices which actual agent currently resides on it. This type of message is denoted
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Comparative analysis of different meat traceability systems using multiple criteria and a social network approach

Comparative analysis of different meat traceability systems using multiple criteria and a social network approach

ABSTRACT: The adoption of a proper traceability system is being incorporated into meat production practices as a method of gaining consumer confidence. The various partners operating in the chain of meat production can be considered a social network, and they have the common goal of generating a communication process that can ensure each characteristic of the product, including safety. This study aimed to select the most appropriate meat traceability system “from farm to fork” that could be applied to Brazilian beef and pork production for international trade. The research was done in three steps. The first used the analytical hierarchy process (AHP) for selecting the best on- farm livestock traceability. In the second step, the actors in the meat production chain were identified to build a framework and defined each role in the network. In the third step, the selection of the traceability system was done. Results indicated that with an electronic traceability system, it is possible to acquire better connections between the links in the chain and to provide the means for managing uncertainties by creating structures that facilitate information flow more efficiently.
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The Impact of the Expansion of the Bolsa Familia Program on the Time Allocation of Youths And Their Parents Lia Chitolina Miguel Nathan Foguel Naercio Menezes-Filho

The Impact of the Expansion of the Bolsa Familia Program on the Time Allocation of Youths And Their Parents Lia Chitolina Miguel Nathan Foguel Naercio Menezes-Filho

household, the program caused an increase of more than 11 percentage points in the probability of attending school. One possible reason for this increase in the estimated impact is the fact that his/her family is only receiving any transfers from the Bolsa Familia program because he/she is attending school. The fear of losing access to the program, which means that it may take time to come back to it in case of harder times ahead, may stimulate parents to monitor their kids’ school attendance more strongly. When these two features were combined — i.e. male youngsters who were the youngest child — the probability of attending school increased by 16.2 percentage points and it is statistically significant at the 1 per cent level.
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Meta-stable Solidification Created by the Detonation Method of Coating Formation

Meta-stable Solidification Created by the Detonation Method of Coating Formation

The DGS, HVOF and plasma spraying techniques applied to production of coatings from powders offer practically unlimited abilities in the establishing composition of a coating. A control of the spraying parameters with the objective to obtain coatings with unique exploiting properties like resistance to abrasive wear, erosion, corrosion, high temperature corrosion or thermal shocks, as well as good adhesion and low porosity was the subject of number of works [1-6].

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General Fault Admittance Method Solution of a Line-to-Line Fault

General Fault Admittance Method Solution of a Line-to-Line Fault

134 he line to line fault is assumed to be at busbar 1, the load busbar. Various impedances to simulate the line- to-line fault are considered: purely resistive, a resistive and inductive combination, and a purely inductive value. The line-to-line fault is described by the impedances in the respective faulted phases. In the general fault admittance method, the impedances to be input are those in the b and c phases. The open circuit values for the a phase and ground path are not input since their respective fault admittances are zero. The initial fault impedance values were of the order of 10 -3 Ω. The sequence fault currents were calculated for the initial value. A second value of the fault impedances was used, obtained by multiplying the initial value by a factor of 10 -1 . The second values of sequence fault currents were then calculated. The absolute value of the change in the positive sequence current was compared against a tolerance of 10 -8 , and if smaller the solution was considered converged. If the absolute value of the change was larger than the tolerance, the fault impedance was again reduced and another value calculated. The iterative process was repeated until either convergence or non- convergence. Note that the order of the initial value of the fault impedances was much smaller than any of the components of the positive sequence impedances. The presence of the delta earthed-star transformer poses a challenge in terms of its modelling. In the computer program, the transformer is modelled in one of two ways: as a normal star connection, for the positive and negative sequence networks or as a delta star transformer with a phase shift. In the former model, the phase shifts are incorporated when assembling the sequence currents to obtain the phase values. In particular on the delta connected side of the transformer the positive sequence currents’ angles are increased by the phase shift while the angle of the negative sequence currents are reduced by the same value. The zero sequence currents, if any, are not affected by the phase shifts. Both models for the delta star transformer gave same results. (The √3 line current factor was used to find the line currents on the delta side of the delta star transformer).
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BrO/SO<sub>2</sub> molar ratios from scanning DOAS measurements in the NOVAC network

BrO/SO<sub>2</sub> molar ratios from scanning DOAS measurements in the NOVAC network

Figure 10. Daily averages of the BrO/SO 2 molar ratios from Nevado del Ruiz from November 2009 to June 2013. The ratio drops down to values below 5×10 −5 , and increases again after the eruption on 30 June 2012 (red vertical bar). Also shown at the bottom of the image is the volcanic activity risk level defined by the Colombian Geological Survey (coloured horizontal bars). The black-orange line shows a running mean around seven data points (from both instruments). Top: the criterion for a valid measurement is a BrO column density above four times the BrO DOAS retrieval error. Center: for this figure an SO 2 threshold of 7 × 10 17 molecules cm −2 was chosen as the criterion for valid data points. Bottom: Running mean of the BrO/SO 2 time series retrieved in different wavelength ranges, with an SO 2 threshold of of 7 × 10 17 molecules cm −2 as the criterion for valid data points.
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A fault-location technique for Java implementations of algebraic specifications

A fault-location technique for Java implementations of algebraic specifications

The remainder of this paper is organized as follows: section 2 gives an overview of the Flasji approach both in the broader context of ConGu and GenT approaches, and isolated; it also presents an example that will be used throughout the paper; section 3 gives the reader some background information about GenT test generation since the techniques proposed in this paper use several of GenT’s artifacts to draw conclusions and generate additional information; four probing techniques are presented in section 4 whose main objective is to collect suspect methods and decide the guilty one; section 5 integrates these techniques resulting in the Flasji approach; an evaluation experiment of Flasji is presented in section 6 and results are compared with the ones obtained using two fault- location tools [22, 23]; section 7 discusses related work and, finally, section 8 concludes and identifies topics to be improved.
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The Research on an Algorithm of Three-Dimensional Topology Control of Wireless Sensor Network

The Research on an Algorithm of Three-Dimensional Topology Control of Wireless Sensor Network

Nowadays, the research about three-dimensional topology control has focused on ensuring the connectivity of the networks, and has seldom considered the balance between neighbor node degree and energy consumption minimum path. In order to solve this problem, this paper proposes an adjustable topology control algorithm in three-dimensional wireless sensor networks, and this algorithm can dynamically adjust the network topology control structure through changing the adjustment factor r(0<r<1), and make the network has good neighbor node degree while retaining some energy consumption minimum paths. The simulation experiment data shows that the algorithm can make the network topology with good adjustability and sparsity, and also proves that the algorithm has good effect on prolonging the network’s lifetime and saving the node power.
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Egg Hatchability Prediction by Multiple Linear Regression and Artificial Neural Networks

Egg Hatchability Prediction by Multiple Linear Regression and Artificial Neural Networks

The parameters used in the input layer included the entire 496-h incubation period (incubation and hatcher data). Hatchability and data electronically recorded by the sensors inside the incubators and the hatchers were randomly divided in two sets – training and Validation. The training set consisted of data from six incubations out of the eight carried out, and included the mean and standard deviation of each parameter. Data were normalized in a 0-1 range, and submitted to ANN in the format of a matrix with eleven columns (process variables) and 496 lines (corresponding to the number of hours of the total incubation period). Data normalization is essential when unit values have different magnitudes (Yin et al., 2003). The remaining incubations were divided in two sets – Test and Validation. Figure 1 presents the architecture of the applied neural network.
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A New Scheme for Fault Detection and Classification Applied to DC Motor

A New Scheme for Fault Detection and Classification Applied to DC Motor

ABSTRACT. This study presents an approach for fault detection and classification in a DC drive system. The fault is detected by a classical Luenberger observer. After the fault detection, the fault classification is started. The fault classification, the main contribution of this paper, is based on a representation which combines the Subctrative Clustering algorithm with an adaptation of Particle Swarm Clustering.

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An Adaptive Two-Stage BPNN–DCT Image Compression Technique

An Adaptive Two-Stage BPNN–DCT Image Compression Technique

images can be regarded as two-dimensional signals with the spatial coordinated as independent variables, image compression has been an active area of research since the inception of digital image processing. It is extremely important for efficient storage and transmission of image data. Since it was an area of interest of many researchers, many techniques have been introduced. Images are in wide use today, and decreasing the bandwidth and space required by them is a benefit. With images, lossy compression is generally allowed as long as the losses are subjectively unnoticeable to the human eye. The human visual system is not as sensitive to changes in high frequencies [1]. This piece of information can be utilized by image compression methods. After converting an image into the frequency domain, we can effectively control the magnitudes of higher frequencies in an image.
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The Influence Of CSR Awareness On Consumer Purchase Decision Of A Telecommunication Network In Ghana A Case Of La Nkwantanag Madina Municipality

The Influence Of CSR Awareness On Consumer Purchase Decision Of A Telecommunication Network In Ghana A Case Of La Nkwantanag Madina Municipality

Corporations need to address their social obligations more consciously. It is important to understand what kinds of responsibilities construct CSR before involving in any CSR act ivities. One widely acknowledged theory of CSR‘s components is Carroll‘s four-part theory. Carroll (1991) developed his four-part theory of CSR, arguing that CSR is constituted by four kinds of social responsibilities: economic, legal, ethical, and philanthropic responsibilities. The economic responsibilities are the primary part of the four responsibilities. It is the fundamental layer of Carroll‘s CSR pyramid. All corporations are responsible for providing goods and services that are needed by the society. Consequently, profits from selling goods and services go to shareholders and other investors to keep a company survive and grow. Economic responsibilities of a company are the base for providing legal, ethical and philanthropic responsibilities. Legal responsibilities are the second layer of the CSR pyramid and are coexisting with economic responsibilities as fundamental precepts of the free enterprise system. Firms are expected to operate under the legal system and regulations while creating profits for shareholders. Firms are fulfilling the "social contract" between firms and the society by being legally responsible. Legally responsible also reflects the "codified ethics" of business operation, as well as the pursuit of economic responsibilities set by lawmakers (Carroll, 1991). Ethical responsibilities involve activities and practices that are expected by the society and done by firms voluntarily regarding fair, justice and the respect for or protection of stakeholders' moral rights. Ethical responsibilities are voluntary choices of firms, since they are not codified into any law or regulation. These responsibilities reflect social norms, expectations and concerns of consumers, employees, shareholders and the community. Ethical responsibilities go further than legal responsibilities because they involve newly emerging values and norms that the public expects a firm to comply with and are at a higher standard of business practices than that current legal system required. However, ethical responsibilities are not easy to deal with for firms because new expectations from the public keep emerging and this makes the legitimacy of ethical responsibilities continually under debate (Carroll, 1991). Philanthropic responsibilities involve firms‘ activities that are
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Wavelet Based Fault Detection, Classification in Transmission System with TCSC Controllers

Wavelet Based Fault Detection, Classification in Transmission System with TCSC Controllers

Wavelet Transform (WT) is an efficient means of analyzing transient currents and voltages. Unlike DFT, WT not only analyzes the signal in frequency bands but also provides non-uniform division of frequency domain, i.e. WT uses short window at high frequencies and long window at low frequencies. This helps to analyze the signal in both frequency and time domains effectively. A set of basis functions called Wavelets, are used to decompose the signal in various frequency bands, which are obtained from a mother wavelet by dilation and translation. Hence the amplitude and incidence of each frequency can be found precisely. Wavelet Transform is defined as a sequence of a function {h(n)}(low pass filter) and {g(n)}(high pass filter). The scaling function (t) and wavelet (t) are defined by the following equations (t) = 2 h (n) (2t-n), (t) = 2 g (n) (2t-n)
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Design of An Efficient Neural Key Generation

Design of An Efficient Neural Key Generation

The goal of any cryptographic system is the exchange of information among the intended users without any leakage of information to others who may have unauthorized access to it. A common secret key could be created over a public channel accessible to any opponent. Neural networks can be used to generate common secret key. In case of neural cryptography, both the communicating networks receive an identical input vector, generate an output bit and are trained based on the output bit. The two networks and their weight vectors exhibit a novel phenomenon, where the networks synchronize to a state with identical time-dependent weights. The generated secret key over a public channel is used for encrypting and decrypting the information being sent on the channel .
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Key factors of clinical research network capacity building

Key factors of clinical research network capacity building

While capacity refers to the ability or power to finish, change, tackle, develop, or experience some objective or activity, capacity building indicates the interventions, procedures or activities aiming to produce sustained change or improvement to perform activities at levels of individuals, organizations, systems, national and/or international entities [1, 2]. Thus, clinical research network capacity building refers to programs aimed at enhancing networks of researchers to conduct clinical research. In the literature, there have been many exam- ples of local, national and global clinical research networks with successful capacity development that perform and utilize health research resources effi- ciently, cost-effectively, and sustainably. Nevertheless, although there is a large body of research on how to develop and build capacity in clinical research net- works, the conceptualizations and implementations remain controversial and challenging [2, 3]. The experi- ences learnt from the past accomplishments and failures can assist in the future capacity building efforts to be more practical, effective and efficient. Therefore, the objective of this paper is to provide an overview of
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