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Go to Editorial ManagerThis paper introduces a radial distribution feeder protection scheme based on certain features extraction from current signals measurement at the substation. The features are captured using the discrete wavelet transform (DWT). Two digital signals processing methods are used to introduce those features to the 1) fault detection 2) identification and 3) localization schemes; the first one is the energy method and the second one is the root mean square method. For the purpose of fault type identification, two systems are tested and compared, a Fuzzy Inference System (FIS) and Artificial Neural Network (ANN). Fault location scheme is then built based on ANNs. An effort is made to reduce the computational burden and the speed of detection provided by the fault detection and identification schemes. Since the short circuit faults are the most likely types of faults that can occur in power systems, the ten types of these faults taking into account different fault resistances are simulated in MATLAB environment and the protection scheme is built based on the idea of over current. The power quality disturbances such as switching transient events on the feeder is also taken into account in order to build a reliable and secure protection scheme.
The discovery and identification of damages in engineering structures is very important in the field of engineering maintenance, as it is a great challenge in presenting new methods in measuring vibrations and discovering damages with the development in the field of automation and high accuracy in discovering damages. In this study, natural frequencies and mode shapes of transverse vibration for damage detection in structures are investigated. The study is performed for various crack depth and crack location. And suggested a new technique based on Continuous Wavelet Transform (CWT) and Convolution Neural Network (CNN). The comparison will be done by simulating the oscillations of a cantilever steel beam with and without defect as a numerical case. The proposed new technique proved to outperform classical methods and has achieved a100% accuracy in the identification of defect position for the data studied.
The paper deals with neural networks identification of ultimate moment capacity of steel-concrete composite beams on base of experimental results. Basic information on artificial neural networks and its parameters suitable for analysis of experimental results are given. Two types of neural network algorithms are used. Results of identification are reported. The results show that artificial neural networks are highly suitable for assessing the ultimate moment capacity of composite section. The proposed neural network was also used to explore the effect of the various parameters on the behaviour of composite beams.
A method based on experimentally calibrated rotor model is proposed in this work for unbalance identification of flexible rotors without trial runs. Influence coefficient balancing method especially when applied to flexible rotors is disadvantaged by its low efficiency and lengthy procedure, whilst the proposed method has the advantage of being efficient, applicable to multi-operating spin speeds and do not need trial runs. An accurate model for the rotor and its supports based on rotordynamics and finite elements analysis combined with experimental modal analysis, is produced to identify the unbalance distribution on the rotor. To create digital model of the rotor, frequency response functions (FRFs) are determined from excitation and response data, and then modal parameters (natural frequencies and mode shapes) are extracted and compared with experimental analogies. Unbalance response is measured traditionally on rotor supports, in this work the response measured from rotating disks instead. The obtained results show that the proposed approach provides an effective alternative in rotor balancing. Increasing the number of balancing disks on balancing quality is investigated as well.
In this paper, a new approach for the positioning (localization) of multi-node systems is presented. Each node including the beacon node contains two types of sensors: one for the distance sensing and the other type is for communication. The main idea of our proposed approach is to use the control of beacon to construct a nodes' tree which is going to be used later by the nodes to know the paths in which the information will flow. During the tree construction the identities of nodes will be known. Every node except the beacon will use the information obtained from its previous neighbor in the tree to find its own location and orientation. Several simulations using visual basic 2012 are implemented to discern the performance of this algorithm.
This paper is concerned with performance on the widely used control technique: adaptive control for synchronization between two identical chaotic systems embedded in the Master and Slave. It is assumed that the parameters of slave system are unknown. The required stability condition is derived to ensure the stability of error dynamics. Adaptive control laws are designed using appropriate parameters estimation law. The system parameters are asymptotically synchronized; thus the slave parameters can be identified. As an application, the proposed scheme is applied to secure communication system. The information signal is transmitted and recovered on the basis of identification parameters also the system is tested under the consideration of the noisy channel. Finally, through Numerical simulation results, the proposed scheme was success in the communication application.
Hydrodynamics and heat transfer in a fully developed laminar incompressible reciprocating channel flow subjected to a constant heat flux have 'been investigated analytically using similarity transfo1mat ion. An exact analytical solution for the velocity, local, and bulk temperature as well as the Nusselt number has been obtained. The effect of the parameters Pr, Ao, y, and X/Dh on u, T, Tt, Nux, and Nux are presented. The results showed that the local Nusselt number is increased with increasing Womersly number (A.) while the dimensionless temperature is increased with Womersly and decreases with amplitude (Ao). The Prandtl number has a significant effect on the local Nusselt number. The results were found in very good agreement with those obtained numerically using the finite volume method. The comparison with the experimental results of other authors gave a reasonable identification.
This paper is concerned with a stress analysis in a bearing under unbalanced fon:es of the jownal. Some aspects of mathematical modeling of rotating structW'Cs were considered. "Finite Element Method'' is fom1ulated for modeling rotating structures. As an application, a test rotor mounted on two-lobe hydrodynamic bearings is presented. Unbalance response calculations for various unbalance magnitudes are ca1Ticd out in the bearing location. The bearing coefficients were found at rotational speed of 4,000 rpm. An accurate identification of bearing force parameters, i.e. stiffness and damping coefficients is presented by a classical linearized model. The bearing support forces in tlexiblc rotor-bearing systems are presented as a function of unbalance response of the journal. The calculation of the bearing stress due to rotor w1balance are carried out using ANSYS. The ANSYS program gives a good aids in understanding the ~tress analysis in the bearing under the action of journal rotation.
In this paper, a Neural Network (NN) model system for self-organization fish school system is identified. Monitoring and data extraction from fish school video has been achieved by using image processing technique in order to generate the data suitable for parameter identification of NN model system. Data obtained have been used to identify the parameters of a model based on a black-box represented by nonlinear autoregressive exogenous model (NARX). The obtained results show that this system can be used for multi robot formation system.