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This study is related to determination of the ferroresonance phenomenon for Seyitomer-Isiklar part of the Electric Power System of 380 kV in Turkey. In this manner, Simulation data, which is produced from the Matlab-Simulink model, is considered for voltage variation of the R-Phase of the power system and then the continuous wavelet transform (CWT) is applied to this voltage variation. Results are examined on the time-scale plane and most dominant scale can be determined as a feature of the ferroresonance phenomenon. Hence, it can be shown that this extracted feature provides all pre-definitions of the ferroresonance event. Consequently, the ferroresonance phenomenon can be determined by overvoltage variations from the sub-scales of the original voltage variation. [ABSTRACT FROM AUTHOR]

In this experimental study, it was achieved to detect internal or surface cracks that can occur in the production of ceramic plates by using the method of impulse noise. This method is a test technique which was not used in the applications. In this experimental study, a pulse is applied on the ceramic material. This pulse is produced by pendulum mechanism. With the reflected sound material, the deformation of the material is analyzed. The application of the study was performed on ceramic plates. Ceramic double plates, made from same materials, with same characteristics, but in different models, were used. One of these ceramic double plates, both of which are entirely of the same kind, is un-defonned plate, and the other is cracked plate. Impulse noises, having arisen from the equal impulse power applied on these intact and cracked plates of the same model, were examined by wavelet analysis. By means of the applied method, it was seen to have shown significant results in distinguishing intact and cracked plates. Having the frequency and magnitude dimensions of the deformations on the plates also examined by means of the applied analysis, important distinguishing findings were attained therefrom. [ABSTRACT FROM AUTHOR]

This study is focused on the investigating of a power plant generator in reverse power condition. For this purpose, reverse power data were collected from a Combined Heat and Power Plant generator protection relay. The reverse power conditions were evaluated by means of the time-frequency and time-scale methods. As a result of this evaluation, it can be said that the time-frequency and time-scale properties of the signals like current, voltage, frequency, active and reactive powers were extracted with all details. [ABSTRACT FROM AUTHOR]

Renewable energy sources will come at the beginning of the future energy resources. In particular, wind power is among the most discussed sources in Turkey and all over the world. In this study, a model of the three-blade horizontal-axis wind turbine was designed. Analysis of fault, which may occur as a result of a possible blade deformation, was performed with the model. Comparison is provided between generator rotor speeds and torques for the states of healthy and damaged blades. Continuous wavelet transform (CWT) approach was adopted as the analysis method. The state of the healthy blade and the broken one were clearly identified by means of the CWT. [ABSTRACT FROM AUTHOR]

In this study, an auto-associative neural network (AANN) is designed as a fault detector using the cybernetic concepts. In this sense, an artificial neural network structure is connected with a finite state system or a finite automata and an AANN topology is described as a virtual detector. In terms of the practical application, vibration signals, which are taken from an induction motor of 5 HP for both the healthy and faulty motor cases, are considered in the spectral domain. The vibration signal presented in the healthy motor case is separated into 4 blocks and the spectral set of these blocks is used as input and target pattern sets during the training of the AANN. After the training process, a new vibration spectrum, which is defined in the faulty motor case is applied to this trained network and the faulty case is determined by the error variation at output nodes of the AANN. In this application, the error signal shows huge amplitudes between 2 and 4 kHz as an indicator of the bearing damage. [ABSTRACT FROM AUTHOR]