Cengiz Polat Uzunoglu

Modeling and Suppression of Chaotic Ferroresonance in a Power System by Using Memristor-based System

Abstract Power systems contain capacitances and inductances that can initiate chaotic ferroresonance due to the non-linear operating characteristics of ferromagnetic materials, such as power transformer cores. Chaotic ferroresonance is a highly disturbing complex, non-linear phenomenon that may cause over-voltages and over-currents; hence, it can compromise regular system operation. The memristor is a non-linear passive two-terminal electricalcomponent, foreseen and introduced as the fourth ideal circuit element. In this studys novel approach, a memristive system is proposed for modeling chaotic ferroresonance in power systems. Employing the memristive circuit as the source of ferroresonance, flexible and adjustable solutions of the dynamic system can be obtained easily. The chaotic characteristic of the proposed system is verified by using a bifurcation diagram and Lyapunov exponent analysis. To reduce the effects of chaotic ferroresonance, a memristor-based system is proposed for damping chaotic oscillations of the system as a protection element. simulation program with integrated circuit emphasis (SPICE) simulations have been carried out for the performance analysis of the proposed system during ferroresonance, and suppression modes are presented.

Classification of chaotic circuit output patterns with probabilistic neural networks

This study focused on the classification of chaotic circuit behaviors with probabilistic neural network (PNN). Although, chaotic circuit outputs track similar traces for the defined parameters, still the circuit outputs preserve their own random characteristics at each trial. PNN is an effective tool for classification of pattern recognition problems. Inherited features of PNN are very compatible with the chaotic circuit output classification problem and it provides satisfying performance. The selection of the proper features in the feature extraction step defines the performance of the classification significantly. In order to, compare classification performance of the PNN, different feature vectors are employed in the training process. Moreover, the spread parameter is a considerably vital factor for the performance of the network. The simulation results and the corresponding illustrations for the performance analysis are also given.

Fractal dimension analysis of Uroflowmetry signals

The Uroflowmetry test is a commonly used method, which evaluates urine flow rates and volumetric analysis of patients. Flow rates and volumetric analysis are employed for assessing various urinary disorders such as Urethral Stricture, Bladder control problems, prostate etc. In this study, ten different Uroflowmetry signals of both abnormal and normal patients are processed. The Higuchis fractal dimension computation is proposed for analyzing and investigating the characteristics of these signals. The Higuchis method is an effective tool for measurement of fractal dimension of self-affine signals which is proposed for online monitoring of Uroflowmetry signals of patients and hence accelerated diagnose of urinary disorders.

Dedection of eye-blink movements with sensors

During the lifetime, one of the most involuntary or voluntary movements of the human body is blinking eyes. The main purpose of ongoing scientific researches is to obtain a meaningful and fundamental data by observing eye blink movement, which might give some valuable information for other research subjects. Eye blink data is being frequently analyzed and processed for different application fields such as wearable technologies, intelligent driver warning systems, etc. In this study, eye blink data is obtained as biomechanical outcome for application of biomedical purposes in treatment of diseases caused by eye blink complications. The motivation of the study is based on facial paralysis disease, which may be resulted in eye blink difficulty especially in one eye. The proposed treatment system operates by triggering eyelid muscles by an appropriate synchronizing data obtained by healthy eye blink movements. The synchronizing data is acquired by using QRD1114 sensor module which contains IR led and phototransistör. The proposed system with its ergonomic structure does not block the angle of vision.

Modelling of chaotic surface tracking on the polymeric insulators with Hidden Markov Models

In this study, chaotic surface tracking patterns observed on polymeric high voltage (HV) outdoor insulation materials were investigated and simulated. The polymeric samples are tested according to the IEC 587 Inclined Plane Tracking Test Standard. Since the chaotic surface tracking patterns manifests smutty and disordered images, they are preprocessed and purified by image processing tools. Internal and external effects may severely decrease insulation performance. In order to examine external effects, samples are subjected to moisture and vibration effect. Polymer samples are investigated by their fractal dimension which is a prominent tool for analyzing chaotic images. To simulate these chaotic surface tracking patterns Hidden Markov Models (HMM) are used.

Amplitude and frequency estimation of power system signals using independent component analysis

In this paper independent component analysis (ICA) method for amplitude and frequency estimation of distorted power system signals is proposed. In order to protect system and keep it in safe operation mode the amplitude and frequency estimation should be conducted accurately. Transient disturbances of the power system may reduce estimation performance due to the distortion strength. In this study white noise and pulse noise which are very common for power systems, are used to contaminate power system signal. Thus, the proposed method is employed to decompose noise from system signal and hence to improve the efficiency of the estimation. Computer simulations have been carried out for the performance analysis of the ICA method and the comparison of the results of the proposed method with the conventional filters are displayed by using mean square error (MSE) values.

Frequency estimation of power system signals with chaotic oscillations using music and esprit algorithms

Chaotic ferroresonance is one of the disturbances of a power system, which may cause chaotic oscillations with over voltages and over currents. In order to protect system and keep it stable the frequency estimation should be fulfilled accurately. In this study first chaotic oscillations of ferroresonance are modeled with forced Duffing oscillators dynamical equations. MUSIC (Multiple Signal Classification) and ESPRIT (Estimation of Parameters by Rotationally Invariant Technique) methods are proposed for frequency estimation of chaotically distorted power system signals. Frequency is estimated efficiently by using the MUSIC and ESPRIT methods. Finally, computer simulations have been carried out for the performance analysis of the proposed methods and the comparison results of the proposed methods based on the SNR (Signal to noise ratio) values are given.

Three dimentional acoustic source location estimation with maximum likelihood estimation using Kalman filter

In this study, a novel algorithm based on the Maximum Likelihood Estimator (MLE) is proposed to estimate three dimensional position of an acoustic source which is located in a field with randomly deployed sensors. The received sensor signals have been filtered by using Kalman filter in order to eliminate the background noise. Filtered signals have been subjected to the estimation procedure to obtain the three dimensional source location. Consequently, the usage of Kalman filter has provided better estimation performance in accordance with the case without Kalman filter. Moreover, the performance decreases due to the larger parameter space in the procedure of estimating three dimensional location of the source. In the three dimensional estimation scenario Kalman filter drastically improves the performance of the estimator. The performances of the estimation procedures with and without Kalman filter has been investigated by plotting the estimator variances. The performance of the algorithm has been analyzed by comparing the simulation results and the Cramer Rao Bound expressions.