Okan Ozgonenel

Time-domain simulation of nonlinear transformers displaying hysteresis

This paper introduces a novel technique for modeling, in the time domain, a power transformer with nonlinear and hysteretic behavior. The model is particularly suitable for harmonic or protection studies where the transformer is driven into the nonlinear regime. The technique uses a single-phase two-winding transformer model based on the Jiles-Atherton model of ferromagnetic hysteresis. It includes eddy-current loss by adding an extra single-turn winding so that the transients are modeled as fully as possible.

Modeling and real-time fault identification in transformers

Abstract In this paper, a different internal fault modeling and an identification algorithm are presented. There has been an increasing concern about turn-to-turn faults in transformers because of the high costs of unexpected outages. It is not always possible to analyze the transformer behavior under such faults at rated conditions, since the tests are highly destructive. To develop transformer internal fault detection technique, a transformer model to simulate internal faults is required. This paper describes a novel technique and methodology for modeling and identifying transformer internal faults by using transmission line method (TLM) and fuzzy reasoning technique based on dynamic principal component analysis (PCA), respectively. The transformer has been modeled considering non-linearities as hysteresis and saturation. Transformer internal fault currents are successfully discriminated from the rated currents. The degree and priority of transformer internal faults are obtained by the proposed method. It is suited for implementation on computers because of no computation complexity. Hence, the proposed algorithm can be used effectively in real-time fault identification problems.

A New Method for Fault Detection and Identification of Incipient Faults in Power Transformers

Abstract This article presents a new scheme for incipient fault detection and its identification in transformers. The new approach is actually based on adaptive modeling of transformers using the transmission line method (TLM) obtained from the hysteresis model. The adaptive TLM observer representing no-load, quarter-load, half-load, and rated-load conditions is used for faults detection. The continuous wavelet transform (CWT) is performed on residuals that are obtained by comparing real system currents and calculated TLM observer currents in order to extract the features for fault identification. An adaptive fuzzy reasoning technique is used to identify incipient faults in the transformer. The sum of CWT coefficients of residuals is applied to the adaptive fuzzy rule-based decision-making unit to indicate the type of faults. The main advantage of the suggested scheme is that different types of incipient faults in the transformer can be correctly identified. The test results verify the effectiveness of the suggested method.

Use of artificial neural network in differentiation of subgroups of temporomandibular internal derangements: a preliminary study.

PURPOSE Artificial neural networks (ANNs) have been developed in the past few decades for many different applications in medical science and in biomedical research. The use of neural networks in oral and maxillofacial surgery is limited. The aim of this study was to determine the use of ANNs for the prediction of 2 subgroups of temporomandibular joint (TMJ) internal derangements (IDs) and normal joints using characteristic clinical signs and symptoms of the diseases. MATERIALS AND METHODS Clinical symptoms and diagnoses of 161 patients with TMJ ID were considered the gold standard and were employed to train a neural network. After the training process, the symptoms and diagnoses of 58 new patients were used to verify the networks ability to diagnose. The diagnoses obtained from ANNs were compared with diagnoses of a surgeon experienced in temporomandibular disorders. The sensitivity and specificity of ANNs in predicting subtypes of TMJ ID were evaluated using clinical diagnosis as the gold standard. RESULTS Eight cases evaluated as bilaterally normal in clinical examination were evaluated as normal by ANN. In detecting unilateral anterior disc displacement with reduction (ADDwR; clicking), the sensitivity and specificity of ANN were 80% and 95%, respectively. In detecting unilateral anterior disc displacement without reduction (ADDwoR; locking), the sensitivity and specificity of ANN were 69% and 91%, respectively. In detecting bilateral ADDwoR, the sensitivity and specificity of ANN were 37% and 100%, respectively. In detecting bilateral ADDwR, the sensitivity and specificity of ANN were 100% and 89%, respectively. In detecting cases of ADDwR at 1 side and ADDwoR at the other side, the sensitivity and specificity of ANN were 44% and 93%, respectively. CONCLUSION The application of ANNs for diagnosis of subtypes of TMJ IDs may be a useful supportive diagnostic method, especially for dental practitioners. Further research, including advanced network models that use clinical data and radiographic images, is recommended.

The Design and Implementation of Adsorptive Removal of Cu(II) from Leachate Using ANFIS

Clinoptilolite was investigated for the removal of Cu(II) ions from industrial leachate. Adaptive neural fuzzy interface system (ANFIS) was used for modeling the batch experimental system and predicting the optimal input values, that is, initial pH, adsorbent dosage, and contact time. Experiments were studied under laboratory batch and fixed bed conditions. The outcomes of suggested ANFIS modeling were then compared to a full factorial experimental design (23), which was utilized to assess the effect of three factors on the adsorption of Cu(II) ions in aqueous leachate of industrial waste. It was observed that the optimized parameters are almost close to each other. The highest removal efficiency was found as about 93.65% at pH 6, adsorbent dosage 11.4 g/L, and contact time 33 min for batch conditions of 23 experimental design and about 90.43% at pH 5, adsorbent dosage 15 g/L and contact time 35 min for batch conditions of ANFIS. The results show that clinoptilolite is an efficient sorbent and ANFIS, which is easy to implement and is able to model the batch experimental system.

Identification of Transformer Internal Faults by Using an RBF Network Based on Dynamical Principle Component Analysis

In this paper; a method is proposed to detect and identify parameter faults in nonlinear dynamical systems. The approach is based on the principal component analysis (PCA) and artificial neural networks (ANNs) based on radial basis functions (RBFs). A nonlinear systems input and output data is manipulated without taking consideration any model in the approach. The method is applied to a three phase custom built transformer in order to detect and identify internal short circuit faults. It is obsered theughgh various application examples that the proposed method leads to satisfactory results in terms of detecting parameter faults in non-linear dynamical systems.

A Novel Selection Algorithm of a Wavelet-Based Transformer Differential Current Features

In this paper, a novel selection algorithm of wavelet- based transformer differential current features is proposed. The minimum description length with entropy criteria are employed for an initial selection of the mother wavelet and the resolution level, respectively; whereas stepwise regression is applied for obtaining the most statistically significant features. Dimensionality reduction is accordingly achieved, with an acceptable accuracy maintained for classification. The validity of the proposed algorithm is tested through a neuro-wavelet- based classifier of transformer inrush and internal fault differential currents. The proposed algorithm highlights the potential of utilizing synergism of integrating multiple feature selection techniques as opposed to an individual technique, which ensures optimal selection of the features.

Wavelet Transform Based Protection of Stator Faults in Synchronous Generators

Abstract This article presents a novel wavelet power based algorithm for protection of stator windings of a three-phase synchronous generator. The proposed algorithm is based on the comparison of the instantaneous power of the wavelet packet transform coefficients of voltage and current samples of different faulted and normal conditions acquired from the terminals of the generator. The wavelet powers of the second level high frequency details of fault currents and voltages using a selected mother wavelet show distinctive features between different faulted and normal conditions. The proposed wavelet power based detection algorithm is developed and implemented in real-time using the ds1102 digital signal processor board. The protection technique is tested on-line on a laboratory 1.6 kW three-phase synchronous generator. Different types of stator faults at generator terminals for testing the technique in real-time, faults were identified promptly and properly, and the trip signal was initiated almost at the instant or within one cycle of fault occurrence.

Study of montmorillonite clay for the removal of copper (II) by adsorption: full factorial design approach and cascade forward neural network.

An intensive study has been made of the removal efficiency of Cu(II) from industrial leachate by biosorption of montmorillonite. A 24 factorial design and cascade forward neural network (CFNN) were used to display the significant levels of the analyzed factors on the removal efficiency. The obtained model based on 24 factorial design was statistically tested using the well-known methods. The statistical analysis proves that the main effects of analyzed parameters were significant by an obtained linear model within a 95% confidence interval. The proposed CFNN model requires less experimental data and minimum calculations. Moreover, it is found to be cost-effective due to inherent advantages of its network structure. Optimization of the levels of the analyzed factors was achieved by minimizing adsorbent dosage and contact time, which were costly, and maximizing Cu(II) removal efficiency. The suggested optimum conditions are initial pH at 6, adsorbent dosage at 10 mg/L, and contact time at 10 min using raw montmorillonite with the Cu(II) removal of 80.7%. At the optimum values, removal efficiency was increased to 88.91% if the modified montmorillonite was used.

Feature vector extraction by using empirical mode decomposition for power quality disturbances

This work presents a relatively new method known as empirical mode decomposition (EMD) for power quality disturbances. In a comprehensive and wider range of approaches and engineering activities, there is a increasing concern for power system disturbances monitoring techniques. The need of increasing performances in terms of accuracy and computation speed is permanently demanding new efficient processing techniques on power system visualization. For system monitoring, feature extraction of a disturbed power signal provides information that helps to detect and diagnose the responsible fault for power quality disturbance. Traditionally, monitoring spectral and harmonic analysis of dynamic systems is based on Fourier based transforms and the wavelets. The Fourier transform usually has been used in the past for analysis of stationary and periodic signals. Qualification to providing a more accurate ‘real-time’ demonstration of a signal without any artifacts imposed by the non-locally adaptive limitations of the fast Fourier transform (FFT) and wavelet processing. In this work, the first step of Hilbert-Huang transform (HHT), EMD, has been regarded as a powerful tool for adaptive analysis of non-linear and non-stationary signals.