Application of dynamics wavelet networks to Load Frequency Control

Frekans kararlılığı açısından iki veya daha fazla sayıda güç bölgesi içeren enterkonnekte güç sistemlerinde, her bir bölgedeki üretim planlı bir güç alışverişini sürdürebilmek için gereklidir. Bu çalışmada içerisinde geri dinamikleri, ortogonal olmayan ana dalgacık aktivasyon fonksiyonları ve iç bağlantı ağırlıkları bulunan bir “Dinamik Dalgacık Ağı’na (DDA)”  dayalı yeni bir uyarlamalı yük frekans denetleyicisi (YFD) tasarımı amaçlanmıştır. Bunun için bir DDA iki bölgeli bir güç sistemi örneğinde bölgeler arasına bağlanmıştır. Adaptasyon, DDA parametrelerinin ayarlanmasına dayanır. Bu da yük frekans hata masraflarını içeren bir ölçütün en aza indirilmesi ile sağlanır. Gerekli olan ölçütün ağ parametrelerine göre gradyanları, ek duyarlılık analizi ile hesaplanmıştır.Yapılan benzetim çalışmaları, bu denetim yaklaşımının geleneksel integral denetime göre daha başarılı olduğu bir iki bölgeli güç sistemi üzerinde göstermiştir.Anahtar Kelimeler: Yük frekans denetimi, güç sistem denetimi, dalgacık dönüşümü, dinamik dalgacık ağı, zeki denetim.Frequency stability is one of the stability criteria for large-scale stability of power system. In interconnected power systems with two or more areas, the generation within each area has to be controlled so as to maintain scheduled power interchange. Load frequency control scheme has two main control loops such as primary and secondary control. In primary loop, a steady state frequency error can occur forever. Secondary loop controls the active power at the tie line between areas. This paper proposes a new adaptive load frequency controller based on a ?Dynamic Wavelet Network (DWN)? that has lag dynamics, non-orthogonal mother wavelets as activation function and interconnection weights. A DWN is connected between the two area power systems. The input signals of the DWN are the ACEs and their changes. The outputs of the DWN are the control signals for the two-area load frequency control. Adaptation is based on adjusting parameters of DWN for load frequency control. This is done by minimizing the cost functional of load frequency errors. The cost gradients with respect to the network parameters are calculated by adjoint sensitivity method. It is illustrated that this control approach is more successful than conventional integral controller for load frequency control in two area systems.Keywords: Load frequency control, power system control, wavelet transformation, dynamic wavelet networks, intelligent control

Performance improvement of dynamic buffered ATM switch

Performance of ATM networks depends on switch performance and architecture. This paper presents a simulation study of a new dynamic allocation of input buffer space in ATM switching elements. The switching elements are composed of input and output buffers which are used to store received and forwarded cells, respectively. Efficient and fair use of buffer space in an ATM switch is essential to gain high throughput and low cell loss performance from the network. In this paper, input buffer space of each switching element is allocated dynamically as a function of traffic load. A shared buffer pool is provided with threshold-based virtual partition among input ports, which supplies the necessary input buffer space as required by each input port. The system behaviour under varying traffic loads has investigated using a simulation program. Also, a comparison with a static allocation scheme shows that the threshold based dynamic buffer allocation scheme ensures an increased network throughput and a fair share of the buffer space even under bursty loading conditions. (c) 2005 Elsevier Ltd. All rights reserved

Estimation and evaluation of sub-bands on LF and HF base-bands in HRV for Ventricular Tachyarrhythmia patients

Heart Rate Variability (HRV) is an efficient tool for assessment of Sympathovagal Balance (SB) and classification of cardiac disturbances. However, its index may be not enough for classification and evaluation of some disease. This study presents 32 new sub-bands over LF and HF base-bands that are accepted in the literature. Moreover, it determines dominant sub-bands over both base-bands in VTA database. These sub-bands are obtained using Wavelet Packet Transform (WPT) and evaluated using Multilayer Perceptron Neural Networks (MLPNN). Results are compared with obtained results from normal datasets. The domination effects of these sub-bands are assessed according to comparison of each other related to MLPNN training and test accuracy percentages by selecting different width of windows. As a result, obtained results showed that the LF zone including LF1, LF2 and LF3 sub-bands on 0.0390625-0.0859375 Hz frequency range is the most dominant over the LF base-band and, the HF zone including HF1, HF2 and HF3 on 0.1953125-0.28125 Hz frequency range is the most dominant over the HF base-band. In normal datasets, distinctive domination effect has not been determined. (C) 2009 Elsevier Ltd. All rights reserved

Automatic seizure detection in EEG using logistic regression and artificial neural network

The detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. In this study, multiple signal classification (MUSIC), autoregressive (AR) and periodogram methods were used to get power spectra in patients with absence seizure. The EEG power spectra were used as an input to a classifier. We introduce two fundamentally different approaches for designing classification models (classifiers); the traditional statistical method based on logistic regression (LR) and the emerging computationally powerful techniques based on artificial neural networks (ANNs). LR as well as multilayer perceptron neural network (MLPNN) based classifiers were developed and compared in relation to their accuracy in classification of EEG signals. The comparisons between the developed classifiers were primarily based on analysis of the receiver operating characteristic (ROC) curves as well as a number of scalar performance measures pertaining to the classification. The MLPNN based classifier outperformed the LR based counterpart. Within the same group, the MLPNN-based classifier was more accurate than the LR-based classifier. (c) 2005 Published by Elsevier B.V

Weak signal propagation through noisy feedforward neuronal networks

We determine under which conditions the propagation of weak periodic signals through a feedforward Hodgkin-Huxley neuronal network is optimal. We find that successive neuronal layers are able to amplify weak signals introduced to the neurons forming the first layer only above a certain intensity of intrinsic noise. Furthermore, we show that as low as 4% of all possible interlayer links are sufficient for an optimal propagation of weak signals to great depths of the feedforward neuronal network, provided the signal frequency and the intensity of intrinsic noise are appropriately adjusted. NeuroReport 21:338-343 (C) 2010 Wolters Kluwer Health vertical bar Lippincott Williams & Wilkins

Optimization of resistive loading of EMI/EMC near field probe

Near field studies must be carried out to insulate printed circuit boards and other circuits entirely from outer space. It can be achieved by utilizing the screen properly. These studies must always be made in the near field. The best way known to sense Radio Frequency energy is using a proper loop antenna. The loop probe can be used both as a transmitter and a receiver. If it is loaded resistively, desired flat response between low and high cut off frequency can be obtained. To get accurate results, its physical and electrical parameters must be chosen properly. The most important electrical parameter is inductance of such a loop conductor. Common inductance equations are in general form and they are derived for general calculations. However, loop probes have narrow physical range; they have diameters of a few-centimeters, a few-turn, and have a very thin conductor. So, a certain equation can be derived more accurately. It can be achieved by using conventional empirical derivations or by using modem optimization techniques. (C) 2004 Elsevier B.V. All rights reserved

Comparison of subspace-based methods with AR parametric methods in epileptic seizure detection

Electroencephalography is an important clinical tool for the evaluation and treatment of neurophysiologic disorders related to epilepsy. Careful analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. The detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. In this study, we have proposed subspace-based methods to analyze and characterize epileptiform discharges in the form of 3-Hz spike and wave complex in patients with absence seizure. The variations in the shape of the EEG power spectra were examined in order to obtain medical information. These power spectra were then used to compare the applied methods in terms of their frequency resolution and the effects in determination of epileptic seizure. Global performance of the proposed methods was evaluated by means of the visual inspection of power spectral densities (PSDs). Graphical results comparing the performance of the proposed methods with that of the autoregressive techniques were given. The results demonstrate consistently superior performance of the proposed methods over the autoregressive ones. (C) 2004 Elsevier Ltd. All rights reserved

Lecture Notes in Computer Science

This paper proposes a new controller based on neural network and fuzzy logic technologies for load frequency control to allow for the incorporation of both heuristics and deep knowledge to exploit the best characteristics of each. A "Dynamical Fuzzy Network (DFN)" that contains dynamical elements such as delayers or integrators in their processing units is used in the adaptive controller design for load frequency control. A DFN is connected between the two area power systems. The input signals of the DFN are the ACEs and their changes. The outputs of the DFN are the control signals for the two area load frequency control. Adaptation is based on adjusting parameters of DFN for load frequency control. This is done by minimizing the cost functional of load frequency errors. The cost gradients with respect to the network parameters are calculated by adjoint sensitivity. In this paper, it is illustrated that this control approach is more successful than conventional integral controller for load frequency control in two area systems

Determination of a New VLF Band in HRV for Ventricular Tachyarrhythmia Patients

This study presents a new very low frequency (VLF) band range in ventricular tachyarrhythmia patients and involves an approach for estimation of effect of VLF band on ventricular tachyarrhythmia patients. A model based on wavelet packets (WP) and multilayer perceptron neural network (MLPNN) is used for determination of effective VLF band in heart rate variability (HRV) signals. HRV is decomposed into sub-bands including very low frequency parts and variations of energy are analyzed. Domination test is done using MLPNN and dominant band is determined. As a result, a new VLF band was described in 0.0039063-0.03125 Hz frequency range. This method can be used for other bands or other arrhythmia patients. Especially, estimation of dominant band energy using this method can be helped to diagnose for applications where have important effect of characteristic band