Iranian Journal of Electrical and Electronic Engineering

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A new fuzzy maximum power point tracker (MPPT) for photovoltaic systems is proposed. Fuzzy controller input parameters dI dP , ) dI dP ( D and variation of duty cycle ( DC D ) are used to generate the optimal MPPT converter duty cycle, such that solar panel maximum power is generated under different operating conditions. A photovoltaic system including a solar panel, a fuzzy MPP tracker and a resistive load is designed, simulated and constructed. The fuzzy MPP tracker includes a buck dc/dc converter, fuzzy controller and interfacing circuits. Theoretical and experimental results are used to indicate the advantages and limitations of the proposed technique.

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A modified circular patch antenna design has been proposed in this paper, the bandwidth of this antenna is optimized using the genetic algorithm (GA) based on fuzzy decision-making. This design is simulated with HP HFSS Program that based on finite element method. This method is employed for analysis at the frequency band of 1.4 GHz- 2.6 GHz. It gives good impedance bandwidth of the order of 15.5% at the frequency band of 1.67GHz- 1.95GHz and 10.6% at 2.23GHz- 2.48GHz. It means that impedance bandwidth increases above 4.9% than the impedance bandwidth of ordinary circular patch antennas and band width rise from 1.78GHz- 1.98GHz (10.6%) to 1.67GHz- 1.95GHz (15.5%) and 2.23GHz- 2.48GHz (10.6%). The antenna fabricated with two slots on circular patch antenna. The measured results of the optimized antenna validate a high compatibility between the simulation and the measurements.

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The concepts of robust classification and intelligently controlling the search process of genetic algorithm (GA) are introduced and integrated with a conventional genetic classifier for development of a new version of it, which is called Intelligent and Robust GA-classifier (IRGA-classifier). It can efficiently approximate the decision hyperplanes in the feature space. It is shown experimentally that the proposed IRGA-classifier has removed two important weak points of the conventional GA-classifiers. These problems are the large number of training points and the large number of iterations to achieve a comparable performance with the Bayes classifier, which is an optimal conventional classifier. Three examples have been chosen to compare the performance of designed IRGA-classifier to conventional GA-classifier and Bayes classifier. They are the Iris data classification, the Wine data classification, and radar targets classification from backscattered signals. The results show clearly a considerable improvement for the performance of IRGA-classifier compared with a conventional GA-classifier.

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The operation of Fuel Cell Distributed Generation (FCDG) systems in distribution systems is introduced by modeling, controller design, and simulation study of a Solid Oxide Fuel Cell (SOFC) distributed generation (DG) system. The physical model of the fuel cell stack and dynamic models of power conditioning units are described. Then, suitable control architecture based on fuzzy logic control for the overall system is presented in order to active power control and power quality improvement. A MATLAB/Simulink simulation model is developed for the SOFC DG system by combining the individual component models and the controllers designed for the power conditioning units. Simulation results are given to show the overall system performance including active power control and voltage regulation capability of the distribution system.

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Dealing with uncertainty is one of the most critical problems in complicated

pattern recognition subjects. In this paper, we modify the structure of a useful Unsupervised

Fuzzy Neural Network (UFNN) of Kwan and Cai, and compose a new FNN with 6 types of

fuzzy neurons and its associated self organizing supervised learning algorithm. This

improved five-layer feed forward Supervised Fuzzy Neural Network (SFNN) is used for

classification and identification of shifted and distorted training patterns. It is generally

useful for those flexible patterns which are not certainly identifiable upon their features. To

show the identification capability of our proposed network, we used fingerprint, as the most

flexible and varied pattern. After feature extraction of different shapes of fingerprints, the

pattern of these features, “feature-map”, is applied to the network. The network first

fuzzifies the pattern and then computes its similarities to all of the learned pattern classes.

The network eventually selects the learned pattern of highest similarity and returns its

specific class as a non fuzzy output. To test our FNN, we applied the standard (NIST

database) and our databases (with 176×224 dimensions). The feature-maps of these

fingerprints contain two types of minutiae and three types of singular points, each of them

is represented by 22×28 pixels, which is less than real size and suitable for real time

applications. The feature maps are applied to the FNN as training patterns. Upon its setting

parameters, the network discriminates 3 to 7 subclasses for each main classes assigned to

one of the subjects.

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This paper presents a novel optimization based methodology to allocate Flexible AC Transmission Systems (FACTS) devices in an attempt to improve the previously mentioned researches in this field. Static voltage stability enhancement, voltage profile improvement, line congestion alleviation, and FACTS devices investment cost reduction, have been considered, simultaneously, as objective functions. Therefore, multi-objective optimization without simplification has been used in this paper to find a logical solution to the allocation problem. The optimizations are carried out on the basis of location, size and type of FACTS devices. Thyristor Controlled Series Compensator (TCSC) and Static Var Compensator (SVC) are utilized to achieve the determined objectives. The problem is formulated according to Sequential Quadratic Programming (SQP) problem in the first stage. This formulation is used to accurately evaluate static security margin with congestion alleviation constraint incorporating voltage dependence of loads in the presence of FACTS devices and estimated annual load profile. The best trade-off between conflicting objectives has been obtained through Genetic Algorithm (GA) based fuzzy multi-objective optimization approach, in the next stage. The IEEE 14-bus test system is selected to validate the allocated devices for all load-voltage characteristics determined by the proposed approach.

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This paper presents design and implementation of three new Infrared Counter-Countermeasure (IRCCM) efficient methods using Neural Network (NN), Fuzzy System (FS), and Kalman Filter (KF). The proposed algorithms estimate tracking error or correction signal when jamming occurs. An experimental test setup is designed and implemented for performance evaluation of the proposed methods. The methods validity is verified with experiments on IR seeker reticle based on a Digital Signal Processing (DSP) processor. The practical results emphasize that the proposed algorithms are highly effective and can reduce the jamming effects. The experimental results obtained strongly support the potential of the method using FS to eliminate the IRCM effect 83%.

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The aim of this study is to address a new feature extraction method in the area of the heart arrhythmia classification based on a metric with simple mathematical calculation called Curve-Length Method (CLM). In the presented method, curve length of the under study excerpted segment of signal is considered as an informative feature in which the effect of important geometric parameters of the original signal can be found. To show merits of the presented method, first the original electrocardiogram (ECG) in lead I is pre-processed by removing its baseline wander then by scaling it in the [-1,1] interval. In the next step, using a trous method, discrete wavelet scales 23 and 24 and smoothing function scale 22 are extracted. Afterwards, segments including samples of the QRS complex, P and T waves are estimated via an approximation criterion and CLM is implemented to extract corresponding features from aforementioned scales, smoothing function and also from each original segment. The resulted feature vector (including 12 components) is used to tune an Adaptive Network Fuzzy Inference System (ANFIS) classifier. The presented strategy is applied to classify four categories found in the MIT-BIH Arrhythmia Database namely as Atrial Premature Beat (APB), Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB) and Premature Ventricular Contraction (PVC) and average values of Se = 99.81%, P+ = 99.80%, Sp = 99.81% and Acc = 99.72% are obtained for sensitivity, positive predictivity, specifity and accuracy respectively showing marginal improvement of the heart arrhythmia classification performance.

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Here, a new fuzzy direct torque control algorithm for induction motors is proposed. As in the classical direct torque control, the inverter gate control signals directly come from the optimum switching voltage vector look-up table, the best voltage space vector selection is a key factor to obtain minimum torque and flux ripples. In the proposed approach, the best voltage space vector is selected using a new fuzzy method. A simulation model is built up and the torque and flux ripples of basic direct torque control and the proposed method are compared. The simulation results show that the torque and flux ripples are significantly decreased and in addition, the switching frequency can be fixed.

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This paper consist of two sections: control and stabilizing approach for chaotic behaviour of converter is introduced in first section of this paper for the removal of harmonic caused by the chaotic behaviour in current converter. For this work, a Time- Delayed Feedback Controller (TDFC) control method for stability chaotic behaviour of buck converter for switching courses in current control mode is presented. This behaviour is demonstrated by presenting a piecewise linear discrete map for this converter and then combining the feedback equation to obtain the overall equation of the converter. A simple time-delay feedback control method is applied to stabilize the Unstable Periodic Orbits (UPOs). In second section is studied the effect of a parallel metal oxide surge arrester on the ferroresonance oscillations of the transformer. It is expected that the arresters generally cause ferroresonance drop out. Simulation has been done on a three phase power transformer with one open phase. Effect of varying input voltage has been studied. The simulation results reveal that connecting the arrester to the transformer poles, exhibits a great mitigating effect on ferroresonant over voltages. Phase plane along with bifurcation diagrams are also presented. Significant effect on the onset of chaos, the range of parameter values that may lead to chaos and magnitude of ferroresonant voltages has been obtained, shown and tabulated.

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The paper addresses a new QRS complex geometrical feature extraction technique as well as its application for electrocardiogram (ECG) supervised hybrid (fusion) beat-type classification. To this end, after detection and delineation of the major events of ECG signal via a robust algorithm, each QRS region and also its corresponding discrete wavelet transform (DWT) are supposed as virtual images and each of them is divided into eight polar sectors. Then, the curve length of each excerpted segment is calculated and is used as the element of the feature space. To increase the robustness of the proposed classification algorithm versus noise, artifacts and arrhythmic outliers, a fusion structure consisting of three Multi Layer Perceptron-Back Propagation (MLP-BP) neural networks with different topologies and one Adaptive Network Fuzzy Inference System (ANFIS) were designed and implemented. To show the merit of the new proposed algorithm, it was applied to all MIT-BIH Arrhythmia Database records and the discrimination power of the classifier in isolation of different beat types of each record was assessed and as the result, the average accuracy value Acc=98.27% was obtained. Also, the proposed method was applied to 8 number of arrhythmias (Normal, LBBB, RBBB, PVC, APB, VE, PB, VF) belonging to 19 number of the aforementioned database and the average value of Acc=98.08% was achieved. To evaluate performance quality of the new proposed hybrid learning machine, the obtained results were compared with similar peer-reviewed studies in this area.

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Long-term demand forecasting presents the first step in planning and developing future generation, transmission and distribution facilities. One of the primary tasks of an electric utility accurately predicts load demand requirements at all times, especially for long-term. Based on the outcome of such forecasts, utilities coordinate their resources to meet the forecasted demand using a least-cost plan. In general, resource planning is performed subject to numerous uncertainties. Expert opinion indicates that a major source of uncertainty in planning for future capacity resource needs and operation of existing generation resources is the forecasted load demand. This paper presents an overview of the past and current practice in long- term demand forecasting. It introduces methods, which consists of some traditional methods, neural networks, genetic algorithms, fuzzy rules, support vector machines, wavelet networks and expert systems.

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This paper proposes a Trust-Region Based Augmented Method (TRALM) to solve a combined Environmental and Economic Power Dispatch (EEPD) problem. The EEPD problem is a multi-objective problem with competing and non-commensurable objectives. The TRALM produces a set of non-dominated Pareto optimal solutions for the problem. Fuzzy set theory is employed to extract a compromise non-dominated solution. The proposed algorithm is applied to the standard IEEE 30 bus six-generator test system. Comparison of TRALM results with the various algorithms, reported in the literature shows that the solutions of the proposed algorithm are very accurate for the EEPD problem.

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This paper proposes a method for determining the price bidding strategies of market participants consisting of Generation Companies (GENCOs) and Distribution Companies (DISCOs) in a day-ahead electricity market, while taking into consideration the load forecast uncertainty and demand response programs. The proposed algorithm tries to find a Pareto optimal point for a risk neutral participant in the market. Because of the complexity of the problem a stochastic method is used. In the proposed method, two approaches are used simultaneously. First approach is Fuzzy Genetic Algorithm for finding the best bidding strategies of market players, and another one is Mont-Carlo Method that models the uncertainty of load in price determining algorithm. It is demonstrated that with considering transmission flow constraints in the problem, load uncertainty can considerably influences the profits of companies and so using the second part of the proposed algorithm will be useful in such situation. It is also illustrated when there are no transmission flow constraints, the effect of load uncertainty can be modeled without using a stochastic model. The algorithm is finally tested on an 8 bus system.

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Nowadays, the Fuzzy C-Means method has become one of the most popular clustering methods based on minimization of a criterion function. However, the performance of this clustering algorithm may be significantly degraded in the presence of noise. This paper presents a robust clustering algorithm called Bilateral Weighted Fuzzy CMeans (BWFCM). We used a new objective function that uses some kinds of weights for reducing the effect of noises in clustering. Experimental results using, two artificial datasets, five real datasets, viz., Iris, Cancer, Wine, Glass and a speech corpus used in a GMM-based speaker identification task show that compared to three well-known clustering algorithms, namely, the Fuzzy Possibilistic C-Means, Credibilistic Fuzzy C-Means and Density Weighted Fuzzy C-Means, our approach is less sensitive to outliers and noises and has an acceptable computational complexity.

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This paper presents a Closed Loop CLL-T (capacitor inductor inductor) Series Parallel Resonant Converter (SPRC) has been simulated and the performance is analysised. A three element CLL-T SPRC working under load independent operation (voltage type and current type load) is presented in this paper. The Steady state Stability Analysis of CLL-T SPRC has been developed using State Space technique and the regulation of output voltage is done by using Fuzzy controller. The simulation study indicates the superiority of fuzzy control over the conventional control methods. The proposed approach is expected to provide better voltage regulation for dynamic load conditions. A prototype 300 W, 100 kHz converter is designed and built to experimentally demonstrate, dynamic and steady state performance for the CLL-T SPRC are compared from the simulation studies.

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Abstract- In a typical competitive electricity market, a large number of short-term and long-term contracts are set on basis of energy price by an Independent System Operator (ISO). Under such circumstances, accurate electricity price forecasting can play a significant role in improving the more reasonable bidding strategies adopted by the electricity market participants. So, they cannot only raise their profit but also manage the relevant market more efficiently. This conspicuous reason has motivated the researchers to develop the most accurate, though sophisticated, forecasting models to predict the short-term electricity price as precisely as possible. In this article, a new method is suggested to forecast the next day's electricity price of Iranian Electricity Market. The authors have used this hybrid model successfully in their previous publications to predict the electric load data of Ontario Electricity Market [1] and of the Spinning Reserve data of Khorasan Electricity Network [2] respectively.

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An integrated fuzzy guidance (IFG) law for a surface to air homing missile is introduced. The introduced approach is a modification of the well-known proportional navigation guidance (PNG) law. The IFG law enables the missile to approach a high maneuvering target while trying to minimize control effort as well as miss-distance in a two-stage flight. In the first stage, while the missile is far from the intended target, the IFG tends to have low sensitivity to the target maneuvering seeking to minimize the overall control effort. When the missile gets closer to the target, a second stage is started and IFG law changes tactic by increasing that sensitivity attempting to minimize the miss-distance. A fuzzy-switching point (FSP) controller manages the transition between the two stages. The FSP is optimized based on variety of scenarios some of which are discussed in the paper. The introduced scheme depends on line-of-sight angle rate, closing velocity, and target-missile relative range. The performance of the new IFG law is compared with PNG law and the results show a relative superiority in wide variety of flight conditions.

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Steer-by-wire is the electrical steering systems on vehicles that are expected with the development of an optimal control system can improve the dynamic performance of the vehicle. This paper aims to optimize the control systems, namely Fuzzy Logic Control (FLC) and the Proportional, Integral and Derivative (PID) control on the vehicle steering system using Imperialist Competitive Algorithm (ICA). The control systems are built in a cascade, FLC to suppress errors in the lateral motion and the PID control to minimize the error in the yaw motion of the vehicle. FLC is built has two inputs (error and delta error) and single output. Each input and output consists of three Membership Function (MF) in the form of a triangular for language term "zero" and two trapezoidal for language term "negative" and "positive". In order to work optimally, each MF optimized using ICA to get the position and width of the most appropriate. Likewise, in the PID control, the constant at each Proportional, Integral and Derivative control also optimized using ICA, so there are six parameters of the control system are simultaneously optimized by ICA. Simulations performed on vehicle models with 10 Degree Of Freedom (DOF), the plant input using the variables of steering that expressed in the desired trajectory, and the plant outputs are lateral and yaw motion. The simulation results showed that the FLC-PID control system optimized by using ICA can maintain the movement of vehicle according to the desired trajectory with lower error and higher speed limits than optimized with Particle Swarm Optimization (PSO).

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Reactive power plays an important role in supporting real power transmission, maintaining system voltages within proper limits and overall system reliability. In this paper, the production cost of reactive power, cost of the system transmission loss, investment cost of capacitor banks and absolute value of total voltage deviation (TVD) are included into the objective function of the power flow problem. Then, by using particle swarm optimization algorithm (PSO), the problem is solved. The proposed PSO algorithm is implemented on standard IEEE 14-bus and IEEE 57-bus test systems and with using fuzzy satisfying method the optimal solutions are determined. The fuzzy goals are quantified by defining their corresponding membership functions and the decision maker is then asked to specify the desirable membership values. The obtained results show that solving this problem by using the proposed method gives much better results than all the other algorithms.