JOURNAL OF IRANIAN ASSOCIATION OF ELECTRICAL AND ELECTRONICS ENGINEERS
Year:2011 | Volume:8 | Issue:1|Papers Count:7

In this paper a new structure based on Bayesian networks is presented to improve mobile robot behavior, in which there exist faulty robot sensors. If a robot likes to follow certain behavior in the environment to reach its goal, it must be capable of making inference and mapping based on prior knowledge and also should be capable of understanding its reactions on the environment over time. Old learning models for knowledge learning, especially on dynamic environment, are quite complex and have uncertainty in sensors. In this paper a new structure based on Bayesian network is presented for knowledge learning on robot behavior when the malfunction sensors exist. In this paper successful door crossing behavior is explained. In this issue the belt of ultrasonic sensors is used to receive environment information. Simulation results show that using the Bayesian network is very effective in robot behavior with faulty sensors.

In this paper a direct ac-ac converter with low distortion input/output waveforms is investigated. Direct ac-ac converters using self-commutated semiconductor switches, also known as matrix converters, have recently attracted the attention of researchers. However, these converters are still under development due to the problems associated with the commutation of bidirectional switches employed in these converters. In this paper, an ac-ac converter is proposed based on the realization of matrix converters. Absence of bulky and unreliable electrolytic capacitors, low distortion input/output waveforms with independent fundamental frequency and employing two conventional power electronic building blocks are among important advantages of the proposed converter. The PWM patterns of two converters must be synchronized in order to achieve harmonic-free waveforms. This calls for an advanced PWM generation method as compared to conventional PWM strategies. Detailed derivation of the PWM signals based on double space-vector strategy is presented. Simulation results show various aspects of the system operation. The theoretical results are verified using a laboratory prototype. Experimental and simulation results show good agreement.

In this paper CCM/CVM operation and modeling of the improved Sheppard-Taylor based power factor correction (PFC) rectifier is presented and a predictive control strategy is introduced for this rectifier. Compared with conventional boost or buck–boost PFC's, this topology allows a better current tracking at the AC side, with a relatively reduced voltage at the DC side. Consequently, the high frequency AC filters required by the buck PFCs are avoided, and the voltage stresses on the boost switches are significantly reduced. Furthermore In predictive control strategy the duty cycle required to achieve unity power factor in a half line period can be calculated in advance. The main advantage of this digital control PFC method is achieving on high dynamics directly depending on the processing speed of micro processor. Simulation results show that an improved Sheppard-Taylor converter controlled by this strategy can achieve sinusoidal current waveform with good dynamic response. Input voltage feed-forward compensation makes the output voltage insensitive to the input voltage variation and guarantees sinusoidal input current even if the input voltage is distorted.

Direct torque control (DTC) is a fast and simple torque control method for induction motor without speed sensor.Conventional AC-DC-AC converter has a large DC link capacitor and low power factor in the input power line. In the matrix converters the DC link capacitor can be reduced and the unity input power factor can be obtained. In this paper a new method for direct torque and stator flux control with unity power factor using matrix converter is suggested. The proposed matrix converter consists of 9 uni-direction switches with a clamp circuit. Reference voltage vector for matrix converter is calculated based on input-output feedback linearization with a constant switching frequency. Simulation results confirm the effectiveness of the proposed method and compared with the classic DTC method of matrix converter fed induction motor.

Transformers are one of the important and at the same time expensive components of power systems. On timely diagnosis of fault in such systems is still among the researchers interest. Fault diagnosis of transformers based on the dissolved gas analysis is a new technique in the field of fault diagnosis of power transformers. IEC, Roger's and Dornenburg techniques are the mostly used techniques. However, each have a limited range for diagnosis and may fail a correct diagnosis when the amount of dissolve gases are close to the predefined margins. This problem can be solved by using intelligent techniques such as Fuzzy logics or neural networks. In this paper, a combined technique is proposed for the fault diagnosis of power transformers. It employs a combination of fuzzy Roger's, fuzzy IEC, and fuzzified dissolved gas thresholds for the fault diagnosis. The proposed technique is applied on some transformers with a sum of over 100 available data on their conditions and showed an accuracy of over 98%.

Transmission system is one of the most important parts of the restructured power system. Insufficient or inappropriate expansion of transmission system is considered to be a major barrier to competition in electricity market. Therefore many researches have been focused on developing suitable approaches for transmission expansion planning in deregulated power systems.This paper presents a new approach for transmission expansion planning in long term based on dynamic modeling while the method is based on the maximization of the social welfare, it also considers the view point of system policy maker through weighting generating units. The suggested method can be efficiently used for systems which are in the way of experiencing restructuring. The suggested method is tested on IEEE 14-bus system and the results are discussed.

Generation expansion planning (GEP) in the competitive environment of electric industry is an important and complex problem, which is normally performed for about the next 10-30 years horizon. This problem has presented a challenge for both market managers and suppliers regarding the safe provision of loads and acquiring minimum profit for suppliers over the planning interval. In this paper, the aim is to establish dynamic balance between energy supply and demand by adjustment and optimal design of GEP over the horizon of study such that the expected profit to be provided for generating plants. To do so, in this paper the uncertainties of demand and system capacity have been modelled through two stochastic processes. In addition, the market price dynamics and their mutual effects on the GEP are considered. Based on this model, the intention of private sectors and their influences on the obtained results are observable. This nonlinear dynamic problem is solved using an optimization method based on genetic algorithm. The efficiency and ability of the proposed method is examined on a test power system.