فصلنامه روشهای هوشمند در صنعت برق
پیاپی 14 (تابستان 1392)

Reliability of a system is the ability of a system to do prospected duties in future and the probability of desirable operation for doing predetermined duties. Power system elements failures data are the main data of reliability assessment in the network. Determining antiseptic parameters is the goal of reliability assessment by using system history data. These parameters help to recognize week points of the system. In other words، the goal of reliability assessment is operation improving and decreasing of the failures and power outages. This paper is developed to assess reliability indices of Bakhtar Regional Electricity Company up to 1393 and the improving methods and their effects on the reliability indices in this network. DIgSILENT Power Factory software is employed for simulation. Simulation results show the positive effect of improving methods in reliability indices of Bakhtar Regional Electricity Company.

Nowadays، the control of machenical systems with fewer inputs than outputs (Underactuated systems) has become a challenging problem for control engineers. The 2DOF Acrobot system is one of the appealing example of this category. The goal of this paper، is designing، modeling and implementation of an Acrobot system to use in linear control، digital control، robotics and artificial intelligence labs. So، at first، a suitable mechanism (simple and low price) was designed by SolidWorks. Then، the designed mechanism was made carefully. In the next step، dynamical model of the system was obtained based on Euler-Lagrange method. After implementation of some practical subsystem in the Simulink environment، the Acrobot system was linked with the MATLAB using a Data Acquisituon card.

In this paper a suitable mathematical model of the two terminal HVDC system is provided for optimal power flow (OPF) and optimal location based on OPF such power injection model. The ability of voltage source converter (VSC) -based HVDC to independently control active and reactive power is well represented by the model. The model is used to develop an OPF-based optimal location algorithm of two systems two terminal HVDC to minimize the total fuel cost and active power losses as objective function. The optimization framework is modeled as non-linear programming (NLP) and solved by Matlab and GAMS softwares. The proposed algorithm is implemented on the IEEE 14- and 30-bus test systems. The simulation results show ability of two systems two terminal HVDC in improving the power system operation. Furthermore، two systems two terminal HVDC is compared by PST and OUPFC in the power system operation from economical and technical aspects.

Considering the growing trend of the consumption of the electric power and the global tendency to substitute new renewable sources of energy، this paper proposes a Monte Carlo based method to determine an optimal level of this change. Considering the limitation of the wind farms in continuous supply of electric power، hydrostatic power storage facilities are used beside wind farms so that the electric power could be stored and fed in a continuous flow into power systems. Due to the gradual exclusion of conventional generators and 5 percent annual load increments، LOLE index was used in order to calculate the amount of the wind power and the capacity of the necessary power storage facility. To this end، LOLE index was calculated for the first year as the reference index for the estimation of the amount of wind power and the capacity of the storage facility in consequent years. For the upcoming years، calculations have been made to account for the gradual exclusion of conventional generators in proportion to load increments. The proposed method has been implemented and simulated on IEEE-RTS test system.

Researchers tendency to use different collective intelligence as the search methods to optimize complex engineering problems has increased because of the high performance of this algorithms. Gravitational search algorithm (GSA) is among these algorithms. This algorithm is inspired by Newton''s laws of physics and gravitational attraction. Random masses are agents who have searched for the space. This paper presents a new Fuzzy Population GSA model called FPGSA. The proposed method is a combination of parametric fuzzy controller and gravitational search algorithm. The space being searched using this combined reasonable and accurate method. In the collective intelligence algorithms، population size influences the final answer so that for a large population، a better response is obtained but the algorithm execution time is longer. To overcome this problem، a new parameter called the dispersion coefficient is added to the algorithm. Implementation results show that by controlling this factor، system performance can be improved.

Induction motors are critical components in many industrial processes. Therefore، swift، precise and reliable monitoring and fault detection systems are required to prevent any further damages. The online monitoring of induction motors has been becoming increasingly important. The main difficulty in this task is the lack of an accurate analytical model to describe a faulty motor. A fuzzy logic approach may help to diagnose traction motor faults. This paper presents a simple method for the detection of stator winding faults (which make up 38% of induction motor failures) based on monitoring the line/terminal current amplitudes. In this method، fuzzy logic is used to make decisions about the stator motor condition. In fact، fuzzy logic is reminiscent of human thinking processes and natural language enabling decisions to be made based on vague information. The motor condition is described using linguistic variables. Fuzzy subsets and the corresponding membership functions describe stator current amplitudes. A knowledge base، comprising rule and data bases، is built to support the fuzzy inference. Simulation results are presented to verify the accuracy of motor’s fault detection and knowledge extraction feasibility. The preliminary results show that the proposed fuzzy approach can be used for accurate stator fault diagnosis.

Computational Grids have developed as a new approach to solve large scale problems in scientific، engineering and business areas. Open Grid services architecture is an adaptation of the service-oriented architecture which presents the Grid operation a set of service-oriented softwares. Grid services composition provides the possibility for users to submit their complex requirements as a single request. QoS-aware Grid service composition algorithms try to construct a composite Grid service which satisfies the user-defined constraints as well as to optimize the QoS parameters. All of the presented service composition approaches in the literature discard using the Grid services with unknown QoS values in the composition process. However، estimation of unknown QoS values of Grid services provides an opportunity for them to be selected as component Grid services in the constructed composite service. In this paper، a probabilistic QoS model is presented for estimating the unknown QoS values using Bayesian network. Experimental results indicate that estimating the unknown QoS values has high accuracy and leads to more efficient composite Grid services.

This paper presents a T-F masking method for convolutive blind source separation based on hermitian angle concept. The hermitian angle is calculated between T-F domain mixture vector and reference vector. Two different reference vectors are assumed for calculating two different hermitian angles، and then these angles are clustered with k-means or FCM method to estimate unmixing masks. The well-known permutation problem is solved based on k-means clustering of estimated masks which are partitioned to small groups. The experimental results show an improvement in performance when using two different reference vectors compared to only one.