COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING)
Year:2011 | Volume:2 | Issue:2|Papers Count:6

Software performance engineering in early life cycle of software development (software modeling) is very useful and cost effective, but not fully automated yet. This paper presents an optimization method based on multi-objective particle swarm optimization (MOPSO) for exploring the software design space automatically and proposes the best configuration in terms of performance evaluation. The software model is transformed in to a performance model, which is based on Layered Queueing Networks (LQNs). Then, the model will be optimized and feedback to the software model. One case study is utilized for evaluating the proposed method. The results obtained apparently show the optimization performance of MOPSO.

Intelligent methods are widely used in fault detection and diagnosis in electrical machine, transformers and in general in all parts of industry. One of the most advanced methods in fault detection is fuzzy Inference system. Since, extraction of effective rules is so hard in complicated problems, using fuzzy Inference is impossible. In this paper, in order to determine the amount of inter-turn stator winding fault in Permanent Magnet Synchronous Motor (PMSM),the fuzzy logic set has been used. This research has been fulfilled in two steps. In first step, a mathematical model of PMSM under fault condition has been simulated in MATLAB/SIMULINK and required data; deferential current of each phase and speed of motor have been extracted, in second step, a fuzzy set has been created using Adaptive Neuro-Fuzzy Inference System (ANFIS) and this fuzzy set has been used in simulation for on-line test. Obtained results show that proposed method can follow fault in short time and can present amount of fault very precise.

Nowadays, the optimal application of phasor measurement units (PMUs) in power systems is increasing because of their advantages such as the capability for dynamical state estimation and improvements in the speed of control and protection systems. In this paper, a new method is presented using binary integer linear programming for the optimal placement of PMUs to guarantee full observability of a power system. Moreover, the problem of the optimal placement of these units in the case of a single line outage is investigated. In all of the investigations, the effect of zero-injection buses in the power system was considered. The ability of the proposed method in solving any of these problems was demonstrated by applying to the 14-, 30-, 39-, 57- and 118-bus IEEE standard systems, 42-bus transmission network of Esfahan Regional Electric and also two very large-scale systems with 2383 and 2746 buses. The simulation results verified the effective and appropriate performance of the proposed method in comparison with other methods.

In This Paper, two different feature extraction methods were studied and their performances in pattern recognition based- P300 detection were compared. These two methods were Common Spatial Pattern (CSP) and intelligent segmentation. Data set II (P300 speller) from the BCI competition 2005 was used. After pre-processing and feature extraction, these features were compared. For this purpose, first, a statistical analysis had been applied for evaluating the fitness of each feature in discriminating between target and non-target signals. Then, each of these two groups of features was evaluated by a Linear Discriminant Analysis (LDA) classifier. Furthermore by using Stepwise Linear Discriminant Analysis (SWLDA), the best set of features was selected. Finally in this research, the best result for P300 detection was 95.25% for intelligent segmentation as a feature extraction method. This result shows that intelligent segmentation is better than CSP method for P300 detection.

This paper deals a control strategy developed for optimizing the power flow in a Fuel Cell Hybrid Vehicle (FCHV) structure. This method implements an on-line power management based on the neuro-fuzzy controller between dual power sources that consist of a battery bank and a fuel cell (FC). This structure included battery and fuel cell and its power train system include an Electric Motor (EM) and vehicle dynamics. The proposed control method involves an intelligent controller which captures all of possible operation modes and predicts the driver intention. Moreover, there are local controllers to regulate the set points of each subsystems to reach the best performance and acceptable operation indexes. Simulation results of hybrid system illustrate improvement in the operation efficiency of the FCHV and the battery state of charge and fuel cell utilization factor have been maintained at a reasonable level.

In this paper, a new approach is presented for reactive power management considering security and economic objectives in order to meet optimum and secure operation of power systems. Reactive power management issue is formulated as a nonlinear multi-objective optimization problem with continuous and discrete variables, in which the active losses reduction is selected as economic goal and the voltage profile improvement and its stability are also selected as security goals. The above optimization problem is solved by using the objectives fuzzification approach and a new developed version of particle swarm optimization algorithm that has highly ability to provide the best solution in less time. In this paper, the optimal production of generator’s reactive power and the minimum eigenvalue of Jacobin matrix are two effective indices that considered to assessment the system’s voltage stability under normal operation. Moreover, a new index is proposed for optimal production of generator’s reactive power that used to voltage stability assessment. The proposed model has been implemented on IEEE 30 Bus power system. The simulation results confirm acceptable performance of the proposed algorithm in management of reactive power with respect to both security and economic conditions.