COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING)
Year:2011 | Volume:1 | Issue:1 (1)|Papers Count:7

This paper presents a new online Power System Stabilizer (PSS) design based on fuzzy wavelet network (FWN) to damp the multi-machine power system low frequency oscillations. The FWN, inspired by the wavelet theory and fuzzy concepts, is used to simultaneous design of two PSSs, in which error between system desired output and output of control object is directly utilized to tune the network parameters. The orthogonal least square (OLS) algorithm is used to determine network dimension, purify the wavelets for selecting efficient wavelets, and determine the number of sub- wavelet neural networks and fuzzy rules. In this paper, Shuffled Frog Leaping Algorithm (SFLA) is employed for learning of FWN parameters and to find the optimal values of the controller parameters. To illustrate the capability of the proposed approach, some numerical results are presented on a 2-area 4-machine system. To show the effectiveness and robustness of the designed supplementary controllers, a line-to-ground fault and also a three phase fault are applied at a bus. Furthermore, to make a comparison, two conventional PSSs are designed in which a lead-lag structure is considered for each PSS and its parameters are tuned using SFLA. The simulation results show the superiority and capability of the FWN based PSSs.

In this paper, a robust Artificial Bee Colony (ABC) algorithm based on Integral Time Absolute Error (ITAE) is proposed for solution of a Load Frequency Control (LFC) problem in a restructured power system that operates under deregulation according to a bilateral policy scheme. Precise tuning of the objective function is significant in achieving the desired level of robust performance in the proposed method. Simulation result suggests that optimal control parameters for power system can be designed with much less effort using ABC in which the objective function is chosen based on ITAE method on the frequency deviation and Area Control Error (ACE). The effectiveness of the proposed method is demonstrated on a four-area restructured power system with possible contracted scenarios under large load demand and area disturbances in comparison with conventional tuning of control parameters through ITAE performance indices. The evaluation results show that the proposed optimization strategy achieves good robust performance for a wide range of system parameters and load changes in the presence of system nonlinearities and is therefore superior to other controllers.

The magnetizing inrush current phenomenon is a large transient condition, which occurs when a transformer is energized. The inrush current magnitude may be as high as ten times of transformer rated current that causes mal-operation of protection systems. Indeed, the similarity between signatures of Inrush current and internal fault condition make this failure. So, for safe running of a transformer, it is necessary to distinguish inrush current from fault currents. In this project, an Artificial Neural Network (ANN) which is trained by two different swarm based algorithms; Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO) have been used to discriminate inrush current from fault currents in power transformers. GSA works based on gravity laws and in opposite of other swarm based algorithms, particles have identity and PSO is based on behaviors of bird flocking. Proposed approach has two general stages, in first step, obtained data from simulation have been processed and applied to ANN, and then in step two, using training data considered ANN has been trained by GSA & PSO. Proposed method has been compared with one of the common training approach which is called Back Propagation (BP) and Results show that proposed method is so quick and can do discrimination very accurate.

In this paper, a multi-objective version of Gravitational search algorithm (GSA) is proposed which is based on optimal Pareto concepts for solving the multi objective problems. To show the effectiveness of the proposed algorithm, it is tested on different benchmark functions and an engineering problem known as placement of Static Var Compensators (SVC) for VAr planning in a large-scale power system. Taking advantages of the SVCs depends greatly on how these devices are placed in the power system, namely on their location and size. The VAr planning problem is formulated as a multi-objective optimization problem, which represent minimizing voltage deviation, losses and the cost of installation resulting in the maximum system VAr margin. The results obtained show the effectiveness of the proposed algorithm.

In this paper, the fuzzy control method in the speed control of permanent magnet synchronous motor is presented. In this controlle two speed and current regulators are used. The speed controller is a fuzzy controller and the current controller is a classic controller. The coefficients of the controller are optimized by using Genetic Algorithm (GA). The aim of the optimization is the reduce the delay and the low steady state error in order to achieve the best possible response. In this paper the fuzzy and classic methods of control are compared to understand advantages and disadvantages of these two methods. In these two methods the motor parameters changs, the discanection of one phone and the measurement noise are studied and the results show that the fuzzy controller is stable in comparison with the classic controller.

Unit commitment is one of the main problems in power systems operation and since there are plenty of constraints and parameters it is too complicated. In this paper, a new method based on artificial bee colony algorithm has been proposed to solve the problem of unit commitment. In the proposed method, a novel coding approach is presented which uses integer numbers (for satisfying minimum up/down time constraints) and binary numbers (for satisfying spinning reserve constraint). One of the advantages of the proposed coding approach is elimination of using penalty factors in the optimization process to constraints handling. The total cost of unit commitment can be truly minimized using the proposed algorithm. In comparison with other existing methods in this regard, the simulated results and numerical studies show better convergence of the proposed algorithm.

One of the most important reasons of Low Frequency Oscillations (LFO) occurrence in power systems is lack of damping torque against the power system disturbances. In the recent past Power System Stabilizer (PSS) was used to damp the LFO. FACTs devices, such as Thyristor Controlled Series Compensator (TCSC), can control power flow, reduce sub-synchronous resonance and increase transient stability. So, TCSC may be used to damp LFO instead of PSS. In this paper, the TCSC is employed to damp the LFO using neuro-fuzzy controller. In proposed method of this paper, the linearized model of synchronous machine (Heffron-Philips) connected to infinite bus (Single Machine-Infinite Bus: SMIB) with TCSC is used and also in order to damp LFO, adaptive neuro-fuzzy controller for TCSC is designed. The proposed method is simulated for various types of loads and for different disturbances. Simulation results show good performance of neuro-fuzzy controller in damping LFO.