فهرست مطالب

Artificial Intelligence in Electrical Engineering - Volume:3 Issue: 10, 2015
  • Volume:3 Issue: 10, 2015
  • تاریخ انتشار: 1394/04/04
  • تعداد عناوین: 6
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  • Shahin Shafei* Pages 1-7
    Image data require huge amounts of disk space and large bandwidths for transmission. Hence, image compression is necessary to reduce the amount of data required to represent a digital image. Therefore an efficient technique for image compression is highly pushed to demand. Although, lots of compression techniques are available, but the technique which is faster, memory efficient and simple, surely hits the user requirements. In this paper, the image compression, need of compression, its principles, how image data can be compressed, and the image compression techniques are reviewed and discussed. Also, wavelet-based image compression algorithm using Discrete Wavelet Transform (DWT) based on B-spline factorization technique is discussed in detail. Based on the review, some general ideas to choose the best compression algorithm for an image are recommended. Finally, applications and future scopes of image compression techniques are discussed considering its development on FPGA systems.
    Keywords: Image compression, Discrete Wavelet Transform, Decomposed lifting algorithm (DLA), Huffman, coding
  • Mohammad Esmaeil Akbari, Noradin Ghadimi* Pages 8-15
    In this paper an adaptive PID controller for Wind Energy Conversion Systems (WECS) has been developed. The adaptation technique applied to this controller is based on Reinforcement Learning (RL) theory. Nonlinear characteristics of wind variations as plant input, wind turbine structure and generator operational behavior demand for high quality adaptive controller to ensure both robust stability and safe performance. Thus, a reinforcement learning algorithm is used for online tuning of PID coefficients in order to enhance closed loop system performance. In this study, at start the proposed controller is applied to two pure mathematical plants, and then the closed loop WECS behavior is discussed in the presence of a major disturbance.
    Keywords: Adaptive control, WECS, Reinforcement learning
  • Roya Abdollahi* Pages 16-19
    We first describe how to “fuzzify” the estimated binary columns to create a [0,1]-valued column. We refer to this [0,1] -valued column as the soft segmentation column of the noisy spectrogram column. Similarly to the collection of soft segmentation columns as the soft segmentation image, or simply as the soft segmentation. The band-dependent posterior probability that the hard segmentation column value of pixel is 1, given that bin and the binary values in the neighborhood configuration of the pixel are equal. Symbolically, each pixel of the soft segmentation column is set to the soft segmentation column value of the pixel in a row was set to zero.
  • Zolekh Teadadi, Hassan Changiziyan Pages 20-23
    In the near future the use of distributed generation systems will play a big role in the production of electrical energy. One of the most common types of DG technologies, fuel cells, which can be connected to the national grid by power electronic converters or work alone Studies the dynamic behavior and stability of the power grid is of crucial importance. These studies need to know the exact model of dynamic elements. In this paper, a new method based on a neural network algorithm for controlling the fuel cell voltage is provided. The effects of load change the output voltage characteristic of the fuel cell unit is checked Simulations in MATLAB / SIMULINK. The results show that the prosecution is conducted in an appropriate manner Voltage Stabilization time.
    Keywords: Fuel cell, dynamic behavior, neural networks, hydrogen, neural network controller
  • Milad Babakhani Qazijahan* Pages 24-36
    In this article, the sliding mode control of frequency load control of power systems is studied. The study area consists of a system of water and heat. First, a mathematical model of the proposed system disturbances is made and then sliding control mode for frequency load control is provided. By the system simulation and sliding mode control, it can be shown that the damping of oscillations is well led.
    Keywords: frequency Load control, sliding mode control, dual zone system
  • Vahid Mansouri*, Mohammad E. Akbari Pages 37-50
    Review and classification of electric load forecasting (LF) techniques based on artificial neural networks (ANN) is presented. A basic ANNs architectures used in LF reviewed. A wide range of ANN oriented applications for forecasting are given in the literature. These are classified into five groups: (1) ANNs in short-term LF, (2) ANNs in mid-term LF, (3) ANNs in long-term LF, (4) Hybrid ANNs in LF, (5) ANNs in Special applications of LF. The major research articles for each category are briefly described and the related literature reviewed. Conclusions are made on future research directions.
    Keywords: Artificial Neural Networks (ANNs), Load Forecasting(LF), Short Term LF, Mid Term LF, Long Term LF, Peak LF, Unit Commitment(UC)