Surekha Bhanot

Frequency Stabilization for Multi-area Thermal–Hydro Power System Using Genetic Algorithm-optimized Fuzzy Logic Controller in Deregulated Environment

Abstract—This article develops a model of load frequency control for an interconnected two-area thermal–hydro power system under a deregulated environment. In this article, a fuzzy logic controller is optimized by a genetic algorithm in two steps. The first step of fuzzy logic controller optimization is for variable range optimization, and the second step is for the optimization of scaling and gain parameters. Further, the genetic algorithm-optimized fuzzy logic controller is compared against a conventional proportional-integral-derivative controller and a simple fuzzy logic controller. The proposed genetic algorithm-optimized fuzzy logic controller shows better dynamic response following a step-load change with combination of poolco and bilateral contracts in a deregulated environment. In this article, the effect of the governor dead band is also considered. In addition, performance of genetic algorithm-optimized fuzzy logic controller also has been examined for various step-load changes in different distribution unit demands and compared with the proportional-integral-derivative controller and simple fuzzy logic controller.

Implementing Edge Detection for Detecting Neurons from Brain to Identify Emotions

Edges of an image are considered a type of crucial information that can be extracted by applying detectors with different methodology. Edge detection is a basic and important subject in computer vision and image processing In this Paper we discuss several Digital Image Processing Techniques applied in edge feature extraction. Firstly, Linear filtering of Image is done is used to remove noises from the image collected. Secondly, some edge detection operators such as Sobel, Log edge detection, canny edge detection are analyzed and then according to the simulation results, the advantages and disadvantages of these edge detection operators are compared. It is shown that the canny operator can obtain better edge feature. Finally, Edge detection is applied to identify neurons in Brain. After this the Neurons are classified and feature vector will be calculated. KeywordsFilters, Sobel, Canny, Log, Distortion, Edge Detection Introduction (Heading 1)

A fast learning algorithm for training feedforward neural networks

This paper deals with a fast and computationally simple Successive Over-relaxation Resilient Backpropagation (SORRPROP) learning algorithm which has been developed by modifying the Resilient Backpropagation (RPROP) algorithm. It uses latest computed values of weights between the hidden and output layers to update remaining weights. The modification does not add any extra computation in RPROP algorithm and maintains its computational simplicity. Classification and regression simulations examples have been used to compare the performance. From the test results for the examples undertaken it is concluded that SORRPROP has small convergence times and better performance in comparison to other first-order learning algorithms.

Radial basis function neural network-based face recognition using firefly algorithm

This paper presents an adaptive technique for obtaining centers of the hidden layer neurons of radial basis function neural network (RBFNN) for face recognition. The proposed technique uses firefly algorithm to obtain natural sub-clusters of training face images formed due to variations in pose, illumination, expression and occlusion, etc. Movement of fireflies in a hyper-dimensional input space is controlled by tuning the parameter gamma (γ) of firefly algorithm which plays an important role in maintaining the trade-off between effective search space exploration, firefly convergence, overall computational time and the recognition accuracy. The proposed technique is novel as it combines the advantages of evolutionary firefly algorithm and RBFNN in adaptive evolution of number and centers of hidden neurons. The strength of the proposed technique lies in its fast convergence, improved face recognition performance, reduced feature selection overhead and algorithm stability. The proposed technique is validated using benchmark face databases, namely ORL, Yale, AR and LFW. The average face recognition accuracies achieved using proposed algorithm for the above face databases outperform some of the existing techniques in face recognition.

Evolutionary design of Multiquadric radial basis functions neural network for face recognition

In this paper, it is proposed to use Multiquadric basis functions at hidden layer of radial basis functions neural networks (RBFNN) for face recognition. The performance of RBFNN depends on the design of the structure of RBFNN, which includes optimal center selection and spread of RBF units, number of neurons at hidden layer, weights etc. Design of hidden layer of RBFNN also includes the choice of basis functions which is proposed to be of Multiquadric basis functions. The shape of Multiquadric basis function plays an important role in the performance of RBFNN in face recognition. A novel evolutionary shape parameter optimization technique inspired by the attractiveness of the natural fireflies is proposed and is used in the design of Multiquadric basis functions for the given face database. The algorithm is tested on two benchmarked face databases ORL and Indian face databases. The proposed technique significantly outperforms the performance of the Gaussian basis functions based RBFNN in terms of face recognition accuracy.

Remote experimentation of “No-load tests on a transformer” in electrical engineering

Currently, rapid developments are taking place to increase the efficiency and outreach of engineering education. Remote-laboratories for remote experimentation is a highly significant and effective development in this area. However, Electrical Engineering experiments are generally difficult to automate due to the risks of high voltages/currents associated with them. In addition digitally controllable electrical machines are expensive and not widely found in many smaller universities. In this paper, remote experimentation of the important experiment “No load tests on a transformer” using PSoC and Labview is presented. More experiments in allied fields can be automated by drawing on this work. In addition it would also serve as an impetus to stronger efforts in this field enabling increased access to high-end laboratories even among the universities with lesser financial capabilities.

Modified functional link ANN based controller for thermal processes

In this paper, Modified Functional Link ANN (M-FLANN) based controller has been designed and implemented on simulated water bath temperature control system. The performance of this controller has been compared with Multilayer Perceptron (MLP), Direct Linear Feed-through ANN (DLFANN), Functional Link ANN (FLANN) and Fuzzy Logic (FLC) and proportional-integral-derivative (PID) controllers on the same process. Their performances have been evaluated under identical conditions with respect to set-point regulation, effect of unknown disturbances and variable lag times. It has been found that the M-FLANN controller has better performance compared to other controllers. The results have been further confirmed by implementing and comparing the four ANN controllers on Continually Stirred Tank Heater (CSTH); a multi-input multi-output (MIMO) process.

Self-tuned fuzzy logic control of a pH neutralization process

On-line implementation of self-tuning mechanism based adaptive fuzzy logic control of a pH neutralization process which takes care of steady state error and time taken to reach steady state under varying operating conditions has been presented in this paper. The pH neutralization system is Armfield pH Sensor Accessory (PCT42) in conjunction with Process Vessel Accessory (PCT41) and Multifunction Process Control Teaching System (PCT40). The proposed adaptive scheme updates the normalized universe of discourse of output fuzzy membership functions with varying scaling factors based on error and change of error values. The speed of response of the adaptive controller is taken care by use of coarse control technique whereas amount of deviation under steady state is accounted with the help of fine control technique. The performance of adaptive scheme is tested for pH control at equivalence point. LabVIEW software is used for online communication, control and display.

Neural Control of Neutralization Process using Fuzzy Inference System based Lookup Table

Over a number of years, pH control of neutralization process is recognized as a benchmark for modeling and control of nonlinear processes. This paper first describes dynamic modeling of pH neutralization process. Thereafter fuzzy logic based pH control scheme for neutralization process is developed. Further, a two-dimensional (2-D) lookup table is generated based on defuzzification mechanism of fuzzy inference system (FIS). Finally, using this lookup table, a neural network control for pH neutralization process is developed. Performances of fuzzy logic based control and lookup table based neural network control for servo and regulatory operations are compared based on integral square error (ISE) and integral absolute error (IAE) criterions. Results indicate that lookup table based neural network control performs better than fuzzy logic based control. General Terms Nonlinear process control, fuzzy logic control, neural network control.

Optimized adaptive neuro-fuzzy inference system for pH control

pH control plays an important role in many modern industrial plants due to strict environment regulations. This paper presents fuzzy logic based pH control scheme for neutralization process in which genetic algorithm is used to optimize the various membership functions of fuzzy inference system. Further, using this optimized fuzzy inference system, adaptive neuro-fuzzy inference system for pH neutralization process is developed. Performances of both control schemes are compared for servo and regulatory operations. Results indicate that adaptive neuro-fuzzy inference system based control uses fewer rules as compared to optimized fuzzy logic based control.