Pandian Saraswathi Yadav Engineering College

Sivagangai, India

Pandian Saraswathi Yadav Engineering College

Sivagangai, India

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Senthamarai Kannan S.,Pandian Saraswathi Yadav Engineering College
Asian Journal of Information Technology | Year: 2016

At present, Camouflaging worm attack constitute a large part of internet peer servers. Due to the increasing traffic in internet services, it has become inevitable to take into account its effects on network management. Generally, studies on resisting Camouflaging Worm attack have involved analysis with power spectral density distribution via spectrum-based scheme. However, with several facilities provided by spectrumbased scheme, its network traffic volume in internet severs is increasing day by day increasing the malicious traffic rate. In this research proposal plan is to develop efficient identification of C-Worm propagation and restriction of uncontrolled malicious traffic in the internet by applying Enhanced Hidden Markov Chain-based C-Worm Detection (EHMC-CWD) technique. The C-Worm replicates the abnormal traffic on its own and propagates throughout the network and cause damages to the internet services. Enhanced Hidden Markov Chain (EHMC) identifies the camouflaging abnormal traffic replicated across the internet. Next, EHMC adapted a dynamic Bayesian network to evaluate camouflaging worm propagation by means of optimal non linear filtering. Therefore the replicated traffic generated by C-Worm reveals the information about the sequence of traffic in which it is propagated. The performance of EHMC-CWD is evaluated by extensive simulations. Simulation results show that our proposal can considerably reduce the execution time for C-Worm detection and memory space and also improves high detection rate to a certain degree. © Medwell Journals 2016.


Premkumar K.,Pandian Saraswathi Yadav Engineering College | Manikandan B.V.,Mepco Schlenk Engineering College, Sivakasi
Neurocomputing | Year: 2015

In this paper, two different speed controllers i.e., fuzzy online gain tuned anti wind up Proportional Integral and Derivative (PID) controller and fuzzy PID supervised online ANFIS controller for the speed control of brushless dc motor have been proposed. The control system parameters such as rise time, settling time, peak time, recovery time, peak overshoot and undershoot of speed response of the brushless dc motor with the proposed controllers have been compared with already published controllers such as anti wind up PID controller, fuzzy PID controller, offline ANFIS controller, PID supervised online ANFIS controller and On-line Recursive least square-error back propagation algorithm based ANFIS controller. In order to validate the effectiveness of the proposed controllers, the brushless dc motor is operated under constant load condition, varying load conditions and varying set speed conditions. The simulation results under MATLAB environment have predicted better performance with fuzzy PID supervised online ANFIS controller under all operating conditions of the drive. © 2015 Elsevier B.V.


Premkumar K.,Pandian Saraswathi Yadav Engineering College | Manikandan B.V.,Mepco Schlenk Engineering College, Sivakasi
Journal of Intelligent and Fuzzy Systems | Year: 2015

This paper deals with the application of GA-PSO optimized online Adaptive Neuro Fuzzy Inference System (ANFIS) for the speed control of Brushless DC motor. Learning parameters, i.e., Learning Rate (n), forgetting factor (λ) and steepest descent momentum constant (α) of online ANFIS controller is optimized for different speed-torque operating conditions of Brushless DC motor using hybrid GA-PSO algorithm. The overall speed control system is simulated and validated using MATLAB. The performance of the proposed controller is analyzed and compared with offline ANFIS controller and Proportional Integral Derivative (PID) controller. In order to validate the effectiveness of the proposed controller, simulation is performed under constant load conditions, varying load conditions and varying set speed conditions. Also speed tracking response is investigated for different set speed conditions and different loading conditions. In addition, for effective comparison of the controllers, four performance measures such as maximum overshoot, steady state error, integral of absolute error, and integral of time multiplied absolute error are evaluated and tested for the considered controllers. It has been proved that the proposed controller easily overcomes the drawbacks of offline ANFIS controller and Proportional Integral Derivative (PID) controller. © 2015-IOS Press and the authors.


Premkumar K.,Pandian Saraswathi Yadav Engineering College | Manikandan B.V.,Mepco Schlenk Engineering College, Sivakasi
Neurocomputing | Year: 2014

In this paper, a novel controller for brushless DC (BLDC) motor has been presented. The proposed controller is based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and the rigorous analysis through simulation is performed using simulink tool box in MATLAB environment. The performance of the motor with proposed ANFIS controller is analyzed and compared with classical Proportional Integral (PI) controller, Fuzzy Tuned PID controller and Fuzzy Variable Structure controller. The dynamic characteristics of the brushless DC motor is observed and analyzed using the developed MATLAB/simulink model. Control system response parameters such as overshoot, undershoot, rise time, recovery time and steady state error are measured and compared for the above controllers. In order to validate the performance of the proposed controller under realistic working environment, simulation result has been obtained and analyzed for varying load and varying set speed conditions. © 2014 Elsevier B.V.


Premkumar K.,Pandian Saraswathi Yadav Engineering College | Manikandan B.V.,Mepco Schlenk Engineering College, Sivakasi
Applied Soft Computing Journal | Year: 2015

In this paper, speed control of Brushless DC motor using Bat algorithm optimized online Adaptive Neuro-Fuzzy Inference System is presented. Learning parameters of the online ANFIS controller, i.e., Learning Rate (η), Forgetting Factor (λ) and Steepest Descent Momentum Constant (α) are optimized for different operating conditions of Brushless DC motor using Genetic Algorithm, Particle Swarm Optimization, and Bat algorithm. In addition, tuning of the gains of the Proportional Integral Derivative (PID), Fuzzy PID, and Adaptive Fuzzy Logic Controller is optimized using Genetic Algorithm, Particle Swarm Optimization and Bat Algorithm. Time domain specification of the speed response such as rise time, peak overshoot, undershoot, recovery time, settling time and steady state error is obtained and compared for the considered controllers. Also, performance indices such as Root Mean Squared Error, Integral of Absolute Error, Integral of Time Multiplied Absolute Error and Integral of Squared Error are evaluated and compared for the above controllers. In order to validate the effectiveness of the proposed controller, simulation is performed under constant load condition, varying load condition and varying set speed conditions of the Brushless DC motor. The real time experimental verification of the proposed controller is verified using an advanced DSP processor. The simulation and experimental results confirm that bat algorithm optimized online ANFIS controller outperforms the other controllers under all considered operating conditions. ©2015 Elsevier B.V. All rights reserved.


Pandiarajan K.,Pandian Saraswathi Yadav Engineering College | Babulal C.K.,Thiagarajar College of Engineering
International Journal of Electrical Power and Energy Systems | Year: 2016

This paper proposes the integration of fuzzy logic system with harmony search algorithm (FHSA) to find the optimal solution for optimal power flow (OPF) problem in a power system. The objective of the method is to minimize the total fuel cost of thermal generating units having quadratic cost characteristics and severity index (SI). The generator active power, generator bus voltage magnitude, transformer taps, VAR of shunts and the reactance of thyristor controlled series capacitor (TCSC) are taken as the control variables. The adjustment of proposed algorithm parameters such as pitch adjustment rate (PAR) and bandwidth (BW) is done through fuzzy logic system (FLS). The effectiveness of the proposed method has been tested on the standard IEEE 30 bus, IEEE 57 bus and IEEE 118 bus systems in MATLAB environment and their results are compared with conventional harmony search algorithm (HSA) and other heuristic methods reported in the literature recently. © 2015 Elsevier Ltd. All rights reserved.


Pandiarajan K.,Pandian Saraswathi Yadav Engineering College | Babulal C.K.,Thiagarajar College of Engineering
COMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering | Year: 2016

Purpose - The electric power system is a complex system, whose operating condition may not remain at a constant value. The various contingencies like outage of lines, transformers, generators and sudden increase of load demand or failure of equipments are more common. This causes overloads and system parameters to exceed the limits thus resulting in an insecure system. The purpose of this paper is to enhance the power system security by alleviating overloads on the transmission lines. Design/methodology/approach - Fuzzy logic system (FLS) with particle swarm optimization based optimal power flow approach is used for overload alleviation on the transmission lines. FLS is modeled to find the changes in inertia weight by which new weights are determined and their values are applied to particle swarm optimization (PSO) algorithm for velocity and position updation. Findings - The proposed method is tested and examined on the standard IEEE-30 bus system under base case and increased load conditions at different contingency. This method gives better results in terms of optimum fuel cost and fast convergence under base case and could alleviate the line overloads at different contingency with optimum generation cost, when compared to adaptive particle swarm optimization (APSO) and PSO. Originality/value - FLS is modeled in MATLAB environment. The effectiveness of the proposed method is tested and examined on the standard IEEE-30 bus system and their results are compared with APSO and PSO under MATPOWER environment. The results show that the proposed algorithm is capable of improving the transmission security with optimum generation cost. © Emerald Group Publishing Limited.


Karthigai Pandian M.,Pandian Saraswathi Yadav Engineering College | Balamurugan N.B.,Thiagarajar College of Engineering
Journal of Electrical Engineering and Technology | Year: 2014

In this paper, we propose new physically based threshold voltage models for short channel Surrounding Gate Silicon Nanowire Transistor with two different geometries. The model explores the impact of various device parameters like silicon film thickness, film height, film width, gate oxide thickness, and drain bias on the threshold voltage behavior of a cylindrical surrounding gate and rectangular surrounding gate nanowire MOSFET. Threshold voltage roll-off and DIBL characteristics of these devices are also studied. Proposed models are clearly validated by comparing the simulations with the TCAD simulation for a wide range of device geometries. © 2014 Korean Institute of Electrical Engineers. All rights reserved.


Pandiarajan K.,Pandian Saraswathi Yadav Engineering College | Babulal C.K.,Thiagarajar College of Engineering
Journal of Electrical Systems | Year: 2014

A power system configuration undergoes frequent changes due to contingencies and/or disturbances. If the power system survives after the disturbance, it will be operating in a new steady state in which one or more transmission lines may be overloaded. A corrective control action must be taken to eliminate such overloads. The most practiced technique for overload alleviation is generator rescheduling and/or load shedding. This paper proposes an Adaptive Neuro-Fuzzy Inference System (ANFIS) based generation reschedule to alleviate transmission line overloads. The system parameters such as overload factor (OF), generation shift sensitivity factor (GSSF) and sensitivity of vulnerability index of generation system (SVIGS) are given to the ANFIS as inputs. The output (ANFISOUT) from the ANFIS gives the quantity of real power to be rescheduled. The effectiveness of the proposed approach has been tested for modified IEEE 30 bus system, 39 bus New England system and modified IEEE 57 bus system in MATLAB environment and their results are compared with fuzzy logic based approach. © JES 2014.


Pandiarajan K.,Pandian Saraswathi Yadav Engineering College | Babulal C.K.,Thiagarajar College of Engineering
Journal of Electrical Systems | Year: 2014

This paper proposes an application of hybrid differential evolution with particle swarm optimization (DEPSO) for transmission line management in power system network. Generation rescheduling is performed to reinstate the system from abnormal to normal operating condition. The identification of overloaded lines is based on computation of overload factor (OLF). The objective of the proposed approach is to alleviate the transmission line overload by reducing severity index (SI) subjected to the power balance, voltage and generator limit constraints. The effectiveness of the proposed approach is demonstrated for different contingency cases in IEEE 30 and IEEE 118 bus systems in MATLAB environment and their results are compared with other evolutionary algorithms like Particle swarm optimization (PSO) and Differential evolution (DE). The results show DEPSO approach well proves its ability to remove the line overloads with a minimum rescheduling cost. © JES 2014.

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