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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. Source


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. Source


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. Source


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. Source


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. Source

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