S.M. Abd Elazim

PI controller design using ABC algorithm for MPPT of PV system supplying DC motor pump load

AbstractMaximum power point tracking (MPPT) is used in photovoltaic (PV) systems to maximize its output power. A new MPPT system has been suggested for PV–DC motor pump system by designing two PI controllers. The first one is used to reach MPPT by monitoring the voltage and current of the PV array and adjusting the duty cycle of the DC/DC converter. The second PI controller is designed for speed control of DC series motor by setting the voltage fed to the DC series motor through another DC/DC converter. The suggested design problem of MPPT and speed controller is formulated as an optimization task which is solved by artificial bee colony (ABC) to search for optimal parameters of PI controllers. Simulation results have shown the validity of the developed technique in delivering MPPT to DC series motor pump system under atmospheric conditions and tracking the reference speed of motor. Moreover, the performance of the ABC algorithm is compared with genetic algorithm for various disturbances to prove its robustness.

PI controller design for MPPT of photovoltaic system supplying SRM via BAT search algorithm

Maximum power point tracking (MPPT) is used in photovoltaic (PV) systems to maximize its output power. This paper introduces a new MPPT control design to PV system supplied switched reluctance motor (SRM) based on PI controller. The developed PI controller is used to reach MPPT by monitoring the voltage and current of the PV array and adjusting the duty cycle of the DC/DC converter. The design task of MPPT is formulated as an optimization problem which is solved by BAT algorithm to search for optimal parameters of PI controller. Simulation results have shown the validity of the suggested technique in delivering MPPT to SRM under atmospheric conditions. Also, the performance of the developed BAT algorithm is compared with particle swarm optimization for various disturbances to confirm its robustness.

PI controller design using artificial bee colony algorithm for MPPT of photovoltaic system supplied DC motor‐pump load

Maximum Power Point Tracking (MPPT) is used in Photovoltaic (PV) systems to maximize its output power. A new MPPT system has been suggested for PV-DC motor pump system by designing two PI controllers. The first one is used to reach MPPT by monitoring the voltage and current of the PV array and adjusting the duty cycle of the DC/DC converter. The second PI controller is designed for speed control of DC series motor by setting the voltage fed to the DC series motor through another DC/DC converter. The suggested design problem of MPPT and speed controller is formulated as an optimization task which is solved by Artificial Bee Colony (ABC) to search for optimal parameters of PI controllers. Simulation results have shown the validity of the developed technique in delivering MPPT to DC series motor pump system under atmospheric conditions and tracking the reference speed of motor. Moreover, the performance of the ABC algorithm is compared with Genetic Algorithm for various disturbances to prove its robustness.

Mine blast algorithm for environmental economic load dispatch with valve loading effect

Economic load dispatch (ELD) is the process of allocating the required load between the available generation units such that the cost of operation is minimized. The ELD problem is formulated as a nonlinear constrained optimization problem with both equality and inequality constraints. The dual-objective combined economic emission dispatch (CEED) problem is considering the environmental impacts that accumulated from emission of gaseous pollutants of fossil-fueled power plants. In this paper, an implementation of mine blast algorithm (MBA) to solve ELD and CEED problems in power systems is discussed. Results obtained by the proposed MBA are compared with other optimization algorithms for various power systems. The results introduced in this paper show that the proposed MBA outlasts other techniques in terms of total cost and computational time.

Speed control of SRM supplied by photovoltaic system via ant colony optimization algorithm

This paper proposes a speed control of switched reluctance motor supplied by photovoltaic system. The proposed design of the speed controller is formulated as an optimization problem. Ant colony optimization (ACO) algorithm is employed to search for the optimal proportional integral (PI) parameters of the proposed controller by minimizing the time domain objective function. The behavior of the proposed ACO has been estimated with the behavior of genetic algorithm (GA) in order to prove the superior efficiency of the proposed ACO in tuning PI controller over GA. Also, the behavior of the proposed controller has been estimated with respect to the change of load torque, variable reference speed, ambient temperature and radiation. Simulation results confirm the better behavior of the optimized PI controller based on ACO compared with optimized PI controller based on GA over a wide range of operating conditions.