E.S. Ali

A hybrid particle swarm optimization and bacterial foraging for power system stability enhancement

A novel hybrid approach involving particle swarm optimization PSO and bacterial foraging optimization algorithm BFOA called bacterial swarm optimization BSO is illustrated for designing static var compensator SVC in a multimachine power system. In BSO, the search directions of tumble behavior for each bacterium are oriented by the individuals best location and the global best location of PSO. The proposed hybrid algorithm has been extensively compared with the original BFOA algorithm and the PSO algorithm. Simulation results have shown the validity of the proposed BSO in tuning SVC compared with BFOA and PSO. Moreover, the results are presented to demonstrate the effectiveness of the proposed controller to improve the power system stability over a wide range of loading conditions.

Stability improvement of multimachine power system via new coordinated design of PSSs and SVC

In this article, the assessment of new coordinated design of power system stabilizers PSSs and static var compensator SVC in a multimachine power system via statistical method is proposed. The coordinated design problem of PSSs and SVC over a wide range of loading conditions is handled as an optimization problem. The bacterial swarming optimization BSO, which synergistically couples the bacterial foraging with the particle swarm optimization PSO, is used to seek for optimal controllers parameters. By minimizing the proposed objective function, in which the speed deviations between generators are involved; stability performance of the system is enhanced. To compare the capability of PSS and SVC, both are designed independently, and then in a coordinated manner. Simultaneous tuning of the BSO-based coordinated controller gives robust damping performance over wide range of operating conditions and large disturbance in compare to optimized PSS controller based on BSO BSOPSS and optimized SVC controller based on BSO BSOSVC. Moreover, a statistical T test is executed to validate the robustness of coordinated controller versus uncoordinated one.

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.