On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Journal of Global Optimization
Expert algorithm based on adaptive particle swarm optimization for power flow analysis
Expert Systems with Applications: An International Journal
An application of PSO technique for harmonic elimination in a PWM inverter
Applied Soft Computing
A new social and momentum component adaptive PSO algorithm for image segmentation
Expert Systems with Applications: An International Journal
A modified artificial bee colony algorithm
Computers and Operations Research
A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation
IEEE Transactions on Evolutionary Computation
An adaptive bacterial foraging algorithm for fuzzy entropy based image segmentation
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Hi-index | 0.00 |
Solving harmonic estimation problems in power quality signals has attained significant importance in recent times. Stochastic optimization algorithms have been successfully employed to determine magnitude of this information in an unknown signal contaminated with noise or containing additive dc decaying components. The present paper shows how a recently proposed stochastic optimization algorithm, called artificial bee colony algorithm, can be hybridized with least square algorithm to solve these problems effectively. The proposed algorithm has been tested for a series of case studies employing different benchmark environment situations and our extensive simulation tests validate the usefulness of the proposed algorithm and it could largely outperform several competing simulation algorithms, proposed in the recent past. The effectiveness of the proposed algorithm is further demonstrated for those situations where the number of harmonics present in the signal is also not known, along with the magnitude and phase of each harmonic.