This paper deals with reconfiguration of distribution system to minimize power losses, deviation of nodes voltages and branches current constraint violations. This means alternation of feeders' topological structure by changing the status of tie and sectionalizing switches. The work presented here is a step forward to define the reconfiguration problem closer to reality by considering the effect of harmonic loads. To solve this complicated combinatorial, non-differentiable constrained optimization problem, novel heuristic optimization techniques such as Shuffled Frog Leaping Algorithm (SFLA) and Imperialist Competitive Algorithm (ICA) are employed. The methods are compared with Discrete Particle Swarm Optimization (DPSO) algorithm to examine their performance including computational time, convergence rate, and accuracy of results. In this context, experiments are conducted on 69-bus and 84-bus distribution test systems for the short- and long-term reconfiguration scenarios. The obtained results establish that harmonic losses are a major deciding factor in reconfiguration and harmonic loads cannot be represented with only fundamental components.

Purpose: The purpose of this paper is to implement a simple, fast and accurate heuristic method for parameter determination of Jiles‐Atherton (JA) hysteresis model for representing magnetization in electrical steel sheets. The performance of the method is validated using measured data and comparison with previous methods.Design/methodology/approach: JA model requires five parameters to represent the hysteretic behavior of ferromagnetic materials. In order to determine these parameters, measured hysteresis loop is used here to calculate a fitness function which is defined by comparing the measured and simulated magnetization loops. This fitness function is minimized by optimization algorithms.Findings: In total, four different measured hysteresis loops are studied in this paper. Each optimization algorithm is executed 50 times to investigate the convergence, speed, and accuracy of six methods. All methods begin with the same randomly generated initial parameters. Physical boundaries are used for parameters to avoid unaccepted results. Thorough examination of results shows that the proposed method is more appropriate than previously implemented methods for the parameter determination of Jiles‐Atherton model in all studied cases. The required parameters for each optimization method are also presented.Originality/value: Shuffled frog leaping algorithm (SFLA) is implemented for the first time for JA model parameter determination. The results show that SFLA is faster and more accurate in comparison with other methods. Furthermore, this algorithm is easy to implement and tune.

This study presents a novel approach for modelling inrush current transient in power transformers taking into account the hysteresis effect. Inverse Jiles–Atherton (JA) model is used to represent the hysteresis phenomenon in the magnetic core. The parameters of this model are optimally determined using inrush current measurements by shuffled frog-leaping algorithm (SFLA). Then, an adaptive technique to enhance the accuracy of the proposed model for simulation of inrush current in other possible conditions is proposed. The method is based on artificial neural network which is used for updating the inverse JA hysteresis model parameters in each hysteresis loop during a power frequency cycle. SFLA optimisation is used for accurate parameter determination from measured inrush currents in training stage. The measurements are performed on a single-phase power transformer and the results verify the performance of the proposed neuro-SFLA approach.