Dynamic economic load dispatch is one of the most important roles of power generation’s operation and control. It determines the optimal controls of production of generator units with predicted load demand over a certain period of time. Economic dispatch at minimum production cost is one of the most important subjects in the power network’s operation, which is a complicated nonlinear constrained optimization problem. Since dynamic economic load dispatch was introduced, several intelligent methods have been used to solve this problem. In this paper, an Improved Particle Swarm Optimizer (IPSO) and Water Cycle optimizer (WCO), as swarm-based optimization algorithms, have been proposed to solve dynamic economic load dispatch problem and their results compare with each other. These algorithms are applied to a dynamic economic dispatch problem for 6-unit power systems with a 24-h load demand at each one hour time intervals. The goal of the research is categorized in two parts; first of all, introduction of application of new heuristic method for solving economic load dispatch problem and second, comparison between two swarm-based algorithms. Obtained results show that WCO is very fast and also reach to better results and minimum. پرونده مقاله

One of the major problems that may occur in the differential protection systems of power transformers is mal-operation of the protection relays in sake of internal fault detection, because of similarity between this current and inrush current. This paper presents a novel approach for discriminating inrush current from internal fault in power transformers based on Improved Gravitational Search Algorithm (IGSA). For this purpose, an Artificial Neural Network (ANN) which is trained by IGSA has been applied to discrete sample data of internal fault and inrush currents in the transformers. Results show that, the used approach can discriminate between these two kinds of phenomenon, very well and also, has high accuracy and excellent reliability, in addition, it has less computational burden and complexity. پرونده مقاله