No abstract

This article introduces a new method for particle swarm optimizationalgorithm.This method will improve the speed of reaching minimization point of cost value. Then we applied three typical methods on Economic dispatch problem with the similar terms for three system and after comprehensive tests on these system, we observed that our new method improves mean time and mean cost of our application.At the end of the each part of the article, results were compared to each other in the separate table based on mean time, best cost and mean cost with specific values of generation.

Large scale integration of wind generation capacity into power systems introduces operational challenges due to wind power uncertainty and variability. Therefore, accurate wind power forecast is important for reliable and economic operation of the power systems. Complexities and nonlinearities exhibited by wind power time series necessitate use of elaborative and sophisticated approaches for wind power forecasting. In this paper, a local neurofuzzy (LNF) approach, trained by the polynomial model tree (POLYMOT) learning algorithm, is proposed for short-term wind power forecasting. The LNF approach is constructed based on the contribution of local polynomial models which can efficiently model wind power generation. Data from Sotavento wind farm in Spain was used to validate the proposed LNF approach. Comparison between performance of the proposed approach and several recently published approaches illustrates capability of the LNF model for accurate wind power forecasting.

Recently, because of wide utilization of renewable resources, in multi terminal direct current (MTDC) grids are considered. MTDC grids control is very important. Vector control method has been taken as the method of controlling MTDC grid, which includes inner current controllers (ICC) and outer controllers. Also, in this controlling method, outer controllers are composed of active power controllers and DC voltage as active channel and reactive power controllers and AC voltage as reactive channel. Voltage margin control method is used to control DC voltage. Also MTDC grids' controllers are optimized by using Honey-bee Mating Optimization Algorithm (HMBO) and the results are compared with classic regulation mode. In this paper, a DC four-terminal test grid has been used for evaluating performance of HBMO algorithm.