Improved self‐adaptive differential evolution algorithm for reactive power optimization of smart distribution network with wind energy. (7th July 2016)

• Record Type: Journal Article
• Authors: Li, Timing; Yuan, Rongxiang; Deng, Xiangtian
• Is Part Of: International transactions on electrical energy systems. Volume 26:Number 12(2016:Dec.)
• Page Start: 2744
• Page End: 2758
• Publication Date: 2016-07-07
• Subjects: optimal reactive power flow -- improved self‐adaptive differential evolution algorithm -- distribution system -- doubly fed induction generator; Electric power -- Periodicals; Electric power systems -- Periodicals; Electrical engineering -- Periodicals; 621.3
• DOI: 10.1002/etep.2232
• Languages: English
• ISSNs: 2050-7038

Abstract

Summary: With the development of wind energy, many wind turbines have been fed into the distribution system. Considering that many wind turbines have the capability of reactive power support, which are represented by the doubly fed induction generators, a reactive power optimization model of the distribution with wind turbine is proposed. In order to solve the combinatorial optimization problem, an improved self‐adaptive differential evolution algorithm is developed. In the algorithm, the mutation direction is guided by elite group, which can greatly improve the algorithm convergence rate. Then a self‐learning strategy that self‐adaptively adjusts the individual mutation rate and crossover rate is introduced in the process of individual evolution; the robustness of algorithm is increased. To avoid the difficulty in selecting penalty function coefficient, Deb criteria is used to deal with the constraint limits. The effectiveness of the model and algorithm has been verified on modified Institute of Electrical and Electronics Engineers (IEEE) 33‐node system and PG&E 69‐node system. Copyright © 2016 John Wiley & Sons, Ltd.