ANN-Based STATCOM Tuning for Performance Enhancement of Combined Wind Farms. Issue 1 (20th January 2019)

• Record Type: Journal Article
• Authors: Rashad, Ahmed; Kamel, Salah; Jurado, Francisco; Abdel-Nasser, Mohamed; Mahmoud, Karar
• Is Part Of: Electric power components and systems. Volume 47:Issue 1/2(2019)
• Page Start: 10
• Page End: 26
• Publication Date: 2019-01-20
• Subjects: squirrel cage induction generators -- doubly fed induction generators -- combined wind farm -- STATCOM -- artificial neural network -- multiobjective genetic algorithm -- whale optimization algorithm; Electric machinery -- Periodicals; 621.3104205
• DOI: 10.1080/15325008.2019.1570052
• Languages: English
• ISSNs: 1532-5008

Abstract

Abstract: Although the wind farms based on squirrel cage induction generators (SCIG) is cheaper than the wind farms based on doubly fed induction generators (DFIG), it is always in desperate need for reactive power compensation. Nevertheless, the wind farms based on DFIG are expensive compared with the SCIG wind farm, it features by its ability to control the active power independent of reactive power. However, combined wind farm (CWF) has been developed to collect the benefits of SCIG and DFIG wind turbines in the same wind farm. In this article, artificial neural network (ANN) is used to evaluate gain parameters of static synchronous compensator (STATCOM) in order to improve the stability performance of CWF. The impact of tuned STATCOM on the performance of CWF during gust wind speed and during three-phase fault is comprehensively investigated. The performance of CWF with STATCOM tuned by ANN is compared with its performance when the STATCOM tuned by the multiobjective genetic algorithm (MOGA) and whale optimization algorithm (WOA). The results show that the performance of CWF can be enhanced using STATCOM tuned by ANN more than MOGA and WOA.