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A Novel Hybrid Ant Colony-Particle Swarm Optimization Techniques Based Tuning STATCOM for Grid Code Compliance

机译:基于新型混合蚁群粒子粒子优化技术,用于网格代码合规性

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Integrating wind power plants (WPPs) into power systems are increasing dramatically now a day. However, the dynamic performance of power systems will be affected by the large penetration level of such renewable sources of energy. From this context power system operators and transmission system operators have put regulation rules to keep pushing wind power plants to safeguard limits that keep power system more stable and reliable. One of these rules is providing a low voltage ride through (LVRT) for wind farms without disconnecting it from the power system. The current paper implements the STATCOM as a LVRT for a 9 MW wind farm connected to the grid through transmission system of 120 kV. For enhancing the dynamic performance of STATCOM, two types of optimization methodologies: ant colony (ACO) and particle swarm optimization (PSO), are proposed to fine tune the coefficients of PI controllers to optimally manage the STATCOM dynamics.
机译:将风力发电厂(WPP)集成到动力系统现在每天都在急剧增加。然而,电力系统的动态性能将受到这种可再生能源源的大的渗透水平的影响。从这种上下文电力系统运营商和传输系统运营商已经进行了规则规则,以继续推动风电厂以保护电力系统更稳定可靠的限制。其中一个规则是通过用于风电场的低电压骑行(LVRT),而不将其与电力系统断开。目前纸张通过120 kV的传动系统实现9兆瓦风电场的LVRT。为了提高Statcom的动态性能,提出了两种类型的优化方法:蚁群(ACO)和粒子群优化(PSO),以微调PI控制器的系数,以最佳地管理Statcom Dynamics。

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