摘要
以分布式电源接入配电网运行时产生的有功网损最小并能改善节点电压质量为目标,提出将免疫算法与教与学算法相结合的配电网无功优化方法。为了提高教与学优化算法的收敛性能,将免疫算法的免疫记忆和自我调节机制引入到教与学算法,并采用个体浓度机制的多样性保持策略,同时利用免疫算法的"接种疫苗"和"免疫选择"来提高个体全局搜索能力,加快算法收敛速度。通过对改进后的含DG的IEEE33节点配电系统进行仿真分析,结果表明在接入DG的基础上进行无功优化,系统电压水平得到稳定提升,系统网损也大大降低,从而验证了所提算法的有效性。
To reach the goal of minimum active power loss and improved node voltage quality, an improved teaching-learning-based optimization algorithm combined with immunity algorithm is proposed for reactive power optimization. To improve the convergence of teaching-learning-based algorithm, the immunological memory and self-regulation mechanism of immunity algorithm are introduced into teaching-learning based algorithm, meanwhile the search process is guided by the ＂vaccination＂ and ＂immune selection＂ of the im- mune algorithm. Simulation is carried out based on a modified IEEE33-bus system with distributed genera- tors, and the results show that the voltage quality of distributed network is improved and the network loss is reduced when reactive power optimization is adopted by distribution network with distributed genera- tors, and the global convergence and stability of the proposed algorithm is verified.
作者
丁士高
高桂革
曾宪文
DING Shi-gao GAO Gui-ge ZENG Xian-wen(Shanghai DianJi UniversityElectrical Institute, Shanghai 201306, China Shanghai DianJi University Electronic Information Institute, Shanghai 201306 ,China)
出处
《电力学报》
2016年第5期378-383,共6页
Journal of Electric Power
关键词
配电网
分布式电源
无功优化
教与学算法
免疫教与学算法
distribution network
distributed generators
reactive power optimization
teaching-learningbased algorithm l teaching-learning-based algorithm with immunity
