摘要
在短期负荷预测的基础上,利用遗传算法求解整个电网的无功优化问题,得到的优化结果为各个变电站VQC的合理限值。该方法将全局优化、集中控制与分散控制的优点结合起来,克服了各变电站无功、电压就地最优控制的弊端,提高了系统电压的合格率、降低了系统的总线损,节电效益显著。
In the short-term load forecasting's foundation, solves the entire power system's reactive power optimization question using the genetic algorithm, and obtains optimized result for each transformer substation VQC reasonable limiting value. This method the global optimization, the common control and the dispersion control's merit unifies, has overcome various transformer substations reactive power optimization, the voltage optimum control malpractice, raised the systemic voltage qualified rate, to reduce system's main line to damage, the electricity saving benefit was remarkable.
出处
《山东电力高等专科学校学报》
2008年第2期72-75,共4页
Journal of Shandong Electric Power College
关键词
短期负荷预测
人工神经网络
无功优化
遗传算法
short-term load forecasiing
artificial neural net-work
reactive power optimization
genetic algo-rithm