CAJ | 학술논문

电力系统模型参数的不确定将给仿真结果带来不确定性,概率分配法(probabilistic collocation method,PCM)通过少数几次仿真建立起仿真结果与不确定性参数之间的多项式关系,为仿真不确定性评估提供了有力的工具.但随着不确定性参数个数的增加,PCM所需仿真次数将呈指数增长,为减小计算量,有必要识别出主导不确定性参数.提出了一种基于一阶PCM拟合的主导不确定性参数的选择指标,克服了传统灵敏度指标依赖于参数取值,以及无法考虑参数概率分布和取值范围等缺点,能有效地选出主导不确定性参数.进而仅对主导不确定性参数进行分析,提高PCM的适应性.算例结果验证了所提指标的有效性.
전력계통모형삼수적불학정장급방진결과대래불학정성,개솔분배법(probabilistic collocation method,PCM)통과소수궤차방진건립기방진결과여불학정성삼수지간적다항식관계,위방진불학정성평고제공료유력적공구.단수착불학정성삼수개수적증가,PCM소수방진차수장정지수증장,위감소계산량,유필요식별출주도불학정성삼수.제출료일충기우일계PCM의합적주도불학정성삼수적선택지표,극복료전통령민도지표의뢰우삼수취치,이급무법고필삼수개솔분포화취치범위등결점,능유효지선출주도불학정성삼수.진이부대주도불학정성삼수진행분석,제고PCM적괄응성.산례결과험증료소제지표적유효성.
Uncertain parameters that exist in power system will bring uncertainty to simulation result. Probabilistic collocation method (PCM) can establish a polynomial relationship between the simulation result and uncertain parameters through a few simulations on smartly chosen parameter values, and provides a powerful tool for uncertainty evaluation. Although PCM is touted as a very economic technique, computational complexity nevertheless grows exponentially with the number of uncertain parameters. Key uncertain parameters which influence the uncertainty of result most should be recognized to reduce computational complexity. An index for key parameter selection based on first-order PCM fitting is proposed, which can overcome the shortcomings brought by traditional sensitivity method. For example, the sensitivity method relies on the value of parameter. Besides, it can’t take into account the probability distribution and vary range of parameter. Polynomial relationship can be established with a few key parameters through PCM. Thus the adaptability of PCM is improved. Testing results of case studies demonstrate the validity of the proposed method.