补偿负荷不平衡时,通常以理想三相电压源电势作为公共连接点(point of common coupling,PCC)参考电压,实际系统中据此设计的补偿网络并不能完全平衡。对此,针对负荷不平衡的三相三线制电路,为更符合PCC处三相电压不对称的实际情形,该文...补偿负荷不平衡时,通常以理想三相电压源电势作为公共连接点(point of common coupling,PCC)参考电压,实际系统中据此设计的补偿网络并不能完全平衡。对此,针对负荷不平衡的三相三线制电路,为更符合PCC处三相电压不对称的实际情形,该文考虑系统导纳,将系统侧等效为戴维南模型后,建立了不平衡负荷的负序加权等效模型;基于等效负荷参数,运用Steinmetz平衡化理论给出了相间补偿网络电纳的负序加权计算公式。网络参数是以加权系数为变量的函数,可兼容已有的针对三相三线制电路设计的平衡化补偿网络方法,为补偿的优化提供了理论基础。结合具体算例,该文给出了负序加权系数和补偿容量的三维关系图,以补偿容量最小为目标,确定出平衡化补偿的最优方案,仿真与物理实验验证了所提模型和平衡化方法的正确性。展开更多
Short-term forecasting is a difficult problem because of the influence of non-linear factors and irregular events.A novel short-term forecasting method named TIK was proposed,in which ARMA forecasting model was used t...Short-term forecasting is a difficult problem because of the influence of non-linear factors and irregular events.A novel short-term forecasting method named TIK was proposed,in which ARMA forecasting model was used to consider the load time series trend forecasting,intelligence forecasting DESVR model was applied to estimate the non-linear influence,and knowledge mining methods were applied to correct the errors caused by irregular events.In order to prove the effectiveness of the proposed model,an application of the daily maximum load forecasting was evaluated.The experimental results show that the DESVR model improves the mean absolute percentage error(MAPE) from 2.82% to 2.55%,and the knowledge rules can improve the MAPE from 2.55% to 2.30%.Compared with the single ARMA forecasting method and ARMA combined SVR forecasting method,it can be proved that TIK method gains the best performance in short-term load forecasting.展开更多
文摘补偿负荷不平衡时,通常以理想三相电压源电势作为公共连接点(point of common coupling,PCC)参考电压,实际系统中据此设计的补偿网络并不能完全平衡。对此,针对负荷不平衡的三相三线制电路,为更符合PCC处三相电压不对称的实际情形,该文考虑系统导纳,将系统侧等效为戴维南模型后,建立了不平衡负荷的负序加权等效模型;基于等效负荷参数,运用Steinmetz平衡化理论给出了相间补偿网络电纳的负序加权计算公式。网络参数是以加权系数为变量的函数,可兼容已有的针对三相三线制电路设计的平衡化补偿网络方法,为补偿的优化提供了理论基础。结合具体算例,该文给出了负序加权系数和补偿容量的三维关系图,以补偿容量最小为目标,确定出平衡化补偿的最优方案,仿真与物理实验验证了所提模型和平衡化方法的正确性。
基金Projects(70671039,71071052) supported by the National Natural Science Foundation of ChinaProjects(10QX44,09QX68) supported by the Fundamental Research Funds for the Central Universities in China
文摘Short-term forecasting is a difficult problem because of the influence of non-linear factors and irregular events.A novel short-term forecasting method named TIK was proposed,in which ARMA forecasting model was used to consider the load time series trend forecasting,intelligence forecasting DESVR model was applied to estimate the non-linear influence,and knowledge mining methods were applied to correct the errors caused by irregular events.In order to prove the effectiveness of the proposed model,an application of the daily maximum load forecasting was evaluated.The experimental results show that the DESVR model improves the mean absolute percentage error(MAPE) from 2.82% to 2.55%,and the knowledge rules can improve the MAPE from 2.55% to 2.30%.Compared with the single ARMA forecasting method and ARMA combined SVR forecasting method,it can be proved that TIK method gains the best performance in short-term load forecasting.
