电网技术
電網技術
전망기술
POWER SYSTEM TECHNOLOGY
2013年
8期
2194-2200
,共7页
颜全椿%卫志农%孙国强%王超%孙维真
顏全椿%衛誌農%孫國彊%王超%孫維真
안전춘%위지농%손국강%왕초%손유진
抗差估计%加权最小绝对值%原对偶内点法%多预测-校正内点法%状态估计
抗差估計%加權最小絕對值%原對偶內點法%多預測-校正內點法%狀態估計
항차고계%가권최소절대치%원대우내점법%다예측-교정내점법%상태고계
robust estimation%weighted least absolute value%primal-dual interior point method%multiple predictor-corrector interior point method%state estimation
针对预测-校正内点法(predictor-corrector primal-dual interior point method,PCPDIPM)加权最小绝对值状态估计(weighted least absolute squares, WLAV)可能发生校正方向指向错误方向的不足,提出一种基于多预测-校正内点法(multiple PCPDIPM,MPCPDIPM)的WLAV抗差状态估计算法。该算法在PCPDIPM的基础上,通过多次校正,对中心参数动态估计,并采用2阶段线性搜索法确定校正方向在总的牛顿方向中的最优比重,从而保证迭代点向中心轨迹靠拢。最后,通过IEEE算例仿真和我国某省网的测试结果验证了所提方法的有效性。与含不良数据辨识功能的加权最小二乘状态估计相比较,所提方法的收敛速度及抗差能力具有明显的优势。
針對預測-校正內點法(predictor-corrector primal-dual interior point method,PCPDIPM)加權最小絕對值狀態估計(weighted least absolute squares, WLAV)可能髮生校正方嚮指嚮錯誤方嚮的不足,提齣一種基于多預測-校正內點法(multiple PCPDIPM,MPCPDIPM)的WLAV抗差狀態估計算法。該算法在PCPDIPM的基礎上,通過多次校正,對中心參數動態估計,併採用2階段線性搜索法確定校正方嚮在總的牛頓方嚮中的最優比重,從而保證迭代點嚮中心軌跡靠攏。最後,通過IEEE算例倣真和我國某省網的測試結果驗證瞭所提方法的有效性。與含不良數據辨識功能的加權最小二乘狀態估計相比較,所提方法的收斂速度及抗差能力具有明顯的優勢。
침대예측-교정내점법(predictor-corrector primal-dual interior point method,PCPDIPM)가권최소절대치상태고계(weighted least absolute squares, WLAV)가능발생교정방향지향착오방향적불족,제출일충기우다예측-교정내점법(multiple PCPDIPM,MPCPDIPM)적WLAV항차상태고계산법。해산법재PCPDIPM적기출상,통과다차교정,대중심삼수동태고계,병채용2계단선성수색법학정교정방향재총적우돈방향중적최우비중,종이보증질대점향중심궤적고롱。최후,통과IEEE산례방진화아국모성망적측시결과험증료소제방법적유효성。여함불량수거변식공능적가권최소이승상태고계상비교,소제방법적수렴속도급항차능력구유명현적우세。
In allusion to the defect that during the weighted least absolute squares (WLAV) state estimation based on predictor-corrector primal-dual interior point method (PCPDIPM) it is possible the corrector direction possibly points to wrong direction, a multiple PCPDIPM based robust WLAV state estimation algorithm is proposed. On the basis of PCPDIPM, through multiple correction the proposed algorithm dynamically estimates the centrality parameter and by use of two-stage linear searching method the optimal proportion of the corrector direction in the Newton direction is determined to ensure that the iteration points draw close to centrality parameter. Finally, the effectiveness of the proposed method is verified by simulation results of IEEE 14-bus system and test results of a certain provincial power grid in China. The convergence speed and robustness of the proposed method are much better than the weighted least square state estimation with the function of bad data identification.