廣義負(fù)荷建模中縱橫聚類策略研究
發(fā)布時間：2019-06-05 01:03
【摘要】：考慮風(fēng)電接入后廣義負(fù)荷的不確定性,提出縱橫聚類策略。針對聚類客觀性與復(fù)雜場景分析需求,引入高質(zhì)量的仿射傳播(affinity propagation,AP)聚類算法。利用包含縱向聚類與橫向聚類的縱橫聚類策略對全年實測有功響應(yīng)空間客觀聚類,實現(xiàn)大尺度與小尺度數(shù)據(jù)在統(tǒng)一時間框架下的聚類分析:通過三步聚類實現(xiàn)全年大時間尺度下的縱向聚類,結(jié)果體現(xiàn)了細(xì)化季節(jié)特性;通過將縱向類內(nèi)全部數(shù)據(jù)聯(lián)排統(tǒng)一聚類,實現(xiàn)較小時間尺度下的精細(xì)橫向聚類,結(jié)果體現(xiàn)日時段特性。引入帶概率信息的廣義負(fù)荷建模方法建模,并檢驗聚類策略的有效性。仿真結(jié)果表明,所提策略實現(xiàn)的聚類客觀、合理,便于精確建模與現(xiàn)場應(yīng)用,可為后續(xù)仿真分析與調(diào)度控制提供輔助參考。
[Abstract]:Considering the uncertainty of generalized load after wind power access, a vertical and horizontal clustering strategy is proposed. In order to meet the needs of clustering objectivity and complex scene analysis, a high quality affine propagation (affinity propagation,AP) clustering algorithm is introduced. The vertical and horizontal clustering strategy, which includes longitudinal clustering and transverse clustering, is used to objectively cluster the measured active power response space in the whole year. To realize the clustering analysis of large-scale and small-scale data in a unified time frame: the longitudinal clustering of large-scale and small-scale data in the whole year is realized by three-step clustering, and the results reflect the refined seasonal characteristics. Through the unified clustering of all the data in the longitudinal class, the fine lateral clustering at a small time scale is realized, and the results show the characteristics of the daily period. The generalized load modeling method with probability information is introduced, and the effectiveness of clustering strategy is tested. The simulation results show that the clustering of the proposed strategy is objective, reasonable, convenient for accurate modeling and field application, and can provide an auxiliary reference for subsequent simulation analysis and scheduling control.
【作者單位】： 電網(wǎng)智能化調(diào)度與控制教育部重點實驗室(山東大學(xué));
【基金】：國家自然科學(xué)基金資助項目(51177091) 國家863高技術(shù)基金項目(2011AA05A101)~~
【分類號】：TM614
[Abstract]:Considering the uncertainty of generalized load after wind power access, a vertical and horizontal clustering strategy is proposed. In order to meet the needs of clustering objectivity and complex scene analysis, a high quality affine propagation (affinity propagation,AP) clustering algorithm is introduced. The vertical and horizontal clustering strategy, which includes longitudinal clustering and transverse clustering, is used to objectively cluster the measured active power response space in the whole year. To realize the clustering analysis of large-scale and small-scale data in a unified time frame: the longitudinal clustering of large-scale and small-scale data in the whole year is realized by three-step clustering, and the results reflect the refined seasonal characteristics. Through the unified clustering of all the data in the longitudinal class, the fine lateral clustering at a small time scale is realized, and the results show the characteristics of the daily period. The generalized load modeling method with probability information is introduced, and the effectiveness of clustering strategy is tested. The simulation results show that the clustering of the proposed strategy is objective, reasonable, convenient for accurate modeling and field application, and can provide an auxiliary reference for subsequent simulation analysis and scheduling control.
【作者單位】： 電網(wǎng)智能化調(diào)度與控制教育部重點實驗室(山東大學(xué));
【基金】：國家自然科學(xué)基金資助項目(51177091) 國家863高技術(shù)基金項目(2011AA05A101)~~
【分類號】：TM614
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