首页|Data-model Hybrid Driven Topology Identification Framework for Distribution Networks
Data-model Hybrid Driven Topology Identification Framework for Distribution Networks
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维普
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Extensive penetration of distribution energy re-sources(DERs)brings increasing uncertainties to distribution networks.Accurate topology identification is a critical basis to guarantee robust distribution network operation.Many algo-rithms that estimate distribution network topology have already been employed.Unfortunately,most are based on data-driven alone method and are hard to deal with ever-changing distri-bution network physical structures.Under these backgrounds,this paper proposes a data-model hybrid driven topology identi-fication scheme for distribution networks.First,a data-driven method based on a deep belief network(DBN)and random forest(RF)algorithm is used to realize the distribution network topology rough identification.Then,the rough identification results in the previous step are used to make a model of distribution network topology.The model transforms the topol-ogy identification problem into a mixed integer programming problem to correct the rough topology further.Performance of the proposed method is verified in an IEEE 33-bus test system and modified 292-bus system.
维普
万方数据
