In practical power systems,operators generally keep interface flowing under the transient stability constrained with interface real power flow limits(TS-IRPFL)to guarantee transient stability of the system.Many method...In practical power systems,operators generally keep interface flowing under the transient stability constrained with interface real power flow limits(TS-IRPFL)to guarantee transient stability of the system.Many methods of computing TS-IRPFL have been proposed.However,in practice,the method widely used to determine TS-IRPFL is based on selection and analysis of typical scenarios as well as scenario matching.First,typical scenarios are selected and analyzed to obtain accurate limits,then the scenario to be analyzed is matched with a certain typical scenario,whose limit is adopted as the forecast limit.In this paper,following the steps described above,a pragmatic method to determine TS-IRPFL is proposed.The proposed method utilizes data-driven tools to improve the steps of scenario selection and matching.First of all,we formulate a clear model of power system scenario similarity.Based on the similarity model,we develop a typical scenario selector by clustering and a scenario matcher by nearest neighbor algorithm.The proposed method is pragmatic because it does not change the existing procedure.Moreover,it is much more reasonable than the traditional method.Test results verify the validity of the method.展开更多
基金This work was supported by National Key R&D Program of China(2018YFB0904500)and State Grid Corporation of China。
文摘In practical power systems,operators generally keep interface flowing under the transient stability constrained with interface real power flow limits(TS-IRPFL)to guarantee transient stability of the system.Many methods of computing TS-IRPFL have been proposed.However,in practice,the method widely used to determine TS-IRPFL is based on selection and analysis of typical scenarios as well as scenario matching.First,typical scenarios are selected and analyzed to obtain accurate limits,then the scenario to be analyzed is matched with a certain typical scenario,whose limit is adopted as the forecast limit.In this paper,following the steps described above,a pragmatic method to determine TS-IRPFL is proposed.The proposed method utilizes data-driven tools to improve the steps of scenario selection and matching.First of all,we formulate a clear model of power system scenario similarity.Based on the similarity model,we develop a typical scenario selector by clustering and a scenario matcher by nearest neighbor algorithm.The proposed method is pragmatic because it does not change the existing procedure.Moreover,it is much more reasonable than the traditional method.Test results verify the validity of the method.
