IEEE Transactions on Power Systems · 2019 · 42 citations · 30 references
ArchitectureComputational EngineeringElectrical EngineeringMachine LearningRenewable Energy IntegrationRenewable Energy SystemsDistributed Energy ResourcesEnergy Management SystemsEnergy SimulationEnergy System ModelingPower Grid AnalysisSolar Photovoltaic SystemsDistribution Network ModelingDynamic ResponseElectric Power System ModelingHybrid ModelingLong Short-term Memory NetworksPhotovoltaic System ModelingPhotovoltaic System SimulationResponse Error
Accurate modeling is an important method for dynamic response analysis and control strategy verification of high photovoltaic (PV) penetration distribution networks. This paper proposes a precise dynamic modeling framework for the two-staged PV station cluster, namely as deep learning clustering hybrid modeling framework. It includes clustering-based equivalent model and error correction model (ECM). A long short-term memory network is used to form the ECM, which models the dynamic response error between the existing equivalent model and the detailed model. The competence of this framework is validated by numerous case studies based on a practical PV cluster construction. The simulation results reveal that the proposed method is featured of low complexity and fast response speed as the equivalent model but has much higher accuracy.
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