IEEE Transactions on Instrumentation and Measurement · 2021 · 10 citations · 18 references
EngineeringElectrical EngineeringEnergy SystemsMachine LearningPower ElectronicsElectrical System DiagnosticsFault DetectionPower Grid AnalysisPower SystemsSmart Grid SystemsSmart GridsCurrent MeasurementDemand Response ControlFault AnalysisInrush CurrentInrush Current MitigationLoad IdentificationMachine Learning For Smart GridsPower System Modeling
Training the load identification algorithm for a power-system monitor (PSM) begins with characterization or observation of the loads. This article demonstrates a phase-controlled switch that, in situ, accounts for the effects of source and line impedance on load transients at controllable phase angles with respect to the ac voltage line-cycle. The phase angle significantly affects the inrush for many loads, such as lighting, induction motors, and power supplies. The circuitry and signal identification techniques presented in this article can provide a full set of training data for machine learning algorithms for power system monitoring. The system can also be used as the foundation of a “smart switch” in grid applications for automatic demand response control.
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