Gaussian process regression based inertia emulation and reserve estimation for grid interfaced photovoltaic system. (October 2018)

• Record Type: Journal Article
• Authors: Kanwal, S.; Khan, B.; Ali, S.M.; Mehmood, C.A.
• Is Part Of: Renewable energy. Volume 126(2018)
• Page Start: 865
• Page End: 875
• Publication Date: 2018-10
• Subjects: Deloading -- Machine learning -- Microgrid -- Photovoltaic systems -- Regression -- Solar power forecasting; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2018.04.012
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: Accurate power reserve estimation for a Photovoltaic Generator (PVG) is of paramount importance to combat frequency changes in a smart grid. Standalone PVG lacks inertia, or an internal power reserve due to power electronic converter grid-interface. Operating a PVG at deloaded percentage of its maximum power capacity mimics an internal power reserve, simulating the Automatic Generation Control (AGC) feature of synchronous machines. Thus, a deloaded PVG releases or absorbs the reserve according to the frequency variations for the grid stability. Moreover, an efficient switching between various reserves during grid operation is required. The common reserve estimation technique is to apply PVG manufacturer's specification based deterministic approach. In this work, we compare the deterministic modeling results with a statistical learning model of Gaussian Process Regression (GPR). The GPR model is trained by dataset of PVG maximum power values evaluated by load line analysis in a simulation, according to the irradiance and historical temperature of Abbottabad, Pakistan. The trained model performance is compared with the deterministic model in a simulation, where the PVG is saturated to turn on a synchronous generator. Time difference of turning on the backup generator between GPR model and deterministic modeling validates the importance of accurate reserve estimation. Highlights: Gaussian Process Regression (GPR) technique for reserve estimation of PV system is developed. Inertia is emulated in PV system based on ambient conditions and grid frequency. Results showed that GPR modeling yields better response than deterministic modeling of PV system.