Please use this identifier to cite or link to this item: https://idr.l3.nitk.ac.in/jspui/handle/123456789/9727
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dc.contributor.authorPrusty, B.R.
dc.contributor.authorJena, D.
dc.date.accessioned2020-03-31T06:51:21Z-
dc.date.available2020-03-31T06:51:21Z-
dc.date.issued2019
dc.identifier.citationInternational Transactions on Electrical Energy Systems, 2019, Vol.29, 5, pp.-en_US
dc.identifier.uri10.1002/2050-7038.2819
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/9727-
dc.description.abstractThe probabilistic steady-state forecasting of a PV-integrated power system requires a suitable forecasting model capable of accurately characterizing the uncertainties and correlations among multivariate inputs. The critical and foremost difficulties in the development of such a model include the accurate representation of the characterizing features such as complex nonstationary pattern, non-Gaussianity, and spatial and temporal correlations. This paper aims at developing an improved high-dimensional multivariate spatiotemporal model through enhanced preprocessing, transformation techniques, principal component analysis, and a suitable time series model that is capable of accurately modeling the trend in the variance of uncertain inputs. The proposed model is applied to the probabilistic load flow carried out on the modified Indian utility 62-bus transmission system using temperature-augmented system model for an operational planning study. A detailed discussion of various results has indicated the effectiveness of the proposed model in capturing the aforesaid characterizing features of uncertain inputs. 2019 John Wiley & Sons, Ltd.en_US
dc.titleA spatiotemporal probabilistic model-based temperature-augmented probabilistic load flow considering PV generationsen_US
dc.typeArticleen_US
Appears in Collections:1. Journal Articles

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