Probabilistic Load Flow for Wind Integrated Power System Considering Node Power Uncertainties and Random Branch Outages

This paper proposes an analytical probabilistic load flow (PLF) approach that considers conventional generator outages, load variability, and random branch outages. The branch outages are modeled as 0-1 distributions of fictitious power injections at the appropriate nodes. The distribution of state...

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Bibliographic Details
Published in:IEEE transactions on sustainable energy 2023-01, Vol.14 (1), p.482-489
Main Authors: Singh, Vikas, Moger, Tukaram, Jena, Debashisha
Format: Article
Language:English
Subjects:
Algorithms
Branch outage
Computer applications
Contingency
Contingency management
correlation
cumulant method
Electrical loads
Fuzzy logic
Generators
gram-charlier expansion
Load modeling
Mathematical models
Monte Carlo simulation
nataf transformation
Outages
Performance indices
Power flow
probabilistic load flow
Probabilistic logic
Sequences
Sequential analysis
Series expansion
Uncertainty
Wind power
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Summary:This paper proposes an analytical probabilistic load flow (PLF) approach that considers conventional generator outages, load variability, and random branch outages. The branch outages are modeled as 0-1 distributions of fictitious power injections at the appropriate nodes. The distribution of state variables and line power flows is then obtained using a combined Cumulant and Gram-Charlier series expansion approach. The proposed PLF performs contingency sequencing with fuzzy logic to eliminate random line checking and avoid masking mistakes faced by performance index-based algorithms. The Jacobian inverse calculation in the traditional Cumulant method is eliminated to conserve storage space and speed up the computation using the Gauss-Jordan method. The correlations among loads and wind power generations has been modeled using the Nataf transformation process. Results of 24-bus and 259-bus equivalent systems of the Indian southern and western power grids are analyzed and validated with those obtained using the Monte Carlo simulation method. The suggested method's efficacy is justified by its accuracy and low computational burden.
ISSN:1949-3029
1949-3037
DOI:10.1109/TSTE.2022.3216914