NDLI: New multivariate linear regression real and reactive branch flow models for volatile scenarios

Content Provider	IEEE Xplore Digital Library
Author	Appalasamy, S. Jones, O.D. Moin, N.H. Tan Ching Sin
Copyright Year	2015
Description	Author affiliation: Dept. of Math. & Stat., Univ. of Melbourne, Melbourne, VIC, Australia (Appalasamy, S.; Jones, O.D.) \|\| Inst. of Energy Policy & Res. (IEPRe), Univ. Tenaga Nasional (UNITEN), Kajang, Malaysia (Tan Ching Sin) \|\| Inst. of Math. Sci., Univ. of Malaya, Kuala Lumpur, Malaysia (Moin, N.H.)
Abstract	Nonlinearity of power flow equations is one of the major underlying factors in a power systems operation complexity. The need for a robust and less complex models rises in a volatile, dynamic and real time scenario. This paper introduces new empirical models using multivariate linear regression (MLR) methods with least squares for both real and reactive branch flows. The models do not make prior assumptions and do not depend on a particular base case. Instead they are trained on either simulated or historical data. Tests using the IEEE 14 bus system show that given similar input variables to DC models, the MLR models performs significantly better. They also show that the MLR models have good prediction accuracy in scenarios with high volatility.
Starting Page	1
Ending Page	5
File Size	192171
Page Count	5
File Format	PDF
ISBN	9781467380409
DOI	10.1109/PESGM.2015.7285669
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Mathematical model Data models Predictive models Biological system modeling Reactive power Accuracy multivariate linear regression real branch flows reactive branch flows robust flexible
Content Type	Text
Resource Type	Article

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