NDLI: Extremum Seeking control of smart inverters for VAR compensation

Content Provider	IEEE Xplore Digital Library
Author	Arnold, D.B. Negrete-Pincetic, M. Stewart, E.M. Auslander, D.M. Callaway, D.S.
Copyright Year	2015
Description	Author affiliation: Environ. Energy Technol. Div., Lawrence Berkeley Nat. Lab., Berkeley, CA, USA (Stewart, E.M.) \|\| Energy & ResourcesGroup, U.C. Berkeley, Berkeley, CA, USA (Callaway, D.S.) \|\| EECS Dept., U.C. Berkeley, Berkeley, CA, USA (Negrete-Pincetic, M.) \|\| Dept. of Mech. Eng., U.C. Berkeley, Berkeley, CA, USA (Arnold, D.B.; Auslander, D.M.)
Abstract	Reactive power compensation is used by utilities to ensure customer voltages are within pre-defined tolerances and reduce system resistive losses. While much attention has been paid to model-based control algorithms for reactive power support and Volt Var Optimization (VVO), these strategies typically require relatively large communications capabilities and accurate models. In this work, a non-model-based control strategy for smart inverters is considered for VAR compensation. An Extremum Seeking control algorithm is applied to modulate the reactive power output of inverters based on real power information from the feeder substation, without an explicit feeder model. Simulation results using utility demand information confirm the ability of the control algorithm to inject VARs to minimize feeder head real power consumption. In addition, we show that the algorithm is capable of improving feeder voltage profiles and reducing reactive power supplied by the distribution substation.
Starting Page	1
Ending Page	5
File Size	1256714
Page Count	5
File Format	PDF
ISBN	9781467380409
DOI	10.1109/PESGM.2015.7286378
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	Reactive power Inverters Substations Optimization Voltage control Linear programming Power demand
Content Type	Text
Resource Type	Article

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