The effects of precision and small impedance branches on power flow robustness

Daniel Tylavsky; Peter E. Crouch; Leslie F. Jarriel; Jagjit Singh; Rambabu Adapa

doi:10.1109/59.317563

The effects of precision and small impedance branches on power flow robustness

Daniel Tylavsky, Peter E. Crouch, Leslie F. Jarriel, Jagjit Singh, Rambabu Adapa

Electrical Engineering

Research output: Contribution to journal › Article › peer-review

33 Scopus citations

Abstract

Renewed interest at EPRI on improved power flow techniques is due to the recent improvements in the robustness of fast power flow algorithms. In this paper we investigate two interacting parameters which affect convergence in power flow algorithms, limited precision and small impedance branches. Analytical methods and experimental results are given for predicting the minimum convergence tolerance achievable by an algorithm as a function of the infimum of the set of branch impedance values, and the number of bits of mantissa precision. Methods are suggested for increasing the virtual precision at minimal computational cost and for scanning power flow input data to determine whether available precision is sufficient.

Original language	English (US)
Pages (from-to)	6-14
Number of pages	9
Journal	IEEE Transactions on Power Systems
Volume	9
Issue number	1
DOIs	https://doi.org/10.1109/59.317563
State	Published - Feb 1994

Keywords

Load Flow
Power Flow
Precision
Robustness

ASJC Scopus subject areas

Energy Engineering and Power Technology
Electrical and Electronic Engineering

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10.1109/59.317563

Cite this

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title = "The effects of precision and small impedance branches on power flow robustness",

abstract = "Renewed interest at EPRI on improved power flow techniques is due to the recent improvements in the robustness of fast power flow algorithms. In this paper we investigate two interacting parameters which affect convergence in power flow algorithms, limited precision and small impedance branches. Analytical methods and experimental results are given for predicting the minimum convergence tolerance achievable by an algorithm as a function of the infimum of the set of branch impedance values, and the number of bits of mantissa precision. Methods are suggested for increasing the virtual precision at minimal computational cost and for scanning power flow input data to determine whether available precision is sufficient.",

keywords = "Load Flow, Power Flow, Precision, Robustness",

author = "Daniel Tylavsky and Crouch, {Peter E.} and Jarriel, {Leslie F.} and Jagjit Singh and Rambabu Adapa",

note = "Funding Information: Support for this research was supplied by the Electric Power Research Institute under agreement RP2473-38, and by Salt River Project through the Electric Power Research Laboratory at Arizona State University, and by the National Science Foundation through grant #ECS 900574 1.",

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AU - Tylavsky, Daniel

AU - Crouch, Peter E.

AU - Jarriel, Leslie F.

AU - Singh, Jagjit

AU - Adapa, Rambabu

N1 - Funding Information: Support for this research was supplied by the Electric Power Research Institute under agreement RP2473-38, and by Salt River Project through the Electric Power Research Laboratory at Arizona State University, and by the National Science Foundation through grant #ECS 900574 1.

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N2 - Renewed interest at EPRI on improved power flow techniques is due to the recent improvements in the robustness of fast power flow algorithms. In this paper we investigate two interacting parameters which affect convergence in power flow algorithms, limited precision and small impedance branches. Analytical methods and experimental results are given for predicting the minimum convergence tolerance achievable by an algorithm as a function of the infimum of the set of branch impedance values, and the number of bits of mantissa precision. Methods are suggested for increasing the virtual precision at minimal computational cost and for scanning power flow input data to determine whether available precision is sufficient.

AB - Renewed interest at EPRI on improved power flow techniques is due to the recent improvements in the robustness of fast power flow algorithms. In this paper we investigate two interacting parameters which affect convergence in power flow algorithms, limited precision and small impedance branches. Analytical methods and experimental results are given for predicting the minimum convergence tolerance achievable by an algorithm as a function of the infimum of the set of branch impedance values, and the number of bits of mantissa precision. Methods are suggested for increasing the virtual precision at minimal computational cost and for scanning power flow input data to determine whether available precision is sufficient.

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KW - Power Flow

KW - Precision

KW - Robustness

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The effects of precision and small impedance branches on power flow robustness

Abstract

Keywords

ASJC Scopus subject areas

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