Metaheuristic Approach for Online Optimal Reactive Power Management in Near-Shore Wind Power Plants

J.L. Rueda Torres; A.M. Theologi; M. Ndreko; I. Erlich; P. Palensky

doi:10.1109/ISGT-LA.2017.8126706

Metaheuristic Approach for Online Optimal Reactive Power Management in Near-Shore Wind Power Plants

J.L. Rueda Torres, A.M. Theologi, M. Ndreko, I. Erlich, P. Palensky

Intelligent Electrical Power Grids

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

5 Citations (Scopus)

Abstract

Mean-variance mapping optimization (MVMO) is an emerging metaheuristic optimization algorithm, whose evolutionary mechanism performs within a normalized search space. The most remarkable aspect of this mechanism resides in the application of a special mapping function to generate new values of the optimization variables based on their statistical significance throughout the search process. This paper concerns with the feasibility of the MVMO to tackle the problem of online optimal reactive power management in near-shore wind power plants. The main challenges reside in the restricted computing budget and mix-integer nature of the problem. To this aim, MVMO is configured to evolve a single solution throughout the search process, and a new mapping function is proposed to improve the global search capability. Numerical tests on a benchmark system proposed by the IEEE Working Group on Modern Heuristic Optimization as well as a real world wind power plant demonstrate the effectiveness of MVMO.

Original language	English
Title of host publication	2017 IEEE PES Innovative Smart Grid Technologies Conference - Latin America (ISGT Latin America)
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	1-6
Number of pages	6
ISBN (Electronic)	978-1-5386-3312-0
DOIs	https://doi.org/10.1109/ISGT-LA.2017.8126706
Publication status	Published - 2017
Event	2017 IEEE PES Innovative Smart Grid Technologies Conference - Latin America - JW Marriott Hotel, Quito, Ecuador Duration: 20 Sept 2017 → 22 Sept 2017 http://ieee-isgt-latam.org/files/2017/08/Technical-Program_IEEE_ISGT_LA_2017.pdf

Conference

Conference	2017 IEEE PES Innovative Smart Grid Technologies Conference - Latin America
Abbreviated title	2017 ISGT Latin America
Country/Territory	Ecuador
City	Quito
Period	20/09/17 → 22/09/17
Internet address	http://ieee-isgt-latam.org/files/2017/08/Technical-Program_IEEE_ISGT_LA_2017.pdf

Keywords

Metaheuristic Optimization
Reactive Power Control
Wind Power Plant
On-Load Tap Changer
Mean-Variance Mapping Optimization

Access to Document

10.1109/ISGT-LA.2017.8126706

Cite this

@inproceedings{3e59a5e013cd4452bc9aa98883d7cad8,

title = "Metaheuristic Approach for Online Optimal Reactive Power Management in Near-Shore Wind Power Plants",

abstract = "Mean-variance mapping optimization (MVMO) is an emerging metaheuristic optimization algorithm, whose evolutionary mechanism performs within a normalized search space. The most remarkable aspect of this mechanism resides in the application of a special mapping function to generate new values of the optimization variables based on their statistical significance throughout the search process. This paper concerns with the feasibility of the MVMO to tackle the problem of online optimal reactive power management in near-shore wind power plants. The main challenges reside in the restricted computing budget and mix-integer nature of the problem. To this aim, MVMO is configured to evolve a single solution throughout the search process, and a new mapping function is proposed to improve the global search capability. Numerical tests on a benchmark system proposed by the IEEE Working Group on Modern Heuristic Optimization as well as a real world wind power plant demonstrate the effectiveness of MVMO.",

keywords = "Metaheuristic Optimization, Reactive Power Control, Wind Power Plant, On-Load Tap Changer, Mean-Variance Mapping Optimization",

author = "{Rueda Torres}, J.L. and A.M. Theologi and M. Ndreko and I. Erlich and P. Palensky",

year = "2017",

doi = "10.1109/ISGT-LA.2017.8126706",

language = "English",

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booktitle = "2017 IEEE PES Innovative Smart Grid Technologies Conference - Latin America (ISGT Latin America)",

publisher = "IEEE",

address = "United States",

note = "2017 IEEE PES Innovative Smart Grid Technologies Conference - Latin America, 2017 ISGT Latin America ; Conference date: 20-09-2017 Through 22-09-2017",

url = "http://ieee-isgt-latam.org/files/2017/08/Technical-Program_IEEE_ISGT_LA_2017.pdf",

}

Rueda Torres, JL, Theologi, AM, Ndreko, M, Erlich, I & Palensky, P 2017, Metaheuristic Approach for Online Optimal Reactive Power Management in Near-Shore Wind Power Plants. in 2017 IEEE PES Innovative Smart Grid Technologies Conference - Latin America (ISGT Latin America). IEEE, Piscataway, NJ, pp. 1-6, 2017 IEEE PES Innovative Smart Grid Technologies Conference - Latin America, Quito, Ecuador, 20/09/17. https://doi.org/10.1109/ISGT-LA.2017.8126706

Metaheuristic Approach for Online Optimal Reactive Power Management in Near-Shore Wind Power Plants. / Rueda Torres, J.L.; Theologi, A.M.; Ndreko, M. et al.
2017 IEEE PES Innovative Smart Grid Technologies Conference - Latin America (ISGT Latin America). Piscataway, NJ: IEEE, 2017. p. 1-6.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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T1 - Metaheuristic Approach for Online Optimal Reactive Power Management in Near-Shore Wind Power Plants

AU - Rueda Torres, J.L.

AU - Theologi, A.M.

AU - Ndreko, M.

AU - Erlich, I.

AU - Palensky, P.

PY - 2017

Y1 - 2017

N2 - Mean-variance mapping optimization (MVMO) is an emerging metaheuristic optimization algorithm, whose evolutionary mechanism performs within a normalized search space. The most remarkable aspect of this mechanism resides in the application of a special mapping function to generate new values of the optimization variables based on their statistical significance throughout the search process. This paper concerns with the feasibility of the MVMO to tackle the problem of online optimal reactive power management in near-shore wind power plants. The main challenges reside in the restricted computing budget and mix-integer nature of the problem. To this aim, MVMO is configured to evolve a single solution throughout the search process, and a new mapping function is proposed to improve the global search capability. Numerical tests on a benchmark system proposed by the IEEE Working Group on Modern Heuristic Optimization as well as a real world wind power plant demonstrate the effectiveness of MVMO.

AB - Mean-variance mapping optimization (MVMO) is an emerging metaheuristic optimization algorithm, whose evolutionary mechanism performs within a normalized search space. The most remarkable aspect of this mechanism resides in the application of a special mapping function to generate new values of the optimization variables based on their statistical significance throughout the search process. This paper concerns with the feasibility of the MVMO to tackle the problem of online optimal reactive power management in near-shore wind power plants. The main challenges reside in the restricted computing budget and mix-integer nature of the problem. To this aim, MVMO is configured to evolve a single solution throughout the search process, and a new mapping function is proposed to improve the global search capability. Numerical tests on a benchmark system proposed by the IEEE Working Group on Modern Heuristic Optimization as well as a real world wind power plant demonstrate the effectiveness of MVMO.

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KW - Reactive Power Control

KW - Wind Power Plant

KW - On-Load Tap Changer

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ER -

Metaheuristic Approach for Online Optimal Reactive Power Management in Near-Shore Wind Power Plants

Abstract

Conference

Keywords

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