Stochastic Petri Nets for very short-term wind speed modeling

Ratna Ika Putri; Ardyono Priyadi; Mauridhi Hery Purnomo

doi:10.1109/CIVEMSA.2015.7158619

Stochastic Petri Nets for very short-term wind speed modeling

Institut Teknologi Sepuluh Nopember

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

To overcome the limitations of fossil energy and protect the environment from emissions of greenhouse gases, it is essential to develop the use of renewable energy as a substitute. At present, one of the renewable sources of energy is wind energy, which has the advantage of being pollution free and inexhaustible. However, the use of wind energy is strongly influenced by wind speed, which is not constant. Such varying wind speeds lead to the creation of fluctuated wind power. Consequently, there is a need for modeling and the accurate prediction of wind speed to help optimize the design of the turbine and control system in a wind energy conversion system to maintain system stability. This paper presents the modeling of very short-term wind speed using Stochastic Petri Nets (SPN) that is based on the measurement results of wind speed in Nganjuk. In this study, Stochastic Petri Nets was designed by using 7 places and 7 transitions. Transition to the SPN is defined as a function that generates random values using a uniform function. Wind speed data that was generated during a 500 seconds interval, was compared with the observed wind speeds. The comparison of the generated wind speed and observed ones shows that both its statistical characteristic have similar value.

Original language	English
Title of host publication	2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781479960910
DOIs	https://doi.org/10.1109/CIVEMSA.2015.7158619
Publication status	Published - 14 Jul 2015
Event	2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015 - Shenzhen, China Duration: 12 Jun 2015 → 14 Jun 2015

Publication series

Name	2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015

Conference

Conference	2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015
Country/Territory	China
City	Shenzhen
Period	12/06/15 → 14/06/15

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Modeling
Stochastic Petri Net
Wind Speed

Access to Document

10.1109/CIVEMSA.2015.7158619

Cite this

Putri, R. I., Priyadi, A., & Purnomo, M. H. (2015). Stochastic Petri Nets for very short-term wind speed modeling. In 2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015 Article 7158619 (2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CIVEMSA.2015.7158619

Putri, Ratna Ika ; Priyadi, Ardyono ; Purnomo, Mauridhi Hery. / Stochastic Petri Nets for very short-term wind speed modeling. 2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015. Institute of Electrical and Electronics Engineers Inc., 2015. (2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015).

@inproceedings{26228c2b428241eab66c9e2fb8d442e4,

title = "Stochastic Petri Nets for very short-term wind speed modeling",

abstract = "To overcome the limitations of fossil energy and protect the environment from emissions of greenhouse gases, it is essential to develop the use of renewable energy as a substitute. At present, one of the renewable sources of energy is wind energy, which has the advantage of being pollution free and inexhaustible. However, the use of wind energy is strongly influenced by wind speed, which is not constant. Such varying wind speeds lead to the creation of fluctuated wind power. Consequently, there is a need for modeling and the accurate prediction of wind speed to help optimize the design of the turbine and control system in a wind energy conversion system to maintain system stability. This paper presents the modeling of very short-term wind speed using Stochastic Petri Nets (SPN) that is based on the measurement results of wind speed in Nganjuk. In this study, Stochastic Petri Nets was designed by using 7 places and 7 transitions. Transition to the SPN is defined as a function that generates random values using a uniform function. Wind speed data that was generated during a 500 seconds interval, was compared with the observed wind speeds. The comparison of the generated wind speed and observed ones shows that both its statistical characteristic have similar value.",

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author = "Putri, \{Ratna Ika\} and Ardyono Priyadi and Purnomo, \{Mauridhi Hery\}",

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Putri, RI, Priyadi, A & Purnomo, MH 2015, Stochastic Petri Nets for very short-term wind speed modeling. in 2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015., 7158619, 2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015, Institute of Electrical and Electronics Engineers Inc., 2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015, Shenzhen, China, 12/06/15. https://doi.org/10.1109/CIVEMSA.2015.7158619

Stochastic Petri Nets for very short-term wind speed modeling. / Putri, Ratna Ika; Priyadi, Ardyono ; Purnomo, Mauridhi Hery.
2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015. Institute of Electrical and Electronics Engineers Inc., 2015. 7158619 (2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Stochastic Petri Nets for very short-term wind speed modeling

AU - Putri, Ratna Ika

AU - Priyadi, Ardyono

AU - Purnomo, Mauridhi Hery

PY - 2015/7/14

Y1 - 2015/7/14

N2 - To overcome the limitations of fossil energy and protect the environment from emissions of greenhouse gases, it is essential to develop the use of renewable energy as a substitute. At present, one of the renewable sources of energy is wind energy, which has the advantage of being pollution free and inexhaustible. However, the use of wind energy is strongly influenced by wind speed, which is not constant. Such varying wind speeds lead to the creation of fluctuated wind power. Consequently, there is a need for modeling and the accurate prediction of wind speed to help optimize the design of the turbine and control system in a wind energy conversion system to maintain system stability. This paper presents the modeling of very short-term wind speed using Stochastic Petri Nets (SPN) that is based on the measurement results of wind speed in Nganjuk. In this study, Stochastic Petri Nets was designed by using 7 places and 7 transitions. Transition to the SPN is defined as a function that generates random values using a uniform function. Wind speed data that was generated during a 500 seconds interval, was compared with the observed wind speeds. The comparison of the generated wind speed and observed ones shows that both its statistical characteristic have similar value.

AB - To overcome the limitations of fossil energy and protect the environment from emissions of greenhouse gases, it is essential to develop the use of renewable energy as a substitute. At present, one of the renewable sources of energy is wind energy, which has the advantage of being pollution free and inexhaustible. However, the use of wind energy is strongly influenced by wind speed, which is not constant. Such varying wind speeds lead to the creation of fluctuated wind power. Consequently, there is a need for modeling and the accurate prediction of wind speed to help optimize the design of the turbine and control system in a wind energy conversion system to maintain system stability. This paper presents the modeling of very short-term wind speed using Stochastic Petri Nets (SPN) that is based on the measurement results of wind speed in Nganjuk. In this study, Stochastic Petri Nets was designed by using 7 places and 7 transitions. Transition to the SPN is defined as a function that generates random values using a uniform function. Wind speed data that was generated during a 500 seconds interval, was compared with the observed wind speeds. The comparison of the generated wind speed and observed ones shows that both its statistical characteristic have similar value.

KW - Modeling

KW - Stochastic Petri Net

KW - Wind Speed

UR - https://www.scopus.com/pages/publications/84943194566

U2 - 10.1109/CIVEMSA.2015.7158619

DO - 10.1109/CIVEMSA.2015.7158619

M3 - Conference contribution

AN - SCOPUS:84943194566

T3 - 2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015

BT - 2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015

Y2 - 12 June 2015 through 14 June 2015

ER -

Putri RI, Priyadi A , Purnomo MH. Stochastic Petri Nets for very short-term wind speed modeling. In 2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015. Institute of Electrical and Electronics Engineers Inc. 2015. 7158619. (2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2015). doi: 10.1109/CIVEMSA.2015.7158619

Stochastic Petri Nets for very short-term wind speed modeling

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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