{"title":"Stochastic Petri Nets for very short-term wind speed modeling","authors":"R. I. Putri, A. Priyadi, M. Purnomo","doi":"10.1109/CIVEMSA.2015.7158619","DOIUrl":null,"url":null,"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.","PeriodicalId":348918,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","volume":"08 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIVEMSA.2015.7158619","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
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.
为了克服化石能源的局限性,保护环境免受温室气体的排放,必须发展可再生能源的使用作为替代品。目前,可再生能源之一是风能,它具有无污染、取之不尽、用之不竭的优点。然而,风能的利用受到风速的强烈影响,而风速不是恒定的。这种变化的风速导致产生波动的风力。因此,需要对风力进行建模和准确的风速预测,以帮助风电转换系统中风机和控制系统的优化设计,以保持系统的稳定性。本文介绍了利用随机Petri网(Stochastic Petri Nets, SPN)在Nganjuk风速测量结果的基础上建立的极短期风速模型。在本研究中,随机Petri网采用7个位置和7个过渡设计。向SPN的转换被定义为使用统一函数生成随机值的函数。每隔500秒产生的风速数据与观测到的风速进行了比较。产生风速与观测风速的比较表明,两者的统计特征值相近。