Abstract
The massive penetration of wind generators in existing electrical grids is causing several critical issues, which are pushing system operators to enhance their operation functions in order to mitigate the effects produced by the intermittent and non-programmable generation profiles. In this context, the integration of wind forecasting and reliability models based on experimental data represents a strategic tool for assessing the impact of generators and grid operation state on the available power profiles. Unfortunately, field data acquired by Supervisory Control and Data Acquisition systems can be characterized by outliers and incoherent data, which need to be properly detected and filtered in order to avoid large modeling errors. To deal with this challenging issue, in this paper a novel methodology fusing Fuzzy clustering techniques, and probabilistic-based anomaly detection algorithms are proposed for wind data filtering and data-driven generator modeling
Highlights
Wind energy is recognized as one of the most promising technology for the effective implementation of modern sustainable energy policies
The massive penetration of wind generators in existing electric grids caused several side-effects, which determines the need for improving the robustness of system control and protection functions, mitigating the impacts of the large uncertainties induced by the intermittent and not-programmable nature of the wind power profiles [1]
The combination of effective wind forecasting tools and reliable generator models, implemented by knowledge discovering from experimental data streaming, represents one of the most promising enabling methodology, especially in assessing the impacts of wind power profiles on power system security and spinning reserve optimization [2,3], and in enhancing asset maintenance [3] and optimal bidding in electrical markets [4,5]
Summary
Wind energy is recognized as one of the most promising technology for the effective implementation of modern sustainable energy policies. The simplest solution methodology that could be adopted to solve these problems is based on the deployment of average models, which can be identified by curve-fitting of experimental data according to the procedure described in the IEC-61400-12 standard [6] These first-order models allow the assessment of wind power production as a function of a limited number of observable variables, mainly the wind speed, and are characterized by a low level of accuracy, since they are not able to take into account several important features characterizing the real generator operation. Detailed experimental results obtained on a real case study is presented and discussed in order to demonstrate the effectiveness of the proposed technique for on-line wind generators modeling
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
