A case study of space-time performance comparison of wind turbines on a wind farm. (June 2021)

• Record Type: Journal Article
• Authors: Ding, Yu; Kumar, Nitesh; Prakash, Abhinav; Kio, Adaiyibo E.; Liu, Xin; Liu, Lei; Li, Qingchang
• Is Part Of: Renewable energy. Volume 171(2021)
• Page Start: 735
• Page End: 746
• Publication Date: 2021-06
• Subjects: Data science -- Machine learning -- Power production performance -- Wind farms -- Wind turbines; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2021.02.136
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: This paper presents an academia-industry joint case study, which was conducted to quantify and compare multi-year changes in power production performance of multiple turbines scattered over a mid-size wind farm. This analysis is referred to as a space-time performance comparison. One key aspect in power performance analysis is to have the wind and environmental inputs controlled for. This research employs, in a sequential fashion, two principal modeling components to exercise tight control of multiple input conditions—a covariate matching method, followed by a Gaussian process model-based functional comparison. The analysis method is applied to a wind farm that houses 66 turbines on a moderately complex terrain. The power production and environmental data span nearly four years, during which period the turbines have gone through multiple technical upgrades. The space-time analysis presents a quantitative and global picture showing how turbines differ relative to each other as well as how each of them changes over time. Highlights: This paper presents a case study for quantifying the turbine performance change and comparing turbines in space and time. The method entails two major modeling components to control for stochastic environmental changes. The outcome presents a useful decision-support for wind farm owners and operators. The study used more than 1 GB actual wind farm operation data from 66 turbines spanning nearly four years.