A Hidden Markov Model Representing the Spatial and Temporal Correlation of Multiple Wind Farms

Jiakun Fang, Chi Su, Weihao Hu, Zhe Chen

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

Abstrakt

To accommodate the increasing wind energy with stochastic nature becomes a major issue on power system reliability. This paper proposes a methodology to characterize the spatiotemporal correlation of multiple wind farms. First, a hierarchical clustering method based on self-organizing maps is adopted to categorize the similar output patterns of several wind farms into joint states. Then the hidden Markov model (HMM) is then designed to describe the temporal correlations among these joint states. Unlike the conventional Markov chain model, the accumulated wind power is taken into consideration. The proposed statistical modeling framework is compatible with the sequential power system reliability analysis. A case study on optimal sizing and location of fast-response regulation sources is presented.
OriginalsprogEngelsk
TitelProceedings of the IEEE Power & Energy Society General Meeting, PES 2015
Antal sider5
ForlagIEEE Press
Publikationsdatojul. 2015
Sider1-5
ISBN (Trykt)978-1-4673-8040-9
DOI
StatusUdgivet - jul. 2015
BegivenhedIEEE Power & Energy Society General Meeting, PES 2015 - Sheraton Denver Downtown Hotel 1550 Court Place, Denver, Colorado, USA
Varighed: 26 jul. 201530 jul. 2015

Konference

KonferenceIEEE Power & Energy Society General Meeting, PES 2015
LokationSheraton Denver Downtown Hotel 1550 Court Place
LandUSA
ByDenver, Colorado
Periode26/07/201530/07/2015

    Fingerprint

Citationsformater

Fang, J., Su, C., Hu, W., & Chen, Z. (2015). A Hidden Markov Model Representing the Spatial and Temporal Correlation of Multiple Wind Farms. I Proceedings of the IEEE Power & Energy Society General Meeting, PES 2015 (s. 1-5). IEEE Press. https://doi.org/10.1109/PESGM.2015.7286389