A hybrid neuro-fuzzy power prediction system for wind energy generation. (January 2016)

• Record Type: Journal Article
• Authors: Saleh, Ahmed E.; Moustafa, Mohamed S.; Abo-Al-Ez, Khaled M.; Abdullah, Ahmed A.
• Is Part Of: International journal of electrical power & energy systems. Volume 74(2016:Jan.)
• Page Start: 384
• Page End: 395
• Publication Date: 2016-01
• Subjects: Power prediction -- Wireless sensor network -- SCADA -- Neuro fuzzy; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2015.07.039
• Languages: English
• ISSNs: 0142-0615

Abstract

Abstract: Wind energy generation is expected to increase in future electric grids. The generated wind power has an intermittent nature which may affect power system stability and increase the risk of blackouts. Therefore, a prediction system for wind power generation is essential for optimum operation of a power system with a significant share of wind energy conversion systems. In this paper, a hybrid neuro-fuzzy wind power prediction system is proposed. A wireless sensor network (WSN) is used to measure and transmit the required parameters for the prediction model at the operator centre. Those parameters are the major factors affecting wind farm output power, namely air temperature, wind speed, air density and air pressure. Considering all these factors will increase the prediction accuracy of the proposed model. The proposed prediction model is designed and tested using fuzzy rules with adaptive network. To decide the optimal number of fuzzy rules, the clustering of the data using modified Fuzzy C-Means (FCM) is used to implement hybrid optimization method. The prediction model is tested using four subsets of data divided into four seasons of year. The proposed prediction model is implemented using Matlab. Analysis of results shows that the proposed model has good prediction accuracy and provides a useful qualitative description of the prediction system.